摘要
电力线损是衡量配电网运行效率的重要指标,受技术与非技术多重因素影响。伴随着智能电表和用电终端的普及化,利用大数据进行的线损分析成为提升供电质量的关键要点,依靠数据清洗、特征提取及相关性分析工作,结合随机森林、支持向量机、BP神经网络、LightGBM等各类机器学习算法,完成了线损预测和异常诊断模型的构建与验证,实验结果说明,模型在预测精度以及异常识别效果上均强于传统方法,明显提高了诊断效率以及配网优化档次,有力促进线损管理实现精细化与智能化升级。
The power line loss is an important indicator to measure the operational efficiency of distribution networks,which is influenced by multiple technical and non-technical factors.With the popularization of smart meters and power consumption terminals,the line loss analysis carried out by big data has become a key point to improve the power supply quality.Relying on data cleaning,feature extraction and correlation analysis,this paper combines various machine learning algorithms such as random forest,support vector machine,BP neural network and LightGBM to complete the prediction of line loss and the construction and verification of abnormal diagnosis model.The experimental results show that the model is superior to the traditional method both in prediction accuracy and anomaly identification effect.It significantly improves the diagnosis efficiency and the optimization level of distribution network,and effectively promotes the refined and intelligent upgrade of line loss management.
作者
唐龙庆
Tang Longqing(Daqing Refining&Chemical Company,Daqing 163000,China)
出处
《防爆电机》
2025年第4期128-131,共4页
Explosion-proof Electric Machine
关键词
线损分析
机器学习
特征选择
异常诊断
配电网优化
Line loss analysis
machine learning
feature selection
abnormal diagnosis
distribution network optimization
