As a new practical and economical solution to the aging problem of overhead line (OHL) assets, the technical policies of most power grid companies in the world experienced a gradual transition from scheduled preventive maintenance to a risk-based approach in asset management. Even though the accumulation of contamination is predictable within a certain degree, there are currently no effective ways to identify the risk of the insulator flashover in order to plan its replacement. This paper presents a novel machine learning (ML) based method for estimating the flashover probability of the cup-and-pin glass insulator string. The proposed method is based on the Extreme Gradient Boosting (XGBoost) supervised ML model, in which the leakage current (LC) features and applied voltage are used as the inputs. The established model can estimate the critical flashover voltage (U50%) for various designs of OHL insulators with different voltage levels. The proposed method is also able to accurately determine the condition of the insulator strings and instruct asset management engineers to take appropriate actions.

翻译：作为应对架空线路（OHL）资产老化问题的一种新型实用且经济的解决方案，全球大多数电网公司的技术政策在资产管理中经历了从定期预防性维护向基于风险的方法的逐步转变。尽管污染积累在一定程度上是可预测的，但目前尚无有效方法识别绝缘子闪络风险以规划其更换。本文提出了一种基于机器学习（ML）的新方法，用于估算杯盘式玻璃绝缘子串的闪络概率。该方法基于极端梯度提升（XGBoost）监督ML模型，以泄漏电流（LC）特征和外施电压作为输入。所建立的模型能够估算不同电压等级、多种设计的OHL绝缘子的临界闪络电压（U50%）。该方法还能准确判断绝缘子串的状态，并指导资产管理工程师采取适当措施。