E3S Web Conf.
Volume 256, 20212021 International Conference on Power System and Energy Internet (PoSEI2021)
|Number of page(s)||5|
|Section||Smart Grid Technology and Power System Regulation Modeling|
|Published online||10 May 2021|
Nontechnical Loss Detection using Neural Architecture Search and Outlier Detection
1 Chongqing University, Chongqing, 400044, China
2 State Grid Cooperation of China, Shang Qiu, Henan Province, 476000, China
* Corresponding author’s e-mail: firstname.lastname@example.org
Electricity supply is essential to economy growth and improvement of people’s life. For a long time, illegal electricity theft not only affects the supply of power, but also causes significant economic loss. Traditional techniques for detecting electricity theft are inefficient and time-consuming. Data-based detecting algorithms become a new solution. This article analyses the features of electricity consumption, current, voltage and opening records under various electricity theft modes and proposes a new simulation method for electricity theft users. Based on the simulation dataset, a feature extraction method based on neural architecture search (NAS) is proposed. The advantage of this feature extraction model is demonstrated in the comparison experiments with other feature extraction model. Finally, the effectiveness and accuracy of the electricity theft detection method based on NAS model and outlier detection are verified through an industrial case study.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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