A review of soft computing methods for harmonics elimination PWM for inverters in renewable energy conversion systems

For a renewable energy (RE) system, an inverter is normally required to condition the dc power to ac, so that it could be connected to the electrical grid. At the heart of the inverter is the modulation strategy that synthesized the ac waveform by chopping the dc voltage using power electronics switches. Among the numerous modulation techniques, the harmonics elimination PWM (HEPWM) is preferable due to its superior harmonics profile; the elimination of low order harmonics results in reduced switching losses, hence improved inverter efficiency. However, the non-linear and transcendental nature of the HEPWM equations poses a challenge for the conventional computational methods (mostly calculus-based). With the advent of low cost and powerful computers, the soft computing (SC) approach seems to be a better approach and well suited to handle the complexity of the HEPWM problem. This review paper attempts to summarize the operation of the nine SC methods, as well as highlighting their advantages and limitations. Furthermore, the work also presents a critical evaluation on the performance of the three prominent SC techniques, namely, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is envisaged that the information gathered in this single reference will be useful for researchers, designers and practitioners that utilize HEPWM to design energy conversion system.