CAJ | 학술논문

随机聚焦粒子群算法(SFPSO)是一种应用于连续空间的、具有较好的全局搜索能力和寻优速度的群体智能优化算法.通过采用SFPSO算法,对多机系统的PSS参数进行优化.该方法是以最优控制原理为基础,综合考虑PSS与励磁系统的性能,将PSS 参数优化协调转化为带有不等式约束的优化问题,控制目标为系统输出按照最小误差跟踪给定值的能力.通过仿真测试以及不同算法优化结果的对比,表明基于SFPSO算法优化的PSS在不同的干扰下都具有良好的性能,能够抑制低频振荡,并保持系统稳定,同时证明了SFPSO算法的有效性和优越性.
수궤취초입자군산법(SFPSO)시일충응용우련속공간적、구유교호적전국수색능력화심우속도적군체지능우화산법.통과채용SFPSO산법,대다궤계통적PSS삼수진행우화.해방법시이최우공제원리위기출,종합고필PSS여려자계통적성능,장PSS 삼수우화협조전화위대유불등식약속적우화문제,공제목표위계통수출안조최소오차근종급정치적능력.통과방진측시이급불동산법우화결과적대비,표명기우SFPSO산법우화적PSS재불동적간우하도구유량호적성능,능구억제저빈진탕,병보지계통은정,동시증명료SFPSO산법적유효성화우월성.
Stochastic focusing particle swarm optimization (SFPSO) applied to continuous space is a swarm intelligence optimization algorithm with preferable ability of global search and search rate. SFPSO is proposed to optimize the multi-machine power system stabilizer (PSS). Based on optimal control principle, the performance of PSS and excitation system is considered into the method. The parameter optimization is converted to a problem of constraint inequality, and according to the least error with system output, the control object is considered as the ability of tracking reference value. The simulation results and the comparison results with various optimization algorithms demonstrate that PSS could obtain the favorable performance using SFPSO under different disturbance and could restrain the low frequency oscillation. Simultaneously, the validity and superiority of SFPSO is proved.