电力系统保护与控制2024，Vol.52Issue(3)：128-140,13.DOI:10.19783/j.cnki.pspc.230867
基于集成深度神经网络的配电网分布式状态估计方法
Distribution network distributed state estimation method based on an integrated deep neural network
摘要
Abstract
With the integration of a large number of distributed energy sources,the operation and control methods of distribution systems have become increasingly complex.In response to the problems faced by distribution network state estimation methods such as difficulty in identifying distributed power source fluctuation data,low estimation accuracy,poor robustness and estimation timeliness,a distribution network distributed state estimation method based on integrated deep neural networks is proposed.First,the data identification technique of measuring data correlation testing is used to identify bad data and new energy fluctuation data.From this,the bad data is corrected using a temporal convolutional network(TCN)-bidirectional long short term memory(BILSTM).Then,an integrated deep neural network(DNN)state estimation model is established,and the maximum relevance-minimum redundancy(MRMR)method is used to optimize the training samples,thereby improving accuracy and robustness.Finally,a distributed integrated DNN model is established to compensate for the slow speed of centralized state estimation and improve efficiency.The numerical analysis based on an IEEE123 distribution network shows that the proposed method can more accurately identify distributed power source fluctuation data and bad data,while improving the accuracy and efficiency of state estimation,and is very robust.关键词
状态估计/最大相关-最小冗余/分布式/集成深度神经网络Key words
state estimation/maximum relevance-minimum redundancy/distributed/integrated deep neural network引用本文复制引用
张汪洋,樊艳芳,侯俊杰,宋雨露..基于集成深度神经网络的配电网分布式状态估计方法[J].电力系统保护与控制,2024,52(3):128-140,13.基金项目
This work is supported by the National Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01C365 and No.2022D01C662). 新疆维吾尔自治区自然科学基金项目资助(2022D01C365,2022D01C662) （No.2022D01C365 and No.2022D01C662）
2022 天山英才培养计划项目资助(2022TSYCLJ0019) （2022TSYCLJ0019）
