基于WAMS的电力系统实时状态估计和预报  

Power System Real-time State Estimation and Prediction Based on WAMS

作　　者：李虹[1] 李卫国[1] 毕天姝[1] 熊浩清[2] 

机构地区：[1]华北电力大学电力系统保护与动态安全监控教育部重点实验室,北京市102206 [2]河南电力调度通信中心,河南省郑州市450052

出　　处：《电力系统自动化》2009年第16期35-39,49,共6页Automation of Electric Power Systems

基　　金：教育部新世纪优秀人才支持计划资助项目(NECT-05-0216)~~

摘　　要：根据广域测量系统/数据采集与监控(WAMS/SCADA)系统的测量数据,提出一种实时状态估计和预报算法。采用递推增广最小二乘法辨识和修正状态转移矩阵,并采用渐消记忆指数加权法在线估计线性化后的模型误差协方差矩阵,同时由量测量的标准差在线计算量测权重,这使得在利用观测数据进行滤波的同时,状态估计模型中不精确(或未知)参数和噪声协方差矩阵不断地在线辨识和修正,以适应环境的干扰和过程的时变性,减少状态估计误差,达到良好的滤波效果。仿真结果表明,所提出的方法在正常情况、存在坏数据、负荷突变/发电机输出功率突变、网络拓扑结构误判等情况下具有较好的滤波效果。A real-time state estimator based on WAMS is presented, in which the recursive extended least-squares algorithm is used to identify the state transition matrix, and the fading memory exponential weighted algorithm is employed to estimate the error covariance matrix of the linearized model. At the same time, the measurement weights are calculated on-line by the measurement standard deviations. When the filter is done by using observed data, the imprecise （or unknown） parameters and the noise covariance matrixes of the state estimation model are constantly identified and corrected on-line simultaneously, which makes the model follow the contingencies and the time-varying process adaptively. Therefore the estimated errors are reduced, and the filtering precision is high. Simulation results show that the proposed method has good performance under different scenarios such as normal operation, bad measurements, sudden load change/drastic generation variation and topology errors. This work is supported by Program for New Century Excellent Talents in University （No. NCET-05-0216）.

关 键 词：实时状态估计 广域测量系统 扩展卡尔曼滤波 数据采集与监控 电力系统 

分 类 号：TM76[电气工程—电力系统及自动化]