基于GBDT的电力计量设备故障预测

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 392-396.

基于GBDT的电力计量设备故障预测

刘金硕¹, 刘必为², 张密³, 刘卿⁴

武汉大学国家网络安全学院武汉430070¹;
武汉大学计算机学院武汉430070²;
中国电力科学研究院北京100089³;
天津电力科学研究院天津300041⁴

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 刘金硕博士,副教授,主要研究方向为数据挖掘、大数据,E-mail:liujinshuo@whu.edu.cn(通信作者)。
基金资助:
本文受国网公司总部科技项目,国家自然科学基金(61672393)资助。

Fault Prediction of Power Metering Equipment Based on GBDT

LIU Jin-shuo¹, LIU Bi-wei², ZHANG Mi³, LIU Qing⁴

School of Cyber Science and Engineering,Wuhan University,Wuhan 430070,China¹;
School of Computer Science,Wuhan University,Wuhan 430070,China²;
China Electric Power Research Institute,Beijing 100089,China³;
Electric Power Science & Research Institute of Tianjin Electric Power Company,Tianjin 300041,China⁴

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： 电力计量设备的故障风险预测可以减少国家电网因为故障风险带来的损失。文中首先进行了数据的预处理和特征选取;其次,设计了基于GBDT的故障大类、故障小类以及设备寿命周期的预测;最后,对设计的模型进行了有效性和先进性的验证。实验在中国电力科研研究院提供的数据上进行。由实验结果可知,所提算法对6种故障类型的预测准确率为90.56%,查全率为92.95%,F1值为91.71%。相比回归、BP神经网络、Adaboost、决策树算法,梯度提升决策树算法在参数调优条件下的性能最优。

关键词: GBDT, 计量风险预测, 数据清洗

Abstract: The fault risk prediction of power metering equipment can reduce the loss caused by the fault risk of the national grid.Firstly,the data preprocessing and feature selection are carried out.Secondly,the GBDT-based fault categories,fault subclasses and equipment life cycle prediction are designed.Finally,the validity and advancement of the designed model are verified.Data used in the experiment are provided by China Electric Power Research Institute.The experimental results show that the prediction accuracy of the six fault types by using the proposed algorithm is 90.56%,the recall rate is 92.95%,and the F1 value is 91.71%.Compared with regression,BP neural network,Adaboost and decision tree algorithm,the gradient lifting decision tree algorithm has the best performance under parameter tuning conditions.

Key words: Data cleaning, GBDT, Measurement risk prediction

中图分类号:

TP206+.3

刘金硕, 刘必为, 张密, 刘卿. 基于GBDT的电力计量设备故障预测[J]. 计算机科学, 2019, 46(6A): 392-396. https://doi.org/

LIU Jin-shuo, LIU Bi-wei, ZHANG Mi, LIU Qing. Fault Prediction of Power Metering Equipment Based on GBDT[J]. Computer Science, 2019, 46(6A): 392-396. https://doi.org/

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