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基于负荷尖峰特征LSTM自编码器的窃电识别方法

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成果类型:
期刊论文
作者:
刘康;刘鑫;张蓬鹤;薛阳;李彬;...
通讯作者:
Su, Sheng(eessheng@163.com)
作者机构:
[刘康; 李彬; 苏盛] Hunan Key Laboratory of Smart Grid Operation and Control, Changsha University of Science and Technology, Changsha
410114, China
[刘鑫] Marketing Service Center of State Grid Hunan Electric Power Company (Metering Center), Changsha
[张蓬鹤; 薛阳] China Electric Power Research Institute Co., Ltd., Beijing
100180, China
通讯机构:
[Su, S.] H
Hunan Key Laboratory of Smart Grid Operation and Control, China
语种:
中文
关键词:
窃电;长短期记忆;神经网络;自编码器;用电特征;异常检测
关键词(英文):
anomaly detection;autoencoder;electricity theft;long short-term memory (LSTM);neural network;power consumption characteristics
期刊:
电力系统自动化
ISSN:
1000-1026
年:
2023
卷:
47
期:
2
页码:
96-104
基金类别:
This work is supported by National Natural Science Foundation of China (No. 51777015), Key Projects of Hunan Provincial Department of Education (No. 19A011) and Education Department of Hunan Province of China (No. 19C0349).
机构署名:
本校为其他机构
院系归属:
机械与电气工程学院
摘要:
近年来,面向高损线路的窃电检测方法得到大面积工程应用,对降低窃电检测误报率和推动数据驱动窃电检测的工程应用起到了重要作用。但如何准确检出非高损线路的专变窃电用户,仍是亟待解决的难题。基于实践经验中部分窃电用户存在用电量异常尖峰这一特点,提出基于负荷尖峰特征长短期记忆(LSTM)自编码器的用户窃电识别方法。首先,分析典型窃电用户曲线形态,提炼了区分正常及窃电用户的用电量尖峰特征。然后,结合该特征和用户分时数据周期性规律,构建LSTM自编码模型重构输入得到拟合值,基于拟合值与真实值的均方误差设定自适应阈值,从而识别窃电嫌疑用户并提供具体预警尖峰时段。最后,应...
摘要(英文):
In recent years, electricity theft detection methods for high-loss lines have been applied in a large area of engineering, which plays an important role in reducing the false alarm rate of electricity theft detection and promoting the engineering application of data-driven electricity theft detection. However, it is still a difficult problem to accurately detect the specialty transformer users of non-high loss lines. Based on the characteristic that some users who steal electricity have abnormal power consumption peaks in the practical experien...

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