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.

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.

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.

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.

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.

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.

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.

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.

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.