The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts

Solar energy has been one of the environmental friendly sources of energy. The low cost solution with minimal maintenance motivates towards photovoltaic (PV) cells based energy harnessing methods to meet energy demands. However, majority of conventional PV systems suffer from low energy conversion ratio (ECR) due to improper selection of the PV parameters and maximum power point tracking (MPPT) algorithm. Even ECR is adversely affected under varying environmental conditions. Therefore, accurate estimation of PV parameter can be of paramount significance for efficient PV model design. In addition, the development of a robust MPPT algorithm in conjunction with the effective PV design parameter can enable optimal ECR achievement. In this review paper, a number of literatures pertaining to PV parameter estimation and MPPT algorithms are discussed. Different methods including analytical, iterative and evolutionary computing algorithms are assessed for their efficacy towards PV parameter estimation. This review paper revealed that the analytical approaches suffer from singularity problem as well as limited mathematical calculation that confine its efficacy for optimal PV parameter estimation under dynamic irradiation pattern. The iterative approaches too are limited due to dynamic environment conditions. Our study has revealed that the evolutionary computing approaches, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), etc. have played vital role in PV design parameter estimation and classical approaches suffer from local minima and convergence issues. This manuscript reveals that to enable an optimal PV design parameter estimation there is an inevitable need to incorporate either evolutionary computation schemes or apply an efficient multi-objective optimization measures. This as a result can not only alleviate local minima and convergence issues but can also enable swift and precise parameter estimation to assist optimal PV design and augmented ECR performance.

Parameter estimation for photovoltaic system under normal and partial shading conditions: A survey

Adaptive Genetic Algorithm Based Multi-Objective Optimization for Photovoltaic Cell Design Parameter Extraction

Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm

Parameter identification of a photovoltaic (PV) cell is essential to simulate the behavior and to optimize the different characteristics of the PV generator. Therefore, the prediction of the PV system behavior will be possible; this allows a better energy management and a good operation reliability. There are several models that express the physical behavior of a PV cell to reproduce well the I-V curve in real conditions. In this paper, we focus on metaheuristic methods; two algorithms were used and compared, Genetic Algorithm (GA) and Particle Swarm method (PSO) with experimental results.

Abderrezzak Bouharchouche

Papers

Parameter identification of photovoltaic cell/module using genetic algorithm (GA) and particle swarm optimization (PSO)