Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid

Xiaoquan Lu, Yu Zhou, Zhongdong Wang, Yongxian Yi, Longji Feng and Fei Wang
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Xiaoquan Lu: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 210019, China
Yu Zhou: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 210019, China
Zhongdong Wang: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 210019, China
Yongxian Yi: State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 210019, China
Longji Feng: State Grid Nanjing Power Supply Company, Nanjing 210000, China
Fei Wang: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2019, vol. 12, issue 18, 1-18

Abstract: Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.

Keywords: non-technical losses; smart grid; semi-supervised learning; knowledge embed; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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