电力系统自动化2019，Vol.43Issue(1)：119-125,7.DOI:10.7500/AEPS20180630013
基于堆叠去相关自编码器和支持向量机的窃电检测
Nontechnical Loss Detection Based on Stacked Uncorrelating Autoencoder and Support Vector Machine
摘要
Abstract
The performance of existing consumption-pattern based models is not sufficiently satisfactory for application.This is partly because most of them have focused on the selection of classification algorithms rather than the design of features, and existing feature design methods with respect to electricity nontechnical loss (NTL) detection remain inefficient and unsatisfactory.A deep-learning based feature extraction model is proposed for NTL detection, namely the stacked uncorrelating autoencoder (SUAE).Due to the deep architecture and powerful uncorrelating ability of SUAE, features are extracted from load profiles concisely and effectively, which has thus enabled a great improvement in final NTL detection performance.Support vector machines (SVM) are applied as classifiers, which use the features extracted by SUAE to output a judgment result.Case studies on real datasets have demonstrated that the proposed NTL model has high relevance ratio and low false alarm rate, as well as the powerful feature extraction ability of the SUAE.关键词
非技术性损失/窃电检测/深度学习/去相关自编码器/支持向量机Key words
nontechnical losses/nontechnical loss detection/deep learning/uncorrelating autoencoder/support vector machines引用本文复制引用
胡天宇,郭庆来,孙宏斌..基于堆叠去相关自编码器和支持向量机的窃电检测[J].电力系统自动化,2019,43(1):119-125,7.基金项目
国家重点研发计划资助项目(2017YFB0903000) （2017YFB0903000）
国家自然科学基金创新研究群体科学基金资助项目(51621065) This work is supported by National Key R&D Program of China (No. 2017YFB0903000) and Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 51621065). （51621065）
