After the high proportion of wind power is connected, it brings a series of problems to the stability of the frequency and voltage, and the grid needs wind power to assume the main responsibility for ensuring power supply. Existing studies have shown that the virtual synchronous grid-forming equipment, including grid-forming wind turbines, has good frequency/voltage active temporary and steady-state support capabilities, and has better temporary and steady-state adaptability in weak grid scenarios. Based on these advantages, grid-forming wind turbines are expected to play a greater role in the future grid supported by low inertia and weak voltage. However, the maximum power point tracking (MPPT) operation mode of the wind turbine and its own limited rotational energy lead to the restriction of the transient support capacity of the grid-forming permanent magnet synchronous motor(PMSG), while the grid-forming PMSG based wind-storage generator has an additional energy source due to its access to energy storage, and its transient support performance has been greatly improved, which is an effective solution. At present, the access mode of energy storage is mostly parallel energy storage on the DC side or AC side, in which the energy storage driven by grid-following control adopts the passive mode of responding to the frequency acquisition signal to support the grid frequency, and most of the energy storage driven by virtual synchronous grid-forming control is connected to the AC side of the wind turbine, and the energy storage cannot be incorporated into the virtual synchronous control system. Therefore, this study is dedicated to proposing a transient support capacity improvement strategy for grid-forming PMSG based on wind-storage integration.

Firstly, the power energy storage represented by the supercapacitor was selected to form a grid-forming PMSG based Wind-storage generator with grid-forming PMSG, and the dynamic model of the grid-forming PMSG and grid-forming PMSG based wind-storage generator were established, and the constraints of the wind turbine dynamics on the frequency support capacity and transient stability of the grid-forming PMSG were summarized by analyzing the transient response of the grid-forming PMSG under frequency and voltage drops.

Then, combined with the energy flow characteristics of the grid-forming PMSG based wind-storage generator in the transient support process, the transient response power of the Grid-forming control is decomposed into inertia response power signal and damping response power signal to drive energy storage, and a transient support capacity improvement strategy for grid-forming PMSG based on wind-storage integration is formed, which realizes the flexible allocation and invocation of rotor kinetic energy and energy storage, and incorporates the rotor kinetic energy and energy storage into the active support system of virtual synchronous control to improve the frequency support capacity and transient stability of the grid-forming PMSG based wind-storage generator.

Finally, after simulation verification under various conditions, the strategy can improve the transient support capacity of the grid-forming PMSG based wind-storage generator, including: (1) The frequency support capacity has been improved, which reduces the constraints of MPPT on the frequency support capacity of the wind turbine. (2) The fault ride-through capability is improved, the transient fluctuation of rotor speed and DC bus voltage is effectively suppressed, the redundant energy generated by fault ride-through is effectively absorbed. (3) Combined with the fault ride-through power angle stability control strategy, the redundant energy during the fault period is converted into energy storage energy, so as to avoid the reduction of wind turbine power generation efficiency and reduce energy waste.

Grid-forming energy storage technology serves as a critical solution for enhancing power system stability. Transformerless energy storage systems, characterized by high efficiency, modularity, and direct medium/high-voltage grid integration, have emerged as the preferred choice for large-scale grid-connected energy storage. However, the reduced electrical distance between transformerless systems and the grid results in significantly lower grid impedance, posing severe challenges to the stability of grid-forming control. The underlying mechanism lies in the voltage-source operation of grid-forming converters: under low grid impedance conditions, minor voltage deviations between the converter and grid can trigger substantial current surges, ultimately leading to instability. To address these challenges, this study establishes a full order small signal model to analyze the impact of low grid impedance on stability and proposes impedance enhancement strategies.

The research begins by developing a dynamic model that integrates virtual synchronous generator (VSG) control, voltage-loop regulation, and grid interactions. Pole trajectory analysis reveals two critical instability mechanisms: 1) Excessively low grid inductance shifts system poles to the right-half plane, inducing instability; 2) Insufficient grid resistance reduces damping ratios, exacerbating oscillatory behavior. These combined effects diminish system stability margins and may provoke subsynchronous oscillations. To mitigate these issues, a dual-layer impedance enhancement strategy is proposed: (1) Physical impedance reconstruction: The equivalent internal voltage control strategy repurposes filter inductance as coupling impedance by relocating the controlled voltage from the point of common coupling (PCC) to the converter side. This hardware-free modification enhances physical coupling impedance without requiring additional components. (2) Adaptive virtual impedance: A composite virtual impedance module combines static impedance for damping optimization and a dynamic current-limiting component. The static virtual impedance elevates damping ratios near to 0.707, while the current-limiting module dynamically adjusts impedance parameters based on real-time overcurrent thresholds, ensuring fault current suppression.

In the analysis of impedance enhancement effect, it is shown that equivalent internal voltage control causes the dominant pole of the system under strong power grid to shift to the left into the stable region, while the introduction of adaptive virtual impedance further enhances damping characteristics and improves dynamic response performance. The proposed impedance enhancement strategy enhances the system stability by introducing filtering impedance at the physical level and superimposing virtual impedance at the control level, thereby increasing the equivalent coupling impedance of the system from a single grid impedance to the combined effect of the three.

Experimental validation on a cascaded H-bridge transformerless energy storage platform under zero grid impedance conditions confirms the strategy's effectiveness. The proposed method eliminates oscillatory instability observed in conventional approaches, achieving smooth active power step responses without overshoot. During grid frequency fluctuations (±0.5 Hz), the system provides 0.67(pu) active power support, demonstrating effective grid-forming capabilities. Under symmetrical voltage sags (0.5(pu)), it delivers 0.5(pu) reactive power while constraining currents within 1.2(pu) safety thresholds, validating robust fault ride-through performance. Experimental and theoretical analyses confirm: (1) The proposed impedance enhancement architecture synergizes physical-layer reconstruction with control-layer virtual compensation, demonstrating superior stability improvement over conventional methods through coordinated impedance augmentation. (2) A pole trajectory analysis-based parameter optimization framework achieves concurrent enhancement of stability and dynamic performance, with virtual impedance implementation optimizing damping ratios to eliminate oscillatory instabilities. This work validates the effectiveness of the proposed strategy in extreme low-impedance scenarios, providing technical support for grid-forming transformerless energy storage applications in power grids.

The dynamic characteristic of grid-forming inverter (GFM) is mainly affected by the control strategy, and the interaction with the power grid may cause instability such as oscillation. At the same time, the interactive coupling between different time-scale controllers in GFM makes the stability analysis more complicated. Modal analysis based on the state-space model (MASS) uses the participation factor (PF) to quantify the contribution of each state variable to a particular pattern. However, the number of electrical components in new power systems is increasing explosively, and the difficulty of state-space modeling of the whole system is increasing rapidly. In addition, state-space modeling requires detailed system structure topology and complete control parameters of each electrical component, and inverters usually only have impedance models that describe the characteristics of voltage and current ports, with gray box or black box characteristics.

In order to explore the interaction characteristics among all electrical components of the system, the dynamic model of the whole system is constructed by the closed-loop feedback formula of the whole system dynamic matrix. Based on this foundation, the modal analysis based on impedance model (MAI) can evaluate the contribution of each power device to oscillation modes at the device level. However, MAI treats inverters as single, holistic components, which limits its ability to identify dominant system dynamics at the control loop or state variable level. Decomposing different control loops into equivalent circuit components enables the stability analysis of internal inverter dynamics. However, the decomposition of synchronization control loops remains to be explored. This paper proposes an extended modal analysis based on impedance model (EMAI) method to address the current challenges faced by MAI.

First, a decomposition method for the GFM impedance model based on the matrix inversion lemma was proposed, dividing GFM dynamics into synchronous dynamics (SD), dominated by the power frequency synchronization loop (PFL), and electromagnetic dynamics (ED), governed by the voltage control loop (VCL). The detailed categorization of dynamics facilitates an in-depth exploration of the complex coupling mechanisms among controllers operating on different time scales. Subsequently, overall impedance participation factors and participation ratios (PR) were introduced to characterize different internal dynamics of GFM, enabling the evaluation of SD and ED contributions at the control loop level. These metrics help identify the dominant system dynamics and trace the root causes of system instability. Finally, an explicit parameter PF was introduced to precisely locate the critical control parameters of identified loops, serving as a metric for optimizing control parameters and enhancing system damping.

The analysis yields the following conclusion: as the frequency of oscillation modes decreases, the dominant dynamics within each GFM gradually shift from ED to SD. MAI can provide an overall assessment of GFM participation but fails to capture the dominant dynamics of individual GFMs. EMAI not only identifies interactions between various GFMs and the grid but also evaluates the contributions of ED and SD within GFM through overall impedance participation factors, thereby pinpointing the primary causes affecting system dynamics to specific control loops. Moreover, the results of EMAI and MASS in assessing the participation levels of different GFM dynamics are highly consistent, validating the effectiveness of the EMAI method. Furthermore, the explicit parameter PF provides effective recommendations for improving system damping and enhancing stability. EMAI offers nuanced insights into system stability analysis, enabling the rapid identification of the root causes of system instability.

With a high proportion of power electronic devices connected to the power system, the new power system presents low inertia, low impedance, weak stability and other characteristics, and the risk of operational security increases. In this regard, the grid-forming energy storage converter should be emerged, the grid-forming energy storage converter gives inner loop voltage control the amplitude and phase angle through the power external loop control, presenting the voltage source characteristics. It has active anti-interference, active support characteristics, can effectively solve the problems faced by the new power systems. However, when the system is disturbed and the voltage falls to different degrees, the grid-forming energy storage is limited by the power angle curve of the power outer loop and the fixed active and reactive reference values, which will result in a large power angle instability and a disturbance current of more than 5 times. It threats the security and stability of the system operation. To address this problem, this paper firstly establishes a model of grid-forming energy storage converter. Based on the established model, the droop control power angle curve is plotted, and the transient destabilization mechanism of the grid-forming energy storage converter is analyzed under large disturbances. After analyzing the system, it is known that the stability of the system during large disturbances depends on the existence of an intersection between the system power angle curve and the active power reference value. At the same time, the size of the system disturbance current is affected by the degree of power angle change to a certain extent. Secondly, the disturbance current characteristics and its determining factors are analyzed, and the effect of direct current limiting control on the transient stability of the system is revealed. The analysis results show that the direct current limiting control tends to destabilize the system and cannot be directly used to limit the disturbance current. After theoretical analysis in this paper, it is found that the disturbance current size of the system is positively correlated with the difference between the converter out put voltage and the grid-side voltage, and the converter out put voltage size is correlated with the reference value of the power outer loop reactive power of the structural network type control. Therefore, during the disturbance period, the disturbance current can be limited by adjusting the system reactive power and then controlling the converter out put voltage. Based on the above theoretical analysis, an adaptive low-voltage ride-through (LVRT) control strategy for grid-forming energy storage converter is proposed, which can adjust the active and reactive reference values according to the degree of system perturbation, without switching the control strategy and changing the structure of the grid-forming control strategy. The energy storage converter still exhibits the characteristics of the voltage source during the distribution period, and it has the ability of active support for the system. It realizes effective limitation of the distribution current in the course of maintaining the stability of the system. At the same time, the disturbance current is effectively limited. Finally, the effectiveness of the proposed control strategy is verified by simulation and semi-physical experiment.

Under the impetus of "dual carbon" targets, new energy sources are increasingly integrated into the power grid through power electronic converters, leading to a gradual decline in the proportion of synchronous machines. To enhance the stability of "highly renewable and highly flexible" systems, the flexible controllability of converters can be leveraged by employing grid-forming control to provide reliable voltage and frequency support to the system. Virtual synchronous generator (VSG) control emulates the operating characteristics of synchronous generators to achieve voltage and frequency regulation, providing active frequency and voltage support capabilities while effectively increasing the inertia level of new energy units. VSG control has garnered significant attention due to its active support features; however, the factors influencing its voltage support capability are not yet fully understood, necessitating further research on VSG control strategies that balance voltage support with short-circuit current limitations.

To address these issues, this paper first analyzes the equivalent impedance of each control stage of grid-forming converters based on VSG control during steady-state operation and establishes an equivalent circuit model of the system. Secondly, based on the system's equivalent circuit, the expression for terminal voltage is derived, quantifying the relationship between terminal voltage, internal electromotive force, and system impedance, and analyzing the factors affecting the voltage support capability of VSG. Subsequently, improvements to VSG control are made considering both current limitation requirements and voltage support capability, proposing adaptive control strategies for virtual impedance and voltage compensation coefficients. Finally, the accuracy of the theoretical analysis and the effectiveness of the proposed strategy are verified using the Matlab/Simulink electromagnetic simulation platform.

The analysis results show that reducing virtual impedance, reactive power voltage droop coefficient, or increasing the voltage compensation coefficient can enhance the voltage support capability of VSG. However, decreasing virtual impedance and reactive power voltage droop coefficient reduces the system's equivalent impedance, while increasing the voltage compensation coefficient increases the system's internal electromotive force, thus imposing higher demands on the system's current-limiting capacity. By adopting the proposed adaptive control strategy for virtual impedance and voltage compensation coefficients, virtual impedance can be self-adaptively configured according to the system state, ensuring voltage support capability under the premise of meeting current-limiting requirements.

Through theoretical analysis and simulation experiments, the following conclusions can be drawn: (1) When the grid-forming converter system based on VSG control enters a steady state, its various control stages can be represented by equivalent impedance, which characterizes the relationship between terminal voltage, internal electromotive force, and system impedance. (2) The voltage support capability of VSG is related to virtual impedance, reactive power voltage droop coefficient, and voltage compensation coefficient. Reducing the reactive power voltage droop coefficient, decreasing virtual impedance, and adding voltage compensation control to the reactive power loop can all improve voltage support capability. (3) Voltage support capability and short-circuit current limitation of VSG interact. Through adaptive control of virtual impedance and voltage compensation coefficients, short-circuit currents can be fully utilized, maximizing the voltage support capability of VSG without exceeding the short-circuit current limit.

In China, the current renewable energy resources mainly use grid-following converters as grid-connected interfaces, which cannot provide inertia and damping support for power systems. In order to enhance the support capacity of renewable energy resources, grid-forming inverters are emerging as a promising solution as they can emulate the dynamic property of synchronous generator and provide support. However, the grid-forming inverter faces significant risks of transient synchronous instability. Current research primarily focuses on single grid-forming inverter systems, which cannot be applied to multi-machine systems due to complex interactions between converters. Quantitative transient analysis and the method of stability region estimation for multiple paralleled grid-forming inverter systems are absent.

To fill this gap, taking transient interaction and power coupling into consideration, the large-signal equivalent model of multiple grid-forming inverters system is established. Based on this model, a set of Lyapunov functions is constructed, which accounts for damping dissipation, reactive power loop dynamics, and transient interactions, enabling intuitively and accurately plotting the stability region for multi-machine system. Then, by comparing the sizes of the stability regions, the impact of control parameters and grid parameters on the stability boundaries of grid-forming multi-machine systems is quantified. Furthermore, the influence of damping dissipation, reactive power loop dynamics, and transient interactions on the transient stability margin is explored. Finally, hardware-in-the-loop experiments validate the accuracy of the estimated maximum stability region.

The following conclusions can be drawn from the analysis in this paper: (1) Due to the complex interaction, the equivalence model and transient characteristics of multi-machine system are more complex than those of single-machine system. (2) The Lyapunov function set, which takes into account voltage dynamics, damping dissipation and transient interaction, can accurately estimate the maximum stability region of multiple grid-forming inverter systems, and predict the transient synchronization stability via the location of the fault clearing point. (3) By comparing the size of the stability region, the increase of reference power, fault depth, and line impedance will reduce the stability region, and the increase of damping coefficient, inertia, and reactive droop coefficient will enlarge the stability region. The voltage dynamics and damping dissipation can increase the stability margin of the system, and the transient interaction between units can reduce the stability margin.

The global consensus has emerged to replace traditional fossil fuel-based power generation with renewable energy sources such as photovoltaic and wind power, leading to the formation of renewable energy delivery systems (REDSs). Within these systems, a trend towards the integration of grid-following (GFL) and grid-forming (GFM) devices has emerged. The REDS incorporating GFL and GFM devices exhibit high dynamic order, with complex dynamic interactions between heterogeneous equipment clusters and between equipment clusters and the network, posing challenges for the mechanism analysis and quantitative computation of small-signal stability. This paper proposes an eigen-subsystem computation method for the small-signal stability analysis of REDSs. It defines the double-infeed eigen-subsystem (DIES), which includes a GFL device and a GFM device. By equivalently reducing the complex, high-dimensional REDS to several low-dimensional DIES, the method preserves the dynamic interactions both between devices and between devices and the network. This approach enables efficient and accurate small-signal stability analysis of REDSs.

Firstly, for a REDS incorporating GFL and GFM devices, a full-order small-signal model of the system is constructed. The general approach for deriving the eigen-subsystem is briefly outlined, which involves reducing the complex high-dimensional system to several simple low-dimensional eigen-subsystems through decoupling. Subsequently, for a REDS with an equal number of n GFL devices and n GFM devices, based on the full-order model of the system, a matrix block diagonalization method is proposed on top of the matrix diagonalization method. A fast algorithm based on the Givens method is presented to solve for P⊗I₄ (P∈R^n×n), thus decoupling the REDS into n DIESs. Stability criteria for the DIES are also provided. When the device parameters are given, the stability operating region Ω of the DIES can be determined. The DIES remains stable if its network impedance falls within Ω. Thirdly, for more generalized scenarios, a node-splitting method is introduced to increase the number of less abundant devices, addressing the imbalance in the number of GFL and GFM devices. An eigen-subsystem-based method for small-signal stability analysis of REDSs is proposed. The REDS is stable if the set of network impedances Ω₁, formed by all decoupled DIESs, lies within the stability region Ω. Otherwise, the REDS becomes unstable and exhibits the same stability issues as the unstable DIES. Finally, time-domain simulations are conducted, and a 3-machines 9-nodes system as well as a 54-machines system are used to validate the effectiveness and correctness of the proposed method in the small-signal stability analysis of REDSs incorporating GFL and GFM devices. Experimental comparisons show that, compared to traditional eigenvalue analysis methods, the proposed method significantly improves computational efficiency.

The following conclusions can be drawn: (1) The REDS is mode-equivalent to its DIESs, and the stability characteristics of the original system can be traced back through DIESs. (2) For general REDS with n GFL devices and m GFM devices, the system can be decoupled and reduced in order by constructing a mode-equivalent system through node-splitting. This results in m DIESs, and (n-m) eigen-subsystems of single GFL devices (where n>m, or vice versa). (3)When the device parameters are given, the stability operating region Ω of the device-side characteristics can be determined. The network-side information of the eigen-subsystems obtained from the decoupling of the REDS forms a set of network impedances Ω₁. By checking whether Ω₁ belongs to Ω, the stability of the original system can be quickly assessed. Currently, small-signal synchrony stability has primarily been analyzed for the DIES. A future challenge is how to comprehensively analyze system stability under interactions among different components and quantify the stability margin of hybrid delivery systems.

As modern power system toward high renewable energy integration with wind, solar, and storage sources, the increasing share of inverter-based resources leads to stability challenges dominated by multi-loop control with wide-band frequency characteristics. In systems with high penetration of renewable energy, converter-based systems have presented new features, such as large-scale integration and long-distance transmission, causing system faults to exhibit large-signal transient characteristics. The hybrid connection of grid-following (GFL) and grid-forming (GFM) converters has emerged as a potential solution to enhance the stability and efficiency of new energy transmission. However, the high order and strong nonlinearity of these hybrid systems pose challenges to the assessment of their transient stability. Therefore, this study is dedicated to designing an effective method for evaluating the transient stability of GFL/GFM converter hybrid systems.

The research methodology starts with the construction of a detailed fourth-order nonlinear model of the hybrid system, integrating phase-locked loops and virtual synchronous generators, which serves as the basis for the proposed transient stability solution method based on alternating calculation. Further, by calculating the mutation portion at the failure moment, the method derives the computed initial values for each system of the transient process. The essence of the rotation calculations lies in performing energy calculations and resolving the angular velocities in the power angle domain, subsequently mapping them back to the time domain. In the method implementation, energy calculations are first performed for a certain converter system, the dynamics of this system is used to further estimate the motion of the other system in this step, and the order of calculations for the two systems is exchanged to perform the alternating calculations. In this process, the correspondence between the power angles of GFL and GFM control is established, which enables the complex interactive motion patterns of the hybrid system under severe disturbances to be evaluated. During the alternating computation process, for the GFL/GFM system, the equivalent kinetic energy change over the step is computed by integrating the relevant equations that take into account the damped power and kinematic properties, avoiding the uncertainty associated with neglecting damping. During the continuous iterative computation process, the computed values are exchanged and updated between the two systems to ensure accurate transient behavior of the system. Eventually, the computation is stopped after the judgment condition of stability is satisfied.

The experimental and simulation results confirm the feasibility and effectiveness of the proposed method. It accurately depicts the variations in power angles, angular velocities, and GFM converter voltages during the transient processes of the hybrid system. The computational time of this method is significantly reduced compared to existing numerical methods, with at least an order of magnitude improvement. Additionally, the method is applicable to calculating the critical clearing time (CCT), achieving a resolution within 5 ms in the presented examples. It can also accurately characterize the out-of-sync operation of GFL and GFM converters during the fault recovery process.

In conclusion, this study provides a practical solution for evaluating the transient stability of hybrid converter systems. The developed method based on alternating calculation in the discrete domain exhibits clear physical mechanisms and relatively low computational requirements. It has the potential to be incorporated as a subsystem in large-scale simulations to accelerate the simulation speed.

Similar to synchronous generators, the grid-forming converter mostly uses power synchronization or inertial synchronization control strategies, which can provide inertia and damping support to the grid. However, the similar external characteristics of the grid-forming converter and synchronous generator result in its susceptibility to sub-synchronous oscillations when connected to the grid through aseries capacitor compensation line. In view of this, this paper carries out a comprehensive research work on the stability analysis and sub-synchronous oscillation suppression strategy for the grid-forming converter connected to the grid via a series capacitor compensation line.

Firstly, the self-impedance and the accompanying impedance models of the grid-forming converter are established by using the complex variable representation method. The self-impedance and the accompanying impedance are verified using the frequency scanning method, and the scanning results were consistent with the analytical model, verifying the correctness of both. The established the self-impedance and the accompanying impedance models can accurately explain and characterize the single-frequency input and dual-frequency output of the grid-forming converter. Afterwards, the equivalent impedance model of the system with single input and single output of the grid-forming converter is derived, taking into account the frequency coupling effect and the influence of the series complementary lines.

Secondly, the stability of the grid-connected system at different series compensation degrees is analysed by using the Nyquist stability criterion based on an equivalent impedance model that accounts for thefrequency coupling effect. It is found that the larger series compensation degree is, the worse the system stability is. In addition, the impedance stability analysis taking into account the frequency coupling effect is more accurate under certain operating conditions.

Then, a current feedback-based impedance reshaping strategy is proposed for the phenomenon of sub-synchronous oscillations generated by the interaction between the grid-forming converter and the series-complementary line. The strategy is that the grid-connected current passes through the notch filter and the feedback coefficient as part of the modulation wave output to achieve system impedance reshaping. The function of the trap filter is to maintain the fundamental frequency output impedance and avoid the working point offset of the converter. And the current feedback coefficient was introduced into the equivalent impedance model, the feedback coefficient-frequency binary equivalent impedance model was established, and the amplitude-phase contour stability criterion was used to parameterize the current feedback coefficients. It is found that the larger the feedback coefficient k is, the larger the phase margin of the system is, and the more stable the system is. In addition, after the system is shaped by impedance, the phase-frequency curve moves down as a whole, especially in the frequency band below 50 Hz, the phase-frequency curve moves down greatly, resulting in the phase difference at the resonance point less than 180°, and the oscillation is suppressed.

Finally, the grid-connected system model of the grid-forming converter via series-complementary line is built through simulation and experiment, and the impedance remodeling control strategy is implemented on the damping controller to verify the correctness of the theoretical analysis as well as the parameter design. This study draws the following conclusions. (1) The interaction between the grid-forming converter and the series compensation line is easy to cause sub-synchronous oscillation, and the greater the series compensation degree, the higher the oscillation risk. In addition, under certain operating conditions, impedance analyses that take into account frequency coupling effect are more accurate and their influence cannot be ignored. (2) The amplitude-phase contour plot can be used to determine intuitively the influence of the feedback coefficient k on the operating characteristics of the system and to derive the range of values of the feedback coefficient k parameter when the system is in a stable or unstable state.

At present, renewable energy generation mainly use grid-following (GFL) control, which is prone to cause small signal stability problems such as broadband oscillation when connecting to the weak grid system. What’s more, the GFL units have insufficient support capacity for the grid. Grid-forming (GFM) technology construct the grid voltage independently through power synchronization control.When the system is disturbed, GFM units can actively support the grid voltage and frequency to improve the stable operation of the system. Under the situation of rapid development of renewable energy, it’s important to carry out GFM technological transformation and upgrading with renewable energy clusters/stations as the main body. How to reasonably plan the access capacity and location of GFM units of renewable energy grid-integration systemand improve the system stability characteristics, has become a key concern for engineering applications. The paper studies the configuration problem of the GFM unitsin renewable energy grid-integration system, with the focus on the small signal stability constraints.

Firstly, the singlerenewable energy converter grid-integration system was established. Based on the small signal model of the renewable energy converter grid-integration system, a closed-loop power-voltage feedback model was constructed, and the consistency between the former and the current-voltage closed-loop feedback model was verified in analyzing the stability characteristics of the system. Secondly, based on the impedance network circuit model, the system stability characteristic analysis method was extended to the multi-machine system. What’s more, the GFM units access capacity and distribution configuration problem of the multi-machine system was set. The objective of the problem is to minimize the total capacity of GFM units in the system, the constraints are that the system has sufficient small signal stability margin and stability support capability. Finally, the small signal stability margin index, the small signal stability support gain growth rate index, the GFM units access point selection index and the configuration method for GFM units of the system were proposed respectively. An analytical example was constructed based on the real renewable energy cluster grid-integration system, and the effectiveness of the proposed method was verified by time-domain simulation.

The conclusions are as follows: (1) Under the constraint of system small signal stability, the reasonable configuration of the capacity and location of GFM units can ensure that the whole system has sufficient small signal stability margin and stability support capability. Furthermore, reducing the capacity of the GFM units configuration can minimize the economic cost of the system. (2) The proposed configuration methodcan enhance the system's stable operation capability, particularly within a specific range of weak grid strength. It appears that when the capacity proportion of GFM units is constant, the weaker the grid characteristics, the more GFM units needs to be accessed. (3) The higher the percentage of GFL units with poor dynamic characteristics in the system, the larger the proportion of GFM units needs to be accessed. Optimization of system control parameters or control strategies, and explore the configuration method of GFM units in complex system scenarios will be the future research direction.

The purpose of the wind-solar complementary system (WSCS) is to couple wind power and photovoltaic (PV) in a complementary way to strengthen the ability to generate power continuously in the medium and long term. However, due to the uncertainty of natural resources, the power output of WSCS is still unstable. In recent years, hybrid energy storage systems (HESS) have been used to match the WSCS to reduce the volatility of system output, but there are still some problems leading to the system's economic cost making it difficult to control. For example, the coupling relationship between wind and solar is linear, and the premise of the fluctuation smoothing strategy is to meet the power demand of load-side or grid-connected. In order to solve the mentioned problems, this paper proposes a method that HESS smooths fluctuations of wind-solar coupling power considering multi-scenario planning. By constructing a nonlinear coupling relationship between wind and solar and optimizing the capacity allocation of power source-side hybrid energy storage, the system accommodation characteristics for power fluctuations are improved.

Firstly, the marginal distributions of the two power sources are constructed using KDE based on the historical data of wind power and PV, and the joint distribution is obtained by preferably using the Gumbel-Copula functions. The multi-scenario set obtained by random sampling of the joint distribution is able to reflect the intensity of the fluctuation changes. Secondly, the FFT and its IFFT are used to analyze the spectral analysis of the unstable power in scenarios set to determine the power borne by each energy storage unit. In this part, since the multi-scenario ensemble originates from a joint distribution, the correlation of each scenario in the ensemble is consistent, which means the cut-off frequency that distinguishes battery and super-capacitor does not change with the change of scenario. Finally, an optimization model is established with the objective function of minimum the cycle operating cost of HESS, and the capacity configuration of the HESS is calculated using an improved PSO. The result of capacity configuration provides room to accommodate fluctuations in power source-side output, which reduces the instability of the system.

The results of the simulation example show that the increase in frequency deviation before the HESS configuration is much larger than that after the HESS configuration. The change rate of RMSE for calculating the frequency deviation before and after the configuration of HESS ranges from 60.0% to 83.5%, the change rate gradually increases with the increase of the number of scenarios in the ensemble. This suggests that the role of HESS in regulating frequency increases as the number of scenarios increases. Meanwhile, with the increase in the number of scenarios in the set, the maximum growth in the rated power and rated capacity of the batteries is 77.1% and 54.9%, respectively. And that of the super-capacitors is 40.0% and 42.4%. However, this makes the increase in equipment cost of the super-capacitor more prominent. Further, the configuration of HESS makes the power fluctuation of the system at adjacent moments smoother. The fluctuation accommodation range of the power source-side within the time intervals of 10 h, 60 h, and 240 h is enhanced by 12.9%, 7.4%, and 6%, respectively. The amplification of the fluctuation accommodation range decreases with the longer of the time intervals. Nevertheless, the HESS still has rechargeable power characteristics when the power source-side output is zero.

From the simulation results, the following conclusions can be drawn: (1) Different numbers of scenarios in the set have consistent correlation, so the dividing frequency of the battery and the super-capacitor does not change with the number of scenarios, which makes the HESS can be effective for the fluctuating power to modulate frequency. (2) The more complex the frequency variations of the fluctuating power at the wind-solar coupling output, the more pronounced the capability of HESS modulating frequency. (3) The results of the HESS configuration reflect that super-capacitors and batteries have greater advantages in the rated power and rated capacity, respectively. (4) Even if the wind-solar coupling output is close to 0 or 0 after the configuration of HESS, the system is still able to ensure that there is a certain margin to counteract the fluctuating impact of sudden power changes.

The output characteristics of magnetostrictive devices usually show a strong bias condition dependence. The bias magnetic field provided by constant current will change nonlinearly with the change of the material permeability under stress excitation (manifested as non-constant bias magnetic field). This change affects the accurate characterization of material magnetization process and the rational design of bias points. At present, the inverse effect models of magnetostriction mainly focus on material characterization under constant bias magnetic field. Models that solely consider a constant magnetic field fail to accurately reflect the output characteristics of devices in their actual operating environments. Establishing a hysteresis model that accounts for the inverse magnetostrictive effect with dynamic variations in the bias magnetic field holds significant research importance.

The models established in this paper include the average model of non-hysteresis energy, the hysteresis constitutive model and the equivalent magnetic circuit model taking into account the variation of the bias magnetic field. Firstly, based on the free energy theory, the expression of the non-hysteretic magnetization is derived, and the average model of the non-hysteretic energy is established. Secondly, using the modeling idea of J-A model for positive hysteresis phenomenon, the first order differential equations of irreversible component M_irr, λ_irr and stress are introduced. The hysteresis constitutive model which can characterize the inverse effect of magnetostrictive materials is obtained. Based on the equivalent theory of magnetic circuit, the influence of stress on magnetic field strength is reflected by the change of magnetoresistance. Finally, an energy average hysteresis model is established which can account for the change of bias magnetic field. Hysteresis models often have difficulty in parameter identification. An improved cuckoo search-grey wolf optimizer (CS-GWO) hybrid algorithm is proposed by introducing nonlinear adaptive step factor α(t). Comparing the optimization results of the traditional optimization algorithms of CS, GWO, and PSO, the CS-GWO algorithm has the highest accuracy and the fastest convergence speed, and can accurately and efficiently identify the globally optimal parameters of the energy-averaged hysteresis model. Model validation is performed in two steps. First, the basic parameters of the model were extracted based on the experiments of Fe₈₁Ga₁₉ alloy bar under -115~0 MPa compressive stress and 22.3~446 Oe constant bias magnetic field. The error between the B-σ curve simulated by the model and the existing experimental data is only 3.85%, which is better than the error calculated by the traditional model of 6.79%. The error of ε-σ curve simulated by the model is 2.93%. Then, based on the experimental data of Fe₈₁Ga₁₉ alloy bar under constant current bias, the parameter H_s is further extracted. The errors of the simulated H-σ curve, B-σ curve and ε-σ curve with experimental data are 4.74%, 4.31% and 3.97%, respectively. The simulation results can accurately describe the tendency of the bias field to increase nonlinearly with the increase of stress, which also leads to a shallower sensing response under constant current bias than under constant field bias.

The proposed model, in addition to predicting the sensing response of the device, can also be used to track the optimal bias conditions of the material as well as to predict the trend of the ΔE effect of the material. The model can provide theoretical guidance for the performance tuning and variable stiffness design of devices such as sensors and energy harvesters based on the inverse effect of magnetostrictive materials.

For bundled conductors, the shadowing effect of upwind sub-conductor will affect the airflow and droplets distributions of downwind one, resulting in the difference in icing characteristics. Traditional icing calculation process generally ignored these differences and, hence, only giving an identical icing mass result of each sub-conductor. This affects the study of the aerodynamic characteristics and deicing methods of icing bundled conductor. Although some scholars have pointed out that the shadowing effect between sub-conductors will influence the icing process, there is no quantitative study. Therefore, this paper further explores the shadowing effect and relevant influencing factors of bundled conductors through numerical simulation and test research. Furthermore, based on the analysis of the shadowing effect and the superposition principle, a rapid calculation method of ice mass accreted on the bundled conductor is proposed.

Firstly, the distributions of airflow and droplets around bundled conductor are solved by Eulerian-Eulerian two-phase flow model. Secondly, combined with the mass and thermodynamic balance equations, the icing mass and shape accreted on bundled conductor under various icing environments are obtained. Then a new parameter called shadowing coefficient is defined to investigate the shadowing effect and influencing factors as well. The results show that: Shadowing effect is weakened with increasing absolute value of shadowing angle and bundled-spacing, but intensified with the increase of median volume diameter (M_VD) of droplets; Meanwhile, the shadowing effect experiences a growth and then drops down along with the increase of wind speed, and reach to the max at 15 m/s range 5~20 m/s.

Based on the superposition principle and shadowing effect analysis, a rapid calculating method for ice mass on bundled conductor is proposed. Where iced bundled conductor is regarded as a linear combination of non-shadowed sub-conductor (single conductor) icing intensity and shadowing coefficient, so the icing intensity of various types of bundled conductor can be obtained only requiring the icing intensity of single conductor and the shadowing coefficient in the corresponding environment. Then the rapid icing calculation formulars of 3,4,6,8-bundled conductor under various shadowing angle is given by geometry analysis, respectively, which simplifies the calculation of the icing mass on bundled conductor.

Finaly, a 4-bundled conductor nature icing test was carried out at the Xuefeng Mountain Energy Equipment Safety National Observation and Research Station to validate the accuracy of the numerical simulation and rapid calculation method. Results show that under the environment parameters of ambit temperature T_f = -2℃, M_VD = 25.4 μm, liquid water content L_wc = 0.61 g/m³, wind speed V = 10 m/s and shadowing angle θ = 2°, the difference in icing intensity between rapid calculation and test results was within -4.01% to -19.77%, the icing thickness differences of sub-conductors were between 1.66% to -6.36% and the differences in shadowing coefficient were between 4.05% to 5.33%, which well verifies the accuracy of the rapid calculation method proposed in this paper.

AC/DC hybrid system has become an effective solution for large-scale new energy consumption because of its characteristics of multiple power sources, multiple drop points, large capacity and cross-regional flexible transmission. However, the wide-frequency oscillation problem of AC/DC hybrid power system with high proportion of power electronics is prominent, which threatens the safe and stable operation of the system. At present, the stability analysis of hybrid system is mainly faced with the problem of how to take into account the AC and DC sections of the hybrid system at the same time and how to cover the system equipment with different impedance characteristics, so as to realize the unified analysis of the AC/DC hybrid network with multiple power electronic equipment. To address the above issues, this paper establishes a unified immittance network model for system-level stability assessment of hybrid systems.

First, based on the voltage/current source type device characteristics of the network devices, the impedance/ admittance forms of each network module of the system are standardized to avoid the problem of solving for the right half-plane poles in the process of system stability analysis. Secondly, from the perspective of the AC and DC ports of the system equipment, the AC/DC hybrid system can be divided into mono immittance subsystem and hybrid immittance subsystem. Finally, based on the interaction relationship between system immittance networks, a unified immittance network model containing complete oscillation information of the hybrid system is established. Under the premise of covering the stability information at each AC and DC port of the hybrid system, the unified immittance network model reduces the dimension of the system network model, at the same time expands the system network matrix from a single AC or DC system to an AC/DC system. Furthermore, combined with the derivation and change process of the immittance network, and through the modularization of the system expansion, this paper makes the immittance network applicable to the analysis of objects with different topologies,and extends the unified immittance network to general AC/DC hybrid systems. In addition,in contrast to the matrix model that merely aggregates a single impedance feature or a single admittance feature, the unified immittance network encompasses the AC/DC systems with various impedance characteristics, accomplishing the mutual unification of the new energy unit with admittance properties and the load network with impedance properties. This facilitates the precise establishment of the network mathematical model when confronted with the AC/DC system that concurrently incorporates equipment with current/voltage source characteristics, and realizes the unified coverage of equipment models with dissimilar impedance characteristics.

Based on the immittance network model, a unified immittance network stability criterion covering the interaction relationship of each AC/DC port of the hybrid system is derived. Combined with the expansion analysis of the immittance network, the criterion is extended to the general AC/DC hybrid system. The stability criterion comprehensively covers the stability problems of the AC/DC system, and avoids solving the problem of poles in the right half-plane, simplifying the analysis process. Furthermore, an oscillation traceability method for the hybrid system is given based on the immittance network model. Finally, based on the application examples of the AC/DC hybrid system, the immittance network model and its stability analysis method proposed in this paper are evaluated and verified.

Electric vehicles (EV) have the characteristics of both traffic and mobile load, and their charging behavior will have an interactive impact on the power grid. With the rapid increase in the number of electric vehicle and the continuous improvement of their penetration rate, charging guidance for large-scale EVs has become an important measure to alleviate the contradiction between local limited charging resources and strong charging demand. Therefore, considering the influence of future traffic information changes on navigation strategy, this paper proposes a fast guidance strategy for electric vehicle charging based on dynamic traffic inference.

First of all, a dynamic traffic information prediction model based on spatio-temporal self-supervised learning (ST-SSL) is established. A self-supervised learning (SSL) module for spatial and temporal heterogeneity of traffic data is designed to achieve accurate prediction of multi-period traffic flow information. Secondly, a multi-time dynamic impedance modeling method for urban road network considering future traffic information changes is designed, a charging navigation strategy considering multi-demand scenarios and multi-navigation objectives of users is established, and a solution method based on dynamic Dijkstra algorithm is proposed to realize the selection of the optimal charging station and the planning of the optimal navigation path. Based on the global charging navigation results, the service range of urban charging stations is dynamically evaluated, to achieve rapid charging guidance for electric vehicles. Finally, taking the actual road network of a certain area in Los Angeles as an example, the accuracy of the prediction model and the effectiveness of the guidance strategy are proved, which can effectively perceive the dynamic traffic information and quickly realize the service range division of urban charging stations and the charging guidance for electric vehicles.

In this paper, a fast guidance strategy for electric vehicle charging based on dynamic traffic inference is proposed, based on the case simulation results, the main conclusions can be obtained as follows. (1) The model based on ST-SSL can make full use of the spatial and temporal heterogeneity of traffic data, improve the prediction effect of traffic flow information, and provide an effective data basis for the construction of dynamic traffic impedance. (2) The proposed multi-scenario and multi-objective charging navigation strategy based on dynamic impedance can effectively perceive traffic information and take into account the diversified needs of users, effectively reduce the cost of charging navigation for different users, and reasonably guide the load distribution of electric vehicles. (3) The proposed dynamic Dijkstra algorithm can recommend the optimal path according to the future traffic information, which can be used as a navigation algorithm to plan the driving path, and can also recommend the customized optimal charging station according to the needs of users. (4) The division of charging station service range based on the global charging navigation results can effectively evaluate the service range of charging station, and provide an important reference for the construction planning of charging station. Based on the evaluation results, the charging navigation strategy is quickly assigned to each node, which effectively reduces the computing resource consumption of charging navigation.

Insulator is one of the most common and widely used electrical components in transmission lines, which plays a critical role in electrical insulation and mechanical support, ensuring that the current flows along the specified path and reducing electromagnetic interference with the surrounding environment. Since insulators are installed outdoors, they are exposed to wind, sunlight, rain, ice, frost and other bad weather for a long time, and their surface defects are inevitable. If the insulator appears self-explosion or drop string, which will cause leakage due to the loss of insulation, leading to electric shock accidents, thus resulting in huge economic losses. Relying on computer vision and deep learning technology, insulator defect detection from massive UAV aerial images has become an urgent problem for power operation and maintenance. However, the backgrounds of aerial images from overhead transmission line corridors are complex. Under different lighting conditions, shooting angles, shooting distances, etc., the scale of insulators in aerial images varies greatly, and the insulator strings are prone to occlusion, the defect area of the insulator is much smaller than the insulator itself. Therefore, there are numerous difficulties in detecting insulator defects in practical applications.

In recent years, compared with the traditional object detection methods, deep learning methods can quickly and accurately identify insulators and their defects from power inspection images. There is still a lack of comprehensive review of the latest progress in insulator defect detection in existing literature, without introducing object detection algorithms such as anchor free algorithm, YOLOv7, Transformer, and knowledge extraction techniques. In view of this, this article summarizes and analyzes a large number of visual methods for insulator defects detection, systematically reviews deep learning methods for insulator defect detection in drone aerial images, aiming to select appropriate detection methods for specific insulator defects and provide valuable reference for researchers engaged in transmission lines fault diagnosis.

This paper reviews the research progress of deep learning methods for insulator defect detection in UAV aerial images. Firstly, the current research status of transmission lines inspection based on deep learning is briefly reviewed. Then, the insulator defect detection methods based on deep learning are explained, mainly from the target detection models, lightweight network models, cascade detection models and other methods are summarized, which is conducive to the comparison between different deep learning methods and more helpful for power inspection personnel to select appropriate deep vision detection methods for fault diagnosis of specific electrical component. And the target detection models based on two-stage algorithms, one-stage algorithms and anchor-free algorithms are elucidated. The lightweight network models based on model pruning, knowledge distillation, low-rank decomposition, network quantization and the target detection model based on Transformer are summarized. Next, the self-built and public datasets for insulator defect detection are introduced. Due to the lack of training samples and unified dataset for insulator defect detection, scholars mostly conduct defect detection research through self-built datasets in different detection scenarios. Finally, the challenges faced by insulator defect detection methods based on deep learning are elucidated, including insufficient defect samples, low defect detection accuracy, difficulty in detecting small target defects, and feature extraction, etc. Based on existing deep learning techniques and recent research ideas, several important research directions in the future are pointed out, including expanding insulator defect samples, establishing unified performance evaluation indicators, small and zero sample learning, new defect detection frameworks, multi-level detection of small defects, deep fusion of multiple learning technologies, cloud-edge-end collaborative fusion, and improving network model stability and real-time performance.

In substation robot inspection tasks, high-precision semantic segmentation of 3D point cloud data is one of the key technologies. Traditional point cloud semantic segmentation algorithms have certain limitations, making it difficult to handle complex 3D scenes. Deep learning methods have compensated for the shortcomings of traditional point cloud semantic segmentation algorithms and have become the main method for achieving point cloud semantic segmentation. However, existing point cloud segmentation methods for substations face issues such as high complexity, low accuracy, and gradient vanishing. To address these issues and achieve accurate segmentation of the main equipment point cloud in substations, this paper proposes a high-precision semantic segmentation method for substation main equipment point clouds based on DI-PointNet.

Firstly, on the basis of the PointNet++ network structure, a double-layer consecutive transformer (DLCTransformer) module is introduced. Key points are sampled through the DLCTransformer to enhance information interaction between point clouds and expand the effective receptive field. Secondly, a hierarchical key sampling strategy is adopted. The point cloud data is divided into the original dense point cloud space and a sparse point cloud space formed after farthest point sampling. These are then divided into multiple non-overlapping 3D windows, ultimately generating key values required for self-attention mechanism calculations, thereby reducing computational complexity, improving the model’s receptive field, and aggregating long-range context to achieve information interaction of substation-associated point clouds. Finally, an inverted residual module (InvResMLP) based on residual connections and inverted bottleneck design is added to the network. This enhances the model’s ability to extract complex structural features from substation point clouds while effectively reducing the gradient vanishing problem, making the algorithm more robust in handling complex substation scenarios and improving the accuracy of semantic segmentation of substation main equipment point clouds.

Additionally, to validate the segmentation effectiveness of the algorithm, this paper uses Avia LiDAR equipment to collect point cloud images of different devices at substations such as the Baobei substation in Baoding City. The original data includes transformers, switchgear, steel towers, insulators, maintenance equipment, and others (mainly vegetation and buildings). To simplify the point cloud data while filtering noise, the original input point cloud is first subjected to grid sampling with a grid size of 0.03 m. Data augmentation methods such as z-axis rotation, scaling, perturbation, and color reduction are employed. The initial window size is set to 0.12 m and is doubled after each down-sampling layer. The DI-PointNet is trained using the cross-entropy loss function and Adam optimizer with the following hyperparameters: initial learning rate of 0.001, batch size of 2, and 100 epochs. To ensure the reasonableness and accuracy of the experiments, the comparative algorithms used in this paper are trained using the same hardware platform, environment version, loss function, optimizer, hyperparameters, and training strategies as DI-PointNet.

Through ablation experiments and comparative analysis, the DI-PointNet algorithm proposed in this paper improves the overall accuracy (OA) value of substation point cloud segmentation by 3.4 percentage points compared to before the improvement, while reducing algorithm complexity. The proposed algorithm outperforms other mainstream deep learning algorithms and other point cloud segmentation algorithms in the power sector. The performance of this algorithm is close to the accuracy of manual segmentation and can achieve precise segmentation of substation point clouds.

The increasing distance of offshore wind farms from coastal areas has created an urgent need for the development of long-term extra high voltage direct current (EHVDC) cables. Factory joints are commonly used to connect sections of submarine cables, forming extensive cable systems. Therefore, studying factory joint is crucial for advancing long-length cable lines. This study investigates the physicochemical and dielectric insulation characteristics of XLPE samples under various vulcanization pressures, highlighting the effects of these pressure changes on the properties of 500 kV EHVDC cross-linked polyethylene (XLPE) cable joints.

Commercially available 500 kV EHVDC XLPE pellets were used to prepare the XLPE samples via hot-press method. Initially, a specified quantity of XLPE pellets was distributed between two iron plates. The pellets were preheated at 120℃ for 5 minutes and then heated at 180℃. Cross-linking was subsequently performed under different vulcanization pressures of 1.3 MPa, 1.6 MPa, 1.9 MPa and 2.5 MPa respectively. The fabricated XLPE specimens underwent physical characterization through Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and gel content analysis. While electrical measurements included current density analysis, pulsed electro-acoustic (PEA) analysis, and DC breakdown test.

The physiochemical results indicate that increasing vulcanization pressure enhances the crosslinking degree of XLPE samples, transforming the material from a linear molecular structure to a 3D network structure and breaking macromolecules into smaller, mobile molecules. The increased mobility of these small molecules leads to improved crystallinity, resulting in a higher crystallinity structure. Additionally, the recrystallized macromolecular chains have higher melting temperatures, raising the overall melting temperature of the samples. However, higher vulcanization pressure also produces crosslinking by-products that are difficult to decompose and volatilize. The combination of high temperatures and pressures causes thermal expansion forces perpendicular to the lamellae, increasing lamella spacing, creating more amorphous regions, and effecting the insulation performance of the samples.

Regarding electric insulation performance, the DC breakdown strength and space charge injection threshold strength of the fabricated XLPE samples initially increase and then decrease with the increase in vulcanization pressure. Conversely, conductivity current and average space charge density first decrease and then increase. An optimal vulcanization pressure of 1.9 MPa was identified, at which the XLPE samples exhibited improved electrical insulation properties. Below this pressure, the increased trap energy levels inhibit carrier transport, thereby reducing the number of free carrier paths and hindering the formation of conductive channels, ultimately increasing the breakdown strength of the XLPE samples. However, at vulcanization pressures above 1.9 MPa, the increased crosslinking byproducts create more shallow traps, which lower space charge injection and accumulation, ultimately distorting the sample's internal electric field. Additionally, the increased lamella spacing creates more amorphous regions, reducing the carrier transport barrier and further decrease the breakdown strength of the prepared XLPE samples.

Based on the results, it can be concluded that appropriately increasing the vulcanization pressure of factory joints improves the physicochemical and electrical properties of XLPE. However, excessively high vulcanization pressure can have a detrimental impact on the electrical insulation properties of cable factory joints.

Epoxy resin (EP) possesses advantages such as low cost, high mechanical strength, robust chemical resistance, and excellent electrical insulation properties, making it extensively utilized in various epoxy cast electrical equipment like dry transformers and reactors. Nonetheless, the three-dimensional cross-linked network of resins exhibits non-melting characteristics, posing challenges in the degradation and recycling of retired epoxy electrical equipment. Nowadays, researchers have achieved epoxy resin recycling by incorporating dynamic covalent bonds into the epoxy resin crosslinking network to develop degradable Vitrimer epoxy resin materials. However, when applied to complex environments like high temperature, humidity, and intense electric fields, the internal crosslinking network of epoxy Vitrimers material may deteriorate, impacting its operational longevity. Therefore, besides ensuring favorable electrical, mechanical, and thermal properties, the enduring reliable performance of Vitrimer resin cannot be disregarded. This paper prepared dual-dynamic bonds Vitrimer resin with varying disulfide bond contents. The micromorphology, electrical characteristics, mechanical properties, dynamic thermodynamic properties, and degradation properties of Vitrimer resin at diverse aging stages were regularly investigated and the life evaluation model is constructed at last.

Firstly, dual dynamic crosslinked Vitrimer resin basded on ester bonds and varied disulfide bonds were prepared with 3,3’-dithiodipropionic acid (DTDPA) and hexahydro-4-methylphthalic anhydride (MHHPA) as the curing agent, with Triethanolamine acting as the catalyst. Subsequently, the accelerated thermo-oxygen aging tests were carried out, during which the microscopic morphology, electrical properties, bending characteristics, dynamic thermodynamic attributes, and degradation properties of vitrimer resin were periodically evaluated. Experiment results revealed alterations in the resin's microstructure under hot oxygen aging, leading to random internal cross-linked network fractures that generate abundant free radicals, ultimately causing resin failure. The resin's bending strength diminishes, rigidity increases, toughness notably decreases, and the bending fracture transitions to a brittle fracture pattern. As aging progresses, a denser cross-linked network forms on the resin's surface, elevating T_g. The integration of disulfide bonds makes the resin system more susceptible to oxidation and molecular chain breakage, resulting in reduced breakdown voltage, heightened dielectric loss factor, and increased insulation deterioration. Throughout the aging process, the degradation rate of Vitrimer resin in glycol solution decreases due to surface ester bond reduction and oxide layer formation, while the destruction of the disulfide crosslinking network prevents resin degradation in dithiothreitol solution. Lastly, a life evaluation model for the dual dynamic crosslinked Vitrimer resin was formulated based on the results of bending strength and TGA tests.

The dual dynamic crosslinked Vitrimer resin has excellent comprehensive properties and can realize the recycling of decommissioned epoxy electrical equipment. In this paper, the effect of thermal oxygen aging on the properties of dual dynamic crosslinked degradable resin was studied, which laid the experimental and theoretical foundation for the long-term service of vitrification epoxy resin in electrical equipment.

Metal oxide surge arresters are crucial for overvoltage protection in power systems, determining the insulation level of electrical equipment, with their core component being the ZnO varistor. However, modern stable ZnO varistors exhibit an anomalous decrease in power loss during aging, contradicting the increase in power loss predicted by the classical ion migration model. This discrepancy poses challenges for the condition assessment and life prediction of ZnO varistors due to a lack of theoretical foundations, thereby presenting a potential threat to the power system. Consequently, the study of the anomalous aging mechanism of stable ZnO varistors has been identified as a major challenge for the varistor community by CIGRE in both 2013 and 2017.

In this paper, stable ZnO varistors are subjected to accelerated DC aging at elevated aging temperatures to investigate their long-term stability transition. With increase in aging temperature, power loss trend transitions from a continuous decrease at 120℃ to an initial decrease followed by an increase at 150℃, and a sustained rise at 180℃. The decreasing power loss trend can be fitted by a double exponential decay function, while the increasing power loss is proportional to the square root of the aging time t^0.5. After transitioning to a mixed stable type at 150℃, the aging of stable ZnO varistors becomes irreversible. In-situ high-temperature dielectric measurements reveal that the interface space charge polarization relaxation process shifts to higher frequencies with decreased relaxation time and activation energy decreasing from 0.583 eV to 0.560 eV, indicating the destruction of the grain boundary structure. Low-temperature dielectric tests show that intrinsic point defects of zinc interstitials undergo irreversible consumption after aging. Upon transitioning to an instable type at 180℃, the "crossover" phenomenon of the forward current-voltage (I-U) characteristics disappears at 180℃, and both forward and reverse I-V characteristics shift towards increased leakage current region as a whole. Severe deterioration in reverse electrical parameters was observed, as breakdown voltage U_1mA decreases from 200.5 V to 92.9 V, the nonlinear coefficient α decreases from 16.3 to 2.0, and the leakage current rises from 19.5 μA to 479.3 μA. More importantly, offline physical and chemical structural tests show a reduction in the diffraction angles of ZnO crystal planes and decreased peak intensities. Additionally, a significant decrease in the binding energy of the Zn_2p orbital is observed, with Zn_2p3/2 and Zn_2p1/2 orbitals decreasing from 1 022.5 eV and 1 045.9 eV to 1 022.1 eV and 1 045.2 eV, respectively. This indicates the reduction of zinc interstitials and confirming that the interface states cannot maintain stability at high temperatures, thus becoming neutralized and consumed with zinc interstitials.

These findings demonstrate that the essence of the decreasing power loss in stable ZnO varistors lies in the stable interface states at the grain boundary, which, however, cannot maintain stable at certain high temperatures. The interface states would then neutralize with the zinc interstitials due ion migration, subsequently leading to the reduction of zinc interstitials and the destruction of the ZnO lattice, resulting in significant deterioration of ZnO varistors. Therefore, optimizing the high-temperature stability of the interface states is crucial for enhancing the long-term stability of ZnO varistors.

With global climate warming, the frequency of heavy rainstorm events is increasing. During heavy rainstorms, composite suspension insulator strings are prone to reduced creepage distance utilization and potential rain flashover accidents due to the sudden increase in rain intensity and rain columns bridging the skirt gaps. Since the rod diameter and skirt diameter of composite suspension insulators are smaller than those of large-diameter composite pin insulators, and there are significant differences in skirt structure and creepage distance, the rain flashover characteristics of suspension insulators cannot be equated with pin insulators. Hence, there is insufficient data on the rain flashover characteristics of composite suspension insulators.

This paper conducts artificial rain flashover tests under strong rainfall conditions using five different skirt structures of composite insulators. It investigates the effects of rain intensity and water conductivity on insulator AC flashover characteristics by analyzing the rain column length at the skirt edge, simulation models, arc development paths, and critical leakage currents. A formula for calculating the unit insulator height AC flashover voltage under the combined effects of rain intensity and water conductivity is proposed and verified for accuracy.

Results indicate that the unit insulator height rain flashover voltage is negatively correlated with rain intensity and water conductivity, following an exponential decay function. The voltage gradient reduction can reach 41.9% and 43.8% respectively due to these factors. The rainfall intensity of 9 mm/min can be considered the tipping point between non-extreme and extreme rainfall. Additionally, composite insulators with larger skirt diameters and skirt spacings are more significantly affected by rain intensity, while the impact of water conductivity is consistent across different skirt parameters. When the rainfall intensity is low, the shorter rain column causes the arc to develop along the path of “rain column-air gap-small skirt surface”. When the rainfall intensity is high, the rain column is longer, and the vertical air gap within the rain column is directly broken down, leading to the arc development path of “rain column-vertical air gap-large skirt surface”.

In addition, the maximum length of the rain column at the skirt increases with rain intensity and varies with skirt diameter and spacing. The arc development path differs significantly among insulators with different skirt parameters. Insulators with larger skirt diameters and spacings exhibit a combined air gap and skirt surface discharge path, with reduced rain column bridging the skirt gap due to effective skirt coverage, resulting in higher creepage utilization. Therefore, insulators with larger skirt diameters and spacings can effectively enhance the insulation strength. Due to the influence of the number of charged particles in the water and the area covered by the water film, the average critical leakage current increases with rain intensity and water conductivity, which increases the energy gained by the electric arc and promotes the development of local arcs and the formation of discharge channels. During heavy rainstorms, to reduce surface leakage current on composite insulators and prevent rainwater from bridging the skirts to form a “rain pillar-vertical air gap” continuous arc path, it is recommended to use composite insulators with larger inter-skirt spacing and skirt diameters in regions prone to frequent heavy rainfall.

Accurate prediction of the battery state of charge (SOC) is of great significance to improve the utilization efficiency and safety performance of the battery, and the monitoring of the battery state of charge is very important to help prevent overcharge and overdischarge accidents. The traditional SOC prediction methods are highly dependent on the mechanism model and statistical model, and have problems such as sensitive outliers and limited practical accuracy. In this study, a CNN-LSTM-AM (convolutional neural network - long short term memory neural network - attention mechanism) model is proposed to predict SOC variation trend through battery measurable variables.

The model first uses a one-dimensional convolutional neural network to extract spatial features of measurable variables, including battery current, voltage, temperature and average voltage, and then sends them to bidirectional long and short time memory for time series analysis. Finally, the attention mechanism is introduced to screen key features, reduce the redundancy of feature data, and improve the accuracy and generalization of the model. In addition, CNN-LSTM-AM model adopts rime optimization algorithm to optimize the hyperparameters in the training process, which effectively improves the training efficiency and reduces the training cost.

The actual evaluation on CALCE (Center for Advanced Life Cycle Engineering) data set of lithium iron phosphate shows that the attention mechanism can effectively improve the training performance of the prediction model, and the rime optimization algorithm adopted can help reduce the model hyperparameters, so as to obtain higher prediction accuracy. The performance of CNN-LSTM-AM model was tested under different temperature conditions, and both RMSE and MAE were less than 1%, which was sufficient to confirm the feasibility of the model to predict SOC. In addition, even if the initial SOC is uncertain, the proposed CNN-LSTM-AM model can still accurately track SOC trend changes, and the overall prediction accuracy reaches RMSE<1.5% and MAE<1.5%. The RMSE and MAE results of the network proposed in this study are smaller than those of CNN-LSTM and CNN-LSTM-AM. It shows strong robustness and generalization ability. Finally, in order to comprehensively compare the performance of different SOC prediction methods, the CNN-LSTM-AM model proposed in this study is compared with other experimental results. It can be seen that the method proposed in this study has significantly lower RMSE compared with AT-CNN-LSTM. At the same time, considering that the proposed method uses less training set data, we can also see the advantages of the designed network. Compared with EI-LSTM-CO(extended input-LSTM-constrained output), it can be found that the error is close. In addition, EI-LSTM-CO performs some post-processing on the predicted SOC, which can also reflect the superiority of the proposed method.

The following conclusions are drawn from the simulation analysis: (1) A CNN-LSTM-AM model is proposed and applied to the SOC prediction task of battery, which can effectively capture important input features and improve the prediction accuracy. (2) Design a rime optimization algorithm, which can automatically search the optimal solution of CNN-LSTM-AM model, effectively reduce the time cost of hyperparameter optimization. (3) The influence of different ambient temperatures and initial SOC values on the prediction accuracy of CNN-LSTM-AM was studied, and the performance of CNN-LSTM-AM was compared with that of traditional prediction models to verify its strong robustness and high generalization ability.

With the continuous development of the new energy vehicle industry, lithium-ion batteries are used in large quantities as on-board power batteries. The battery management system (BMS) is responsible for monitoring, evaluating, maintaining, and optimizing the performance and life of Li-ion batteries, and the prediction of c is an important part of the BMS. Accurate prediction of a battery's RUL helps identify batteries that are nearing the end of their life to prevent potential safety risks such as overheating, combustion, or explosion, and allows O&M personnel to schedule battery maintenance and replacements based on the battery’s actual state of health, rather than on a pre-determined schedule, thereby reducing unnecessary O&M costs. However, lithium-ion batteries exhibit nonlinear aging trends due to their complex internal chemical reactions during use, and the aging process of batteries usually exhibits multi-stage degradation, which increases the difficulty of RUL prediction. In view of this, this paper proposes a RUL prediction method for lithium-ion batteries based on public battery data from the University of Maryland and lithium iron phosphate battery data collected by the group's own laboratory, and the main research contributions are as follows:

Aiming at the problem that battery capacity is difficult to be measured directly, and the poor ability of traditional network models to capture multi-feature input information, a method is proposed to optimize the bidirectional gated recurrent unit (BiGRU) network based on the fusion feature and the osprey optimization algorithm (OOA) for RUL prediction of lithium-ion batteries. Simple and easy-to-measure current, voltage and time data during battery aging are collected, from which the health factors that can reflect the aging trend of the battery are extracted. The Savitzky-Golay filtering method is selected to reduce the influence of noise on the prediction accuracy. A fusion feature screening strategy combining filter and wrapper is proposed to reduce the complexity of the model and prevent model overfitting. Considering the insufficient ability of the traditional model to capture battery aging information when dealing with multi-feature inputs, the GRU network, which predicts only based on historical information, is upgraded to the BiGRU network, which is capable of handling both forward and backward information of the sequences. The BiGRU network is able to understand the overall structure and dynamic properties of the sequences in a more in-depth manner, better integrate the multi-dimensional features, and adapt to dependencies in different time scales. OOA is used to effectively optimize the hyper parameters inside the BiGRU model, which improves the prediction accuracy of the model and also realizes the automatic configuration of the parameters. Different types of battery data are used to compare the proposed method with traditional network models to verify the reliability of the proposed OOA-BiGRU model. In addition, the effect of the proposed fusion feature prediction is compared with all feature prediction and filtered feature prediction, which proves that the fusion feature better represents the aging degree of the battery and improves the accuracy of the model prediction.

The research results of this paper provide a new method and idea for RUL prediction of lithium-ion power batteries, which can be applied to the BMS system of new energy vehicles and is of practical significance.