Extraction of Uncertain Parameters of Single-Diode Photovoltaic Module using Hybrid Particle Swarm Optimization and Grey Wolf Optimization Algorithm

EasyChair Preprint 3981

Abstract

Precise photovoltaic (PV) module electrical modelling is essential because of the comprehensive system installation of PV power stations. The scientists have therefore suggested a photovoltaic single-diode model (SDM) for effective PV modelling. The SDM is a simple and non-linear model comprising five unknown parameters. This paper, therefore, presents a novel hybrid approach called particle swarm optimization (PSO) and grey wolf optimization (GWO), in order to ex-tract unknown parameters from the SDM model. This paper also shows a new cost function based on the values of the datasheet instead of using extensive ex-periments. This paper, therefore, used standard test condition (STC) data to esti-mate two parameters by optimizing three remaining parameters by using PSOGWO algorithm. This proposed algorithm is applied to two commercial PV panels, namely KC200GT and SQ85, to find its parameters. Following this, the I-V curves of these PV modules were plotted under STC for five individual runs of the simulation. To prove the performance of the proposed PSOGWO algo-rithm, it is compared based on the statistical results with other algorithms, such as GWO and hybrid GWO-cuckoo search (GWOCS).

Keyphrases: GWO, PSO, Parameter Estimation problem, Photovoltaic module, hybrid psogwo algorithm, mathematical modelling, single diode model

@booklet{EasyChair:3981,
  author    = {Manoharan Premkumar and R. Sowmya and Subramaniam Umashankar and Pradeep Jangir},
  title     = {Extraction of Uncertain Parameters of Single-Diode Photovoltaic Module using Hybrid Particle Swarm Optimization and Grey Wolf Optimization Algorithm},
  howpublished = {EasyChair Preprint 3981},
  year      = {EasyChair, 2020}}