In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used to failure prediction. However, this mathematical model cannot incorporate asset condition data such as inspection or testing results. As a result, the prediction cannot be very specific and accurate for individual assets. To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset statuses. Furthermore, an index called average aging rate is defined to quantify, track and estimate the relationship between asset physical age and conditional age. This approach was applied to an urban distribution system in West Canada to predict medium-voltage cable failures. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates superior performance and practicality for predicting asset class failures in power systems.
翻译:在电力系统中,资产类别是具有相同功能并具有类似电气或机械特点的电力设备组。预测不同资产类别的失败对于电力公用事业制定成本效益高的资产管理战略至关重要。以前,基于实际年龄的Weibull分布被广泛用于预测失败情况。然而,这一数学模型不能纳入资产状况数据,例如检查或测试结果。因此,预测不能对个别资产进行非常具体和准确的预测。为解决这一重要问题,本文件提议根据资产状况数据采取新的和全面的数据驱动方法:K-poins集群作为不受监督的学习方法,用来分析历史资产状况数据的内部结构,并产生资产有条件年龄;作为监督学习方法的后勤倒退在资产实际年龄和有条件年龄中都采用,以对资产状况进行分类和预测。此外,一个称为平均老龄化率的指数是为了量化、跟踪和估计资产实际年龄和有条件年龄之间的关系。这一方法适用于西加拿大的城市分配系统,以预测中压电缆故障情况。案例研究和与标准Webull分布的比较是提供的。拟议方法显示资产系统在等级上的高级性能预测。