基于粒子群算法的电力系统非线性谐波状态估计  

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

作　　者：韩美玉[1] 王艳松[1] 张丽霞[1] 

机构地区：[1]中国石油大学信息与控制工程学院,山东青岛266555

出　　处：《电力系统保护与控制》2013年第22期98-102,共5页Power System Protection and Control

基　　金：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)~~

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。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.

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 

分 类 号：TM711[电气工程—电力系统及自动化]