基于现场实验数据的PSS参数智能优化方法

Intelligent Parameter Optimization of Power System Stabilizer Based on Field Test Results

针对目前电力系统稳定器(PSS)参数优化实验工作中人工参与度高的现状,提出了一种基于现场试验数据的PSS参数智能优化方法。首先通过现场小扰动试验数据,将发电机以外的系统等值为无穷大母线电压Vs和系统电抗Xs,然后利用静态等值系统的线性化Heffron-Philips模型计算励磁控制系统的无补偿相位特性,得到PSS参数优化的目标曲线,并根据这一目标曲线,利用改进粒子群算法(SAPSO)优化PSS的时间参数值。通过在PSASP中对华北华中电网算例进行仿真,仿真结果表明采用本算法优化后的PSS能有效、合理地抑制低频振荡,并且能够适应电网不同的运行方式,具有一定的鲁棒性。

For the high level human participation of the test on parameter optimization of power system stabilizer （PSS）, a new method of intelligent parameter optimization of power system stabilizer （PSS） based on field test results is proposed. First by small disturbance field test results, power system outside the generator can be equaled to an infinite bus voltage Vs and system reactance Xs, Phase lag properties without compensation of excitation system can be calculated by linearized Heffron-Phillips model based on the static equivalent system. Then the objective curve of parame- ter optimization of PSS is obtained,on the basis of which parameter of PSS can be optimized by SAPSO simulated annealing particle swarm optimization. Simulation results of North and Central China power system by PSASP demonstrate that PSS with optimized parameters by this method can control low frequency oscillation effectively and reasonably, and have a certain robustness to adapt to different network operation modes.