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为解决水轮发电机组调速器PID参数优化问题,引入菌群优化(bacterial foraging optimization,BFO)算法。考虑到BFO算法收敛速度慢,而粒子群优化(paticle swarm optimization,PSO)算法具有较好的收敛性,提出BFO-PSO算法。以描述菌体间相互吸引、相互排斥、相互学习的Jcc指标与综合ITAE指标之和构成一种新型适应度函数。数值计算结果表明:与BFO算法、PSO算法相比,BFO-PSO算法收敛速度快,能有效改善水轮机调节系统空载工况和孤网运行条件下过渡过程的动态性能。
In order to solve the PID parameter optimization problem of hydroelectric governor governor, bacterial foraging optimization (BFO) algorithm was introduced. Considering the slow convergence rate of BFO algorithm and the good convergence of particle swarm optimization (PSO) algorithm, the BFO-PSO algorithm is proposed. In order to describe mutual attraction, mutual exclusion and mutual learning, the Jcc index and comprehensive ITAE index constitute a new fitness function. The numerical results show that compared with the BFO and PSO algorithms, the BFO-PSO algorithm has a fast convergence rate and can effectively improve the dynamic performance of the turbine governing system during the no-load condition and the isolated operation condition.