In this paper, a meta-heuristics algorithm Artificial Immune System (AIS) is used for solar PV parameter estimation. Solar PV Double diode model parameter estimation is carried out by applying the new method. Estimated parameter values are substituted in developed MATLAB model and characteristics are obtained. For performance analysis, the results obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm Optimization (PSO). For further validation of the proposed method, error graphs are plotted for two panels using AIS, GA and PSO. The results confirm that the proposed method employing AIS outperforms GA and PSO in terms of convergence characteristics and absolute error. © 2015 The Authors. Published by Elsevier Ltd.

Balasubramanian V

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Babu S

Department of Biotechnology

School of Bio Sciences and Technology

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Jacob B

Azharuddin S.M

Fulltext

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

Energy Procedia

Precise model parameters being the prerequisite for realizing accurate PV models, parameter identification techniques have gained immense interest over the years among the researchers specializing in PV systems. The application of various promising metaheuristic algorithms to optimize the model parameters have lightened up the scope of further enhancements in this field. Ever since, numerous metaheuristic algorithms have deployed for this purpose. With handful of techniques available in this regard, this paper takes up an initiative to review the existing metaheuristic algorithms based parameter extraction techniques with an emphasis on their compatibility, accuracy, convergence speed, range of parameters set and their validating environment. Based on the analysis conducted, accurate models available for 17 different industrial solar cells/modules are identified. Inspired by this review, an unidentified gateway between parameter extraction and fault detection in PV systems have been identified; and has further extended this review to differentiate some models that can help the researchers to achieve accurate, efficient and rapid fault detection. This review is a valuable gathering of statistics from the various researches carried out in PV parameter extraction which can assist enhanced researches for fault detection in PV systems as well. © 2017 Elsevier Ltd

Renewable and Sustainable Energy Reviews

Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems

IEEE Journal of Photovoltaics

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

Over a period of years, Maximum Power Point Tracking has become a mandatory requirement for Solar Photo Voltaic (PV) systems. Being dependent to environmental changes, the PV power constantly fluctuates due to change in irradiation. Under such conditions, large PV array connected in interconnection will experience non-uniform irradiation thus results multiple peaks in P-V characteristics. Although many conventional and soft computing techniques have been proposed in literature, the ability to identify global peak under strong shading conditions is not guaranteed. Particularly, local peak in close agreement to global peak makes most of the algorithms to get trapped in local peaks. This condition often occurs due to insufficient randomness in algorithm hence, a new Flower Pollination Algorithm (FPA) is investigated in this research. Proposed method has dual mode search ability which creates required randomness in every iteration is the key reason to suit FPA for MPPT. Simulation and experimental results verified with different patterns portray FPA excellence under all irradiated conditions. Further performance of FPA is verified with Particle swarm Optimization method and conventional P&amp;O method. © 2016 Elsevier Ltd

Energy

A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)

over a decade, the research interest in solar energy is considerably increased, since it is one of the most promising alternate for decaying fossil fuel. Among the research thrusts, the accurate modeling of solar I-V characteristics is given prior importance. In this article, the authors have proposed a new FPA based solar PV parameter extraction. To avoid computational burden and complexity, single diode modeling is preferred. Solar PV parameters are computed using FPA method. The computed root mean square error and relative error is compared with existing simulated annealing, Pattern search, Genetic algorithm and particle swam optimization techniques. © 2016 IEEE.

2016 IEEE 6th International Conference on Power Systems (ICPS)

Solar PV parameter extraction using FPA

The potential of renewable energy sources is massive as they can in principle meet many times the world's energy demand. In this paper, Solar PV parameter extraction is performed using Artificial Immune System (AIS). A novel objective function based on derivative of maximum power with respect to voltage for solar PV double diode model is proposed. For validation and performance evaluation, the results obtained using the proposed approach employing AIS, are compared with Genetic algorithm (GA) and particle swarm optimization (PSO). As compared to GA and PSO, AIS using the proposed approach has the least error. The results also showcase that the proposed approach with AIS outclasses GA and PSO in terms of absolute error for all the three PV models under various irradiation conditions. © 2015 IEEE.

2015 Annual IEEE India Conference (INDICON)

An innovative method for solar pv parameter extraction for double diode model

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

An improved approach to extract the single-diode equivalent circuit parameters of a photovoltaic cell/panel

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

Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach

Applied Energy

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

Solar PV Modelling and Parameter Extraction Using Artificial Immune System

Journal	Data powered by TypesetEnergy Procedia
Publisher	Data powered by TypesetElsevier BV
ISSN	1876-6102
Open Access	Yes