Affiliations: [a] Department of EEE, Annamalai University, Chidambaram, Tamil Nadu, India
| [b] Department of EEE, S.V. University College of Engineering, Tirupati, Andhra Pradesh, India
Correspondence:
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Corresponding author. Mr. A. Sreenivasulu, Research Scholar, Department of EEE, Annamalai University, Chidambaram 608002, Tamil Nadu, India. E-mail: sreenivasulu136@gmail.com.
Abstract: The world’s energy offer has been beneath an incredible pressure because of the speedy depletion of fossil resources, energy security, environmental issues and therefore the ever-increasing fashionable living sophistication. The problem of persistent hikes in oil costs, climate threats and soaring energy demand has pleased the worldwide interest to exploiting and investment in renewable sorts of energy (RE), alternative energy specially. A electrical phenomenon, PV system is simple to put in, has no moving components, is sort of freed from maintenance, reduced vulnerability to power loss and is expandable. Despite these benefits, PV energy prices significantly on top of fossil fuels. This can be because of its lower effectiveness and better prices. In PV systems tracking MPPT in effective manner is still the problem. In this paper, the 1000 W grid connected PV system has been taken for analysis of various MPPT techniques. Grid connected PV system modeled, tested under totally different irradiation conditions and conjointly for partial shading conditions. additional it’s enforced under partial shading condition for early MPPT ways, improvement methodology,at finally adopted deep learning methodology for the system and therefore the obtained results were compared with different methods.
Keywords: Maximum power point tracking, deep learning, partial shading conditions, efficiency, power
