Interpretable Identification of Power Quality Disturbances in Microgrids Using Time–Frequency Features

Abstract

Power quality disturbances (PQDs) in microgrids pose significant challenges to stable and reliable operation, particularly in tourism-oriented island systems with highly variable and uncertain load patterns. This paper proposes an interpretable PQD identification framework based on time–frequency feature extraction. The method utilizes a short-time Fourier transform to capture the nonstationary characteristics of voltage signals and constructs a compact feature set integrating time-domain, frequency-domain, and time–frequency information for disturbance classification. A supervised learning model is employed to map the extracted features to disturbance categories, while interpretability is achieved through feature contribution analysis, enabling explicit linkage between model decisions and the physical characteristics of PQDs. The proposed approach is validated using a combination of synthetic datasets, simulation data derived from MATLAB/Simulink R2024a microgrid models, and experimentally measured signals from a hardware-based platform. Case study results demonstrate that the proposed framework achieves a high overall classification accuracy of 99.50% across multiple disturbance types, including voltage sag, voltage swell, harmonic distortion, voltage flicker, transient disturbances, and hybrid disturbances. The interpretability analysis further confirms that the identified features are physically consistent with the underlying disturbance mechanisms. Overall, the proposed framework provides an accurate, robust, and interpretable solution for PQD identification, offering practical value for real-time monitoring and intelligent operation of renewable-rich microgrids.