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

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）.