A state of the art a hybrid intelligent strategies of maximum power point tracking: a systematic contemporary

Abstract

Renewable energy sources are among the best substitute sources to fossil fuel due to it is very suitable for mitigating global warming; solar energy is considered the main causes of renewable energy, and solar photovoltaic (PV) generation systems have gained importance worldwide due to several characteristics, including cheap maintenance, low noise, and low fuel costs. However, one of the most difficult challenges facing solar energy systems is changing weather circumstances. The robust control approach stabilizes the PV system's output and maximizes harvested energy. In this study, several maximum power point tracking (MPPT) performances have been presented. Among them, artificial intelligence (AI) based on MPPT methods demonstrates the ability to capture the MPP point. There are several ways to apply AI to MPPT, and this paper presented various intelligent MPPT methods in detail with their benefits and drawbacks and comparison among them to select which technique is suitable and can be used to change weather conditions with<strong> </strong>horse optimization method (HOM) plus neural artificial system (NAS).