What question did this study set out to answer?

This research aims to enhance the recognition of power quality disturbances using advanced deep learning techniques.

February 23, 2026

From Local Patterns to Global Context: A Multimodal Deep Learning Approach for Complex Power Quality Disturbance Recognition

Key Points

This research aims to enhance the recognition of power quality disturbances using advanced deep learning techniques.
Developed a multimodal framework integrating fast Fourier transform and convolutional neural networks.
Constructed a synthetic dataset with 25 disturbance types following IEEE Std. 1159.
Utilized MATLAB/Simulink simulations for real-world data validation.
Achieved an average accuracy of 99.30% on synthetic datasets.
Validation yielded accuracies of 95.88% with real-world data and 97.47% with simulations.
Confirmed the model's adaptability and reliability under various noise conditions.

Abstract

ABSTRACT The increasing penetration of renewable energy sources introduces complex and mixed power quality disturbances (PQDs) that challenge traditional diagnostic approaches. To address the limitations of convolutional neural networks (CNN) in capturing long‐range temporal dependencies, this study proposes an intelligent classification framework integrating fast Fourier transform, one‐dimensional CNN, lightweight Bidirectional Encoder Representations from Transformers (LBERT1d) and a signal‐based cross‐attention (SCA) mechanism. The proposed MCNN1d‐LBERT1d‐SCA framework leverages multimodal time–frequency feature fusion, combining local and global representations to enhance the recognition of concurrent and nonstationary PQDs. A synthetic dataset following IEEE Std. 1159 was constructed encompassing 25 disturbance types with multiple signal‐to‐noise ratios, to ensure robustness and generalisation. Experimental results demonstrate acceptable performance, achieving an average accuracy of 99.30% on the synthetic dataset and maintaining better reliability under noise conditions down to 20 dB. Validation using real‐world IEEE PES data and MATLAB/Simulink simulations yielded accuracies of 95.88% and 97.47%, respectively, confirming the model's strong adaptability and real‐time capability. These results indicate that the proposed hybrid deep learning framework offers a practical and scalable solution for intelligent PQD monitoring contributing to the reliability and stability of modern power systems.

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