面向能源互联网终端用户的异常数据检测方法

doi:10.12204/j.issn.1000-7229.2021.10.003

电力建设 ›› 2021, Vol. 42 ›› Issue (10): 19-27.doi: 10.12204/j.issn.1000-7229.2021.10.003

• 综合多元能源及信息技术的能源互联网规划与运行·栏目主持刘洋副教授、韩富佳博士· • 上一篇下一篇

面向能源互联网终端用户的异常数据检测方法

户艳琴¹, 李海明², 刘念²(), 傅皆恺¹, 黄天翔¹, 李承霖¹, 李珂舟¹, 胡志强³, 范志夫³, 邬小可⁴

1.国网江西综合能源服务有限公司,南昌市330096
2.新能源电力系统国家重点实验室(华北电力大学),北京市 102206
3.国网江西省电力有限公司,南昌市330077
4.国网江西省电力有限公司鹰潭供电分公司,江西省鹰潭市335000

收稿日期:2021-01-25 出版日期:2021-10-01 发布日期:2021-09-30
通讯作者: 刘念 E-mail:nianliu@ncepu.edu.cn
作者简介:户艳琴(1984),女,高级工程师,主要研究方向为反窃电、综合能源。
李海明(1994),男,硕士研究生,主要研究方向为综合能源系统、负荷预测。
傅皆恺(1991),男,工程师,主要研究方向为综合能源、配电网、反窃电。
黄天翔(1991),男,硕士,主要研究方向为电力系统分析、综合能源业务。
李承霖(1991),男,工程师,主要研究方向为反窃电、综合能源。
李柯舟(1982),男,高级工程师,主要研究方向为电气工程、电力通讯工程。
胡志强(1982),男,高级工程师,主要研究方向为电能计量、线损治理、反窃电等。
范志夫(1984),男,硕士,主要研究方向为电能计量、用电信息采集等。
邬小可(1980),男,高级工程师,主要研究方向为电力营销智能用电。
基金资助:
国网江西省电力有限公司科技项目“基于配用电大数据能效提升关键技术研究”(521855200004)

Detection Method of Abnormal Data for End Users of Energy Internet

HU Yanqin¹, LI Haiming², LIU Nian²(), FU Jiekai¹, HUANG Tianxiang¹, LI Chenglin¹, LI Kezhou¹, HU Zhiqiang³, FAN Zhifu³, WU Xiaoke⁴

1. State Grid Jiangxi Integrated Energy Services Co.,Ltd.,Nanchang 330096, China
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University), Beijing 102206, China
3. State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330077, China
4. State Grid Jiangxi Electric Power Co.,Ltd. Yingtan Power Supply Branch, Yingtan 335000, Jiangxi Province, China

Received:2021-01-25 Online:2021-10-01 Published:2021-09-30
Contact: LIU Nian E-mail:nianliu@ncepu.edu.cn

摘要/Abstract

摘要：

随着能源互联网的持续推进,电力系统的信息化程度不断提高,用户侧电量数据迅速增长,为开展基于大数据分析技术的用户用能特征检测提供了数据基础。针对传统的用户异常用电模式检测模型存在投入高、效率低的问题,提出了包含数据清洗-特征筛选-模型训练的用户异常用电全周期检测模型。为了综合考虑用户异常用电模式的影响因素,建立了包含负荷曲线斜率指标、线损指标和告警类指标的评估指标体系;并对初始数据进行数据清洗及缺失值处理以提高用户异常用电模式检测的精确度,然后使用极端梯度提升树(extreme gradient boosting,XGBoost)进行异常检测。最后,通过算例验证了检测模型的有效性,并通过与决策树、随机森林及Adaboost的对比分析,得出了XGBoost在用户异常用电模式检测中以较短的训练时间获得了较高的检测精度的结论。

关键词: 异常用电模式, XGBoost, 评价指标体系, 检测模型, 能源互联网

Abstract:

With the continuous advancement of the energy Internet, the degree of informatization of the power system has been constantly improved and the amount of electricity data on the end-user side has been growing rapidly. The change provides a data foundation for the detection of user energy consumption based on the big data analysis technology. To deal with the high-cost and low-efficiency problem of the traditional detection model for abnormal electricity consumption patterns, a full cycle detection model of abnormal electricity consumption is proposed, which includes data cleaning, feature screening and model training. Besides, for comprehensively considering the factors affecting the abnormal electricity consumption patterns, the evaluation index system including the power consumption slope index, the line-loss index, and the warning information index is built. The data cleaning and missed value preprocess are conducted on the initial data to improve the accuracy of abnormal electricity pattern detection, and XGBoost is used for abnormal detection. Finally, a numerical case is used to verify the availability of the proposed detection method. In terms of the detection accuracy and training time, the detection performance of XGBoost algorithm is the best by comparing it with decision tree, random forest and Adaboost.

Key words: abnormal electricity consumption patterns, XGBoost, evaluation index system, detection model, energy Internet

中图分类号:

TM714

户艳琴, 李海明, 刘念, 傅皆恺, 黄天翔, 李承霖, 李珂舟, 胡志强, 范志夫, 邬小可. 面向能源互联网终端用户的异常数据检测方法[J]. 电力建设, 2021, 42(10): 19-27.

HU Yanqin, LI Haiming, LIU Nian, FU Jiekai, HUANG Tianxiang, LI Chenglin, LI Kezhou, HU Zhiqiang, FAN Zhifu, WU Xiaoke. Detection Method of Abnormal Data for End Users of Energy Internet[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 19-27.

图/表 12

图1 异常用电模式评价指标体系

Fig.1 Evaluation index system of abnormal electricity consumption pattern

表1 XGBoost节点切分算法

Table 1 Node segmentation algorithm for XGBoost

图2 基于XGBoost的异常用电模式检测流程

Fig.2 Flow chart of detection of abnormal electricity consumption pattern based on XGBoost

表2 混淆矩阵

Table 2 Confusion matrix

图3 配电系统结构

Fig.3 The structure of distribution network

图4 迭代次数-准确率曲线

Fig.4 Iteration times - accuracy curve

图5 XGBoost的准确度曲线

Fig.5 Accuracy curve of XGBoost algorithm

图6 训练效果

Fig.6 Training effect

图7 特征贡献度

Fig.7 The contribution of features

表3 数据清洗前后的测试结果

Table 3 Test results before and after data cleaning

表4 5种算法的测试结果

Table 4 Test results of the five algorithms

图8 4种算法的ROC曲线

Fig.8 The ROC curve of the four algorithms

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面向能源互联网终端用户的异常数据检测方法

Detection Method of Abnormal Data for End Users of Energy Internet

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