电气传动2026，Vol.56Issue(5)：52-60,9.DOI:10.19457/j.1001-2095.dqcd26056
基于改进深度强化学习理论的台区微电网电压控制策略
Voltage Control Strategy for Distribution Area Microgrids Based on Improved Deep Reinforcement Learning Theory
摘要
Abstract
The inherent characteristics of distributed energy resources(DERs),including their small scale,volatility,and intermittency,pose significant challenges to the design of operational strategies for distribution area microgrids.Despite the successful integration of diverse DERs and external power grids within microgrid systems,voltage management has become increasingly complex.In light of this,a real-time voltage control strategy for distribution area microgrids was proposed based on deep reinforcement learning theory.Firstly,a recurrent neural network(RNN)model was employed to accurately identify and handle corrupted or missing data in the source-load power data within the system,ensuring data quality.Subsequently,a voltage management model for the distribution area microgrid was constructed,comprehensively considering line losses during power transmission and the potential risk of voltage violations.Given the complex nonlinear constraints inherent in this model,an improved deep reinforcement learning algorithm was adopted for efficient solution,and an ε-greedy decreasing strategy was adopted to guide the agent's action selection,overcoming the limitations of traditional methods.Finally,to validate the effectiveness and feasibility of the proposed strategy,comparative tests were conducted against traditional control strategies.The results show that the voltage control strategy presented exhibits significant advantages in multiple key indicators,including reducing voltage fluctuations and minimizing network losses.关键词
分布式能源/微电网/电压控制/循环神经网络/改进深度强化学习算法Key words
distributed energy resources(DERs)/microgrid/voltage control/recurrent neural network(RNN)/improved deep reinforcement learning algorithm分类
信息技术与安全科学引用本文复制引用
张凌浩,陆晓星,肖小龙,岳付昌,吴凡,李光熹..基于改进深度强化学习理论的台区微电网电压控制策略[J].电气传动,2026,56(5):52-60,9.基金项目
国网江苏省电力有限公司科技项目(J2023167) （J2023167）
