基于K-means聚类和随机森林的电缆风险评估及修复决策

Cable Risk Assessment and Repair Decision Based on K-means Clustering and Random Forest

交联聚乙烯电缆是10 kV配电系统中的重要设备,其安全性至关重要。对电缆的修复决策做出科学判断,有助于提高配电系统的安全性并降低经济成本。鉴于此,本文提出了一种基于K-means聚类和随机森林(Random forest,RF)分类模型的电缆风险评估及修复决策方法。该方法首先根据电缆的绝缘状态,定义电缆的风险等级和风险程度;然后利用K-means聚类算法对多个老化指标进行聚类以实现风险等级区间的划分,从而建立多老化指标风险矩阵;基于多老化指标风险矩阵,利用综合权重法确定多维老化指标所对应的分类标签;最后基于RF算法建立并训练电缆的修复决策分类模型,输出电缆的修复决策结果。所提方法的平均正确率达到99.70%,实现了电缆快速且可靠的修复决策。

Crosslinked polyethylene cable is an important equipment in 10 kV distribution system,and its safety is very important.Making scientific judgments on cable repair decisions can help improve the safety and reduce the economic cost.In view of this,a cable risk assessment and repair decision method based on K-means clustering and random forest(RF)classification model is proposed.The method first defines the risk level and risk degree of the cable based on the insulation status of the cable.Then the K-means clustering algorithm is used to cluster the multi-aging index dataset and classify the risk level intervals to build a multi-aging index risk matrix.Based on the risk matrix of the multi-aging index,the classification labels corresponding to the multi-aging index are determined by using the comprehensive weight method.Finally,the classification model of the repair ways of the cables is established and trained based on the RF algorithm,and the selection results of the repair ways are output.The average accuracy of the proposed method reaches 99.70%,achieving rapid and reliable repair decisions for cables.