GA-Based Optimization and ANN-Based SHEPWM Generation for Two-Level Inverter

Abstract

Selective harmonic elimination (SHE) is a well-known PWM technique applied voltage source inverters (VSI) to control fundamental voltage and eliminate chosen harmonics. This technique requires the determination of optimum switching angles by solving the nonlinear equation set and a look-up table stored the switching times in a real-time application. This paper presents a hybrid genetic algorithm (GA) to optimize offline of optimum 11-switching angles for three-phase two-level inverter. In addition, the paper proposes two Artificial Neural Networks (ANN) based solution. The first ANN was trained by the data obtained from GA to calculate the switching angles without using a look-up table. Second ANN was trained by using these switching angles to generate PWM signals. GA and ANN are performed by using MATLAB environment. The ANN-based SHEPWM was designed to obtain inverter output voltage which has a bipolar waveform with quarter-wave symmetry. The waveforms of inverter output voltage and load current are analyzed with FFT for a RL load.