Real-Time Harmonic Optimization in Multilevel Inverter Using Artificial Neural Network (ANN)

Abstract

Multilevel inverters (MLIs) are increasingly used in real time applications. Among several pulse width modulation (PMW) techniques currently deployed for the control of MLIs, selective harmonic elimination PWM (SHEPWM) technique arguably gives the best performance due to its direct harmonic mitigation capability. However, real time application of SHEPWM technique is presently infeasible due to the heavy computational cost involved in solving the transcendental nonlinear equations known as selective harmonic elimination (SHE) equations, which characterize the harmonics that are selected for elimination or mitigation. This paper presents a twostage approach to the online generation of switching angles that mitigate selected lower-order harmonics in multilevel inverters. The first stage involves an offline solution of SHE equations using ant colony optimisation (ACO). In the second stage, ACO computed results are used to train an artificial neural network (ANN) predictive model. The results obtained from the simulation of the proposed method in MATLAB/SIMULINK environment show that the method is highly efficient and accurate.