用贝叶斯网络+PC学习预测极端天气下输电线跳闸概率
Transmission Line Outage Probability Prediction Under Extreme Events Using Peter-Clark Bayesian Structural Learning
- 基于贝叶斯网络与PC结构学习，建模天气与线路故障关系
- 在有限数据下仍保持高精度，且可扩展性强
- 适合电网风险预警与韧性评估人员参考
近年来极端天气事件频发且强度加剧，导致电力线路故障增多，准确预测输电线路在极端事件下的跳闸概率对电网安全可靠运行至关重要。贝叶斯网络是处理气象不确定性下线路故障预测的有效概率模型，但现有研究多停留在风险定性评估，缺乏具体跳闸概率输出。本文提出一种结合贝叶斯网络与Peter-Clark（PC）结构学习的新方法，实现输电线路跳闸概率的精确预测。该方法在数据有限条件下仍具备良好性能，具有更强的可扩展性与鲁棒性。基于BPA与NOAA数据的案例研究验证了其有效性，与多种现有方法对比进一步凸显其优势。
原文摘要 · Abstract (English)
Recent years have seen a notable increase in the frequency and intensity of extreme weather events. With a rising number of power outages caused by these events, accurate prediction of power line outages is essential for safe and reliable operation of power grids. The Bayesian network is a probabilistic model that is very effective for predicting line outages under weather-related uncertainties. However, most existing studies in this area offer general risk assessments, but fall short of providing specific outage probabilities. In this work, we introduce a novel approach for predicting transmission line outage probabilities using a Bayesian network combined with Peter-Clark (PC) structural learning. Our approach not only enables precise outage probability calculations, but also demonstrates better scalability and robust performance, even with limited data. Case studies using data from BPA and NOAA show the effectiveness of this approach, while comparisons with several existing methods further highlight its advantages.
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