Abstract
Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions.
Highlights
The operating principle and effective performance of a multilevel inverter (MLI) highly depends on its switching operation
Few case studies cannot validate the superiority of an algorithm over other algorithms. This is because the performance of these algorithms can widely vary depending on selective harmonic elimination PWM (SHEPWM) parameters such as the number of voltage levels produced by MLIs, number of targeted harmonics, number of switching angles, and sets of nonlinear equations [4], [15]–[18]
This algorithm effectively overcomes most of the drawbacks hold by the other metaheuristic algorithms as well as mathematical strategies applied for SHEPWM
Summary
The operating principle and effective performance of a multilevel inverter (MLI) highly depends on its switching operation. Few case studies cannot validate the superiority of an algorithm over other algorithms This is because the performance of these algorithms can widely vary depending on SHEPWM parameters such as the number of voltage levels produced by MLIs, number of targeted harmonics, number of switching angles, and sets of nonlinear equations [4], [15]–[18]. This demands an algorithm that can be proven superior to other algorithms under various case studies taking different sets of SHEPWM parameters. The second objective is to have a broad range of solutions that will ensure the flexibility of the MLI or in other words, it can be operated at different modulation indices seemingly
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