基于强化学习与不确定性量化的配电网优化运行风险与异常识别
Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification
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中文总结 AI 辅助
针对配电网优化运行的联合风险与异常识别问题，提出显式感知不确定性的深度强化学习框架，分解不确定性为两类以分别表征风险与异常，仿真验证了方法的性能与有效性。
中文摘要 AI 辅助
现代配电网的可靠运行需要在普遍不确定性下及时识别运行风险与异常事件。实际中，运行人员需识别分布内随机条件下固有的风险，以及对应分布外行为的异常，如异常负荷模式、极端天气或网络物理攻击。本文针对配电网优化运行的联合风险与异常识别问题，提出一种显式感知不确定性的深度强化学习（DRL）框架。我们将分布强化学习与贝叶斯深度强化学习相结合，实现二阶不确定性量化方案，该方案将总不确定性分解为随机不确定性（aleatoric uncertainty）与认知不确定性（epistemic uncertainty），分别用于表征固有风险与分布外异常。所得认知估计既驱动训练阶段的探索，又在部署阶段用于带回退控制的分布外检测；随机估计则用于表征内在运行风险。仿真结果验证了本文DRL智能体的性能及不确定性量化的有效性。
英文摘要
Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-distribution behaviors such as unusual load patterns, extreme weather or cyber-physical attacks. This paper addresses this joint risk and anomaly identification problem for optimal distribution network operation and proposes a deep reinforcement learning framework that is explicitly uncertainty aware. We integrate distributional and Bayesian deep reinforcement learning to realize a second- order uncertainty quantification scheme that decomposes total uncertainty into aleatoric and epistemic components, which are respectively used to characterize inherent risk and out-of- distribution anomalies. The resulting epistemic estimates drive both exploration during training and out-of-distribution detec- tion with fallback control during deployment, whereas aleatoric estimates are used to characterize intrinsic operational risk. Simulation results demonstrate the performance of our DRL agent and the effectiveness of the uncertainty quantification.
发表机构
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）
机构由 AI 辅助整理，请以论文原文为准。
