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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Vydáno v:Neural computing & applications Ročník 35; číslo 3; s. 2267 - 2278
Hlavní autoři: Zeng, Peng, Cui, Shijie, Song, Chunhe, Wang, Zhongfeng, Li, Guangye
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.01.2023
Springer Nature B.V
Témata:
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
Distributed generation
Power system protection
Distribution system
Reinforcement learning
Multiagent
ISSN:0941-0643, 1433-3058
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Shrnutí: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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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-06982-3