论文标题
长期电压稳定性控制的深度加固学习
Deep Reinforcement Learning for Long-Term Voltage Stability Control
论文作者
论文摘要
深钢筋学习（DRL）是一种基于机器学习的方法，适用于复杂和高维控制问题。在这项研究中，为长期电压稳定性事件而开发了基于DRL的实时控制系统。研究了使用需求响应（DR）和能源存储系统（ESS）作为稳定系统的控制措施的系统服务的可能性。 DRL控制的性能在修改的Nordic32测试系统上评估。结果表明，DRL控制很快就会学会有效的控制策略，该政策可以处理DR和ESS时涉及的不确定性。将DRL控制与基于规则的负载脱落方案进行了比较，并且DRL控制显示可稳定该系统的既更快又有较小的负载减少。最后，当测试和评估未包含在训练数据中的负载和干扰方案上的性能时，对照的鲁棒性和泛化能力被证明是有效的。
Deep reinforcement learning (DRL) is a machine learning-based method suited for complex and high-dimensional control problems. In this study, a real-time control system based on DRL is developed for long-term voltage stability events. The possibility of using system services from demand response (DR) and energy storage systems (ESS) as control measures to stabilize the system is investigated. The performance of the DRL control is evaluated on a modified Nordic32 test system. The results show that the DRL control quickly learns an effective control policy that can handle the uncertainty involved when using DR and ESS. The DRL control is compared to a rule-based load shedding scheme and the DRL control is shown to stabilize the system both significantly faster and with lesser load curtailment. Finally, when testing and evaluating the performance on load and disturbance scenarios that were not included in the training data, the robustness and generalization capability of the control were shown to be effective.
