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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2023

07.02.2022 | S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)

A multiagent deep deterministic policy gradient-based distributed protection method for distribution network

verfasst von: Peng Zeng, Shijie Cui, Chunhe Song, Zhongfeng Wang, Guangye Li

Erschienen in: Neural Computing and Applications | Ausgabe 3/2023

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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.
Vorheriger Artikel Information flow-based second-order cone programming model for big data using rough concept lattice
Nächster Artikel Contracting and timing for outsourcing of information system with uncertain requirements

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Metadaten
Titel
A multiagent deep deterministic policy gradient-based distributed protection method for distribution network
verfasst von
Peng Zeng
Shijie Cui
Chunhe Song
Zhongfeng Wang
Guangye Li
Publikationsdatum
07.02.2022
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-022-06982-3

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