Due to the trade-off relationship between breakdown voltage (BV) and specific on-resistance R_on,sp, to minimize the decrease in breakdown voltage while reducing on-resistance, an improved U-trench metal-oxide-semiconductor field-effect transistor (UMOSFET) structure with PN-Bottom is proposed. First, on the basis of traditional 100 V U-trench metal-oxide-semiconductor (UMOS) devices, deep trench etching technology is adopted to introduce a PN-Bottom wrapping region at the bottom of the trench, which wraps the P-type region inside the N-type region. Then, the new structural parameters and fabrication process of the device are introduced. Next, using the TCAD simulation tool, a detailed discussion was conducted on the doping concentration, width, and depth of the N-Bottom and P-Bottom encapsulation regions, and their effects on the breakdown voltage and specific on-resistance of the device were studied. Finally, the simulation results show that under the same conditions, an optimized design with BV of 110 V and R_on,sp of 1.15 mΩ·cm² was obtained.

The health monitoring of the IGBT is crucial for the normal operation of the system, and current research mainly focuses on single aging. The combination of multiple aging states can lead to low applicability and misoperation of some monitoring methods. There are still many shortcomings in the research on the determination and differentiation of multiple aging states. Due to the rich information contained in the U_CE spectrum, different aging will have different effects on it. Therefore, a method for determining and distinguishing bond wire aging and gate aging based on U_CE spectrum is proposed. Firstly, the impact of bonding wire and gate aging on the voltage switch waveform is analyzed. Then, fast Fourier transform (FFT) the switch voltage waveform and perform spectral analysis to explore the impact of different aging on the spectrum, and thus make aging judgments and distinctions. Finally, the circuit is constructed for experimental verification and the impact of different operating conditions on the method is analyzed. The experimental results show that both bond wire aging and gate aging can increase the amplitude of the high-frequency band, and the proposed method can effectively distinguish different aging forms.

The power module is a critical component of the powertrain system in electric vehicles, and its development is driven by the trends of high-frequency operation, miniaturization, high power density and high integration, which brings increasingly severe challenges to thermal management. Thermoelectric cooler (TEC) has attracted widespread attention for its compact structure, high reliability and rapid cooling performance. A temperature feedback control strategy based on thermoelectric cooling is proposed, with phase change materials (PCM) adopted as the interface layers at both cold and hot ends of TEC. Combined with simulation and experimental verification, the layout design of transverse and longitudinal heat transfer paths from the chip to the heat sink is investigated. Compared with the non-TEC cooling scheme, the proposed integrated TEC-based thermal management method reduces the maximum temperature of the SiC chip from 77.6 ℃ to 60.0 ℃, achieving effective thermal regulation and precise temperature feedback control. An efficient thermal management solution for high-power-density SiC power modules is provided and its application potential in electric vehicle power systems is exhibited.

A gallium nitride high electron mobility transistor (GaN HEMT) is widely used in switching power supply design because of its superiority over silicon devices in terms of switching speed, switching loss, and power conversion. Due to the high di/dt and du/dt of GaN HEMT in the switching process, the gate-source of the device in the bridge circuit is easily affected by the parasitic parameters to generate excessive voltage oscillation, which will seriously affect the safety and reliability of the system. To address the above problems, firstly, the bootstrap driver circuit of GaN HEMT is constructed, the mechanism of oscillation is analyzed, and then the RCD circuit based on GaN HEMT is proposed to suppress the negative voltage spike caused by voltage oscillation. Finally, a pulse test circuit is built to experimentally verify the suppression effect of the RCD circuit. Compared with the traditional bootstrap driver circuit, the negative voltage spike after adding the RCD circuit changes from -3.35 V to -1.20 V, and there is no significant delay in the turn-on time of the turn-on process and the turn-off loss process is reduced. The above experimental results verify the effectiveness of the RCD circuit for directional suppression of gate-source oscillation.

Traditional energy storage systems typically include a charging/discharging converter and an equalizer, which are independent of each other. To reduce system complexity and the number of components, a converter for energy storage systems is proposed, which integrates an equalizer into a charge/discharge converter, and achieves automatic voltage balancing between series-connected batteries without the need for voltage measurement circuits, thereby reducing components and simplifying the system. There are two types of proposed converters: type A contains only one active switch, one n-winding inductor and n diodes; type B replaces n diodes in type A with n active switches to improve the efficiency of the circuit. The similarities and differences between type A and type B converter are comparable to those between the asynchronous and the synchronous Buck converter, type B is mainly analyzed and developed a prototype. The experimental results indicate that the proposed converter has good voltage balancing characteristics.

Traditional CLLLC resonant converters do not effectively utilize the switching tubes of the rectifier circuit during operation, resulting in current flowing only through their anti-parallel diodes, leading to high losses and low efficiency. The traditional synchronous rectification scheme based on drain-source voltage is susceptible to the influence of parasitic inductance of switching tubes at high frequencies, which affects the efficiency of synchronous rectification. A detailed analysis of the impact of parasitic parameters on the duty cycle and efficiency of synchronous rectification in CLLLC resonant converters is provided, and an optimization strategy for synchronous rectification based on drain-source voltage is proposed. In a CLLLC resonant converter, the strategy adjusts the turn-off threshold voltage of secondary MOSFET, and improves the efficiency of synchronous rectification by compensating the duty cycle loss time of the synchronous rectifier switching tubes. The experimental results show that the synchronous rectification optimization strategy proposed can effectively reduce the duty cycle loss caused by parasitic parameters and lower the overall losses of CLLLC resonant converter, compared with the traditional synchronous rectification strategy based on drain-source voltage.

The dual active bridge (DAB) microinverter employs a transformer for isolation and step-up before connecting to the grid through bidirectional switches and cycloconverter operation. In comparison to other microinverter solutions, this design boasts numerous advantages, including high conversion efficiency, a broad soft-switching range, a reduced number of power conversion stages, and the ability to provide reactive power support. First, in terms of circuit modulation and control, the circuit topology and switching stages of the DAB DC-AC converter are thoroughly analyzed, a time-domain model is established, the range of transmitted power and soft-switching range are provided, and a feasible closed-loop control framework is proposed. Then, for hardware design, the primary passive components of the DAB microinverter include transformers and inductors. To further enhance power density, a magnetic integration design for transformers and inductors is introduced. By constructing additional magnetic paths on the microinverter transformer, the inductance value of the integrated inductor can be flexibly adjusted, significantly reducing the volume of magnetic components. Finally, a 450 W experimental prototype was built, and experiments were conducted to validate the effectiveness and feasibility of the proposed modulation strategy and magnetic component design method.

An embedded feedforward ripple cancellation (EFFRC) technology with a low-pass filter is proposed to address the issue of poor power supply rejection (PSR) performance in traditional output-capacitorless low-dropout regulator (LDO) operating in the medium and high frequency range. Firstly, an adaptive bias super source follower is inserted into a cascaded flipped voltage followers to drive the power transistor, and Miller compensation and feedforward capacitance compensation techniques are employed to ensure system stability and widen the LDO loop bandwidth. Additionally, a dedicated low-pass filter is integrated into the buffer to effectively eliminate power ripples at the LDO output without consuming additional power, thereby significantly enhancing PSR frequency characteristics. The designed LDO is implemented using SMIC130 nm CMOS process. Simulation results demonstrate that it achieves a low-frequency PSR value of 90 dB across a load change ranging from 0 to 50 mA. Furthermore, the PSR exhibits a frequency bandwidth exceeding 10 MHz, with potential improvements of up to 12 dB in the mid-frequency band. The good PSR characteristics show that the LDO has good dynamic suppression ability for different-frequency noise of input voltage, so it can provide a clean power supply voltage for the circuit driven by it, such as high-performance analog-to-digital converters and low-noise amplifier.

In the process of engineering implementation, the dead-beat current tracking control actually evolves into a one-beat lag control, which leads to the decline of harmonic compensation performance of active power filters. On the basis of analyzing the influence of system delay on digital control, adaptive forward linear prediction and repetitive prediction algorithms are prehensively used to predict the harmonic compensation reference current two sampling times in advance, and discriminant conditions for the switching of the two prediction algorithms are set based on whether the system dynamics occurs. In order to predict the output current of the inverter at one sampling times in advance and effectively suppress the measurement noise, the inverter current prediction link uses both the harmonic reference current to be compensated and the historical data of the output current as inputs. Therefore, the stability of the proposed predictive dead-beat control is analyzed when the output filter inductance and resistance parameters of the main circuit are deviated, and the transmission of current and voltage measurement noise in the system is further analyzed. Finally, simulation and experimental comparison studies on three types of predictive dead-beat control are conducted to verify the effectiveness of the mixed current prediction-based dead-beat control algorithm proposed.

Synchronous compensator can effectively improve the oscillation problem caused by renewable energy connected to a weak grid, but most of the existing regulator modeling methods for oscillation analysis do not take into account the control role of the excitation regulator, which is not accurate enough when applied to the analysis of low-frequency power oscillations, resulting in the limited scope of application of the model. For this reason, a synchronous compensator impedance modeling method that takes into account the excitation response characteristics is proposed. Firstly, a general impedance model of synchronous machine is established. Then, the influence of the self-shunt static excitation system on the impedance characteristics of the compensator is analyzed, and the overall impedance model of the compensator is obtained by associating the excitation model with the synchronous machine model. Finally, the validity of the proposed method is verified by simulation cases, and the analytical results show that: the impedance of the compensator will peak near the industrial frequency (corresponding to low power frequency), and the excitation system will significantly reduce the peak impedance; the self-parallel static excitation system will significantly change the impedance around the power frequency, so that the impedance will be characterized by multiple peaks.

Single-phase phase-locked loop (PLL) is widely used in fields such as power systems. However, the traditional single-phase PLL algorithms face challenges such as high computational complexity and complicated calculation methods. Moreover, their performance is affected in scenarios where the frequency of the input signal undergoes significant changes. A single-phase phase-locked loop based on sinusoidal tracking nonlinear control is proposed to address these issues. Using a Lyapunov stability-based design method, global convergence is achieved when the input signal frequency changes. A simulation model of this new PLL is constructed in PSIM and the application effects of the proposed method are compared with the conventional single-phase PLL based on the second-order generalized integrator, an experimental test is completed using the TMS320F28069 digital signal processor. Simulations and experiments indicate that the algorithm, with its simplicity and fast response, can improve the accuracy degradation issue occurring in traditional second-order generalized integrators during abrupt frequency changes.

Negative inductance is not a traditional physical componentwhich must be implemented through active circuits. Its equivalent implementation schemes include low-power negative inductance based on operational amplifiers and high-power negative inductance based on power electronic converters. Negative inductance exhibits impedance phase characteristics opposite to those of traditional positive inductance, enabling its use of impedance matching or transformation. Currently, its typical applications are concentrated in the microwave field and power systems, with relatively limited applications in power electronics. The definition, characteristics, stability and distinctions from capacitance of negative inductance are first discussed. Then focuses on introducing the principles and control strategies of existing methods for implementing high-power negative inductance based on inverters, comparing these approaches and validating them through simulations. Subsequently, analyzes the application of negative inductance in active common-mode chokes and in simplifying compensation for wireless power transfer. Finally, discusses critical issues requiring urgent resolution in negative inductance research.

Under abusive conditions, the battery cell will experience severe thermal runaway. The heat is then transmitted to the adjacent battery through thermal radiation and convection, which may cause a battery pack and entire electric vehicle to explode. Therefore, the effects of heat abuse of battery cells under various state of charge (SOC) and heating powers, as well as the effects of various electrical connection modes and battery pack arrangement shapes, are investigated on thermal runaway propagation based on an experimental platform that was created by the user. It is of great significance to the safety protection and early warning of electric vehicles. According to the findings, there are four stages of thermal abuse for single cells: heating, secondary heating, thermal runaway, cooling and extinguishing. The battery voltage drop time, exhaust time, and maximum temperature all exhibit a certain linear relationship as SOC increases. Voltage drop, exhaust, and maximum temperature will all advance as heating power increases. The propagation speed of thermal runaway in different electrical connection modes from fast to slow is parallel, series and non-electrical connection. When the shape factors are triangles and diamonds, the propagation speed is the fastest and close, followed by squares.

Solid oxide fuel cell (SOFC) has attracted increasing attention due to its environmental friendliness, high efficiency and broad prospects. The life prediction of SOFC system is critical to the long-term operation of power generation system. However, the accuracy in predicting the life of SOFC using long short-term memory (LSTM) neural network model with numerous parameters is still insufficient. To address this problem, the orthogonal experiment method was employed to systematically optimize the parameters for LSTM. The results indicate that batch size has the greatest influence on the prediction accuracy and calculation time of LSTM. And the root mean square error (RMSE) of optimized model is reduced by 72.36%、18.18%、35.05% and 32.97% when compared with that of recurrent neural network (RNN), gated recurrent unit (GRU), whale optimization algorithm (WOA) optimizates LSTM (WOA-LSTM) and grey wolf optimization (GWO) optimizates LSTM (GWO-LSTM), respectively. The systematically optimized LSTM model exhibits improved performance, which may mitigate fuel cell degradation with other control strategies.

In order to address the shortcomings of existing detection methods, which are unable to effectively capture the complex nonlinear features of photovolatic time series data, a distributed photovolatic anomaly data detection method considering time series clustering is proposed. This method is designed to facilitate the accurate identification and location of abnormal data. Firstly, the similarity of photovolatic power plant time series data is calculated using the dynamic time warping (DTW) method, and a similarity matrix is constructed. Subsequently, spectral clustering is applied to obtain the feature matrix, and the intrinsic connection between photovolatic data points in the feature matrix is analysed. This enables similar photovolatic data to be clustered into a collection. Then, a joint detection model of sub-isolated forest is established based on the collection of photovolatic data clusters. The abnormal score geatures are obtained using the local outlier factor algorithm. The features are used as inputs to the joint detection model to obtain the anomaly score matrix of photovolatic data for each similar clustering set. Finally, the anomaly score matrix is selected and reconstructed, and the anomalies of photovolatic data are identified and located after decision analysis. The experimental results demonstrate that the method is an effective means of capturing photovolatic anomaly data. The precision and recall rates of extracted anomaly samples reach 0.9, thereby enhancing the accuracy of photovolatic anomaly data identification.

Accurate state of health (SOH) estimation is crucial for the safe operation of lithium-ion batteries. However, the complex cycling conditions and random initial charging state of lithium-ion batteries make the inputs of most existing methods unable to be satisfied. To address this problem, we propose an end-to-end SOH estimation method based on relaxation voltage. This method directly establishes the nonlinear relationship between relaxation voltage and SOH using a Bayesian optimization-assisted deep feedforward neural network (BO-DNN), eliminating the laborious feature extraction process. The performance of the estimation method was verified on three different battery aging datasets. The results show that the model achieves a root mean square error (RMSE) of 0.011 0 on the dataset used for model building. A transfer learning model developed by fine-tuning the basic model only relies on less than 3.5% training data, and it achieves an RMSE of less than 0.016 3 on the datasets used for model validation.

A porous carbon material with high specific surface area was prepared from squid ink as carbon source, and a composite material Fe₃O₄/HPBC of Fe₃O₄ and highly porous biomass carbon (HPBC) was prepared by immersion reduction method. The material was used in lithium-ion batteries. At the same time, the effects of different iron ion content and calcination temperature on the electrochemical properties of the composites were also studied. When the prepared Fe₃O₄/HPBC and carbon composite material is used as the negative electrode of lithium-ion batteries, the specific capacities of initial discharge and charge at 0.2 A/g current density are 1 590.0 and 961.0 mAh/g respectively, the coulombic efficiency is 60%, after 300 cycles the capacity without attenuation. After 400 cycles of 5.0 A/g current density, the specific discharge capacity can still reach 484.0 mAh/g, which is higher than the specific discharge capacity of the prepared carbon material electrode, indicating that the Fe₃O₄/HPBC electrode still has good lithium storage performance and cycle stability under large current densities.

The performance degradation of lithium-ion batteries at low temperature is a main bottleneck that restricts the electric vehicle development in cold regions. Low temperature heating (LTH) is an effective solution. A study is conducted on LTH method based on short-term high-current pulse excitation, and the heating characteristics are analyzed. Electric and thermal co-simulations are conducted and verified by experiments. The results show that the presented method can achieve a heating rate of 10.90 °C/min and consumes 4.35% of nominal capacity for raising 20 °C. The temperature consistency and its influencing factors of single cells during the heating process of battery module is investigated by simulation analysis. The results indicate that the difference of resistance is the main cause of inconsistent temperature rise while the inlet wind speed only has a minor impact. When the standard deviation of internal resistance is 4.3 mΩ, the maximum temperature difference can reach over 6 ℃.

With increasing penetration rates of power electronic devices into grid continuously, the threat of new-type power system oscillations becomes much serious. On this basis, the mechanism of a particular form of oscillation, i.e., power-frequency oscillation (or called as synchronous-frequency resonance, synchronous-frequency oscillation) is focused. First, by comparative studies on different models of grid-forming converter tied systems by considering or ignoring current-control loop and/or line dynamics, it is found that the power-frequency oscillation can appear in the single-loop grid-forming converter tied systems. Therefore, for the occurrence of power-frequency oscillation, converter should show the characteristics of a voltage source, and meanwhile, line dynamics should be considered. Then, the mechanism of the power-frequency oscillation is studied under a simple model of two voltage sources connected by a dynamical line. Finally, by time-domain analyses of current and power waveforms under the abc three-phase stationary and dq two-phase synchronous-rotating-frame coordinates, it is found that the power-frequency oscillation comes from the un-damping (or weak-damping) transient component (or free component), whereas the inherent power-frequency sinusoidal oscillation comes from the force component. In addition, it is found that the power-frequency oscillation does not show the frequency-coupling phenomenon. The occurrence conditions and physical origin of power-frequency oscillation are revealed directly in time domain. Clearly, all these findings could help understand the power-frequency oscillation and even use power electronic devices better.

In order to improve the stability and reliability of energy storage auxiliary thermal power units participating in primary frequency regulation, a control strategy for primary frequency regulation of energy storage auxiliary thermal power units considering ultra-short-term load prediction is proposed. The two-layer control strategy is divided into a load prediction layer and a primary frequency modulation layer. The load prediction layer uses a combination prediction model of graph convolutional network-bidirectional gated recurrent units (GCN-BiGRU) and improved temporal convolutional network (iTCN) to predict ultra-short-term load and obtain frequency modulation demand. The primary frequency modulation layer used an improved dragonfly algorithm to dynamically adjust the unit power of virtual droop control and virtual inertial control based on the frequency modulation requirements, with the goal of minimizing frequency deviation. Finally, a simulation model was built on the MATLAB platform to compare and analyze the frequency deviation data and the state of charge (SOC) curves of different control strategies under the working condition of continuous load perturbation, and verify that the control strategy proposed can effectively improve the effect of frequency modulation and maintain the state of charge to a certain extent.

In the inverter grid-connected system with weak grid, the existence of grid impedance and voltage harmonic perturbation will cause the distortion of grid-connected current, and the traditional voltage full feedforward method reduces the steady-state error but affects the quality of current, and even threatens the instability of the system. To address the above problems, it is proposed to improve the grid voltage feedforward, i.e., to add resonant feedforward in the feedforward channel to offset the influence of grid impedance, and at the same time ensure the system open-loop gain, increase the system margin, and improve the stability; and secondly to introduce a proportional multi-resonance controller instead of the traditional proportional integral controller, which is used to suppress the influence of grid harmonics on the grid-connected currents, and then to lower the harmonic content of the grid-connected currents and improve the quality of grid-connected currents. Finally, the effectiveness of the control strategy is verified by simulation.

During the detection of supraharmonics, noise affects the results, and existing harmonic grouping methods were not entirely suitable for their analysis. Consequently, a supraharmonic detection method based on empirical mode decomposition (EMD)-wavelet thresholding and adaptive grouping aggregation was proposed. Initially, high-frequency intrinsic mode function (IMF) components containing significant noise from the EMD method were denoised using wavelet thresholding. The denoised IMF components were then combined with the original low-frequency IMF components to form the final denoised signal. Subsequently, an appropriate grouping bandwidth was determined based on the switching frequency of the supraharmonic sources, and the supraharmonics within the denoised signal spectrum were grouped and aggregated outward from the characteristic frequency center. Finally, simulation and experiments validated the aforementioned method, indicating the feasibility of the EMD-wavelet thresholding and adaptive grouping aggregation approach for supraharmonic detection.

A hybrid power flow controller (HPFC) combines a phase-shifting transformer (PST) with a voltage source type converter (VSC). The combination of VSC can achieve accurate and continuous regulation of the system power flow, but there are overvoltage problems at the AC bus of the converter under different faults. First, in order to suppress the overvoltage caused by fault, a strategy of adaptive reverse transmission of reactive power to compensate voltage for different overvoltage levels is proposed, and the short-time overvoltage caused by the slow regulation speed of traditional controllers is addressed. An adaptive linear active disturbance rejection control (A-LADRC) method for adaptive adjustment of controller bandwidth parameters is designed and thereby the whole process of HPFC fault overvoltage suppression is realized. Then, the working process and overvoltage mechanism of HPFC are analyzed, the most serious scenarios of HPFC overvoltage are identified, and the related strategies and parameters are designed from the level of control methods. Finally, based on RT-LAB real-time simulator and digital signal processor (DSP) controller, a controller-level hardware-in-the-loop simulation platform is built to verify the effectiveness and superiority of the proposed HPFC overvoltage suppression strategy.

Harmonics and three-phase unbalance are two common forms of disturbances in power systems, which interact with each other and jointly cause more complex impacts on power systems. In order to more comprehensively and accurately analyze the distribution law and interaction mechanism of harmonics and three-phase unbalance, the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS) is investigated, and a joint probabilistic emission level modeling method is proposed. First, based on the multivariate correlation coefficients of disturbance source power, harmonic and three-phase unbalanced data, the disturbance source characteristic dataset is screened to realize the data dimensionality reduction. Subsequently, the fuzzy C-means (FCM) algorithm is used to complete the clustering division of the feature data, which lays the foundation for the disturbance source sub-condition analysis. Then, the joint probability model of H-TU-DS is constructed by the nonparametric kernel density estimation method. Finally, the effectiveness and accuracy of the proposed feature dataset screening method, operation condition classification method and joint probability modeling method are proved by comparing and verifying the simulated and measured data.

In order to solve the economic problems caused by the coordinated output of microgrid distributed power generation, an improved jumping spider optimization algorithm (IJSOA) is proposed. First, the best point set strategy is to increase the diversity of the initial population, and the cosine similarity strategy is introduced to enhance the ability to jump out of the local optimal solution, and a non-linear convergence coefficient is set that balances global and local search capabilities, and an improved pheromone update strategy is proposed to accelerate the convergence speed and improve the convergence performance of the algorithm. Then, the grid-connected microgrid economic optimization scheduling model and the optimal operation strategy of the system with the lowest power generation maintenance cost and pollution control cost are established. Finally, simulation experiments are conductes with IJSOA under different strategies, and the results are compared and analyzed with those of other algorithms. The simulation results show that the IJSOA improves the economy by 10.63% compared with the traditional algorithms such as jumping spider optimization algorithm (JSOA) and ant colony algo-rithm (ACO), which verifies the economy and effective-ness of the proposed method.

For the problem of reduced charging performance of wireless charging systems caused by dynamic changes in equivalent load, parameter drift, or system detuning due to factors such as mismatched components, an optimized solution from the perspectives of constant current/voltage charging, system efficiency improvement, and dynamic tuning is proposed. A magnetic flux controllable inductor (MFCI) technology is adopted at the transmitting end to achieve system power control. In contrast, an improved half-controlled rectifier (IHCR) at the receiver end enhances system efficiency and ensures the resonance state. Firstly, the system circuit topology is analyzed to derive equations that show the relationship between key system parameters, charging current/voltage, and system efficiency. The necessity of using a MFCI and an IHCR to enhance system efficiency is explained. Subsequently, these devices are modeled, and their circuit topology and working principles are analyzed. Then, a closed-loop control strategy is analyzed, and the feasibility of the proposed method is verified through PLECS circuit simulation. Finally, a system experimental platform is established, and the experimental results indicate that the designed closed-loop controller can achieve constant current charging (5 A) and constant voltage charging (67.2 V) when the load varies from 9.60 Ω to 134.40 Ω, with a maximum system efficiency of 91.8%.

Collecting parameters such as current and temperature on the motor rotor can effectively prevent rotor side faults and achieve motor vector control better. However, the running rotor rotates at high speed, making it very difficult to provide the signal measured on the rotor side to the controller on the stator side. The electric field coupling wireless signal transmission technology consists of two pairs of axially aligned circular parallel plates forming capacitors, which are installed on both sides of the stator and rotor at the end of the motor. High-frequency and low impedance characteristics of the close-range plate capacitors are utilized to transmit digital signals between the motor stator and rotor, with significantly lower signal delay than other wireless communication technologies. Then modeled the communication circuit, calculated the parameters of each component, analyzed the causes of signal distortion, and the design of a contactless high-frequency signal transmission circuit based on parallel plate coupling capacitors was realized. An experimental platform was built, analog signals from the rotor side were collected and transmitted. By comparing the experimental results, the correctness of communication theory was verified.

Addressing the low carrier ratio characteristics of a dual three-phase permanent magnet synchronous motor during high-speed operation, where traditional deadbeat control fails to ensure adequate current control performance, an improved deadbeat predictive current control method has been proposed. This method incorporates a voltage static error compensator to account for discrete model errors and control delays at low carrier ratios. To address motor parameter mismatch, a discrete sliding mode observer is employed to compensate for voltage disturbance errors. Validation through MATLAB/Simulink simulation and prototype experiments demonstrates that the proposed improved deadbeat current control effectively compensates for motor voltage errors under high-speed operation and resistive-inductive parameter mismatch conditions, offering excellent disturbance rejection and enhancing the system control performance and operational efficiency.

To tackle the challenges of uneven capacitor voltage in the propulsion setup of a three-phase permanent magnet servomotor driven by a T-type three-level inverter, a direct torque control strategy based on zero-sequence voltage injection with carrier modulation is proposed. Firstly, the direct torque control strategy is constructed by motor model calculation. Secondly, the modulation voltage involved in pulse width modulation (PWM) control is obtained by coordinate transformation from the expected voltage, which realizes the combination of carrier modulation and direct torque control. Finally, in order to realize the balanced control of the midpoint potential, the zero-sequence voltage injected is calculated and corrected according to the sampling difference of the upper and lower capacitor voltages. The experimental data demonstrates that the suggested control approach effectively achieves a reduction in torque fluctuations and maintains balanced control of neutral point voltage.

Silicon carbide devices with multi-chip parallel structure are widely used in motor controllers for new energy vehicles in high frequency and high power density applications. The motor controller is a highly coupled electromagnetic-thermal system. The imbalance of chip temperature and stray inductance in the multi-chip parallel structure aggravates the complexity of the coupling system. In order to reveal the deep coupling mechanism of electromagnetic-thermal and the variation law of electromagnetic-thermal domain in the motor controller, multi-physical field coupling analysis under multi-parallel structure is needed. A multi-physical field coupling analysis method based on parameter transfer is proposed. First, the stray inductance model of chip parallel branch at different temperatures is established by magnetic-thermal coupling analysis. Then, through the electro-thermal coupling analysis, the simulation model of silicon carbide chip losses under different temperatures and stray inductance is established. Finally, loss parameter transfer is used to simulate the temperature of the controller under fluid-solid coupling, and the stray inductance model and loss model at this temperature are updated in real time. The electromagnetic-thermal coupling parameters of the chips under the multi-chip parallel structure during the temperature rise of the controller are studied. The accuracy of the multi-physics coupling analysis method is verified by the temperature rise experiment.

Aiming at the problems of high-frequency jitter and phase delay caused by the use of constant sliding mode coefficients, sign functions, and low-pass filters in traditional super twisting sliding mode observer (STSMO), an improved STSMO based on fuzzy control is proposed. Firstly, fuzzy control is used to optimize the constant sliding mode coefficient. Secondly, a new type of continuous function T(s) is used instead of the sign function to reduce jitter, and an Kalman filter is introduced for secondary filtering to further improve the jitter reduction effect. Finally, an enhanced quadrature phase-locked loop (eQPLL) is used to replace the arctangent function to further improve the system and enhance observation accuracy. For time-varying nonlinear permanent magnet synchronous motor (PMSM) control systems, fractional order proportional integral derivative (FOPID) controller can more finely adjust the dynamic response of the system than traditional proportional integral derivative (PID) controller. Therefore, FOPID controller is used to replace the traditional motor speed loop proportional integral (PI) controller. Simulation and experimental results verify that the proposed improved control strategy effectively reduces overshoot, significantly improves the dynamic tracking performance of rotor position and speed, and the improved control system has stronger robustness.

In order to improve the accuracy of photovoltaic array fault diagnosis, an improved artificial neural network (ANN) method for photovoltaic array fault detection is proposed. In ANN, a gated convolutional layer, a flat layer and a fully connected layer are added to replace hidden layer, which solves the problem of gradient disappearance easily in existing ANN. Adaptive Softmax is used as the activation function of the output layer of the model to reduce the computational time incurred due to the large data set size. The simulation model of photovoltaic array was established by using MATLAB to obtain the simulation operation data of different states. Meanwhile, the actual unbalanced operation data set of photovoltaic array was obtained from the literature, and the diagnostic performance of the improved ANN was compared with the six machine learning methods mentioned in the literature, showing the diagnostic performance advantages of the proposed method.

In order to address the model performance degradation caused by sample sparsity and class imbalance in multimode fault diagnosis of automatic guided vehicle (AGV) power batteries, a generative adversarial network incorporating gradient penalty and an independent classifier (GPSC-GAN) was proposed. First, the discriminator loss function was optimized through the introduction of Wasserstein distance with gradient penalty, whereby the distribution divergence between generated samples and real battery fault features was constrained to enhance training stability. Second, an independent classifier module was designed to decouple classification and discrimination functions, enabling high-quality multimode fault samples to be generated for constructing a balanced dataset. Experimental results on AGV battery fault datasets demonstrated that the generated samples achieved structural similarity index measure (SSIM) and maximum mean difference (MMD) values of 0.5 and 0.2 respectively, showing 25% to 66% improvements compared with WGAN-GP, ACGAN, SMOTE, ADASYN, Diffusion GAN and StyleGAN-T models. After integrating synthetic samples, the F1-score of the fault diagnosis model was enhanced to 0.938, with precision and recall rates reaching 94.6% and 93.0% respectively. These metrics outperformed six other comparative methods, verifying the proposed method’s efficiency and robustness in complex industrial scenarios.

High-power high-voltage high-frequency transformers (H³T) are typically subjected to high du/dt pulsed voltage inputs. Their unique operational environment presents significant insulation challenges, making them highly susceptible to partial discharge (PD) initiation. The intense electromagnetic interference generated by high du/dt pulsed voltages substantially compromises the accuracy of PD detection. A novel joint detection method integrating ultra-high frequency (UHF) antennas with photomultiplier tubes (PMTs) is proposed, combining UHF’s advantages of high sensitivity and wide bandwidth with PMT’s superior anti-interference capability and rapid response characteristics. Signal fusion is achieved through amplitude correlation analysis by comparing temporal and spectral consistency between discharge signals acquired from UHF and PMT. The research reveals remarkable consistency in spectral steepness S and kurtosis K between the two detection modalities. The fusion method demonstrates average discharge count enhancements of 66.3% compared to PMT alone and 49.7% compared to UHF alone, significantly improving detection integrity. Furthermore, the analysis of PD characteristics under varying rise times demonstrates that shorter rise times induce earlier and more concentrated PD occurrences. Specifically, the average and maximum discharge amplitudes increase by 37.9% and 37.2%, respectively, while discharge repetition rates and cumulative discharge amplitudes show improvements of 37.1% and 22.1%. This study provides innovative methodology and technical support for achieving high-sensitivity, interference-resistant online PD detection under high du/dt pulsed voltage conditions.

With the widespread random access of distributed resources in the new power system, precise measurement of bus current under complex operating conditions has brought new challenges. Tunnel magnetoresistance (TMR) sensors, as the latest generation of magnetoresistance sensors, integrate the advantages of high sensitivity, low power consumption, and small size of traditional magnetic sensors. They have a wide range of applications in magnetic fields detection, current detection, magnetic memory and other fields. Due to the skin effect of bus current at high frequencies, the current is concentrated in the “skin” part of the conductor, and the surrounding magnetic field exhibits nonlinear characteristics that affect the measurement of the sensor. An optimization method for arranging the position and quantity of tunnel magnetoresistance chips around the distribution bus is proposed, based on the physical characteristics of tunnel reluctance sensors and the disturbance law of skin effect. The corresponding sensor structure is designed to reduce the influence of bus skin effect and stray magnetic field on sensor measurement accuracy.