In this paper, a meta-heuristics algorithm based artificial immune system (AIS) is used for solar PV parameter estimation. A new objective function based on derivative of maximum power with respect to voltage for solar double diode model is proposed. For performance evaluation and validation of the proposed approach using AIS, the results are compared with Genetic algorithm (GA) and particle swarm optimization (PSO) for two different PV modules. The results prove that the proposed approach with AIS outperforms GA and PSO in terms of convergence speed and objective function value for both PV modules. The proposed formulation with AIS can be used for parameter extraction of panel with different make/models. © 2015 IEEE.

Balasubramanian V

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Jacob B

Babu T.S

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Parameter extraction of solar PV double diode model using artificial immune system

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

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. © 2017 Elsevier Ltd

Renewable and Sustainable Energy Reviews

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

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer's datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings. © 2013 Elsevier Ltd.

Solar Energy

Bacterial Foraging Algorithm based solar PV parameter estimation

Numerical calculation of series and shunt resistances and diode quality factor of a photovoltaic cell using the Lambert W-function

Applied Energy

Artificial bee swarm optimization algorithm for parameters identification of solar cell models

Numerical calculation of series and shunt resistance of a photovoltaic cell using the Lambert W-function: Experimental evaluation

Simulated Annealing algorithm for photovoltaic parameters identification

Journal	Data powered by Typeset2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)
Publisher	Data powered by TypesetIEEE
Open Access	0