The power supply load of a high voltage direct current (HVDC) power supply system is prone to be affected by signal interruption, which will result in the unstable operation of power supply system. To effectively improve the management efficiency of power supply system, an optimization method for the management efficiency of HVDC power supply system based on genetic algorithm is proposed. The optimization of management efficiency is transformed into the problem of load distribution, and a load distribution control strategy of efficiency optimization is adopted to reasonably distribute the load current in the HVDC power supply system. The genetic algorithm is used to optimize the management efficiency of the HVDC power supply system, so as to optimize its management efficiency. Experimental results show that the system management efficiency can be adjusted through the independent operation of dual power supplies, thereby improving the efficiency of power supply system. In addition, the running time of this method was maintained at 90-120 s, the optimization time was short, and the efficiency was high.

With the continuous advancement of medical technology, implantable medical devices (IMD) are increasingly applied in clinical practice. Since the traditional battery-powered method will bring additional tissue damage and surgical costs to patients, the use of wireless power transfer (WPT) technology to power IMD will become a trend in the future. However, how to design a high-efficiency IMD-WPT system in a limited space is very challenging. To this end, the performance characteristics of five WPT technologies suitable for IMD are compared. Then, the magnetic resonance WPT technology is taken as an example to introduce the key issues in the design of a magnetic resonance IMD-WPT system. Finally, the application status of part of the magnetic resonance WPT technologies in some typical IMD is combined, and the research direction of IMD-WPT technology in the future is discussed.

To solve the problems of constant-voltage output instability and low efficiency caused by load resistance and coupling coefficient fluctuations in the dynamic charging process of an electric vehicle, a novel dual-side control scheme is proposed. In this scheme, the constant-voltage control output is realized by adjusting the pulse width angle θ of a high-frequency inverter at the transmitter, and the maximum efficiency tracking (MET) control of the system is realized by adjusting the pulse width angle φ of a controllable rectifier at the receiver. Through theoretical analysis, it is proved that when the derivative ∂[sin(θ/2)]/∂[sin(φ/2)] is a specific constant, the system can always work under the operating condition of maximum efficiency. Compared with the same type of MET control scheme, the proposed scheme does not need to install expensive current or power sensors on the transmitter, which reduces the system’s development cost to a certain extent. To verify the rationality of the proposed scheme, an experimental verification device with a rated power of 360 W was built, and experimental results fully proved the rationality and effectiveness of this scheme.

Compared with the traditional silicon(Si) devices, the gallium nitride(GaN) devices have lower parasitic parameters, a faster switching speed and a smaller on-resistance, which will easily lead to the phenomenon of continuous oscillation during their switching-on process and further result in the circuit instability. Therefore, it is necessary to suppress this phenomenon in practical circuits. Under this background, a negative conductance model of a bridge circuit under the conventional driving scheme is established at first, and the oscillation stability of the circuit is analyzed. Then, by adding optimization to the conventional driving scheme, the corresponding negative conductance model is established. The optimization schemes of series damping represented by changing the resistance and adding ferrite beads and those of parallel low impedance represented by adding RC snubber are selected, respectively. With this model, the influence of adding the driving optimization schemes on the oscillation stability of the circuit can be identified, and the changes in the stability before and after the addition were verified by experimental results, providing a reference for the driving circuit to select its appropriate driving optimization scheme.

With the development of wide band gap devices, SiC MOSFET has been widely applied, and the research on its short-circuit protection has become an important topic to ensure the reliability of power electronic equipment. In view of the short short-circuit withstand time of SiC MOSFET and the difficulty in short-circuit fault protection, a short-circuit detection method for SiC MOSFET based on a planar differential Rogowski coil is proposed, which realizes a rapid identification of short-circuit fault by measuring the drain source current of the circuit and has advantages such as a fast response speed, a strong anti-interference capability and complete isolation from the main circuit. First, the working process of the SiC MOSFET short-circuit detection method based on the planar Rogowski coil is introduced. The partial element equivalent circuit (PEEC) modeling method for planar Rogowski coil is studied in detail, and an equivalent model which can reflect the coil’s high-frequency characteristics is obtained. At the same time, the influence of the geometric structure of the planar Rogowski coil on its performance is analyzed, and an optimal design scheme considering both the high gain and high bandwidth is proposed. Aimed at the problem of low measurement accuracy of the Rogowski coil in an environment with strong electromagnetic interference, a scheme of using the differential coil is put forward to improve the anti-interference performance. Finally, the anti-interference per-formance of the designed planar differential Rogowski coil and the reliability of short-circuit protection method based on this coil were verified by experimental results.

To solve the difficulty in online life prediction of silicon carbide metal-oxide-semiconductor field-effect transistor (SiC MOSFET) under practical working conditions, a digital implementation method for SiC MOSFET module life prediction based on particle swarm optimization-back propagation (PSO-BP) neural network was proposed. First, the saturation voltage drop of SiC MOSFET was extracted by a saturation voltage drop platform as the temperature-sensitive electric parameter, and a junction temperature prediction scheme based on experimental data was established. Second, a life prediction scheme based on PSO-BP neural network was established by using a power cycling accelerated aging experimental platform to extract the aging characteristic data. Third, the junction temperature prediction scheme and life prediction scheme were transplanted to field programmable gate array to realize the digitization of SiC MOSFET life prediction. Finally, a circuit was designed to verify the proposed method. Experimental results show that the error between the digital junction temperature and real junction temperature was 4.73 ℃, and the percentage of error between the predicted life times and real life times was 4.1%, which proves that the proposed life prediction method is realized digitally and can accurately predict the life times of SiC MOSFET module.

In the traditional power module automatic layout optimization algorithm, the electrical evaluation is inefficient and takes up a lot of computing time. To solve this problem, lattice Boltzmann method (LBM) is used to replace the traditional evaluation method. Since LBM does not need to solve multiple invertible matrices, it can quickly judge the rationality of electrical interconnection and calculate the voltage/current. With the program of automatic layout design based on the genetic algorithm, an evaluation method of two-dimensional layout is established by using a D2Q4 lattice type, and the accuracy of the evaluation result under the layout scheme is verified by ANSYS Q3D software simulation. A comparative test was conducted in Python 3.10, and results show that LBM reduces the total time of scheme evaluation by 75.4% on average. Moreover, the more the number of loops in the evaluation scheme, the greater the computing advantage of LBM.

Harmonic and electromagnetic interference (EMI) filters are two important output filters used to sup-press the harmonic distortion and EMI noise in grid-connected inverters. Harmonic and EMI filters are combined by planar magnetic integration to reduce the volume and weight. Through the selection of an appropriate magnetic core, the common mode and differential mode inductors are integrated into the same core by drawing PCB planar coil. To integrate the discrete capacitors and further realize the planar magnetic integration of EMI filter, the dielectric is inserted into the PCB and the layer connection mode is reasonably planned. A symmetric LCL filter is used to replace the traditional asymmetric structure of magnetic integration. Furthermore, by designing the air gap in the center pillar of the magnetic core and reasonably arranging the planar windings, the inductors of LCL harmonic filter are also integrated into the same magnetic core unit to form an LCL-EMI planar magnetic integrated filter. A gallium nitride single-phase inverter platform was built, and the LCL-EMI filter with planar magnetic integration was experimentally analyzed to verify the feasibility of the planar magnetic integration method.

The traditional fault diagnosis methods for power transformers cannot detect the power faults accurately or ensure their normal operation. Therefore, a fault diagnosis method for power transformers based on wavelet packet transform and support vector machine (SVM) is proposed. For the power signal collected from a power transformer, the improved minimum noise fraction (MNF) transform denoising is used to denoise, and the noise matrix is estimated by the weighted neighborhood mean method. After the estimation, the improved MNF transform is used to effectively realize image dimensionality reduction and denoising, extract the signal characteristics, and divide the signal into low- and high-frequency part by means of wavelet packet transform to obtain the wavelet packet energy feature vector. The obtained wavelet packet energy feature vector is input into an SVM classifier, and the output results from the SVM classifier are used to realize the state recognition and fault diagnosis of power transformer. Experimental results show that the proposed method can effectively diagnose the faults in the power transformer, such as iron core short-circuit, coil interlayer short-circuit, bushing-to-ground breakdown, coil insulation resistance drop and bushing-to-bushing discharge, and the fault diagnosis accuracy was higher than 98.5%.

With the rapid development of new energy technology, the performance of DC-DC converters continuously increases. In this paper, a novel high-gain DC-DC converter is proposed, which is improved based on the quasi-Z-source topology. Owing to the use of the topology in which three capacitors discharge together to the load, a higher voltage gain is obtained while the voltage stress of capacitors is reduced. The proposed converter has advantages of the traditional quasi-Z-source converter such as simple control, continuous current and small current ripple. The working principle for this converter is analyzed. In addition, its performance was verified through simulation experiments and prototype experiments.