Abstract
The manifested merits associated with solar energy including high sustainability, zero greenhouse gas emission and economic operation have encouraged wide penetration of photovoltaic (PV) systems in the microgrid, during the last few decades. However, the intermittency caused due to the fluctuating nature of solar irradiance demands an efficient maximum power point tracking (MPPT) algorithm for PV-integrated microgrids. The scenarios related to partial shading and faults in PV array impact the voltage–current behaviour resulting in the failure of conventional MPPT techniques in accurately estimating the operating point. The incorrect estimation by MPPT techniques quite often affects the operation of overcurrent protection modules. In this regard, this chapter presents an accurate sine cosine optimization (SCA)-based MPPT algorithm which will search the global operating point irrespective of the condition (i.e. in the event of partial shading or array faults), while avoiding undesired activation of the protection system. Besides this, a reliable protection scheme is proposed to detect and classify the faults in the distribution line under dual operating modes of microgrid (i.e. grid-connected and islanding). The instantaneous voltage–current signals recorded at the relaying bus are preprocessed through discrete wavelet transform (DWT) to obtain the discriminatory attributes, which are further utilized by the hybrid framework of artificial neural network (ANN) and SCA to perform the intended protection tasks under both modes of microgrid operation. The performance of the proposed MPPT technique and protection scheme has been analysed against a wide range of operating scenarios with real-time validation on OPAL-RT digital simulation platform.
Published Version
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