A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Chao Zheng, Shaorong Wang, Yilu Liu, Chengxi Liu, W. Xie, Chen Fang, Shu Liu

Research output: Contribution to journalJournal articleResearchpeer-review

17 Citations (Scopus)
77 Downloads (Pure)

Abstract

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.
Original languageEnglish
Article number2885219
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume30
Issue number9
Pages (from-to)2611 - 2624
Number of pages14
ISSN2162-237X
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • Deep learning
  • Dynamic behaviors
  • Load modeling
  • Long short-term memory (LSTM)
  • Measurement-based approach
  • Recurrent neural network (RNN)

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