The reliability of distribution systems is often challenged under unfavorable weather conditions, where weather-related failures occur with high probability. Predicting the number of weather-related failures in distribution systems can provide guiding information for operation and maintenance decisions, improving the risk management capability of utility companies. This article proposes a novel Bayesian Neural Network (BNN) based model to predict weather-related failures caused by wind, rain and lightning. Superior prediction performance of the BNN based model is verified by contrast experiments with other advanced prediction models under four different evaluation metrics. BNN based prediction model presents remarkable robustness, especially in the prediction of high failure levels. In addition, compared to most previous used prediction models without any prediction confidence feedback, BNN based prediction model has the capability of uncertainty estimation. The confidence interval of prediction results can be obtained, which provides sufficient information for guiding risk management of utility companies. An effective operation and maintenance guiding scheme based on the analysis of prediction uncertainty is proposed, which fully excavates the interpretability of the proposed model and enrich the application value of the model.

中文翻译：

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使用贝叶斯神经网络预测配电系统中与天气相关的故障风险

配电系统的可靠性经常在不利的天气条件下受到挑战，在这种情况下，与天气有关的故障极有可能发生。预测配电系统中与天气相关的故障数量可以为运营和维护决策提供指导信息，从而提高公用事业公司的风险管理能力。本文提出了一种新颖的基于贝叶斯神经网络（BNN）的模型来预测由风，雨和雷引起的与天气相关的故障。在四个不同的评估指标下，通过与其他高级预测模型进行对比实验，验证了基于BNN的模型的出色预测性能。基于BNN的预测模型具有出色的鲁棒性，尤其是在高故障级别的预测中。此外，与大多数先前使用的没有任何预测置信度反馈的预测模型相比，基于BNN的预测模型具有不确定性估计的能力。可获得预测结果的置信区间，为指导公用事业公司的风险管理提供了足够的信息。提出了一种基于预测不确定性分析的有效运维指导方案，充分挖掘了所提模型的可解释性，丰富了该模型的应用价值。