电力可解释因果微分方程网络：一种用于电力系统可解释异常检测和根本原因分析的统一模型
Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems
AI总结：
PICODE网络通过统一模型实现电力系统中异常检测与根本原因分析的可解释性，提升模型的可解释性和减少对标注数据的依赖。
摘要：
异常检测和根本原因分析（RCA）对于确保如电力网络等时序系统安全性和韧性至关重要。然而，现有的时间序列异常检测机器学习模型通常作为黑箱操作，仅提供二元输出，没有任何解释，例如识别异常类型和来源。为了解决这一挑战，我们提出了电力可解释因果微分方程（PICODE）网络，一种统一的、基于因果性的架构，能够同时执行异常检测以及解释为何检测为异常，包括根本原因定位、异常类型分类和异常形状表征。在电力系统中的实验结果表明，PICODE在检测性能上具有竞争力，同时提供改进的可解释性和减少对标记数据或外部因果图的依赖。我们提供了理论结果，证明了异常函数形状与提取因果图权重变化之间的对齐。
英文摘要：
Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as black boxes, offering only binary outputs without any explanation, such as identifying anomaly type and origin. To address this challenge, we propose Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a unified, causality-informed architecture that jointly performs anomaly detection along with the explanation why it is detected as an anomaly, including root cause localization, anomaly type classification, and anomaly shape characterization. Experimental results in power systems demonstrate that PICODE achieves competitive detection performance while offering improved interpretability and reduced reliance on labeled data or external causal graphs. We provide theoretical results demonstrating the alignment between the shape of anomaly functions and the changes in the weights of the extracted causal graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
