结合无监督和有监督学习的电力系统资产类别故障预测
Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
摘要：
在电力系统中，资产类别是一组具有相同功能并具有相似电气或机械特性的电力设备。预测不同资产类别的失败对于电力公司制定具有成本效益的资产管理策略至关重要。基于物理年龄的韦布尔分布已被广泛应用于失效预测。然而，该数学模型不能包含资产状况数据，例如检查或测试结果。因此，对于单个资产，预测不可能非常具体和准确。为解决这一问题，提出了一种基于资产状态数据的数据驱动方法：采用K均值聚类作为无监督学习方法，分析历史资产状态数据的内部结构，生成资产条件年龄;采用逻辑斯谛回归作为有监督学习方法，同时考虑资产物理年龄和条件年龄，对资产状态进行分类和预测。并定义了一个平均老化率指标来量化、跟踪和估计资产物理年龄与条件年龄之间的关系。这种方法被应用到加拿大西部的城市配电系统，以预测中压电缆故障。提供了案例研究和与标准韦布尔分布的比较。所提出的方法表现出优越的性能和实用性，预测电力系统中的资产类故障。
英文摘要：
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.
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。
