Khalid Aqsa, Mustafa Ghulam, Rana Muhammad Rizwan Rashid, Alshahrani Saeed M, Alymani Mofadal
Department of Computer Science, COMSATS University, Islamabad, Pakistan.
University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Punjab, Pakistan.
PeerJ Comput Sci. 2024 Feb 26;10:e1872. doi: 10.7717/peerj-cs.1872. eCollection 2024.
Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.
窃电对分布式电网构成了重大威胁，会导致非技术损耗（NTLs），严重扰乱电网功能。由于电网向连接的用户集中供电，任何未经授权的用电行为都会损害电网，并危及整体供电质量。在处理大量数据时，检测这种欺诈行为具有挑战性。智能电网通过实现双向电流流动提供了解决方案，从而便于检测、分析和实施解决数据流问题的新措施。关键目标是提供一种基于深度学习的融合模型来检测窃电行为并保障智能电网安全。本研究引入了一种创新方法来克服当前窃电检测系统的局限性，这些系统主要依赖于分析一维（1-D）电力数据。这些方法在识别窃电实例时往往准确性不足。为应对这一挑战，本文提出了一种名为RNN-BiLSTM-CRF的集成模型。该模型融合了递归神经网络（RNN）和双向长短期记忆（BiLSTM）架构的优势。值得注意的是，所提出的模型利用了一维（1-D）和二维（2-D）用电数据，从而提高了窃电检测过程的有效性。实验结果表明，在检测窃电方面，该模型的准确率高达93.05%，超过了该领域现有模型的性能。
