粒子群优化自适应最小二乘法的电网谐波估计

Estimation of Harmonics in Power Systems Based on Particle Swarm Optimized Recursive Least Square Model

研究基于粒子群优化自适应最小二乘法的电网谐波估计方法,针对自适应最小二乘(Recursive Least Square, RLS)算法对初始值敏感的问题,本文利用粒子群(Particle Swarm Optimization, PSO)算法得到最优化的电网谐波参数即状态向量的权重初始值,再利用自适应最小二乘法(RLS)对电网谐波参数进行参数估计。对静态和动态的电压信号进行仿真分析,并比较了不同的噪声环境下参数估计效果,最后还应用本文所提方法对电网动态子谐波和间谐波进行了仿真分析。仿真结果表明,与可变约束最小二乘方法(VCLMS),遗传算法(GA)优化参数估计方法相比,本文所提方法估计效果更优。

This paper presents a method for estimating harmonics in power systems based on particle swarm optimized recursive least square (PSO-RLS) model. The PSO is used to get the optimal initial weights and RLS is used to estimate parameters of harmonic signals. In this way, the method resolves the problem that RLS is sensitive to initial weights. This method is used to analyze both steady-state and dynamic voltage signals. And its performance is revealed by comparing results of difference noise environments. In addition, the dynamic sub harmonic and inter harmonics are analyzed using this method. Simulation results show the performance of the proposed method is better than the VCLMS and GA-RLS ones.