In recent years solar energy has received worldwide attention in the field of renewable energy systems. Among the various research thrusts in solar PV, the most proverbial area is extracting maximum power from solar PV system. Application dof Maximum Power Point Tracking (MPPT) for extracting maximum power is very much appreciated and holds the key in developing efficient solar PV system. In this paper, a state of the art review on various maximum power point techniques for solar PV systems covering timeworn conventional methods and latest soft computing algorithms is presented. To date critical analysis on each of the method in terms of (1) tracking speed, (2) algorithm complexity, (3) Dynamic tracking under partial shading and (4) hardware implementation is not been carried out. In this regard the authors have attempted to compile a comprehensive review on various solar PV MPPT techniques based on the above criteria. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for practicing engineers as well as for new researchers. © 2016 Elsevier Ltd

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Ram J.P

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

Renewable and Sustainable Energy Reviews

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The widespread use of Proton Exchange Membrane fuel cell for its unique advantages compelled researchers for precise modelling of its characteristics. Since, modelling becomes extremely important for better understanding, simulation, design, analysis and development of high efficiency fuel cell system. However, due to its non-linearity, multivariate and strongly coupled characteristics; mathematical modelling based on empirical equations was widely adopted. But, the shortage of data, complexity in modelling, and number of unknown parameters favored the use of optimization methods. Many optimization methods have been endeavored to model Proton Exchange Membrane fuel cell characteristics. However, no prior attempt has been made to consolidate the contributions. Hence, this paper comprehensively describes and discusses the various Artificial Intelligence/bio inspired methods applied for fuel cell parameter estimation problem. The methods background theory and its application to the problem is elaborated. It is envisioned that, this review will be a one stop solution to the researchers and engineers working in the area of fuel cell systems. © 2018 Elsevier Ltd

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International Journal of Hydrogen Energy

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Generation of electricity from solar energy has gained worldwide acceptance due to its abundant availability and eco-friendly nature. Even though the power generated from solar looks to be attractive; its availability is subjected to variation owing to many factors such as change in irradiation, temperature, shadow etc. Hence, extraction of maximum power from solar PV using Maximum Power Point Tracking (MPPT) method was the subject of study in the recent past. Among many methods proposed, Hill Climbing and Incremental Conductance MPPT methods were popular in reaching Maximum Power under constant irradiation. However, these methods show large steady state oscillations around MPP and poor dynamic performance when subjected to change in environmental conditions. On the other hand, bio-inspired algorithms showed excellent characteristics when dealing with non-linear, non-differentiable and stochastic optimization problems without involving excessive mathematical computations. Hence, in this paper an attempt is made by applying modifications to Particle Swarm Optimization technique, with emphasis on initial value selection, for Maximum Power Point Tracking. The key features of this method include ability to track the global peak power accurately under change in environmental condition with almost zero steady state oscillations, faster dynamic response and easy implementation. Systematic evaluation has been carried out for different partial shading conditions and finally the results obtained are compared with existing methods. In addition, simulations results are validated via built-in hardware prototype. © 2015 Elsevier B.V.

Applied Soft Computing

Modified Particle Swarm Optimization technique based Maximum Power Point Tracking for uniform and under partial shading condition

The contribution of renewable energy to the field of energy markets has been substantial over the last few years. A large number of PV array installations show the increasing contribution of solar energy to the renewable energy. Partial shading of the PV arrays is one of the most discussed and worked upon concept for the simple reasons that it decreases the power output of the PV array installations and exhibits multiple peaks in the I-V characteristics. As a result, the modules have to be reconfigured to get the maximum power output. This papers presents an optimization based approach for Total cross tied (TCT) connected modules in a PV array. The physical locations of the modules remain unchanged while the electrical connections are altered. The genetic algorithm (GA) as an optimization tool, gives the connection matrix for the new electrical interconnection which fetches the maximum power output from the PV array. This is done to obtain uniform dispersion of shadow throughout the panel. © 2014 Elsevier Ltd. All rights reserved.

Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm

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

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International Journal of Electrical Power & Energy Systems

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Aerospace Science and Technology

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A comprehensive review on solar PV maximum power point tracking techniques

Journal	Data powered by TypesetRenewable and Sustainable Energy Reviews
Publisher	Data powered by TypesetElsevier BV
ISSN	1364-0321
Open Access	0