Mamba-driven linear-time sequence learning architecture for efficient power quality disturbance classification

Abstract

The modernization of electrical power systems has accelerated the transition from conventional centralized grids to intelligent, cyber-enabled smart grids characterized by bidirectional energy flow, advanced sensing, and real-time data-driven control. However, the large-scale integration of inverter-interfaced renewable energy sources and nonlinear loads has introduced significant power quality (PQ) challenges, including voltage sag, swell, harmonics, flicker, transients, and composite disturbances under noisy conditions. Accurate and computationally efficient detection of such disturbances is critical for intelligent digital relaying and real-time monitoring applications. To address these challenges, this paper proposes a deep learning framework based on a Mamba-driven state-space model architecture for comprehensive PQ disturbance classification. The proposed approach converts voltage waveforms into structured two-dimensional patches using convolutional operations, which are then transformed into sequential representations for efficient long-sequence modeling via the Mamba module. Spatial feature extraction and temporal dependency learning are jointly achieved, while maintaining linear-time complexity and reduced memory requirements compared to transformer-based architectures. The extracted latent features are subsequently classified using a deep neural network (DNN)followed by softmax classifier. Eighteen PQ disturbance categories, defined in accordance with IEEE standards, are considered, including single and composite disturbances under noisy operating conditions. Experimental evaluation examines feature representations, convergence characteristics, confusion matrix performance, computational time, and memory usage. Comparative results demonstrate that the proposed Mamba-based framework achieves high classification accuracy with significantly lower computational complexity than conventional transformer models. The findings indicate that the proposed method is well-suited for real-time smart grid monitoring and intelligent protection systems.