Non-technical losses detection in smart grids: An ensemble data-driven approach

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS, 2020, 2020-December, pp. 563-568
Issue Date:
2020-12-01
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Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.
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