Zhang, H, Yue, D, Dou, C et al. (3 more authors) (2021) Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids' System via a Novel Reinforcement Learning Approach. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X
Abstract
With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Microgrids; reinforcement learning (RL); resilient optimal defensive; Takagi-Sugeuo-Kang (TSK) fuzzy system |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Aug 2021 09:12 |
Last Modified: | 13 Mar 2023 16:15 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TNNLS.2021.3105668 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177121 |