Dynamic Equivalent Modeling for Black-box Microgrids Under Multi-operating-point by Using LSTM
作者机构：School of Electrical EngineeringShenyang University of TechnologyShenyang 110819China Department of Energy TechnologyCenter for Research on Microgrids(CROM)Aalborg University9220 Aalborg EastDenmark
出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)
年 卷 期：2024年第10卷第2期
页 面：639-648页
核心收录：
学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 0701[理学-数学]
主 题：Deep learning dynamic behavior dynamic equivalent model microgrid neural network
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 *** is a critical challenge to maintain the effectiveness of the black-box model under a wide operating range and various fault *** 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 *** first,the modeling equivalence and advantages of our basic idea are ***,modeling procedures,including data preparation and design guidelines,are ***,the proposed method is applied to a multi-microgrid testing system for performance *** 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 *** Terms-Deep learning,dynamic behavior,dynamic equivalent model,microgrid,neural network.
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