International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 11 Issue: 6s DOI: https://doi.org/10.17762/ijritcc.v11i6s.6967 Article Received: 30 March 2023 Revised: 24 May 2023 Accepted: 05 June 2023 ___________________________________________________________________________________________________________________ 568 IJRITCC | June 2023, Available @ http://www.ijritcc.org Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method Krupali Kanekar 1 , Prakash Burade 2 , Diraj Magare 3 1 Electrical Department, Sandip University, Nashik, India Electronics and Telecommunication Engg, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, India krupali.kanekar@rait.ac.in 2 Electrical Department, Sandip University, Nashik, India prakash.burade@sandipuniversity.edu.in 3 Electronics and Telecommunication Engg, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, India dhiraj.magare@rait.ac.in Abstract— Industry and academia are becoming more interested in solar energy. The problem of providing an alternative to fossil fuels and limiting the environmental damage brought on by their emissions is what has brought about this increased focus. Solar photovoltaic is the subject of a growing number of studies. The goal of the current investigation is to investigate how various technological modules namely single junction amorphous silicon (a-Si), Hetero junction with Intrinsic Thin-layer (HIT) and multi crystalline silicon (mc-Si) are affected by seasonal spectrum variation respond to Indian climatic circumstances. Compared to many other nations, the entire Indian subcontinent has a relatively distinct climate with distinct seasonal patterns. The four seasons that are considered in this study are summer, winter, monsoon, and post monsoon. The impact of each season varies on the spectrum. Such a study will be helpful to measure the parameter connected to the spectrum and assess its impact on the effectiveness of the PV array. In order to conduct accurate performance investigations, the extraction of the right circuit model parameters is essential. The estimation of the solar PV parameter is done by using Particle swarm optimization (PSO) algorithm. Keywords-photovoltaic, root mean square error; amorphous silicon; multi crystalline silicon; hetero junction with intrinsic thin layer; module temperature; particle swarm optimization; standard test condition. I. INTRODUCTION Our daily lives depend heavily on energy, and there is a growing need for alternative energy sources. Researchers have identified effective strategies to use renewable energy to close the supply-demand gap. A cheap and environmentally friendly energy source, renewable energy is widely accessible all over the world. The solar photovoltaic system has a very low operating cost [1]. Solar energy is a safe form of renewable energy, and because the sun's energy output is so great, it can reliably provide all of the world's energy needs. Over the past ten years and longer, research on solar PV cells has remained active due to the depletion of fossil fuels and the growing demand for renewable energy sources, which has drawn attention to new directions in the study of photovoltaic cells [2]. Modeling has persisted as one of the fundamental methods for studying any complicated system. Models have been used extremely successfully to comprehend and evaluate the operation and effectiveness of photovoltaic cells. Models assist in gaining a thorough understanding of the intricate connections between various external conditions and other innate characteristics that affect the performance of photovoltaic cells. There are several different meta-heuristic optimization strategies, such as the flower pollination algorithm (FPA), genetic algorithm (GA), and particle swarm optimization (PSO) [3]. Presented work offers a fresh perspective on estimating new model coefficients for the site while maintaining the style of the literature reported module temperature models. For three different technology modules deployed at the Gurgaon site in India, amorphous-Si, hetero-junction with intrinsic thin-layer (HIT), and multi-crystalline-Si, the expected module temperature using predicted coefficients was compared with the experimental module temperature. It has been found that the estimation of module temperature using the new equations is precise and error-free when compared to experimental results. The parameters of solar cell models are extracted in this study using a metaheuristic technique based on the Teaching learning based optimization (TLBO) algorithm. Utilizing experimental data sets and objective function, analysis is carried out. It is suggested that the disparity between the estimated and