Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm

Abstract

Abstract Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) has excellent harmonic characteristics, but its nonlinear transcendental system of equations is difficult to be solved, and the practical application encounters a bottleneck. In this paper, a modulation optimization method for seven-level SHEPWM inverter based on the Evolutionary Particle Swarm Optimization (EPSO) algorithm is proposed to address this problem, so that the algorithm quickly converges to the global optimum solution. The EPSO algorithm incorporates a population optimization strategy in two phases to improve the population diversity in real time. In the initialization phase, the initialized population is optimized using Opposition-Based Learning (OBL) to improve the quality of the initial population. In the iterative stage, we combine the adaptive Particle Swarm Optimization (PSO) algorithm, Tunicate Swarm Algorithm (TSA), Adaptive Gaussian Variation, Quasi-Opposition-Based Learning (QOBL) and other optimization methods to solve the problem of insufficient population diversity in the process of searching for the optimal solution, to break through the local optimum, and to improve the convergence speed and accuracy of the algorithm. Experiments of the algorithm in 19 benchmark functions and seven-level SHEPWM inverter optimization modulation show that the optimization ability of the EPSO algorithm is ahead of TSA, INFO, MA (Mayfly Algorithm), EO (Equilibrium Optimizer) and other optimization algorithms. The solution speed is about three times that of PSO, which achieves fast and highly accurate convergence, with a small error in the output of inverter, and better harmonic distortion rate than the standard requirement.

Keywords

• SHEPWM
• EPSO
• OBL
• TSA
• Adaptive Gaussian Variation