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首页> 外文期刊>IEEE Transactions on Power Systems >Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
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Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems

机译：结合无监督学习和有监督学习进行电力系统资产类别故障预测

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摘要

In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical and/or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities toward developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used for failure prediction. However, this mathematical model cannot incorporate asset condition data. 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 operation 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 a medium-voltage cable class in an urban distribution system in West Canada. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates higher accuracy measured by F1-Score than Weibull distribution method for asset class failure prediction.
机译：在电力系统中，资产类别是一组具有相同功能并共享相似电气和/或机械特性的电力设备。预测不同资产类别的故障对于电力公用事业公司制定具有成本效益的资产管理策略至关重要。以前，基于物理年龄的威布尔分布已广泛用于故障预测。但是，该数学模型无法合并资产条件数据。结果，对于单个资产的预测不能非常具体和准确。为了解决这一重要问题，本文提出了一种基于资产状况数据的新颖，综合的数据驱动方法：K-means聚类作为一种无监督的学习方法，用于分析历史资产状况数据的内部结构并产生资产条件年龄;逻辑回归作为一种有监督的学习方法，需要考虑资产的实际年龄和有条件的年龄，以对资产的运行状态进行分类和预测。此外，定义了一个称为平均账龄率的指标，以量化，跟踪和估计资产实际账龄与有条件账龄之间的关系。此方法已应用于加拿大西部城市配电系统中的中压电缆类别。提供了案例研究并与标准的威布尔分布进行了比较。所提出的方法证明了用F1-Score测得的精度要高于Weibull分布方法来预测资产类别的故障。

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