A hybrid approach based on deep learning and support vector machine for the detection of electricity theft in power grids

Abstract

Electricity theft has significant impact on the power grids in terms of generating non-technical losses, which eventually degrading the power quality and minimizing the outfitted profit. In this paper, we proposed a hybrid approach based on deep learning and support vector machine for the detection of energy theft to facilitate and assess energy supplier companies to eliminate the issue of insufficient power, irregular power expenditure and ineffective electricity monitoring. A deep convolutional neural network is proposed for the feature learning using smart meters data in different times, varying from hours to days. Extracted features were further used to train support vector machine, which classify the features in two categories as theft and non-theft. Furthermore, a dropout layer is introduced in convolutional neural network model to avoid over fitting issues. Several careful experiments were carried out on real time customers smart meter data and the results validate the effectiveness of the proposed method in terms of accuracy and less detection error.

Keywords

• Electricity theft
• Non-technical losses
• Smart meter
• Convolutional neural networks
• Support vector machine