基于特征选择和支持向量机的电网线损异常分类与识别研究

Research on Classification and Recognition of Power Grid Line Loss Anomaly Based on Feature Selection and Support Vector Machine

电网线损异常是电力系统中的重要问题,对系统的运行和经济效益具有重要影响。文章旨在基于特征选择和支持向量机方法,进行电网线损异常的分类与识别研究。试验结果表明,提出的方法在电网线损异常的分类与识别中对单一和多元异常识别准确率分别为80.7692%和76.9231%。文章的方法对故障和非故障样本进行有效分类,并成功识别出电网线损隐患,为电力系统运行管理提供了重要支持。

Abnormal power grid line loss is an important issue in the power system,which has a significant impact on system operation and economic efficiency.This study aims to classify and identify abnormal power grid line loss based on feature selection and Support Vector Machine(SVM)methods.Experimental results demonstrate that our proposed method achieves classification and identification accuracies of 80.7692%and 76.9231%for single and multiple abnormality recognition,respectively.The method presented in this study effectively classifies faulty and non-faulty samples and successfully identifies hidden risks of power grid line loss,providing crucial support for power system operation management.