基于改进粒子群算法参数优化的APF预测电流控制

对带有收敛因子的粒子群(PSO)算法进行算法改进,改进后的PSO算法提高了局部探索能力,不易陷入局部最优.在预测电流控制中,采用改进的粒子群收敛因子法对电流预测插值系数进行优化.仿真实验结果表明,该方法可以进一步提高指令电流预测的准确度,使得补偿后系统线路中电流的畸变率减小.特别地,在谐波指令电流突变点附近误差较小,计算量小,动态响应较为平稳,提高APF的补偿效果.

Application of an enhanced particle swarm optimization algorithm for parameter optimization of current predictive control in the active power filter

The paper improves the particle swarm optimization (PSO) algorithm with convergence factor, the improved method improves the ability of local search, it will not fall into local optimum. It uses enhanced PSO to optimize the interpolation points of current predictive control. The accuracy of current prediction has been further enhanced through the analysis of the simulation results, the THD of system's current has been reduced, and the error is little around the break point to the harmonic instruction current, the calculation is little, the response of transient state is relatively stable, and then the compensation effect of the APF can be improved.