| 基于XGBoost算法的电网二次设备缺陷分类研究 |
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| 引用本文： | 陈凯,南东亮,孙永辉,夏响.基于XGBoost算法的电网二次设备缺陷分类研究[J].南京气象学院学报,2019,11(4):483-489. |
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| 作者姓名： | 陈凯 南东亮 孙永辉 夏响 |
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| 作者单位： | 河海大学 能源与电气学院, 南京, 210098,新疆大学 电气工程学院, 乌鲁木齐, 830047,河海大学 能源与电气学院, 南京, 210098,河海大学 能源与电气学院, 南京, 210098 |
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| 基金项目： | 国家自然科学基金（61673161）；国网新疆电力有限公司电力科学研究院科技项目（SGXJDK00DJJS1900094） |
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| 摘 要： | 电网二次设备缺陷严重程度的精确判断可为设备的运行和维护提供重要依据.针对电网二次设备缺陷数据特征量多、人为判断难度大、易出错等问题，提出基于XGBoost（eXtreme Gradient Boosting）的二次设备缺陷分类方法，提高二次设备缺陷分类的准确率.首先，对二次设备历史缺陷数据进行去异常值、编码等一系列预处理工作，并筛选出与设备缺陷相关性高的特征建立特征指标集；然后，利用历史缺陷数据对XGBoost模型进行训练和参数寻优；最后，用训练好的分类模型实现二次设备缺陷的准确分类.本文采用某电厂二次设备缺陷数据对所提算法进行算例分析，并与传统分类器（决策树、逻辑回归等）进行比较，结果表明XGBoost可以实现对二次设备缺陷程度的精确判断，进而可以很好地辅助检修人员进行设备的维护与管理.
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| 关 键 词： | XGBoost算法 二次设备 缺陷分类 机器学习 |
| 收稿时间： | 2019/6/19 0:00:00 |
Defect classification of secondary equipment in power grid based on XGBoost |
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| CHEN Kai,NAN Dongliang,SUN Yonghui and XIA Xiang.Defect classification of secondary equipment in power grid based on XGBoost[J].Journal of Nanjing Institute of Meteorology,2019,11(4):483-489. |
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| Authors: | CHEN Kai NAN Dongliang SUN Yonghui and XIA Xiang |
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| Institution: | College of Energy and Electrical Engineering, Hohai University, Nanjing 210098,School of Electric Engineering, Xinjiang University, Urumqi 830047,College of Energy and Electrical Engineering, Hohai University, Nanjing 210098 and College of Energy and Electrical Engineering, Hohai University, Nanjing 210098 |
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| Abstract: | Accurate determination of the severity of secondary equipment defects in power grid can provide an important basis for the operation and maintenance of equipment.Therefore,in this paper,to address problems such as large quantity of defective data features,and the great difficulty of using error-prone human judgment as an evaluation parameter,a defect classification method based on XGBoost (eXtreme Gradient Boosting) is proposed to improve the accuracy of defect classification of secondary equipment.First,a series of pre-processing work,such as removing outliers and coding,is performed on the secondary equipment historical defect data,and the characteristics highly correlated with equipment defects are extracted to establish the feature index set.Subsequently,the XGBoost model is trained and optimized using historical defect data.Finally,the trained classification model is used to realize the accurate classification of secondary equipment defects.Based on the secondary equipment defective data of a power plant,simulation results are presented to illustrate the effectiveness of the proposed algorithm and are compared with those of traditional classifiers (decision tree,logistic regression,etc.).Simulation results show that XGBoost can accurately determine the defect degree of secondary equipment,to assist the maintenance and management of equipment. |
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| Keywords: | XGBoost secondary equipment defect classification machine learning |
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