基于改进PSO算法的优化特定消谐PWM技术

提出一种改进的粒子群优化（Particle Swarm Optimization，PSO）算法，与传统PSO算法的区别是借鉴模拟退火算法，当微粒个体的适应值小于个体最优值时，以一定的概率接受为个体的最优值。这种处理可以增大算法的全局搜索能力，避免陷入局部最小值。应用改进PSO算法对特定消谐PWM的开关角进行求解，算例证明可以在更小的误差范围内找到方程的解。

Optimization of Selective Harmonic Elimination PWM Technology Based on Impoved PSO Algorithm

An improved particle swarm optimization （PSO） algorithm is proposed, and the difference of which, as compared with traditional PSO, is that it use as a source of reference to simulated annealing algorithm. When individual particles are adapted to individual optimal value, it accepts the optimal value at certain probability. Global searching ability is thus increased avoiding local mini- mum value. Solutions to selective harmonic elimination PWM switch angles should be made by improved PSO algorithm. Examples show that solutions can be found at less error scope.