Nontechnical Loss Detection of Electricity based on Neural Architecture Search in Distribution Power Networks

被引：0

作者：

Dong, Lina ^{[1
]}

Li, Qi ^{[2
]}

Wu, Kejia ^{[1
]}

Fei, Ke ^{[2
]}

Liu, Chuan ^{[1
]}

Wang, Ning ^{[1
]}

Yang, Jun ^{[1
]}

Li, Yigui ^{[2
]}

机构：

[1] State Grid Cooperat China, State Grid Chongqing Elect Power Co, Chongqing, Peoples R China

[2] Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China

来源：

2020 8TH INTERNATIONAL CONFERENCE ON SMART GRID AND CLEAN ENERGY TECHNOLOGIES (ICSGCE 2020) | 2020年

关键词：

non-technical loss; electricity theft; neural architecture search; bayesian optimization;

D O I：

10.1109/icsgce49177.2020.9275605

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Electricity theft has always contributed a large portion of the nontechnical losses (NTLs) for the distribution networks, which usually causes severe concerns both on economics and safety of the power system operation. To cope with the rapid change of electricity theft methods, an auto NTLs detection system is proposed based on Neural Architecture Search (NAS) and Bayesian Optimization (BO). A case study utilizing NAS and BO has been performed on an electricity consumption dataset obtained from real customers of the State Grid Corporation of China. The auto-detection model achieved a F1 scores of 0.582 and an AUC of 0.919 which is competitive to the state-of-art artificial neural network.

引用

页码：143 / 148

页数：6

共 21 条

[1]

[Anonymous], 2016, INT C LEARN REPR

[2]

[Anonymous], 2017, INT C MACH LEARN

[3]

[Anonymous], 2017, ARXIV170805552

[4]

[Anonymous], 2015, ADV NEURAL INFORM PR, V28

[5]

Cheng Junwen, 2019, DISTURBUTION UTILIZA, V36, P75

[6] Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing [J].

Desell, Travis .

PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION), 2017, :127-128

[7]

Dewen Wang, 2020, POWER SYSTEM TECHNOL, V44, P775

[8]

Feng Xinyao, 2020, MICROCOMPUT APPLICAT, V36, P154

[9]

Finardi Paulo, 2020, Electricity theft detection with self-attention

[10]

Hou Xingzhe, 2017, SCI TECHNOLOGY ENG, V17, P96

← 1 2 3 →