Outlier Reconstruction Based Distribution System State Estimation Using Equivalent Model of Long Short-term Memory and Metropolis-Hastings Sampling  

作　　者：Mingchao Xia Jinping Sun Qifang Chen 

机构地区：[1]School of Electrical Engineering,Beijing Jiaotong University(BJTU),Beijing 100044,China [2]IEEE

出　　处：《Journal of Modern Power Systems and Clean Energy》2022年第6期1625-1636,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported by the National Key Research and Development Program(No.2017YFB0902900).

摘　　要：The accuracy of distribution system state estimation(DDSE)is reduced when phasor measurement unit(PMU)measurements contain outliers because of cyber attacks or global positioning system spoofing attacks.Therefore,to enhance the robustness of DDSE to measurement outliers,approximate the target distribution of Metropolis-Hastings(MH)sampling,and judge the prediction of the long short-term memory(LSTM)network,this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling(E-LM model),motivated by the characteristics of the chronological correlations of PMU measurements.First,the target distribution of outlier reconstruction is derived using a kernel density estimation function.Subsequently,the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view.The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations.Moreover,the proposed MH-based forecasting of the LSTM can judge each LSTM prediction,which is independent of its true value.Finally,simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.

关 键 词：Distribution system state estimation(DDSE) outlier reconstruction phasor measurement unit(PMU) equivalent model long short-term memory(LSTM)network Metropolis-Hastings sampling 

分 类 号：TM73[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]