In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

Control strategy is the key technology of power electronic converter equipment. In order to solve the problem of controller design, a general design method is presented in this paper, which is more convenient to use computer machine learning and provides design rules for high-order power electronic system. With the higher order system Lie derivative, the nonlinear system is mapped to a controllable standard type, and then classical linear system control method is adopted to design the controller. The simulation and experimental results show that the two controllers have good steady-state control performance and dynamic response performance.

In a distributed generation system, the all-pass-filter phase-locked loop (APF-PLL) is a commonly used method for grid synchronization. However, the coupling effect between APF-PLL and current control loop increases the risk of oscillation instability for the inverter in the weak grid. At present, there are few effective methods to solve the adverse effect of APF-PLL on the inverter-grid interconnection system in the weak grid. Therefore, a small-signal impedance model of the inverter considering the dual d-q frame brought by APF-PLL is first established. Then the reason for the inverter instability caused by APF-PLL in the weak grid is analyzed. Subsequently, an impedance reshaping method based on a modified first-order filter PLL with a complex coefficient filter (CCF-MFOF-PLL) and its parameter optimization design method are proposed. Finally, the experimental results verify that the proposed method widens the stable range of the inverter and ensures the stable operation of the inverter even with the large grid impedance.

A zero-current-switching (ZCS) two-switch DC-DC forward converter with variable-inductor is presented in this paper. The quasi-resonant technique is employed to achieve ZCS operation. This variable inductor technique is used to reduce the switching losses and the peak value of the output-diode current. Thus, the conversion efficiency can be improved. Moreover, the voltage across the active switch is clamped at the DC-source input voltage. So, the voltage stress of the active switch is less than that of the conventional DC-DC forward converter. Finally, a prototype circuit is implemented to verify the performance.

Different kinds of compensation topologies in wireless power transfer (WPT) systems usually can only achieve constant voltage (CV) or constant current (CC) output at the same time when the load condition varies, such as during the charging process of a battery. This seriously impacts the stability and application of the WPT systems. Therefore, dynamic programming reinforcement learning (DPRL) algorithm is proposed in this work to achieve CV and CC outputs in the common LCC-LCC/S WPT systems. The proposed method does not need parameter identification or accurate mathematical model, and can thereby reduce the complexity and improve the accuracy of the control. An intelligent offline database is trained based on DRPL algorithm by only monitoring the voltage and current in the primary side of the system to obtain an optimal compensation capacitor on the primary side. A kilowatts experimental setup is built, and the results show that the 180 V CV or 5.42 A CC output can both be realized as expected in each system according to the requirements of the load conditions. The maximum and average errors are only 1.29% and 0.93%, respectively, with nearly no impact on the system efficiency, which effectively validates the feasibility of the proposed method.

In distributed generation systems, the inverter is the main power interface and its stability directly determines the reliable operation of the grid-connected system. As a typical topology for a three-phase four-wire inverter, the LCL-type three-phase four-wire split capacitor inverter (LCL-TFSCI) is taken as the research subject of this paper. Compared with the three-phase three-wire inverter, there is an additional zero-sequence path in the LCL-TFSCI. Therefore, it is not only necessary to consider the stability of the positive and negative sequence system, but there is also the need to consider the stability of the zero-sequence system when performing stability analysis for the LCL-TFSCI. In this paper, a small-signal impedance model considering the zero-sequence loop of LCL-TFSCI is firstly established. Subsequently, the instability risk is revealed when LCL-TFSCI is connected to the grid with parallel compensation capacitors. Through instability analysis, an impedance-reshaping method based on the complex filter and combined differential elements is proposed, which can reshape the impedance characteristic of LCL-TFSCI within the wide frequency range and expand the stability domain of the grid-connected system. Finally, the proposed method is verified by simulation and experiment.