A Novel Integral Reinforcement Learning-Based Control Method Assisted by Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell in DC Microgrid

This paper proposes a new online integral reinforcement learning (IRL)-based control algorithm for the solid oxide fuel cell (SOFC) to overcome the long-lasting problems of model dependency and sensitivity to offline training dataset in the existing SOFC control approaches. The proposed method autom...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy Vol. 14; no. 1; pp. 1 - 16
Main Authors: Liu, Yulin, Qie, Tianhao, Yu, Yang, Wang, Yuxuan, Chau, Tat Kei, Zhang, Xinan, Manandhar, Ujjal, Li, Sinan, Iu, Herbert, Fernando, Tyrone
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational modeling
Computer applications
Control methods
Control theory
DC Microgrid
Distributed generation
Fuel cells
Fuel technology
Hardware-In-the-Loop
Integral Reinforcement Learning
Learning
Machine learning
Mathematical models
Microgrids
Neural networks
Optimal control
Solid Oxide Fuel Cell
Solid oxide fuel cells
Training
Tuning
Twin Delayed Deep Deterministic Policy Gradient
Voltage control
ISSN:1949-3029, 1949-3037
Online Access:Get full text
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Summary:This paper proposes a new online integral reinforcement learning (IRL)-based control algorithm for the solid oxide fuel cell (SOFC) to overcome the long-lasting problems of model dependency and sensitivity to offline training dataset in the existing SOFC control approaches. The proposed method automatically updates the optimal control gains through the online neural network training. Unlike the other online learning-based control methods that rely on the assumption of initial stabilizing control or trial-and-error based initial control policy search, the proposed method employs the offline twin delayed deep deterministic policy gradient (TD3) algorithm to systematically determine the initial stabilizing control policy. Compared to the conventional IRL-based control, the proposed method contributes to greatly reduce the computational burden without compromising the control performance. The excellent performance of the proposed method is verified by hardware-in-the-loop experiments.
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content type line 14
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2022.3224179