论文标题
使用深度强化学习来解决发电机故障的最佳功率流问题
Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures
论文作者
论文摘要
深厚的增强学习（DRL）被用于许多领域。 DRL的最大优势之一是它可以持续改进学习代理。其次，DRL框架稳健且灵活，足以适用于不同性质和域的问题。提出的工作是使用DRL技术解决最佳功率流（OPF）问题的证据。已经提出了两种经典算法来解决OPF问题。讨论了香草DRL应用程序的缺点，并建议采用算法来提高性能。其次，提出了针对OPF问题的奖励功能，该功能可以解决DRL中固有问题的解决方案。讨论了DRL发散和变性的原因，并提出了与OPF有关的正确策略。
Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust and flexible enough to be applicable to problems of varying nature and domain. Presented work is evidence of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two classical algorithms have been presented to solve the OPF problem. The drawbacks of the vanilla DRL application are discussed, and an algorithm is suggested to improve the performance. Secondly, a reward function for the OPF problem is presented that enables the solution of inherent issues in DRL. Reasons for divergence and degeneration in DRL are discussed, and the correct strategy to deal with them with respect to OPF is presented.
