Microgrid protection under weather uncertainty using joint probabilistic modeling of solar irradiance and wind speed

Abstract

The present power scenario has witnessed an unprecedented rise in the penetration level of renewable distributed energy resources (DERs). The wider acceptance of photovoltaic (PV) and wind-based DERs in microgrids poses severe protection challenges due to the uncertain variation in solar irradiance level and wind speed. The sporadic variation often results in underreach and overreach of overcurrent relays. With the aim of imparting robustness to the protection scheme against weather intermittency, a technique based on meteorological data-dependent joint probabilistic modeling of the stochastic variation in solar irradiance and wind speed has been integrated with a discrete wavelet transform (DWT) based feature extraction module and an optimal random forest (ORF) based classifier to perform fault detection/classification and zone identification. The performance of the proposed protection scheme has been examined for diverse test cases and compared with other computational intelligence techniques using numerical and real-time simulations.