基于量子遗传算法的水轮机调速器PID参数优化

Optimization of PID parameters of turbine governor based on quantum genetic algorithm

水轮机调速器PID的参数整定对水轮发电机组安全可靠运行具有重要作用,并直接影响电力系统向用户供电的质量及可靠性。为改善水轮发电机组的动态调节品质,利用量子遗传算法对水轮机调速器PID参数实施优化,并将量子遗传算法与传统遗传算法的计算结果进行了比较。仿真结果表明,量子遗传算法(QGA)不但提高了全局的搜寻能力,而且还避免了早熟收敛的问题,有效地解决了传统遗传算法中海明悬崖、计算精度等问题,为水轮机调速器PID参数优化研究提供了新的途径。

PID parameter tuning of turbine governor plays a significant role in the safe and reliable operation of hydro - generating sets, and exerts a direct impact on the quality and reliability of power supply to consumers. In order to improve the quality of dynamic adjustment of generator sets, the PID parameters are optimized by using quantum genetic algorithm （QGA） , the calculation results of QGA and the traditional genetic algorithm are also compared. The simulation results show that, not only does the QGA improve the capability of global search, but also it avoids the problem of premature convergence. The QGA can solve effec- tively the problems of the Hamming cliff, calculating accuracy and other issues appeared in the traditional genetic algorithm, and can provide a new way for the optimization of PID parameters of turbine governor.