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Title: A Novel Equivalent Model of Active Distribution Networks Based on LSTM

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. Here, it reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4];  [4];  [4]
  1. Huazhong Univ. of Science and Technology, Wuhan (China)
  2. Univ. of Tennessee, Knoxville, TN (United States)
  3. Aalborg Univ., Aalborg (Denmark)
  4. State Grid Shanghai Municipal Electric Power Company, Shanghai (China)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1558485
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Neural Networks and Learning Systems
Additional Journal Information:
Journal Volume: 30; Journal Issue: 9; Journal ID: ISSN 2162-237X
Publisher:
IEEE Computational Intelligence Society
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; deep learning; dynamic behaviors; load modeling; long short-term memory (LSTM); measurement-based approach; recurrent neural network (RNN)

Citation Formats

Zheng, Chao, Wang, Shaorong, Liu, Yilu, Liu, Chengxi, Xie, Wei, Fang, Chen, and Liu, Shu. A Novel Equivalent Model of Active Distribution Networks Based on LSTM. United States: N. p., 2019. Web. doi:10.1109/TNNLS.2018.2885219.
Zheng, Chao, Wang, Shaorong, Liu, Yilu, Liu, Chengxi, Xie, Wei, Fang, Chen, & Liu, Shu. A Novel Equivalent Model of Active Distribution Networks Based on LSTM. United States. doi:10.1109/TNNLS.2018.2885219.
Zheng, Chao, Wang, Shaorong, Liu, Yilu, Liu, Chengxi, Xie, Wei, Fang, Chen, and Liu, Shu. Tue . "A Novel Equivalent Model of Active Distribution Networks Based on LSTM". United States. doi:10.1109/TNNLS.2018.2885219.
@article{osti_1558485,
title = {A Novel Equivalent Model of Active Distribution Networks Based on LSTM},
author = {Zheng, Chao and Wang, Shaorong and Liu, Yilu and Liu, Chengxi and Xie, Wei and Fang, Chen and Liu, Shu},
abstractNote = {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. Here, it reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.},
doi = {10.1109/TNNLS.2018.2885219},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
number = 9,
volume = 30,
place = {United States},
year = {2019},
month = {1}
}

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