A novel vision transformer-based power quality disturbance classification method

Accurate and automated classification of power quality disturbances (PQDs) is essential for ensuring the reliability and stability of smart power systems. This study introduces a novel classification framework that combines a Vision Transformer (ViT) model with an innovative signal-to-image transformation technique, which directly reshapes 1D time series (T.S.) signals into 32 × 32 grayscale images, thereby eliminating complex preprocessing steps such as feature extraction or wavelet transforms. Unlike traditional approaches that rely on handcrafted features or spectrogram-based methods, this lightweight conversion preserves temporal characteristics while enabling efficient end-to-end learning through ViT’s attention mechanism. The model was evaluated on a comprehensive dataset comprising 21 distinct PQD classes, systematically generated under real-world conditions (20–50 dB noise levels and ± 0.5 Hz frequency deviations) using two Arbitrary Waveform Generators (AWGs). The proposed system achieved state-of-the-art performance with 99.23 % classification accuracy and an exceptionally fast inference time of 3.58 ms per sample, demonstrating both precision and suitability for real-time applications. Remarkably, the architecture maintained robust performance across all noise levels, confirming its strong generalization capability. Despite using an image-based approach, the method’s computational efficiency, achieved through optimized patch processing and compact input dimensions, makes it deployable in resource-constrained embedded systems. These findings position the framework as a practical foundation for next-generation PQD monitoring systems. The study advances the field by: (1) introducing the first ViT-based solution for raw PQD signal classification, (2) establishing a new benchmark in processing efficiency (3.58 ms runtime), and (3) demonstrating unprecedented robustness to both noise and frequency variations. Overall, this work provides a scalable, accurate, and hardware-friendly solution for intelligent power quality management, showcasing the untapped potential of transformer architectures in T.S. industrial applications.