Enhancing Voltage Stability of Inter-Area Multi-Machine Power Systems using Reinforcement Learning-based STATCOM

M. Iqbal, A. Hossain, Jahidul Islam, Amit Shaha Surja, M. Kabir
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引用次数: 1

Abstract

In this paper, a novel control scheme based on Reinforcement Learning (RL) controller for the Static Synchronous Compensator (STATCOM) is presented for inter-area multi-machine power system. It is a common criterion to develop an improved control strategy for STATCOM to enhance the voltage stability of multi-machine power system. To exemplify on this purpose, a STATCOM controller has been designed which does not depend on the structure and parameters of power system. Moreover, the proposed strategy also replaces the conventional PI controller by RL controller and provides more reliable controlling environment. In addition, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted for this work and the parameters of the controller are being updated by TD3 algorithm using only local information. Finally, a simulated result of inter-area multi-machine power system is given, where the result shows the effectiveness of stable voltage maintenance and preventing voltage collapse.
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基于强化学习的STATCOM增强区域间多机电力系统电压稳定性
针对区域间多机电力系统,提出一种基于强化学习(RL)控制器的静态同步补偿器(STATCOM)控制方案。为提高多机电力系统的电压稳定性,开发一种改进的STATCOM控制策略是一个普遍的准则。为了实现这一目标,设计了一种不依赖于电力系统结构和参数的STATCOM控制器。此外,该策略还以RL控制器取代了传统的PI控制器,提供了更可靠的控制环境。此外,本工作采用双延迟深度确定性策略梯度(TD3)算法,TD3算法仅使用局部信息更新控制器的参数。最后,给出了跨区域多机电力系统的仿真结果,结果表明了该方法在维护电压稳定和防止电压崩溃方面的有效性。
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