基于机器学习集成模型的配电网预测性维护方法研究

Research on Predictive Maintenance Method for Power Distribution Networks Based on Machine Learning Ensemble Model

摘要: 针对智能配电网运行维护中多源运行数据利用不足、故障识别响应滞后及维护计划缺乏状态驱动依据等问题，提出一种基于机器学习集成模型的配电网预测性维护方法。该方法首先采集传感器数据、智能电表数据、电力线路传感器数据、SCADA历史数据、实时监测数据、原始电力负荷数据、环境数据、设备工作状态数据和组件温度数据，并对其进行缺失值填补、异常值处理、标准化及归一化处理；随后提取与组件温度、设备状态和故障分类相关的特征，采用主成分分析筛选关键特征；在此基础上构建机器学习集成模型，实现故障检测、故障分类、组件温度预测及异常告警。模型输出故障类型、温度偏差判断结果和维护建议，并通过准确率、精确率、召回率和F1值评价预测性维护效果。算例验证结果表明，所述方法能够有效识别设备潜在异常并降低误报、漏检风险，为配电网维护调度和状态检修提供辅助决策依据。

Abstract: To address the problems of insufficient utilization of multi-source operational data, delayed fault response, and the lack of condition-driven maintenance scheduling in smart power distribution networks, a predictive maintenance method based on a machine learning ensemble model is proposed. The method first collects sensor data, smart meter data, power line sensor data, historical SCADA data, real-time monitoring data, raw electrical load data, environmental data, equipment operating-state data, and component temperature data, and then performs missing-value filling, outlier processing, standardization, and normalization. Features related to component temperature, equipment status, and fault classification are extracted, and principal component analysis is adopted to select key features. On this basis, a machine learning ensemble model is constructed to implement fault detection, fault classification, component temperature prediction, and abnormal warning. The model outputs fault types, temperature-deviation judgment results, and maintenance suggestions, and its performance is evaluated by accuracy, precision, recall, and F1-score. Case results show that the proposed method can identify potential equipment anomalies and reduce false alarms and missed detections, providing decision support for maintenance scheduling and condition-based maintenance of distribution networks.