Dynamic Equivalent Modeling for Black-box Microgrids Under Multi-operating-point by Using LSTM  

作　　者：Yunlu Li Josep M.Guerrero Junyou Yang Yajuan Guan Guiqing Ma Jiawei Feng 

机构地区：[1]School of Electrical Engineering,Shenyang University of Technology,Shenyang 110819,China [2]Department of Energy Technology,Center for Research on Microgrids(CROM),Aalborg University,9220 Aalborg East,Denmark

出　　处：《CSEE Journal of Power and Energy Systems》2024年第2期639-648,共10页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported in part by the Science Search Foundation of Liaoning Educational Department(No.LQGD2020002).

摘　　要：Since the high penetration of distributed energy sources complicates the dynamics of electrical power systems,accurate dynamic models are indispensable for study on the transient behavior of the microgrid(MG).In some practices,the lack of full detailed information results in failure of dif-ferential equation based dynamic modeling,which leads to a demand for a black-box MG modeling method.It is a critical challenge to maintain the effectiveness of the black-box model under a wide operating range and various fault conditions.In this paper,inspired by the mathematical equivalence between the recurrent neural network(RNN)and differential-algebraic equations(DAEs),a dynamic equivalent modeling method,using long short-term memory(LSTM),is presented to tackle this challenge.At first,the modeling equivalence and advantages of our basic idea are explained.Then,modeling procedures,including data preparation and design guidelines,are presented.Finally,the proposed method is applied to a multi-microgrid testing system for performance evaluation.The results,under various scenarios,reveal that the proposed modeling method has an adequate capability for representing the dynamic behaviors of a black-box MG under grid fault and operating point changing conditions.Index Terms-Deep learning,dynamic behavior,dynamic equivalent model,microgrid,neural network.

关 键 词：Deep learning dynamic behavior dynamic equivalent model MICROGRID neural network 

分 类 号：TM743[电气工程—电力系统及自动化]