View in EDS

A Novel Reactive Power Sharing Control Strategy for Shipboard Microgrids Based on Deep Reinforcement Learning

Saved in:
Bibliographic Details
Title: A Novel Reactive Power Sharing Control Strategy for Shipboard Microgrids Based on Deep Reinforcement Learning
Authors: Wangyang Li, Hong Zhao, Jingwei Zhu, Tiankai Yang
Source: Journal of Marine Science and Engineering ; Volume 13 ; Issue 4 ; Pages: 718
Publisher Information: Multidisciplinary Digital Publishing Institute
Publication Year: 2025
Collection: MDPI Open Access Publishing
Subject Terms: deep reinforcement learning, DQN algorithm, droop control strategy, microgrid control, reactive power sharing
Subject Geographic: agris
Description: Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy.
Document Type: text
File Description: application/pdf
Language: English
Relation: Ocean Engineering; https://dx.doi.org/10.3390/jmse13040718
DOI: 10.3390/jmse13040718
Availability: https://doi.org/10.3390/jmse13040718
Rights: https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.8B941F1C
Database: BASE
Description
Abstract:Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy.
DOI:10.3390/jmse13040718