Adaptive Detection of Islanding and Power Quality Disturbances in a Grid-Integrated Photovoltaic System

Global photovoltaic (PV) generation is increasing steadily at about 30%\growth rate over the last decade. Depleting environment owing to extensive use of fossil fuels is expected to further continue with this growth rate. With such large PV penetration in the utility grid, perturbation-based active islanding detection methods are becoming detrimental, marred with issues like degradation of power quality and deteriorating system stability. This paper uses morphological filters combined with empirical mode decomposition (EMD) to implement an efficient adaptive signal processing-based detection for islanding as well as PQ disturbances. Two-stage morphological median filter (MMF-2) is used to overcome the noise vulnerability associated with EMD. Field programmable gate array implementation is developed for real-time detection of PQ events. Classification of power quality disturbances is obtained using a support vector machine classifier. The results demonstrate fast and accurate real-time detection under various noisy scenarios without applying any parameter perturbations.