A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Zheng, Chao; Wang, Shaorong; Liu, Yilu; Liu, Chengxi; Xie, Wei; Fang, Chen; Liu, Shu

doi:10.1109/TNNLS.2018.2885219

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:

^[1];

^[2];

^[3]; Xie, Wei ^[4]; Fang, Chen ^[4]; Liu, Shu ^[4]

Huazhong Univ. of Science and Technology, Wuhan (China)
Univ. of Tennessee, Knoxville, TN (United States)
Aalborg Univ., Aalborg (Denmark)
State Grid Shanghai Municipal Electric Power Company, Shanghai (China)

Publication Date:: Tue Jan 01 00:00:00 EST 2019

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.

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                    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.  https://doi.org/10.1109/TNNLS.2018.2885219

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                    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.  https://doi.org/10.1109/TNNLS.2018.2885219.  https://www.osti.gov/servlets/purl/1558485.

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@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         = {Tue Jan 01 00:00:00 EST 2019},

  month        = {Tue Jan 01 00:00:00 EST 2019}

}

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