A global and real-time control with embedded hardware artificial neural network (ANN) for imbalance current suppression of parallel connected IGBTs is firstly proposed in this paper. This method focuses on control strategy and control execution. The former one is realized by porting the ANN based PID (ANN-PID) strategy in the control loop to yield the real-time and adaptive characteristics without IGBT quantity limitation. The latter one is realized by designing the IGBT gate quantity of charge regulator (GQR) to execute the command from ANN-PID controller. The evaluation of ANN-PID algorithm results 0.023% mean error in IGBT current control that reveals the feasibility of the proposed method. A full prototype with FPGA based hardware accelerator for ANN-PID computing, including the designed GQR circuit, has been built for realization and qualification in a buck converter with parallel connected IGBTs. The experimental results show that the performance of proposed method in imbalance current suppression is improved about 3.5 to 5.5 times as the load increase from low to high with the advantage of immunity to load change and the current imbalance can be suppressed within 4%.

The soft-switching characteristics of double-sided LCC wireless power transmission systems are susceptible to system parameter disturbances. In order to improve the stability of zero voltage switches in constant-current and constant-voltage charging modes, this paper synthesizes eight resonant conditions which make the system realize constant-current/constant-voltage and Zero Phase Angle. The influence of compensating capacitance parameter disturbance on system impedance angle is studied under constant current/constant voltage operation mode. The parameter configuration methods of compensated capacitance realized by zero voltage switches under the influence of third-order harmonic in constant current/constant voltage operation mode are also given. The system’s experimental prototype verifies the theoretical analysis’s accuracy with capacitive and inductive input impedance.

Launch efficiency has always been the focus of reluctance coil launchers research. In a traditional launch mode, the residual current in the drive coil creates a reverse force on the projectile. This is undesirable since the reverse force is in the opposite direction of movement, reducing the launch efficiency of the system. To address this problem, a multistage discharge circuit with the residual energy recovery and utilization mode is proposed in this article. As the projectile approaches the center of each stage of the drive coil, the current in the drive coil decays rapidly to zero by using the last stage capacitor to absorb the residual energy. The residual energy from the previous stages is eventually recovered by the last stage capacitor. This capacitor with residual energy is used to power the last stage drive coil of the reluctance coil launcher. This mode effectively suppresses the reverse force on the projectile and realizes the reuse of residual energy. Taking a four-stage reluctance coil launcher as an example, this article introduces the working principle of this mode in detail. The feasibility of the model is verified by finite-element simulation and experiments. Both simulation and experimental results show that this mode performs well in the recovery and reuse of residual energy and can effectively improve the launch efficiency of system.

Permanent magnet (PM) motors are widely used in our daily lives. Taking a surface-mounted PM motor as an example, a magnetic circuit model considering the peripheral magnetic flux leakage is established. Maxwell software is used to simulate different loads and thicknesses and the materials of the shielding layers. The magnetic flux leakage around the motor has a sinusoidal distribution in the space. The value of the magnetic flux leakage is inversely proportional to the power of $P+1$ from the surface of the core. The thicker the shielding layer, the smaller the magnetic leakage. The greater the relative permeability of the shielding layer, the better the shielding effect. The results provide ideas and recommendations for the magnetic flux leakage analysis and the design of non-magnetic or low magnetic motors.

There are possible non-convergence problems at load power critical value while using conventional continuous power flow methods. Generally, the step control method is used in the calculation to perform the calculation in small steps at the convergence to the load power critical value, and this method sacrifices the speed of the calculation although the calculation accuracy is high. In this paper, the Levenberg-Marquardt algorithm is used to search for the load power critical value firstly, and after that the step control algorithm is designed to perform fast power flow finding with known load power critical value, which takes into account the computational accuracy and computational efficiency. The case shows that the fast calculation method for continuous power flow of microgrid based on Levenberg-Marquardt algorithm proposed in this paper can guarantee the accuracy while effectively improving the calculation speed, and is suitable for online real-time evaluation of voltage stability and preventive control aided decision online calculation of isolated microgrid with high requirements for real-time.

To solve the problem of low speed and low precision of traditional maximum entropy image segmentation algorithm, a multi-threshold image segmentation algorithm based on improved particle swarm optimization algorithm is proposed. Taking the optimal threshold optimization problem in multi-threshold image segmentation as the research object, the optimal objective function is obtained by using the maximum entropy multi-threshold segmentation method, and then the maximum entropy method and particle swarm optimization algorithm are fused. In order to solve the problem that particle swarm optimization (PSO) is prone to fall into local optimization in the later iteration process, the PSO is improved and the expansion model is added. Finally, the maximum entropy multi-threshold image segmentation method based on the standard particle swarm optimization algorithm and the maximum entropy multi-threshold image segmentation method based on the improved particle swarm optimization algorithm segment the h-component image (hue, saturation, value) of HSV. Images are converted from images. The segmentation results of the two algorithms are evaluated by running time, and the structural similarity of the algorithms is evaluated. evaluation result. Experimental results show that the improved maximum entropy multi-threshold image segmentation algorithm based on particle swarm optimization can better achieve complex image segmentation, and the algorithm has stronger real-time performance.