电力系统保护与控制2024，Vol.52Issue(8)：98-110,13.DOI:10.19783/j.cnki.pspc.231067
基于改进Att-LSTNet与无迹粒子滤波融合的主动配电网预测辅助状态估计
Forecasting-aided state estimation for active distribution networks based on improved Att-LSTNet and unscented particle filter fusion
摘要
Abstract
In response to the issue of inaccurate innovation vectors and unknown measurement noise covariance matrices in the traditional unscented particle filter(UPF),a forecasting-aided state estimation(FASE)method for active distribution network is proposed,which integrates the improved Att-LSTNet and UPF.First,the key parameters of support vector regression(SVR)are optimized using a gravitational search algorithm(GSA),and a GSA-SVR model is established using historical data.This model is then introduced into the output layer of the Att-LSTNet model to create an enhanced forecasting model.Subsequently,the innovation vectors from UPF are used to train this model,and the isolation forest algorithm and box-plot method are employed to monitor and correct the original innovation vectors.Finally,in the case of unknown measurement noise covariance matrices,the corrected innovation vectors and UPF are combined to calculate the unknown measurement noise covariance matrices and perform state estimation.Case study results on the IEEE33-bus and IEEE118-bus test systems demonstrate the superiority of the proposed method in terms of estimation accuracy,generalizability,and robustness.关键词
主动配电网/预测辅助状态估计/Att-LSTNet/无迹粒子滤波/SVRKey words
active distribution networks/forecasting-aided state estimation/Att-LSTNet/UPF/SVR引用本文复制引用
王玥,于越,金朝阳..基于改进Att-LSTNet与无迹粒子滤波融合的主动配电网预测辅助状态估计[J].电力系统保护与控制,2024,52(8):98-110,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.U22B20101). 国家自然科学基金项目资助(U22B20101) （No.U22B20101）
