Machine learning–driven stability and power quality assessment in renewable-integrated power systems

Stability and power quality assessment are critical for the effective and reliable operation of hybrid renewable power grids, where the intermittency of solar and wind generation can lead to significant voltage and frequency fluctuations. This study proposes a framework that integrates an Unscented Kalman Filter (UKF) with a Graph Neural Network (GNN) to simultaneously estimate system states, evaluate dynamic stability, and monitor power quality. The UKF provides accurate voltage and frequency estimation under various disturbances, while the GNN predicts the operating states: stable, marginal, and unstable. The framework is validated under multiple scenarios, including nominal operation, high-load conditions, renewable intermittency, faults, measurement noise, and extreme disturbances. The results demonstrate low estimation errors, with voltage RMSE ranging from 0.008 to 0.06 pu, and frequency RMSE ranging from 0.01 to 0.08 Hz, depending on the operating conditions. The GNN achieves classification accuracy above 94% in most scenarios, reaching up to 98%–99% under nominal conditions. Furthermore, multi-scenario heatmaps and standard power quality indices, such as voltage sag/swell, frequency deviation, and total harmonic distortion (THD), are employed to verify robustness and consistent performance under simulated conditions of the proposed approach. THD remains below 2% under nominal conditions and increases to 5%–5.5% under disturbances, while voltage sag and swell events occur within the ranges of 5%–10% and 3%–6%, respectively. Moreover, the stability index ranges from 0.85 to 0.88 under nominal conditions, decreases to 0.70–0.78 under high-load and renewable-intermittency conditions, and approximately 0.20 under extreme disturbances. Overall, the UKF–GNN framework provides an effective framework for stability and power quality assessment under simulated conditions in renewable-energy-based power systems.