使用机器学习估计XLPE电缆的绝缘健康指数
Adel Ansari1, Sorin Yousef Nia2, Alireza Afshari3
1Tarbiat Modares University, Tehran, Iran. adelansari@modares.ac.ir.
Scientific reports
|November 21, 2025
概括
本研究引入了一种机器学习 (ML) 模型,用于预测中压 (MV) 地下电缆的健康状况. 预测性健康指数模型增强了维护策略,改善了故障预测和降低成本.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 用交联聚乙烯 (XLPE) 绝缘的中压 (MV) 地下电缆对于电力分配至关重要.
- 电缆故障导致重大财务损失和服务中断.
- 传统的反应性维护策略是昂贵和低效的.
研究的目的:
- 为MV XLPE地下电缆开发基于机器学习的强大的健康指数模型.
- 将维护从反应式转变为主动式的方法.
- 在发生关键事件之前识别有故障风险的电缆.
主要方法:
- 利用包括环境因素,电缆特性和历史性能数据在内的数据集.
- 应用机器学习算法:支持矢量机 (SVM),K-最近邻居 (K-NN),人工神经网络 (ANN) 和天真贝叶斯.
- 将电缆分为健康类别:健康到有风险.
主要成果:
- 人工神经网络 (ANN) 显示出卓越的性能,达到94.5%的准确性,比传统方法提高12%.
- 与反应性维护相比,预测模型提高了30%的故障预测.
- 通过积极的干预措施,维护成本减少了25%.
结论:
- 开发的ML健康指数模型为评估MV电缆健康提供了一种可靠的方法.
- 通过这种模型实现的主动维护显著提高了电力分配的可靠性.
- 该研究强调了机器学习在优化关键基础设施资产管理方面的有效性.
关键词:
健康指数 健康指数机器学习是机器学习.中等电压电缆的电压.电力分配的可靠性 电力分配的可靠性预测性维护是指预测性维护.XLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXLPEXL相关概念视频
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