Abstract
In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.
Highlights
The Selective Harmonic Elimination (SHE) and Selective Harmonic Mitigation (SHM) [1,2] has been described for the first time in the 1960s in [3] and disseminated by Patel and Hoft [4,5]
Switching angles calculated by the Grasshopper Optimization Algorithm (GOA) algorithm were implemented into laboratory stand
GOA algorithm, In Inthis the authors authorsinvestigated investigated a novel application of a recently developed for the calculation of switching angles in
Summary
The Selective Harmonic Elimination (SHE) and Selective Harmonic Mitigation (SHM) [1,2] has been described for the first time in the 1960s in [3] and disseminated by Patel and Hoft [4,5]. Iterative techniques such as Newton–Raphson (N–R) [4,5], Gauss–Newton have been employed to solve these equations. The convergence of these methods depends on the initial guess, which is a complex problem and in many cases is not successful. This disadvantage encourages researchers to develop more effective techniques. The combination of Groebner’s bases and symmetric polynomials was applied to solve the mentioned polynomials [8] It generates ambiguous solutions which make it less useful. The main disadvantage of iterative techniques is they do not find an optimum solution
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