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

Area of Science:

• Electrical Engineering
• Computer Science
• Artificial Intelligence

Background:

• Electricity theft causes significant non-technical losses (NTLs) in power grids, impacting grid stability and electricity supply quality.
• Traditional electricity theft detection methods analyzing only one-dimensional (1-D) data lack sufficient accuracy for complex smart grids.
• Smart grids enable advanced solutions for detecting and mitigating issues like electricity theft due to bidirectional data flow.

Purpose of the Study:

• To develop a robust, deep learning-based model for accurate electricity theft detection in smart grids.
• To overcome the limitations of existing 1-D data analysis methods for identifying non-technical losses.
• To enhance the security and reliability of power supply through improved theft detection mechanisms.

Main Methods:

• Proposed an ensemble deep learning model, the Recurrent Neural Network-Bidirectional Long Short-Term Memory-Conditional Random Field (RNN-BiLSTM-CRF).
• Integrated both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data for enhanced analysis.
• Leveraged the strengths of RNN and BiLSTM architectures for sophisticated pattern recognition in consumption data.

Main Results:

• The proposed RNN-BiLSTM-CRF model achieved a high accuracy rate of 93.05% in detecting electricity theft.
• The model demonstrated superior performance compared to existing methods that primarily rely on 1-D data analysis.
• The amalgamation of 1-D and 2-D data significantly improved the effectiveness of the theft detection process.

Conclusions:

• The deep learning-based RNN-BiLSTM-CRF model offers a highly effective solution for detecting electricity theft in smart grids.
• Utilizing multi-dimensional electricity consumption data is crucial for improving the accuracy of non-technical loss detection.
• The developed model contributes to securing smart grids and ensuring a more reliable power supply.