Dynamic Equivalent Modeling for Black-Box Microgrids Under Multi-Operating-Point by Using LSTM

Abstract

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 differential 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.

Keywords

• Deep learning
• dynamic behavior
• dynamic equivalent model
• microgrid
• neural network