摘要
电力变压器是电力系统稳定运行中最为重要的电力设备。对电力变压器的故障识别,一直是所有电力工作的重中之重。近些年随着人工智能的发展,许多智能算法被引入电力变压器故障研究当中。本文提出一种基于决策树算法的电力变压器故障诊断模型,与其他分类模型相比,该模型具有分类精度高、计算速度快、不需要任何领域知识和参数假设、易于实现等优点。通过对实际的故障样本进行诊断,并与支持向量机算法对比,验证了该算法的优越性。
Power transformers are the most important power equipment in the stable operation of power systems.Fault identification of power transformers has always been a top priority for all power work.In recent years,with the development of artificial intelligence,many intelligent algorithms have been introduced into the research of power transformer faults.In this paper,a power tree fault diagnosis model based on decision tree algorithm is proposed.Compared with other classification models,this model has the advantages of high classification accuracy,fast calculation speed,no need for any domain knowledge and parameter assumptions,and easy implementation etc.
作者
王涛
孙志鹏
崔青
张志磊
张天伟
Wang Tao;Sun Zhipeng;Cui Qing;Zhang Zhilei;Zhang Tianwei(Hebei Electric Power Company Shijiazhuang Power Supply Company,Shijiazhuang 050051;College of Electrical Engineering,Northeast Electric Power University,Jilin,Jilin 132012;Beijing Runwei Tianhua Power Technology Co.,Ltd,Beijing 102211)
出处
《电气技术》
2019年第11期16-19,共4页
Electrical Engineering
基金
河北省电力公司科技项目（SGHESJ00YJJS1800793)
关键词
电力变压器
故障诊断
决策树
多值分类
power transformer
fault diagnosis
decision trees
multiclass classification
