基于粒子群算法的电力系统非线性谐波状态估计

韩美玉; 王艳松; 张丽霞

引用本文:	韩美玉,王艳松,张丽霞.基于粒子群算法的电力系统非线性谐波状态估计[J].电力系统保护与控制,2013,41(22):98-102.
	HAN Mei-yu,WANG Yan-song,ZHANG Li-xia.Research on non-linear harmonic state estimation in power system based on particle swarm optimization algorithm[J].Power System Protection and Control,2013,41(22):98-102

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基于粒子群算法的电力系统非线性谐波状态估计
韩美玉¹, 王艳松¹, 张丽霞¹
中国石油大学信息与控制工程学院，山东青岛 266555

摘要:

为增加谐波量测数据的冗余度，提高线性谐波状态估计的可观测度，基于PMU量测数据和SCADA量测数据构成混合量测数据，应用于谐波状态估计，建立非线性谐波状态估计的数学模型。将该非线性数学模型改写为灵敏度模型，并转化为优化问题，应用粒子群算法求解。算例分析表明，非线性谐波状态估计的灵敏度模型是有效的，应用优化算法求解是切实可行的，混合量测数据能提高谐波状态估计的可观测度。

关键词: 谐波状态估计相量量测混合量测量测配置粒子群算法

DOI：10.7667/j.issn.1674-3415.2013.22.016

分类号:

基金项目:国家自然科学基金项目(51207170)；中央高校基本科研业务费专项资金资助(12CX04066A)

Research on non-linear harmonic state estimation in power system based on particle swarm optimization algorithm

HAN Mei-yu,WANG Yan-song,ZHANG Li-xia

Abstract:

To increase the redundancy of measurement data and improve the observability of linear harmonic state estimation, mixed measurements acquired from PMU and SCADA are used to build the mathematical model of non-linear harmonic state estimation. Then, it is rewritten as a sensitivity model, transformed into an optimization problem, and solved by particle swarm optimization algorithm. Example analysis shows the sensitivity model of non-linear harmonic state estimation is efficient. PSO algorithm can be used to solve this optimization problem. Mixed measurements are helpful to improve the accuracy of harmonic state estimation.

Key words: harmonic state estimation phasor measurement mixed measurement measurement configuration particle swarm optimization algorithm

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