A multiagent deep deterministic policy gradient-based distributed protection method for distribution network
Relay protection system plays an important role in the safe and stable operation of distribution network (DN), and the traditional model-based relay protection algorithms are difficult to solve the impact of the increasing uncertainty caused by distributed generation (DG) access on the security of D...
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| Veröffentlicht in: | Neural computing & applications Jg. 35; H. 3; S. 2267 - 2278 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer London
01.01.2023
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | Relay protection system plays an important role in the safe and stable operation of distribution network (DN), and the traditional model-based relay protection algorithms are difficult to solve the impact of the increasing uncertainty caused by distributed generation (DG) access on the security of DN. To solve this issue, first, the relay protection characteristics of DN under DG access are analyzed; second, the DN relay protection problem is transformed into multiagent reinforcement learning (RL) problem; third, a DN distributed protection method based on multiagent deep deterministic policy gradient (MADDPG) is proposed. The advantage of this method is that there is no need to build a DN security model in advance; therefore, it can effectively overcome the impact of uncertainty caused by DG access on DN security . Extensive experiments show the effectiveness of the proposed algorithm. |
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| AbstractList | Relay protection system plays an important role in the safe and stable operation of distribution network (DN), and the traditional model-based relay protection algorithms are difficult to solve the impact of the increasing uncertainty caused by distributed generation (DG) access on the security of DN. To solve this issue, first, the relay protection characteristics of DN under DG access are analyzed; second, the DN relay protection problem is transformed into multiagent reinforcement learning (RL) problem; third, a DN distributed protection method based on multiagent deep deterministic policy gradient (MADDPG) is proposed. The advantage of this method is that there is no need to build a DN security model in advance; therefore, it can effectively overcome the impact of uncertainty caused by DG access on DN security . Extensive experiments show the effectiveness of the proposed algorithm. |
| Author | Wang, Zhongfeng Cui, Shijie Zeng, Peng Song, Chunhe Li, Guangye |
| Author_xml | – sequence: 1 givenname: Peng surname: Zeng fullname: Zeng, Peng organization: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences – sequence: 2 givenname: Shijie surname: Cui fullname: Cui, Shijie email: cuishijie@sia.cn organization: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, University of Chinese Academy of Sciences – sequence: 3 givenname: Chunhe surname: Song fullname: Song, Chunhe organization: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences – sequence: 4 givenname: Zhongfeng surname: Wang fullname: Wang, Zhongfeng organization: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences – sequence: 5 givenname: Guangye surname: Li fullname: Li, Guangye organization: State Grid Liaoning Electric Power Co., Ltd |
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| CitedBy_id | crossref_primary_10_1007_s00521_024_10655_8 crossref_primary_10_1007_s13042_022_01759_5 crossref_primary_10_1007_s40747_024_01529_6 crossref_primary_10_1063_5_0147592 crossref_primary_10_1007_s00521_023_09067_x crossref_primary_10_1109_ACCESS_2023_3335191 crossref_primary_10_3390_en16196867 crossref_primary_10_1007_s11042_024_19871_9 crossref_primary_10_1109_ACCESS_2023_3268283 crossref_primary_10_1109_TPWRD_2024_3447084 crossref_primary_10_1007_s00521_022_07706_3 |
| Cites_doi | 10.1109/TSG.2018.2857449 10.1109/TPWRS.2017.2760011 10.1016/j.trc.2018.12.018 10.1016/j.arcontrol.2019.09.008 10.1016/j.epsr.2018.07.008 10.1109/TSG.2018.2834219 10.1049/iet-rpg.2019.0793 10.1109/ACCESS.2021.3061919 10.1109/TSG.2019.2903756 10.1049/iet-gtd.2018.6230 10.1109/TSG.2018.2879572 10.1007/s00521-021-05795-0 10.1016/j.apenergy.2018.12.061 10.1109/TSG.2016.2629450 10.1109/TSMCB.2009.2026289 10.1002/etep.2532 10.11591/ijeecs.v14.i1.pp319-326 10.1016/j.ipm.2019.102096 10.1016/j.rser.2019.109524 10.3390/en13092149 10.1109/JSYST.2018.2855689 10.23919/JSEE.2020.000006 10.1109/TPWRS.2018.2842648 10.3390/en12030436 10.1109/SEST.2018.8495830 10.1109/ISGTEurope.2019.8905476 10.1109/TSG.2016.2601149 10.1109/EEEIC.2019.8783396 10.1109/ISGT-Asia.2018.8467835 10.1109/CDC40024.2019.9029268 |
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| Keywords | Distributed generation Power system protection Distribution system Reinforcement learning Multiagent |
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| References | Zhang, Li, Yu, Yang (CR27) 2018; 9 Quan, Zhan, Zhang, Xiao, Peng (CR2) 2021; 49 Darabi, Bagheri, Gharehpetian (CR11) 2020; 14 Pereira, Pereira, Contreras, Mantovani (CR7) 2018; 33 Kiliçkiran, Şengör, Akdemir, Kekezoğlu, Erdinç, Paterakis (CR6) 2018; 164 Lu, Hong (CR24) 2019; 236 Dong (CR34) 2021; 45 CR15 CR14 Karupiah, Hussain, Musirin, Rahim (CR13) 2019; 14 CR35 Qi, Luo, Wu, Boriboonsomsin, Barth (CR20) 2019; 99 Xu, Sun, Nikovski, Kitamura, Mori, Hashimoto (CR23) 2019; 10 Lin, Sun, Tan, Liu, Guerrero, Vasquez (CR12) 2019; 14 CR31 CR30 Hussain, Nasir, Vasquez, Guerrero (CR5) 2020; 13 Remani, Jasmin, ImthiasAhamed (CR26) 2019; 13 Barra, Coury, Fernandes (CR3) 2020; 118 Usama (CR4) 2021; 9 Nguyen, Nguyen, Vamplew (CR21) 2021; 33 Zhou, Chen, Yang (CR1) 2018; 39 Wan, Li, He, Prokhorov (CR19) 2019; 10 Kim, Park, Park, Kang (CR33) 2010; 40 Mocanu, Mocanu, Nguyen, Liotta, Webber, Gibescu, Slootweg (CR18) 2019; 10 CR9 Zhang, Zhang, Qiu (CR17) 2020; 6 Saenz-Aguirre, Zulueta, Fernandez-Gamiz, Lozano, Lopez-Guede (CR28) 2019; 12 Glavic (CR16) 2019; 48 Ye, Luo (CR25) 2020; 57 Claessens, Vrancx, Ruelens (CR29) 2018; 9 Shabani, Karimi (CR10) 2018; 28 Zhang, Wang, Yue, Liu, Yao (CR32) 2020; 31 Atteya, El Zonkoly, Ashour (CR8) 2017; 97 Chen, Su (CR22) 2018; 10 Schneider (CR36) 2018; 33 J Zhang (6982_CR32) 2020; 31 B Zhou (6982_CR1) 2018; 39 A Darabi (6982_CR11) 2020; 14 6982_CR14 ZQ Wan (6982_CR19) 2019; 10 6982_CR15 B Kim (6982_CR33) 2010; 40 BJ Claessens (6982_CR29) 2018; 9 M Glavic (6982_CR16) 2019; 48 6982_CR35 T Chen (6982_CR22) 2018; 10 6982_CR30 6982_CR31 6982_CR9 RZ Lu (6982_CR24) 2019; 236 A Saenz-Aguirre (6982_CR28) 2019; 12 L Dong (6982_CR34) 2021; 45 XW Qi (6982_CR20) 2019; 99 Z Zhang (6982_CR17) 2020; 6 N Hussain (6982_CR5) 2020; 13 L Quan (6982_CR2) 2021; 49 HC Kiliçkiran (6982_CR6) 2018; 164 H Lin (6982_CR12) 2019; 14 S Karupiah (6982_CR13) 2019; 14 H Ye (6982_CR25) 2020; 57 PHA Barra (6982_CR3) 2020; 118 T Remani (6982_CR26) 2019; 13 AI Atteya (6982_CR8) 2017; 97 KP Schneider (6982_CR36) 2018; 33 E Mocanu (6982_CR18) 2019; 10 M Shabani (6982_CR10) 2018; 28 M Usama (6982_CR4) 2021; 9 HC Xu (6982_CR23) 2019; 10 XS Zhang (6982_CR27) 2018; 9 K Pereira (6982_CR7) 2018; 33 ND Nguyen (6982_CR21) 2021; 33 |
| References_xml | – volume: 10 start-page: 4338 issue: 4 year: 2018 end-page: 4348 ident: CR22 article-title: Indirect customer-to-customer energy trading with reinforcement learning publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2857449 – volume: 33 start-page: 3181 year: 2018 end-page: 3188 ident: CR36 article-title: Analytic considerations and design basis for the IEEE distribution test feeders publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2017.2760011 – volume: 97 start-page: 463 issue: 5 year: 2017 end-page: 469 ident: CR8 article-title: Adaptive protection scheme for optimally coordinated relay setting using modified PSO algorithm publication-title: J Power Technol – volume: 99 start-page: 67 year: 2019 end-page: 81 ident: CR20 article-title: Deep reinforcement learning enabled self-learning control for energy efficient driving publication-title: Trans Res Part C: Emerg Technol doi: 10.1016/j.trc.2018.12.018 – volume: 48 start-page: 22 year: 2019 end-page: 35 ident: CR16 article-title: (Deep) Reinforcement learning for electric power system control and related problems: a short review and perspectives publication-title: Annu Rev Control doi: 10.1016/j.arcontrol.2019.09.008 – volume: 164 start-page: 89 year: 2018 end-page: 102 ident: CR6 article-title: Power system protection with digital overcurrent relays: a review of non-standard characteristics publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2018.07.008 – volume: 10 start-page: 3698 issue: 4 year: 2019 end-page: 3708 ident: CR18 article-title: On-line building energy optimization using deep reinforcement learning publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2834219 – ident: CR14 – volume: 14 start-page: 1201 issue: 7 year: 2020 end-page: 1209 ident: CR11 article-title: Highly sensitive micro-grid protection using overcurrent relays with a novel relay characteristic publication-title: IET Renew Power Gener doi: 10.1049/iet-rpg.2019.0793 – ident: CR30 – volume: 9 start-page: 35740 year: 2021 end-page: 35765 ident: CR4 article-title: A comprehensive review on protection strategies to mitigate the impact of renewable energy sources on interconnected distribution networks publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3061919 – ident: CR35 – volume: 10 start-page: 6366 issue: 6 year: 2019 end-page: 6375 ident: CR23 article-title: Deep reinforcement learning for joint bidding and pricing of load serving entity publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2019.2903756 – volume: 45 start-page: 4729 issue: 12 year: 2021 end-page: 4738 ident: CR34 article-title: Optimal dispatch of combined heat and power system based on multi-agent deep reinforcement learning publication-title: Power Syst Technol – volume: 14 start-page: 770 issue: 7 year: 2019 end-page: 779 ident: CR12 article-title: Adaptive protection combined with machine learning for microgrids publication-title: IET Gener, Transm Distrib doi: 10.1049/iet-gtd.2018.6230 – volume: 10 start-page: 5246 issue: 5 year: 2019 end-page: 5257 ident: CR19 article-title: Model-free real-time EV charging scheduling based on deep reinforcement learning publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2879572 – volume: 39 start-page: 97 issue: 2 year: 2018 end-page: 105 ident: CR1 article-title: On the power system large-scale blackout in Brazil publication-title: Power Gen Technol – volume: 9 start-page: 2152 issue: 3 year: 2018 end-page: 2165 ident: CR27 article-title: Consensus transfer Q-learning for decentralized generation command dispatch based on virtual generation tribe publication-title: IEEE Trans Smart Grid – ident: CR15 – volume: 33 start-page: 10335 year: 2021 end-page: 10349 ident: CR21 article-title: A prioritized objective actor-critic method for deep reinforcement learning publication-title: Neural Comput Applic doi: 10.1007/s00521-021-05795-0 – volume: 236 start-page: 937 year: 2019 end-page: 949 ident: CR24 article-title: Incentive-based demand response for smart grid with reinforcement learning and deep neural network publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.12.061 – volume: 9 start-page: 3259 issue: 4 year: 2018 end-page: 3269 ident: CR29 article-title: Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2016.2629450 – volume: 40 start-page: 433 issue: 2 year: 2010 end-page: 443 ident: CR33 article-title: Impedance learning for robotic contact tasks using natural actor-critic algorithm publication-title: IEEE Trans Syst, Man, Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2009.2026289 – volume: 28 start-page: e2532 issue: 5 year: 2018 ident: CR10 article-title: A robust approach for coordination of directional overcurrent relays in active radial and meshed distribution networks considering uncertainties publication-title: Int Trans Electr Energy Syst doi: 10.1002/etep.2532 – volume: 14 start-page: 319 issue: 1 year: 2019 end-page: 326 ident: CR13 article-title: Prediction of overcurrent relay miscoordination time using urtificial neural network publication-title: IndonesianJ Electr Eng Comput Sci doi: 10.11591/ijeecs.v14.i1.pp319-326 – ident: CR31 – ident: CR9 – volume: 6 start-page: 213 issue: 1 year: 2020 end-page: 225 ident: CR17 article-title: Deep reinforcement learning for power system applications: an overview publication-title: CSEE J Power Energy Syst – volume: 57 start-page: 102096 issue: 6 year: 2020 ident: CR25 article-title: Deep ranking based cost-sensitive multi-label learning for distant supervision relation extraction publication-title: Inf Process Manag doi: 10.1016/j.ipm.2019.102096 – volume: 118 start-page: 109524 year: 2020 ident: CR3 article-title: A survey on adaptive protection of microgrids and distribution systems with distributed generators publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.109524 – volume: 13 start-page: 2149 issue: 9 year: 2020 ident: CR5 article-title: Recent developments and challenges on AC microgrids fault detection and protection systems–a review publication-title: Energies doi: 10.3390/en13092149 – volume: 13 start-page: 3283 issue: 3 year: 2019 end-page: 3294 ident: CR26 article-title: Residential load scheduling with renewable generation in the smart grid: a reinforcement learning approach publication-title: IEEE Syst J doi: 10.1109/JSYST.2018.2855689 – volume: 31 start-page: 279 issue: 2 year: 2020 end-page: 289 ident: CR32 article-title: Multi-agent system application in accordance with game theory in bi-directional coordination network model publication-title: J Syst Eng Electron doi: 10.23919/JSEE.2020.000006 – volume: 33 start-page: 7064 issue: 6 year: 2018 end-page: 7075 ident: CR7 article-title: A multiobjective optimization technique to develop protection systems of distribution networks with distributed generation publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2018.2842648 – volume: 12 start-page: 436 issue: 3 year: 2019 ident: CR28 article-title: Artificial neural network based reinforcement learning for wind turbine yaw control publication-title: Energies doi: 10.3390/en12030436 – volume: 49 start-page: 63 issue: 8 year: 2021 end-page: 69 ident: CR2 article-title: Adaptive current main protection scheme of distribution network accessed with multiple distributed generations publication-title: Power Syst Protect Control – volume: 40 start-page: 433 issue: 2 year: 2010 ident: 6982_CR33 publication-title: IEEE Trans Syst, Man, Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2009.2026289 – volume: 236 start-page: 937 year: 2019 ident: 6982_CR24 publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.12.061 – volume: 45 start-page: 4729 issue: 12 year: 2021 ident: 6982_CR34 publication-title: Power Syst Technol – volume: 10 start-page: 4338 issue: 4 year: 2018 ident: 6982_CR22 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2857449 – volume: 97 start-page: 463 issue: 5 year: 2017 ident: 6982_CR8 publication-title: J Power Technol – volume: 10 start-page: 3698 issue: 4 year: 2019 ident: 6982_CR18 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2834219 – volume: 9 start-page: 35740 year: 2021 ident: 6982_CR4 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3061919 – volume: 10 start-page: 5246 issue: 5 year: 2019 ident: 6982_CR19 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2018.2879572 – volume: 10 start-page: 6366 issue: 6 year: 2019 ident: 6982_CR23 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2019.2903756 – volume: 118 start-page: 109524 year: 2020 ident: 6982_CR3 publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.109524 – volume: 14 start-page: 1201 issue: 7 year: 2020 ident: 6982_CR11 publication-title: IET Renew Power Gener doi: 10.1049/iet-rpg.2019.0793 – ident: 6982_CR30 doi: 10.1109/SEST.2018.8495830 – volume: 9 start-page: 3259 issue: 4 year: 2018 ident: 6982_CR29 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2016.2629450 – volume: 33 start-page: 3181 year: 2018 ident: 6982_CR36 publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2017.2760011 – ident: 6982_CR15 doi: 10.1109/ISGTEurope.2019.8905476 – volume: 48 start-page: 22 year: 2019 ident: 6982_CR16 publication-title: Annu Rev Control doi: 10.1016/j.arcontrol.2019.09.008 – volume: 57 start-page: 102096 issue: 6 year: 2020 ident: 6982_CR25 publication-title: Inf Process Manag doi: 10.1016/j.ipm.2019.102096 – volume: 9 start-page: 2152 issue: 3 year: 2018 ident: 6982_CR27 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2016.2601149 – volume: 12 start-page: 436 issue: 3 year: 2019 ident: 6982_CR28 publication-title: Energies doi: 10.3390/en12030436 – volume: 39 start-page: 97 issue: 2 year: 2018 ident: 6982_CR1 publication-title: Power Gen Technol – volume: 99 start-page: 67 year: 2019 ident: 6982_CR20 publication-title: Trans Res Part C: Emerg Technol doi: 10.1016/j.trc.2018.12.018 – volume: 28 start-page: e2532 issue: 5 year: 2018 ident: 6982_CR10 publication-title: Int Trans Electr Energy Syst doi: 10.1002/etep.2532 – ident: 6982_CR9 doi: 10.1109/EEEIC.2019.8783396 – volume: 13 start-page: 2149 issue: 9 year: 2020 ident: 6982_CR5 publication-title: Energies doi: 10.3390/en13092149 – volume: 14 start-page: 319 issue: 1 year: 2019 ident: 6982_CR13 publication-title: IndonesianJ Electr Eng Comput Sci doi: 10.11591/ijeecs.v14.i1.pp319-326 – ident: 6982_CR14 doi: 10.1109/ISGT-Asia.2018.8467835 – volume: 164 start-page: 89 year: 2018 ident: 6982_CR6 publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2018.07.008 – volume: 13 start-page: 3283 issue: 3 year: 2019 ident: 6982_CR26 publication-title: IEEE Syst J doi: 10.1109/JSYST.2018.2855689 – volume: 33 start-page: 7064 issue: 6 year: 2018 ident: 6982_CR7 publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2018.2842648 – ident: 6982_CR31 doi: 10.1109/CDC40024.2019.9029268 – volume: 14 start-page: 770 issue: 7 year: 2019 ident: 6982_CR12 publication-title: IET Gener, Transm Distrib doi: 10.1049/iet-gtd.2018.6230 – volume: 31 start-page: 279 issue: 2 year: 2020 ident: 6982_CR32 publication-title: J Syst Eng Electron doi: 10.23919/JSEE.2020.000006 – volume: 33 start-page: 10335 year: 2021 ident: 6982_CR21 publication-title: Neural Comput Applic doi: 10.1007/s00521-021-05795-0 – volume: 6 start-page: 213 issue: 1 year: 2020 ident: 6982_CR17 publication-title: CSEE J Power Energy Syst – ident: 6982_CR35 – volume: 49 start-page: 63 issue: 8 year: 2021 ident: 6982_CR2 publication-title: Power Syst Protect Control |
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| SubjectTerms | Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Distributed generation Electricity distribution Image Processing and Computer Vision Laboratories Machine learning Methods Multiagent systems Neural networks Optimization Power supply Probability and Statistics in Computer Science Relay Robotics S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Security Special Issue on 2021 Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Uncertainty |
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| Title | A multiagent deep deterministic policy gradient-based distributed protection method for distribution network |
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