In a direct current(DC) power transmission system, the stable operation of a valve base electronics(VBE) device is crucial for its safety. However, the traditional methods for detecting the component failures in VBE device circuit boards rely on time-consuming manual inspections or rule-based automation systems, which are often inefficient and limited in the detection accuracy. To address this problem, a method for identifying the component failure areas in VBE boards is proposed in this paper, which uses an enhanced SqueezeNet deep learning model. By incorporating depth-wise separable convolutions and residual connections, the enhanced SqueezeNet model aims to improve the accuracy of component failure detection while reducing the demand for computational resources. Experiments on a VBE board component failure dataset demonstrate that the proposed method outperforms the traditional methods and the standard SqueezeNet model in terms of detection accuracy and computational efficiency, and it achieves an accuracy rate of 95.27%, which is 4.45% higher than that of the standard model. The results of this research not only enhance the efficiency and accuracy of component failure detection in VBE boards, but also provide a novel technical reference for the diagnosis of component failures in similar equipment in power systems.

In the burgeoning field of new energy vehicles, silicon carbide representing a new generation of semi-conductor power devices is progressively replacing silicon-based IGBTs, which also sets higher standards for the motor control performance within the corresponding innovative technological ecosystem. The precision of motor parameters is becoming increasingly critical for enhancing the performance of electric control systems as they evolve from the tradi-tional PI control and direct torque control to advanced algorithms such as model predictive control and neural network control. Aimed at the problem that the classic linear model for permanent magnet synchronous motors cannot adapt to complex and variable conditions due to nonlinear factors such as cross-saturation, a nonlinear magnetic flux identifica-tion method based on Gaussian process regression is proposed. By employing a second-order generalized integrator to acquire the magnetic flux data under dynamic conditions, the system identification is completed. Finally, the effective-ness of the proposed approach and the accuracy of parameter identification were verified through simulation and experi-mental results.

The application of wide bandgap semiconductor devices makes the motor drive system of electric vehicles (EVs) more compact and lightweight, but it also causes more serious electromagnetic interference (EMI), which makes the reliability of the drive system face severe challenges. To solve this problem, a 24 V/2 A EMI DC filter is taken as an example, and through the analysis of noise source, filter principle and impedance influence, the filter parameters are designed according to the index of insertion loss. At the same time, considering the starting impact at the starting time of the whole vehicle, a soft starting circuit is added to further improve the reliability of the EV drive system. Experimental results verified the EMI suppression effect and the soft starting function of the filter, proving the feasibility and effectiveness of the filter design.

In the development of technologies for power electronic devices used in automobiles, the power modules are developing towards the direction of miniaturization and high power density. As a result, the high-frequency switching of power devices used in automobiles will increase the fatigue failure risk of bonding wires. To improve the strength and reliability of bonding, the action mechanism of bonding parameters at different stages was revealed from the perspective of the bonding principle at first, and the optimization intervals for different parameters were obtained using single-factor experiments. Subsequently, a systematic investigation of the influence of wire bonding materials on bonding reliability was conducted through numerical simulations and aging tests. Results indicate that compared with Al bonding wires, Cu bonding wires exhibited higher maximum temperatures and higher maximum equivalent stress. However, due to material properties, Cu bonding wires only achieved half the maximum plastic strain of Al bonding wires. Based on power cycling tests, the lifetime of Cu bonding wires was approximately four times that of Al bonding wires. Moreover, Cu bonding wires exhibited a higher degree of variability in bonding quality, with the phenomenon of stepwise signal escalation due to the detachment of a single wire serving as an early warning signal for potential failures in daily operations.

Aimed at problems such as the setup of an additional excitation source required by active magnetic shielding and the expensive magnetic shielding materials used in passive magnetic shielding, a wireless power transmis-sion (WPT) coupling mechanism with magnetic shielding based on Halbach effect is proposed on the basis of the tradi-tional DD coil structure. First, the topology of WPT coupling mechanism with magnetic shielding effect is proposed, and the principle for the magnetic shielding effect is analyzed theoretically based on the corresponding equivalent magnetic circuit model. Second, the expression for the magnetic field intensity of the coupling mechanism on any plane in the space is derived using the micro-element method. Finally, an experimental platform was built to verify the WPT perfor-mance and magnetic shielding effect of the proposed coupling mechanism. Results show that the Halbach effect coil can effectively weaken the magnetic field intensity outside the coupling mechanism and improve the magnetic shielding ef-fect while ensuring that the power transmission efficiency is basically the same as that of the DD coil.

Aimed at the problem that the power quality control equipment in distribution network is lack of collaborative allocation, an optimal allocation strategy for the control equipment of harmonics, reactive power and three-phase imbalance is proposed, which is based on the multi-objective particle swarm optimization (MOPSO) algorithm. The active power filter (APF) is used to suppress harmonics, the intelligent capacitor is used to compensate reactive power, and the phase-change switch is used to reduce three-phase imbalance. The control effect and operating cost about each power quality issue are taken as optimization objects, and the relevant power quality standards are considered as constraints. Through the MOPSO algorithm, an optimal allocation scheme for the allocation nodes and relevant access capacity of control equipment can be obtained. Furthermore, a power quality assessment model is built, and a simulation model based on an improved IEEE 18-node distribution system is also constructed. The harmonics, reactive power and three-phase imbalance loads are separately connected to simulate power quality issues, and simulation results verify the feasibility of the proposed strategy and its advantages compared with the traditional scheme for power quality control equipment.

Monitoring the power quality of power supply system is an effective method for ensuring the safe operation of power system and user-side equipment. To ensure the power quality of power supply system, a monitoring method for its power quality stability is studied. Based on the Hilbert-Huang transform (HHT) algorithm, the harmonic frequency and amplitude of the power quality signal frame of power supply system are monitored, and the disturbance time amplitude and frequency of the power quality disturbance signal are detected, so as to realize the power quality stability of monitoring system. The frame loss rate, accuracy and transmission delay of the monitored power quality signals under different pressures (with different numbers of power quality signal frames) were tested by experiments, and results show that the proposed method can realize the monitoring of power quality.

The advantages of a Boost-APFC circuit operating in critical conduction mode are introduced. Aimed at the disadvantages of the traditional single-phase CRM-Boost APFC voltage mode control method, such as a long PI parameter debugging time, a poor adjustment effect and increasing unstable factors, a single-phase CRM-Boost APFC voltage mode control method with a static operating point is proposed, and the advantages of this method are verified by PSIM simulations. Considering the shortcomings of the novel interleaved control method, such as a long PI parameter debugging time, increasing unstable factors and the need to use an additional voltage-controlled current source, an improved two-phase interleaved parallel CRM-Boost APFC voltage mode control method is put forward, and the PSIM simulations are completed, with a power factor as high as 99.96%. A 4 kW two-phase interleaved parallel CRM-Boost APFC experimental prototype was made, and it was experimentally debugged, with a power factor of 99.66% and an efficiency of 98.02%.

The hybrid energy storage system can effectively alleviate the frequency instability caused by the strong fluctuation and randomness of wind power output. In this paper, a hybrid energy storage system composed of batteries and super capacitors is taken as the research object, and a hybrid energy storage capacity allocation method is proposed. First, adaptive wavelet transform is adopted to perform a primary distribution of the wind power output, and the grid-connected power and energy storage power satisfying the requirements are obtained. Second, HHT transform is used to decompose the energy storage power, and a series of fluctuating power components and the instantaneous frequency of each component are obtained. Third, the cutoff frequency is determined according to the instantaneous frequency, the power components with a frequency higher than the cutoff frequency are allocated to super capacitors, and the rest are allocated to batteries. Finally, the rated capacity and rated power of the energy storage system are configured according to the energy storage power of batteries and super capacitors, respectively. Simulation results show that adaptive wavelet transform and HHT transform can effectively decompose the wind power output, thus realizing the stabilization of wind power output, as well as the capacity and power allocation of hybrid energy storage system.

The converter with constant-power control in a flexible DC distribution system has characteristics of constant-power load, which will reduce the system damping and adversely affect the system stability. To address this problem, a superconducting magnetic energy storage (SMES) device is introduced to improve the system stability. A feed-back control model of the flexible DC distribution system is derived, and the effect of constant-power load characteris-tics of the converter on system stability is investigated by frequency-domain analysis. Combined with a mathematical model and frequency-domain analysis, it is also pointed out that the SMES device can improve the system stability by introducing positive damping to grid and increasing the phase margin of the system's open-loop transfer function at the shear frequency. To prevent over-high voltage at both ends of the superconducting magnet, the DC/DC converter which connects the SMES device with the DC distribution network needs to have certain voltage regulation performance. Therefore, the SMES device with a modular multilevel DC/DC converter (DC-MMC) is studied, which can adjust the number of sub-modules flexibly to set the voltage ratio of the converter. Moreover, the DC-MMC can control the voltage at both ends of the superconducting magnet while realizing a bidirectional flow of energy in the converter, thus protect-ing storage device. The feasibility and effectiveness of the SMES device with DC-MMC in improving the stability of flex-ible DC distribution system is verified by time-domain simula-tion waveforms.