Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

Haiqing Liu, Zhiqiao Li and Yuancheng Li
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Haiqing Liu: School of Control and Computer, North China Electric Power University, Beijing 102206, China
Zhiqiao Li: School of Control and Computer, North China Electric Power University, Beijing 102206, China
Yuancheng Li: School of Control and Computer, North China Electric Power University, Beijing 102206, China

Energies, 2020, vol. 13, issue 7, 1-16

Abstract: In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.

Keywords: conditional generation network; data set imbalance; stacked convolution noise reduction encoder; LightGBM; power theft detection (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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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