Power Identification of Distributed Generation Based on Back-Propagation Neural Network

Power Identification of Distributed Generation Based on Back-Propagation Neural Network

Distributed generator (DG) is widely used and applied due to the energy and environment issues. Distributed photovoltaic generation is a typical kind of DG. Its output power is random and fluctuant, which has great influence on the safe, stable and economic operation of power system. Thus it is nece...

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Main Authors: Peng Fang, Liu Yang, Wang Feng, Li Chong, Wang Luhao, Cheng Xingong, Zong Xiju
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/32/e3sconf_posei2021_01037.pdf
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spelling doaj-be4c352a5cca4cbe8447bfd70df2a3072021-05-28T12:41:51ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012560103710.1051/e3sconf/202125601037e3sconf_posei2021_01037Power Identification of Distributed Generation Based on Back-Propagation Neural NetworkPeng Fang0Liu Yang1Wang Feng2Li Chong3Wang Luhao4Cheng Xingong5Zong Xiju6School of Electrical Engineering, University of JinanState Grid Shandong Electric Power Research InstituteState Grid Shandong Electric Power Research InstituteState Grid Intelligence Technology Co., LTDSchool of Electrical Engineering, University of JinanSchool of Electrical Engineering, University of JinanSchool of Electrical Engineering, University of JinanDistributed generator (DG) is widely used and applied due to the energy and environment issues. Distributed photovoltaic generation is a typical kind of DG. Its output power is random and fluctuant, which has great influence on the safe, stable and economic operation of power system. Thus it is necessary to identify the power generated by the distributed photovoltaic generation. This paper proposes a power identification method based on BP Neural Network. The sample data comes from simulation by PSCAD and consists of current and active power that are measured in the branch of distributed network connected with DG and active power generated by the DG. The training is based on Matlab. Simulation results verify that the BP Neural Network can identify active power of DG accurately.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/32/e3sconf_posei2021_01037.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Peng Fang
Liu Yang
Wang Feng
Li Chong
Wang Luhao
Cheng Xingong
Zong Xiju
spellingShingle Peng Fang
Liu Yang
Wang Feng
Li Chong
Wang Luhao
Cheng Xingong
Zong Xiju
Power Identification of Distributed Generation Based on Back-Propagation Neural Network
E3S Web of Conferences
author_facet Peng Fang
Liu Yang
Wang Feng
Li Chong
Wang Luhao
Cheng Xingong
Zong Xiju
author_sort Peng Fang
title Power Identification of Distributed Generation Based on Back-Propagation Neural Network
title_short Power Identification of Distributed Generation Based on Back-Propagation Neural Network
title_full Power Identification of Distributed Generation Based on Back-Propagation Neural Network
title_fullStr Power Identification of Distributed Generation Based on Back-Propagation Neural Network
title_full_unstemmed Power Identification of Distributed Generation Based on Back-Propagation Neural Network
title_sort power identification of distributed generation based on back-propagation neural network
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description Distributed generator (DG) is widely used and applied due to the energy and environment issues. Distributed photovoltaic generation is a typical kind of DG. Its output power is random and fluctuant, which has great influence on the safe, stable and economic operation of power system. Thus it is necessary to identify the power generated by the distributed photovoltaic generation. This paper proposes a power identification method based on BP Neural Network. The sample data comes from simulation by PSCAD and consists of current and active power that are measured in the branch of distributed network connected with DG and active power generated by the DG. The training is based on Matlab. Simulation results verify that the BP Neural Network can identify active power of DG accurately.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/32/e3sconf_posei2021_01037.pdf
work_keys_str_mv AT pengfang poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT liuyang poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT wangfeng poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT lichong poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT wangluhao poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT chengxingong poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
AT zongxiju poweridentificationofdistributedgenerationbasedonbackpropagationneuralnetwork
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