An intelligent hybrid GMPPT integrating with accurate PSC detection scheme for PV system using ESSA optimized AWFOPI controller

Abstract

Abstract This study proposes a hybrid global maximum power point tracking (GMPPT) scheme integrating an extreme learning machine with 0.8Voc technique for PV system. An attempt is made to employ an anti-windup fractional-order proportional-integral controller for the MPPT. The controller parameters were tuned using an enhanced salp swarm algorithm. The algorithm integrates via an accurate detection scheme that distinguishes partial shading conditions (PSCs) from an irradiance uniform change. Furthermore, the computed irradiance is used to update PV array open-circuit voltage (Voc_Array), preventing temperature and irradiance sensors from being used. Its performance was studied compared with MPPT controllers, i.e., deterministic particle swarm optimization, hybrid PSO, and Lagrange interpolation PSO. The proposed MPPT technique proved its ability to track GMPP with an average tracking efficiency of 99.20% and 99.10% for uniform and PSCs, respectively. The proposed scheme has significant speed and accuracy in tracking GMPP for complex PSCs and uncertain weather conditions. Irrespective of the environmental uncertainties, it has an average voltage tracking percentage error within\xa0±\xa01% for ten hours test profile. The proposed technique is explored on OPAL-RT 4510 platform. The results depict its ability in GMPP tracking with an average tracking efficiency and tracking time of 99.15% and 0.12\xa0s, respectively.