Optimization techniques for parameter identification and maximum power extraction of solar PV system

Abstract

Power generation from renewable energy (RE) sources is popular due to the increasing energy demands, depleting fossil fuels, and enforcement of carbon emission reduction. There are many RE sources such as wind, solar, tidal, geothermal, biogas and fuel cell are available all over the world. Among this solar energy is the most promising and reliable because of the advantages such as less maintenance, low cost and global availability of sunlight. Further, it is an environmental friendly energy source that can be used to generate electricity through sun- light directly in the name of the photovoltaic (PV) effect. This research work aims to increase the energy obtained from solar PV panels under uniform and shading conditions through an accurate PV model which is designed using PV modeling. In this thesis, PV modeling and maximum power extraction techniques are explored using meta-heuristic techniques and ex- plained in detail. In order to carry out performance analysis of PV panels and control strate- gies, precise PV model is necessary. In this work, a powerful meta-heuristic technique known as the crow search algorithm (CSA) has been explored for effective parameters identification as well as maximum power point tracking process (MPPT). The unknown parameters of single and double diode-based models such as KC200GT, SQ150-PC and ST40 have been success- fully implemented and extracted accurate I-V characteristics of PV models. The modeling performance of CSA, assessed in terms of root mean square error (RME) between extracted and experimental data, it is found to be better to other conventional works. In addition, the PV modeling of CSA under various temperature and irradiation conditions is investigated. Based on the performance analysis, it is derived that CSA is optimistic technique for parameter iden- tification of PV cell/module/array. In further, the power voltage (P-V) characteristics curve of the PV module is nonlinear with respect to environmental conditions and it exhibits multiple local power peaks and a