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GA–PSO approach for optimising space-vector PWM control sequence

机译：GA-PSO方法用于优化空间矢量PWM控制序列<？显示[AQ =“”ID =“Q1”“？>

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摘要

Space-vector pulse width modulation (SVPWM) provides several degrees of freedom, which can be optimised to improve the harmonic performance of the three-phase inverter. Genetic algorithm (GA) and immune algorithm (IA) are the two classical probabilistic optimisation algorithms, which are simple in structure and do not need an accurate mathematical model. However, the optimisation accuracy and reliability are low when they optimise the high-dimensional non-linear problem, such as SVPWM control sequence of the three-phase inverter. To cope with these problems, a genetic algorithm-particle swarm optimisation (GA-PSO) is proposed here, which introduces the mutation of GA into discrete PSO. The global and local optimisation ability of the algorithm is greatly improved by the introduction of mutation operation. The results of MATLAB/SIMULINK simulation show that the weighted total harmonic distortion (WTHD) by the optimal SVPWM control sequence based on GA-PSO is 0.199%, which is much better than that of the PSO, IA, and GA. The average generation number of GA-PSO is only 1/500 of IAs. Further experimental data verify that the WTHD by the optimal SVPWM control sequence based on GA-PSO is lower than that of conventional SVPWM and IA.
机译：空间矢量脉冲宽度调制（SVPWM）提供了多次自由度，可优化以提高三相逆变器的谐波性能。遗传算法（GA）和免疫算法（IA）是两个经典概率优化算法，其结构简单，不需要准确的数学模型。然而，当优化高维非线性问题时，优化精度和可靠性低，例如三相逆变器的SVPWM控制序列。为了应对这些问题，此处提出了一种遗传算法粒子群优化优化（GA-PSO），其将GA的突变引入离散PSO中。通过引入突变操作，算法的全局和局部优化能力大大提高。 Matlab / Simulink仿真的结果表明，基于GA-PSO的最佳SVPWM控制序列的加权总谐波失真（WHD）为0.199％，比PSO，IA和GA更好。 GA-PSO的平均发电人数仅为IAS的1/500。进一步的实验数据验证了基于GA-PSO的最佳SVPWM控制序列的WTHD低于传统SVPWM和IA的WTHD。

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