The breakdown voltage characteristics of power devices are primarily determined by the terminal. Variation of lateral doping (VLD) is a commonly used terminal technology, known for its advantages such as low cost, simple process, and high flexibility. In some cases, traditional linear VLD terminals have limitations, particularly in terms of breakdown voltage. A new nonlinear VLD terminal derived from theoretical analysis is presented, which features superior electrical characteristics and more uniform electric field distribution. This effectively weakens the concentration of the electric field and increases the breakdown voltage. In the same process and material conditions, the breakdown voltage peak of the linear VLD structure is 728 V, while the nonlinear VLD structure can weakens 758 V, achieving a 4.12% enhancement in breakdown voltage characteristics. Moreover, the new structure is relatively easy to implement with minimal increase in process complexity.

As a common power electronic converter, inverters are widely used in various scenarios where DC power supplies supply AC loads, as well as in AC motor speed regulating frequency converters and other instruments. Among them, Y-source inverter (YSI) has a simple structure and high efficiency, but it cannot suppress the starting inrush current well, which is easy to produce leakage inductance peak voltage, resulting in device damage and limited voltage boosting capability. The split-source inverter (SSI) uses fewer passive components, and can achieve the increase of the modulation index and the boost duty ratio at the same time, so the voltage gain can be adjusted more flexibly. However, due to the non-ideal state of the components and the limitations of the topology, the actual voltage gain is limited. Thus, a new type of quasi-split source inverter (QSSI) is proposed. The design of the inverter is a combination of Y-source impedance network and split-source structure, and a voltage clamp unit is introduced to improve voltage gain and solve the problem of voltage oscillation caused by leakageinductance resonance. In addition, QSSI adopts dual-buck structure (DBIS), which effectively inhibits the shoot-through current of the inverter bridge arm, thus improving the reliability of the whole converter. The feasibility of the inverter is verified by theoretical analysis, simulation and experimental results.

A novel DC-DC converter is designed which is a high gain converter combining a dual switching converter, a coupled inductor and a switched capacitor Boost unit. The advantages of this converter are its large voltage gain, low voltage stress on the switching tubes, and the ability to ensure the continuous output current. This converter is made by integrating the switched capacitor and clamped branch with each other, which enhances the voltage input, reduces the voltage stress on the switching tube and also consumes the energy of the leakage inductor with the help of the clamped branch. The voltage input of the converter in continuous conduction mode (CCM), the voltage stress of the switching tubes and the current stress are analyzed in depth. Finally, a test rig is built with 12 V input, 228 V output and 200 W power rating to verify the correctness of theoretical analysis.

For an analog second-order Sigma-Delta modulation class-D power amplifier circuit, the continuous domain mathematical model and discrete time domain iterative mathematical model are constructed. For the power amplifier circuit, the continuous domain mathematical model is derived, which shows that the principle of the second order sigma-delta modulation circuit is self-excited oscillation based on positive feedback, so as to realize the output switching signal. Since the continuous domain mathematical model can only obtain the self-excited oscillation frequency, the continuous domain mathematical model is discretized to obtain the discrete time domain iterative mathematical model. This model can calculate the switching frequency value of any input signal and analyze the stability conditions of second-order Sigma-Delta modulation circuit. The experimental results show that the error between the theoretical switching frequency calculated based on the mathematical model and the experimental value is less than 14%, which verifies the correctness of the mathematical model.

Aiming at the problem of high harmonic content in the output current of traditional nearest level modulation in medium-sized modular multilevel matrix converter (M3C), the pulse width modulation (PWM) mode is introduced into the traditional nearest level modulation (NLM) strategy. And considering the reduction of switching losses and achieve the accurate conversion of submodule states, an improved nearest level pulse width modulation strategy for M3C is proposed to improve the harmonic characteristics of its output current. Compared with the traditional nearest level modulation strategy, the proposed strategy can effectively suppress harmonic components on the M3C output current under the premise of reducing the switching losses. The feasibility and effectiveness of the proposed strategy are verified by M3C simulation model and experimental platform.

Critical conduction mode (CRM), in which the switching frequency of Boost converter changes widely due to the input voltage and load, leading to an increase in switching loss and a decrease in overall efficiency. To solve the above problems, an optimal control strategy for the full power range efficiency of Boost converter is proposed. Firstly, the internal loop control of inductive current is introduced on the basis of the traditional variable amplitude control strategy, and the mathematical relationship between the peak of inductive current and the output power under discontinuous conduction mode (DCM) and critical conduction mode is derived, and the peak of inductive current is accurately controlled accordingly. Secondly, the optimal working mode is selected for the peak value of inductive current at different loads to reduce the current stress and improve the efficiency of the converter. Finally, a 300 W experimental prototype is built to verify the proposed control strategy. The results show that compared with the traditional method, the proposed optimal control strategy can reduce the current stress by at least 8.34% and increase the peak efficiency by 2.15%.

The virtual synchronous generator (VSG) simulates the characteristics of synchronous generators to provide inertia and damping, but inevitably introduces the rotor oscillation characteristics of synchronous generator. Especially in VSG parallel operation systems, when the system is disturbed, it is prone to cause power oscillation problems, which may even damage power electronic equipment. To address this issue, based on state feedback and pole-placement theory, a power oscillation decentralized coordinated suppression strategy for VSG parallel systems based on transient damping enhancement is proposed. By constructing a combination of angular frequency high-frequency component compensation and electromagnetic power high-frequency component compensation, the transient damping of the system is improved, and power oscillation is suppressed without affecting the system steady-state characteristics. Then, the stability of the proposed control strategy is demonstrated by constructing a Lyapunov function. Finally, the effectiveness of the proposed method is verified through simulation.

With the increasing proportion of renewable energy in the grid, the overall damping and inertia of the grid decline, which brings challenges to the safe and stable operation of the grid. Virtual synchronous generator (VSG) technology, by simulating the characteristics of synchronous generator, brings damping and inertia support to the grid. However, it inevitably brings problems such as active low-frequency oscillation and active steady-state deviation. Through the establishment of VSG model, the oscillation mechanism of VSG is analyzed. The small-signal model of active power reveals the principle of low-frequency oscillation and steady-state deviation of active power caused by moment of inertia and damping coefficient. In order to suppress the low-frequency oscillation of active power and eliminate the steady-state deviation of active power, an adaptive VSG control strategy based on power dynamic region division is proposed. According to the different power change rates, the system is divided into the acceleration region and the deceleration region, so as to adaptive control the rate of increase or decrease of the moment of inertia and the damping coefficient, so that VSG has good dynamic characteristics in different working modes. The proposed strategy can effectively suppress the active low-frequency oscillation of VSG, reduce the steady-state deviation of the active power, and improve the active power support capability of VSG. Finally, the system simulation model is built in MATLAB/Simulink, and the correctness of the proposed strategy is verified.

Under weak grid conditions, the grid-connected inverter (GCI) commonly employs a type-II Phase-Locked Loop (PLL) system, which is prone to interaction with the grid impedance, leading to instability issues characterized by low-frequency oscillations. A traditional solution aimed at enhancing GCI stability by reducing the type-II PLL's bandwidth. However, this approach compromises the dynamic performance of the PLL and its adaptability to a wide range of grid impedance variations. To address this, the traditional Type-II PLL was replaced by Type-I PLL, significantly improving the stability of GCI in weak grids without degrading the PLL's dynamic performance. Furthermore, to tackle the phase-locking error issue in type-I PLLs during significant grid frequency fluctuations, an adaptive feedforward method for frequency control is introduced. This enhancement transforms the type-I PLL into a quasi-type-I PLL, eliminating phase tracking errors and enhancing the stability and dynamic performance of GCI in weak grid environments. Both simulation and experimental results confirm the validity and efficacy of the proposed methods.

The traditional parameter design methods based on gain margin (GM) and phase margin (PM) usually result in a low bandwidth of the phase-locked loop (PLL). This means that the fast dynamic of the PLL is sacrificed to improve system stability. Furthermore, the values of GM and PM are of only minor help in determining the maximum available power of a grid-connected inverter (GCI) system. Determining the maximum available power often requires complex multiple simulations or experiments. An operation-margin-based PLL parameter design method is proposed to solve the above problems. A small-signal model of GCI system considering the dynamic characteristics of current loop and PLL dynamics is established. Then, an open-loop transfer function model in complex vector form that can characterize the system is obtained. Based on this model, the three-dimensional control parameter domain under different operating point variables is plotted and the influences of output current and grid voltage on the value of control parameter are analyzed. Finally, the simulations and experiments verify the effectiveness and correctness of the proposed control parameter design method.

During the black start process of a renewable energy station, traditional grid-forming converters usually output symmetrical three-phase currents. At this time, the asymmetrical loads within the station will lead to unbalanced three-phase voltages, thereby triggering power fluctuations between the inverter and the grid, increasing the risk of black start failure. In order to achieve voltage balance during the black start process, the three stages of the black start of a renewable energy station based on the grid-forming energy storage converter are analyzed. By combining the connection mode of the asymmetrical load, it studies the mechanism of voltage unbalance during the zero-to-rise voltage stage considering the constraints of the three-phase three-wire topology. Aiming at the grid-forming characteristics of the energy storage converter, a black start voltage balance strategy based on model predictive control is proposed. This method does not require positive- and negative-sequence separation control, and can effectively suppress the negative sequence voltage generated by the asymmetrical load, and reduce the voltage unbalance factor. The hardware-in-the-loop experiment verifies the effectiveness of the strategy proposed, which can provide a new technical reference for the black start of renewable energy stations.

In off-grid photovoltaic storage microgrids, the DC-side bus voltage amplitude is easily affected by the fluctuation of photovoltaic (PV) output power and load power, the system may experience DC bus voltage oscillation and long voltage recovery time under the traditional control strategies. In order to improve the dynamic stability of bus voltage, a fuzzy-proportional integral (PI) control method based on nonlinear disturbance observer (NDO) power feedforward is proposed. Firstly, the system state equation in storage discharge mode is established, and the disturbance observation link is designed to observe the disturbance power of the PV and load in real time and add it to the current inner loop as a feedforward quantity after correction. Then, a fuzzy-PI controller based on bus voltage deviation is designed to improve the structure of voltage outer loop, and the design process of disturbance observer and controller is given. Finally, the simulation and experimental results verify that the strategy can have good dynamic stability of DC bus voltage under load impact.

Compressed air energy storage (CAES), as an energy storage technology with great development potential, has the advantages of large scale, long life and environmental protection. However, due to its large capacity, if the voltage phase amplitude and frequency are not the same as those of the power grid when it is connected to the power grid, there will be a problem that the grid-connection impulse current is too large and threatens the safe and stable operation of the power system. Therefore, based on linear active disturbance rejection control (LADRC), which is more accurate than traditional PI control, a linear active disturbance rejection control strategy with adaptive adjustment of bandwidth parameters is proposed to achieve smooth grid connection. Finally, the proposed control strategy is verified by simulation experiment and hardware-in-the-loop experiments, and appropriate adjustment parameters are selected. The results show that the proposed adaptive control strategy can effectively reduce the impact of grid-connection and achieve smooth grid-connection, providing a new solution for the safe access of compressed air energy storage to the grid.

The lithium plating is the main reason for the performance degradation, temperature rise and expansion deformation of lithium-ion batteries (LIBs). In order to investigate the lithium plating amount (LPM), temperature variation and the damage of the cathodic particles during the charging of LIBs. Firstly, an electrochemical-thermal-stress coupling model with lithium plating is developed. Then, the influences of charge rate on the cathodic electrical potential and LPM are analyzed. Finally, the effects of the initial temperature, charging mode and cathodic particle radius on the LPM, average temperature and Von Mises stress of cathodic particle are discussed, respectively. Numerical results show that LPM increases with the increase of charge rate. In order to reduce LPM, temperature rise and Von Mises stress during the charge, the charge at lower initial temperature should be avoided, the cathodic particle radius should be reduced or multi-stage constant current and constant voltage charging (4C to 6 A∙h, 3C to the cutoff voltage and constant voltage charging) should be used. This work will provide some guidance for reducing lithium plating.

The initial state of charge (SOC) of the battery is frequently random when users charge the batteries. Therefore, it is difficult to obtain complete charging data of lithium batteries in practical applications. To solve this problem, a state of health (SOH) estimation method of lithium battery based on partial charging data is proposed. Through the established GRU-BP (gated recurrent unit-back propagation) model, a complete charging curve can be obtained from partial charging data. Then the SOH can be estimated by the features extracted from the complete charging curve, which can effectively characterize the aging of battery. This method can achieve SOH estimation under any fixed voltage interval charging data in constant current charging mode. The experimental results show that the proposed method can effectively predict the SOH of lithium batteries with incomplete data, and the estimated root mean square error (RMSE) is below 2%.

Aiming at the problems that the offline parameter identification method can only identify a single fixed parameter which leads to insufficiently accurate state of charge (SOC) estimation, and that the traditional Kalman filtering algorithm is poorly adapted to different temperatures, a fractional-order multi-innovation cubature Kalman filtering combined with fractional-order unscented Kalman filtering (FOMICKF-FOUKF) algorithm is proposed to estimate the SOC of lithium-ion batteries. The algorithm adopts a multi-timescale approach to carry out the online parameter identification with the FOUKF in the macro time scale, and the SOC estimation with the FOMICKF in the micro time scale. The convergence ability of different algorithms varies, and in order to verify the convergence of the algorithms, the algorithms were subjected to the convergence comparison experiment. In addition, it is more difficult to accurately estimate battery SOC at different temperatures where the internal polarization reaction varies significantly. SOC estimation experiments were conducted at 0°C, 25°C and 45°C to verify the adaptability of the proposed algorithm to different temperatures. The results show that the FOMICKF-FOUKF dual Kalman online joint algorithm estimates the SOC with high accuracy and good convergence, and has good adaptability to different temperatures.

Accurate estimation of State of Charge (SOC) for lithium-ion batteries is crucial for implementing battery balancing during charging and discharging, thus extending battery lifespan. Given the complex chemical characteristics of lithium-ion batteries and the non-time-varying nature of SOC estimation using the Kalman filter algorithm, a novel method for SOC estimation for lithium-ion batteries is proposed. Firstly, combining the precision and practicality of lithium-ion battery models, a second-order equivalent model for lithium-ion batteries is presented. Subsequently, utilizing the Adaptive Forgetting Factor Recursive Least Squares (AFFRLS) algorithm with adaptive forgetting factors, the impedance-capacitance parameters of the second-order model are identified online, establishing a filtering relationship for impedance-capacitance parameters. The proposed method integrates Particle Filter (PF) and Cubature Kalman Filter (CKF) algorithms, termed Particle Cubature Kalman Filter (PF-CKF), to estimate SOC for lithium-ion batteries. Finally, Hybrid Pulse Power Characterization (HPPC) experiments are designed to validate the accuracy and stability of this method.

The centralized thermoelectric system exhibits multi-peak characteristics under non-uniform temperature distribution (NUTD). Based on the problem that traditional maximum power point tracking (MPPT) methods are prone to falling into local maximum power point (LMPP) and existing metaheuristic algorithms suffer from energy loss due to long search times caused by imbalanced search capabilities during exploration and exploitation phases, an improved hunter-prey optimization (IHPO) algorithm is proposed. This algorithm relies on the clear exploration and exploitation phases search capability mechanism of standard hunter-prey optimization (HPO) algorithm, and improves the original mechanism for multi-peak characteristics, reducing the large amplitude oscillation caused by frequent random jumps of the algorithm itself while quickly tracking the global maximum power point (GMPP). Based on the centralized thermoelectric system model, IHPO algorithm is simulated and compared with perturbation and observation (P&O) method, particle swarm optimization algorithm (PSO), improved PSO (IPSO) and standard HPO under different operating conditions. The results show that the proposed algorithm can quickly and accurately track GMPP under NUTD, significantly reduce the oscillation of the tracking process, and improve energy output by up to 21.8%. The IHPO algorithm can effectively solve the multi-peak problem of centralized thermoelectric systems.

The soft open point (SOP), as a sort of promising flexible power electronic device in future smart distribution network, can take the place of normal open point to achieve flexible interconnection and power regulation. The shared module soft open point (SMSOP), as a new flexible interconnection topology, has the advantages of high efficiency, small size and low cost. However, SMSOP is prone to overmodulation when using conventional pulse width modulation (PWM) and has high losses if the shared module accounts for more. A new sine-wave-like modulation method is proposed. The sine-wave-like modulation adds a square-wave platform on the basis of the sine-wave modulation. Sine-wave modulation is used at the start and end of each half-cycle to prevent over-modulation at zero-crossing time; square-wave modulation is used in the middle to reduce the switching losses of the shared modules. Finally, the correctness and effectiveness of the new modulation strategy are verified by building a three-phase experimental platform.

In a renewable power collecting system, the number of generators is large, the network topology is complex, and the combination of operating conditions is various, which makes the small-signal stability analysis of the system inefficient and the equivalent modelling difficult. Thus, a small-signal equivalent modelling method of the multi-type renewable energy collecting system is proposed based on partitioned similarity transformation of the transfer function matrix of the generators and the network. First, by combining the models of generators and the network equation of the system, a detailed model of the renewable power collecting system is obtained. Second, considering that the system can be divided into several groups in which dynamics of the generators are approximately the same, via the partitioned equivalent decoupling and order reduction, a single-machine equivalent model is established for each group and hence a multi-machine equivalent model for the system is obtained. Finally, the effectiveness of the proposed method is verified via case studies. The method proposed can deal with the differences of dynamics of the generators and the complex network topology and also effectively reduce the computational burden of the small-signal stability analysis.

In the event of a short-circuit in the AC system where the modular multilevel converter-unified power flow controller (MMC-UPFC) is located, both the series and shunt-side MMCs will be impacted by the fault and be blocked due to overcurrent, leading to the complete shutdown of the MMC-UPFC. The fault characteristics of the AC system where the MMC-UPFC is located are analyzed. It clarifies the main reasons for the overcurrent blocking of the shunt-side MMC. Based on this analysis, a fault ride-through strategy for MMC-UPFC is proposed, which is based on a distributed current limiting module. The working principle of this strategy is analyzed in detail, and the guidelines of its parameter tuning are given. The proposed fault ride-through strategy can effectively suppress the DC bus current by inserting a current limiting resistor during the fault period. This ensures that the shunt-side MMC is not blocked during the fault period, allowing it to continue generating reactive power. Finally, simulations in MATLAB/Simulink are used to verify the effectiveness of the proposed fault ride-through strategy and the correctness of the parameter tuning guidelines.

Long-term operation of distribution networks with persistent single-phase grounding faults will result in insulation deterioration and subsequent phase-to-phase short-circuit faults, and grounding arcs also pose potential fire risks. In order to improve the reliability of power supply and ensure the safety of equipment and environment, the active intervention arc suppression device can effectively reduce the current level of the fault point and extinguish the arc light generated by grounding by introducing the short-circuit current into the substation. However, most of the active intervention arc suppression devices use the zero-sequence voltage as the threshold as the starting criterion, and the resistance value of the single-phase grounding transition resistance has a great influence on the zero-sequence voltage. In order to ensure the efficient operation of the device, analyzes the functional relationship between the transition resistance and the zero-sequence voltage firstly, then deduces the calculation formula of the transition resistance by the vector triangle method and the functional relationship method, and explains the setting method of the zero-sequence starting voltage of the device, and finally verifies the validity and rationality of the calculation method of the endurance transition resistance and the parameter setting of the zero-sequence starting voltage through the simulation experiments and the high-impedance artificial single-phase grounding experiment.

To address the complexities in regulating and operating distribution grids with a high proportion of distributed photovoltaic (PV) systems, which challenges conventional methods in vulnerability identification, a novel methodology utilizing an edge-feature graph attention network (EGAT) is proposed for state estimation and further identification of vulnerabilities via over-limit indicators. Initially, busbars within the distribution network are conceptualized as nodes of a graph, with transmission lines serving as edges, thus forming a topological dataset grounded in their connectivity matrix. Subsequently, the power of each transmission line and the voltages at the connected busbars are consolidated into an edge feature matrix. The employment of EGAT layers facilitates targeted feature extraction and learning, where attention weights are attributed to each node and transmission line. In response to various operational scenarios of the PV distribution network, the EGAT model undergoes training with the integration of transfer learning techniques. Ultimately, by establishing indices for node vulnerability and line vulnerability, a thorough evaluation of the network’s vulnerability under different scenarios is achieved. Demonstrative case studies validate the effectiveness of this approach in recognizing vulnerabilities within distribution networks with substantial integration of distributed PV systems, accurately identifying vulnerable nodes and lines induced by fluctuations in PV output and topological modifications.

In order to increase the energy efficiency of fuel-cell hybrid electric vehicle (FCHEV) and improve the operating environment of fuel cells, an energy management strategy is proposed. Firstly, wavelet transform is used to decompose the vehicle bus power, and the decomposed low-frequency demand power and the state of charge of the power battery are used as inputs to the fuzzy controller. Then, the output power of the fuel cell is calculated via the energy management strategy. Furthermore, genetic algorithm is used to improve the fuzzy rules and improve the control performance. Finally, a vehicle energy management strategy model is built, and a vehicle dynamics model using Simulink and Cruise respectively, and conduct joint simulation under CLTC-P operating conditions. Compared with the power following based control strategy, the proposed strategy reduces the equivalent hydrogen consumption of fuel cells by 14%. While the vehicle is more energy-efficient, it effectively reduces the start stop frequency and load change rate of fuel cells, thereby extending their service life.

In order to correctly identify the operating status of photovoltaic arrays and reduce the missed diagnosis rate of faults, an improved bidirectional long short-term memory network (BiLSTM) fault diagnosis method for photovoltaic arrays is proposed. Firstly, by combining Attention mechanisms to optimize and adjust the attention weight allocation of fault labels, the missed diagnosis rate of photovoltaic array fault diagnosis can be reduced; Secondly, the traditional Harris hawks optimization (HHO) algorithm is further optimized by integrating the best point set, segmented adaptive escape energy mechanism, and golden sine strategy, and the optimized Harris hawks optimization (IHHO) algorithm is used to optimize the hyperparameters of the BiLSTM model. Comparative experiments show that the IHHO-Attention-BiLSTM model achieves a fault diagnosis accuracy of 99.26% for photovoltaic arrays, with a missed diagnosis rate as low as 0.13%.

Accurate estimation of the battery state of health (SOH) can effectively improve the safe operation of system. Aiming at the problem of unstable SOH estimation accuracy under different working conditions, a stacking model-based method for lithium-ion battery SOH estimation is proposed. The method firstly carries out incremental capacity (IC) analysis for the charging data, and extracts the health factor as an effective feature, and then constructs an stacking model by stacking the support vector regression (SVR) and back propagation (BP) neural network model to realize the on-line evaluation of the battery SOH. The experimental results show that the maximum error of the stacking model proposed decreases by 38.1% and 23.7%, and the root mean square error (RMSE) decreases by 47% and 20% compared with the two sub-models. The proposed method can more accurately estimate the SOH under various operating conditions, mitigating the instability factor of the single model estimation.

The state of health (SOH) of Li-ion batteries gradually decreases with the number of charges and discharges, whose features are hidden in the physical information such as battery current and voltage. A data-driven method based on wavelet packet transform (WPT) and sparrow search algorithm (SSA) is proposed for online estimation of SOH, and the feature indices of discharge voltage plateau time (DVPT) and constant current charging time (CCCT) are proposed. The algorithm is verified on the University of Maryland battery public dataset. The results show that the proposed DVPT characteristic metric has a strong correlation with SOH, with an average Pearson correlation coefficient of 98.38% across the four batteries; the model predicted the optimal results with RMSE of 0.019 1, MAE of 0.012 5, and R² of 97.78%.

With the development of wireless power transfer (WPT) technology, the parameter identification technology for WPT systems has attracted extensive attention. Efficient and accurate parameter identification methods can simplify system design and control strategy, and achieve online monitoring of key parameters. The parameter identification and control methods of WPT system are first summarized. Then, the current parameter identification methods of WPT system are divided into three categories: mutual inductance identification, simultaneous identification of mutual inductance and load, and multi-parameter joint identification. Meanwhile, the implementation methods and key problems of each type of identification method are analyzed. Finally, the important problems and technical challenges of parameter identification technology are discussed, and the future application prospects of parameter identification technology are forecasted.

The unmanned platforms have the advantages of small size, low cost and no man-carrying operation, and are widely used in various military missions. At present, the unmanned platforms mainly use electric energy as power source, and their demand for reliable power transmission methods is increasing. In the cases where personnel cannot participate, the charging interfaces are not suitable, and the charging process is low in efficiency, wired charging or battery replacement has drawbacks, which cannot meet the demand for electric power transmission of the unmanned platforms. The system structure is designed to meet the requirements of unmanned platforms wireless power transfer, and based on this structure, a parameter optimization method based on particle swarm optimization (PSO) algorithm is proposed. By analyzing the structure and characteristics of the compensation circuit, coupling coil and energy storage device of the unmanned platform wireless power transfer system, the optimization objectives of the mathematical model of the system parameter optimization and the constraints of power, efficiency and current are proposed, and the algorithm flow of the parameter optimization method of the unmanned platform wireless power transfer system based on PSO algorithm is designed. The results show that the proposed optimization method based on PSO algorithm can reduce the fluctuation amplitude of the output voltage, power and efficiency with the load resistance and coupling coefficient change, and improve the robustness and environmental adaptability of the unmanned platform wireless power transfer system.

Aiming at the problem that the relative position deviation of the transceiver coil in the magnetic coupling resonance wireless power transfer system leads to the rapid reduction of transmission power and efficiency, A power transfer mechanism with wide effective magnetic coupling region is constructed from the perspective of coil physical parameter optimization. The aim is to increase the anti-offset ability of the system under the allowable range of energy efficiency fluctuation. Firstly, the mapping relationship between system parameters and energy efficiency is derived, and the coil parameters which affect the high efficiency transfer of the system are analyzed. Then, the effective magnetic coupling region of power transmission is defined with the goal of high power and high efficiency transfer of the system. Based on this, the coupling coil parameters are analyzed and optimized, and an optimal strategy for selecting universal transceiver coil parameters is formed. Finally, in order to verify the correctness of the quantitative analysis of the effective magnetic coupling region and the effectiveness of the coil parameter optimization, a wireless power transfer system prototype is built. The results show that when the coupling distance is 5 cm and the efficiency is higher than 85% and the power fluctuation range is less than 25%, when the coupling coil is a regular quadrilateral structure and the area ratio of the transmitting and receiving coils is 1:2.89, the power transfer mechanism has the largest effective magnetic coupling area.

The total weight and flight resistance added to the UAV by the wireless charging device during wireless charging of the UAV can reduce its flight efficiency as well as its flight time. A lightweight design method based on the receiver side of UAV wireless charging system is proposed to address this problem. Firstly, the core material and size of the coupling mechanism are optimized. Then the feasibility of the optimization method to realize the lightweighting of the receiving coil by increasing the system frequency is studied and verified, and the impact of increasing the system frequency on the main circuit is analyzed from the perspective of magnetic core loss. Then combined with the actual structure of the UAV, the effect of the carbon fiber fuselage on the performance of the magnetic coupling mechanism of the UAV is analyzed by using the overall modeling and analysis method. Finally, the physical model of wireless charging system for UAVs is built, and the performance tests of the original system and the up-frequency lightweight system are carried out, and the feasibility is verified through comparative experiments.

To improve the pulse waveform quality of high-power microwave sources, a pulsed high-voltage modulator capable of delivering 41.2 kV, 44 A required by a klystron in a high-power microwave system was designed. The modulator with a combined architecture consisting of a Marx module and a pulse transformer achieves a high step-up ratio of approximately 100 times. Especially the dual secondary winding in the pulse transformer easily realizes the high-voltage isolation of the filament power supply. Conceptual design and functional verification are achieved through simulation and experimental research. The modulator is tested to be able to generate a high-voltage pulse of 41.2 kV with a droop of about 4.9% on a 936 Ω normally-operating klystron load at a charging voltage of 260 V. Measured pulse rise-time, fall-time and overshoot are about 800 ns, 900 ns and 3.9%, respectively.

Aiming at eliminating the bulky and expensive line-frequency phase-shifting transformer at the input of the traditional medium/high voltage converter for AC motor drives, a new unidirectional power flow mining converter topology for open-end winding motors is proposed. The rectifier stage of the proposed converter topology adopts three single-phase diode rectifiers + Boost converter circuit structure without line-frequency transformer isolation, which is characterized by high input power factor, low input current harmonics and low switching device voltage stress. The inverter stage is connected to the AC motor by adopting stator open-winding connection for the isolation between phases. On the basis of the proposed topology, further investigates a low common-mode voltage space vector pulse width modulation (SVPWM) strategy, which achieves unity power factor operation of the system, the suppression of the common mode voltage in the rectifier stage, and the elimination of the zero sequence voltage in the open winding motor.

Efficient diagnosis of IGBT open-circuit faults in grid-tied inverters is helpful for quickly detecting the fault and ensuring the stable operation of the grid-tied inverter system and the safety of equipment. The existing IGBT open-circuit fault diagnosis methods have problems such as difficult feature extraction and poor diagnosis accuracy. On this basis, a method is proposed for IGBT open-circuit fault diagnosis of grid-tied inverter based on modified random forest. First, the wavelet packet energy of each frequency band of three-phase current signal is extracted by maximum overlapping discrete wavelet (MODWT) decomposition as the characteristic values for IGBT open-circuit fault diagnosis; Then, the modified random forest method is used to identify different types of faults; Finally, the various IGBT open-circuit faults of grid-tied inverters are diagnosed. The experimental results show that the proposed method has higher diagnosis accuracy and is suitable for IGBT open-circuit fault diagnosis of grid-tied inverters compared with the traditional diagnosis methods such as back propagation (BP) neural network and support vector machine (SVM).

Power electronic transformer (PET) insulation system continue to deteriorate in high frequency, high voltage, short rise time pulse electrical stress and high temperature and other harsh environments; insulation discharge and breakdown risks are increasing significantly. In order to study the effect of high temperature thermal aging for PET on insulation deterioration under real operating conditions, the typical insulation polyimide (PI) of PET was aged at 250 ℃ for 3, 6, 9, 12 days, and then 20 kHz, 4 kV, 300 ns pulsed voltage was used to simulate PET operating conditions. The partial discharge and breakdown voltage of insulation were tested, and time-resolved partial discharge spectrum (TRPD), discharge times, average discharge amplitude, and maximum discharge amplitude were used to characterize the degree of insulation deterioration. The results show that after aging, the TRPD pattern of PI films changes from single peak to multiple peaks, showing a phenomenon of clustering. At the same time, with the increase of aging time, the number of partial discharges, average discharge amplitude and maximum discharge amplitude increase by 47.3%, 61.1% and 41.9%, respectively, and the degree of insulation deterioration was significantly deepened. The breakdown voltage distribution of PI film conforms to Weibull distribution and decreases with aging time, from the initial 4 667 V to 4 457 V, a decrease of 4.5%. Therefore, the effect of aging time on the deterioration of PET insulation is obtained, which has certain guiding significance for the reliability analysis and optimal design of PET insulation.