RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids.

ABSTRACT: 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 l

SUBMITTER: Khalid A 

PROVIDER: S-EPMC10909240 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature