基于粒子群算法的牵引逆变器多目标优化控制策略

Multi-objective Optimization Control Strategy of Traction Inverter Based on Particle Swarm Algorithm

摘要:

为解决中点钳位型三电平牵引逆变器存在的中点电位不平衡以及由此而引起的输出电流谐波无法同时得到有效控制问题，提出了一种基于粒子群算法的牵引逆变器多目标优化控制策略。首先建立谐波抑制和中点电位平衡控制变量的数学模型；然后以输出电流谐波总畸变率最小为目标，以中点电位波动幅值尽可能小为约束条件，采用罚函数法构建了多目标优化模型。通过粒子群算法进行优化求解，实现在有效抑制输出电流谐波的同时最大程度降低中点电位波动幅值。仿真和实验结果验证了所提多目标优化控制策略的有效性。

关键词:谐波畸变率;中点电位波动;多目标优化;粒子群算法

Abstract:

In order to solve the problem that the unbalance of neutral-point voltage and output current harmonics cannot be effectively controlled at the same time, a multi-objective optimal control strategy for three-level traction inverter based on particle swarm optimization was proposed in this paper. Firstly, a mathematical model of harmonic suppression and neutral-point voltage balance control was established. Then the multi-objective optimization model was built with the idea of penalty function. The object function of the optimum problem was to minimize the total harmonic distortion rate of the output currents. And the key constraint was to make the neutral-point voltage fluctuation as small as possible. Finally, the PSO algorithm was applied to solve the optimum problem, and the goal of suppressing the output current harmonics and reducing the neutral-point voltage fluctuation was achieved. Simulation and experimental results verify the effectiveness of the proposed multi-objective optimal control strategy.

Key words:harmonic distortion rate;neutral-point voltage fluctuation;multi-objective optimization;particle swarm algorithm