The topology optimization of transmission networks using Deep Reinforcement Learning (DRL) has increasingly come into focus. Various researchers have proposed different DRL agents, which are often benchmarked on the Grid2Op environment from the Learning to Run a Power Network (L2RPN) challenges. The environments have many advantages with their realistic chronics and underlying power flow backends. However, the interpretation of agent survival or failure is not always clear, as there are a variety of potential causes. In this work, we focus on the failures of the power grid to identify patterns and detect them a priori. We collect the failed chronics of three different agents on the WCCI 2022 L2RPN environment, totaling about 40k data points. By clustering, we are able to detect five distinct clusters, identifying different failure types. Further, we propose a multi-class prediction approach to detect failures beforehand and evaluate five different models. Here, the Light Gradient-Boosting Machine (LightGBM) shows the best performance, with an accuracy of 86%. It also correctly identifies in 91% of the time failure and survival observations. Finally, we provide a detailed feature importance analysis that identifies critical features and regions in the grid.

翻译：利用深度强化学习（DRL）进行输电网拓扑优化的研究日益受到关注。多位研究者提出了不同的DRL智能体，这些智能体通常在"学习运行电力网络"（L2RPN）挑战赛的Grid2Op环境中进行基准测试。该环境具有真实的时序数据和底层潮流计算后端等诸多优势。然而，由于存在多种潜在原因，对智能体存活或故障的解读并不总是清晰的。本研究聚焦于电网故障，旨在识别故障模式并实现先验检测。我们在WCCI 2022 L2RPN环境中收集了三种不同智能体的故障时序数据，总计约4万个数据点。通过聚类分析，我们成功检测到五个不同的簇，识别出多种故障类型。进一步地，我们提出了一种多类别预测方法来预先检测故障，并评估了五种不同模型。其中，轻量梯度提升机（LightGBM）表现出最佳性能，准确率达到86%。该模型对故障与存活观测的识别正确率也达到91%。最后，我们提供了详细的特征重要性分析，识别出电网中的关键特征与区域。