Solar PV cells usually operate at efficiency less than 20%, never generates maximum power due to change in environmental conditions such as irradiation, temperature, partial shading etc. Maximum power can be generated from PV cell with suitable tracking techniques. A modified Particle Swarm Optimization (MPSO) technique is proposed in this paper for tracking maximum power. This proposed evolutionary computation technique assures nearly zero steady state oscillation and faster convergence while tracking maximum power. The proposed PSO algorithm is tested on a CUK converter due to its higher efficiency and ripple free output. Simulations are carried out in MATLAB/SIMULINK and the computed results are validated in an experimental setup. © 2014 Published by Elsevier Ltd.

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Balasubramanian V

Department of Information Security

School of Computer Science and Engineering

Vysakh M

Thakur H.V

Azharuddin S.M

Muralidhar K

Paul D

Jacob B

Babu T.S.

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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

The utilization of renewable energy nowadays has a tremendous growth over the past few years. The important factor which reduces the power output of solar PV array is partial shading. Further the mismatch losses in solar PV array are created by uneven shading. This affects the characteristics of PV array by creating multiple peaks which further reduces the output power. To reduce the effect of partial shading, the shading pattern has to be distributed uniformly which can reduce the mismatch losses as well as to increase the output power. In this article, the PSO algorithm has been proposed to mitigate the problems by partial shading to increase the output yield. © Springer Nature Singapore Pte Ltd. 2018.

Lecture Notes in Electrical Engineering Advances in Smart Grid and Renewable Energy

The Particle Swarm Optimization Algorithm for Maximum Power Extraction of Solar PV Array

Solar Energy

An improved optimization technique for estimation of solar photovoltaic parameters

Photovoltaic Electrical Power Generation System (PV-EPGS) is gaining more importance due to its benefits like no fuel cost, eco-friendliness and less maintenance. However, harnessing the maximum power from large PV-EPGS has become difficult due to the occurrence of a number of peaks in P-V characteristics under partial shaded conditions. This paper presents simulation study of MPPT under partially shaded conditions using the modified version of Particle Swarm Optimization method known as Enhanced Leader Particle Swarm Optimization (EL-PSO). Proposed ELPSO method has advantages over PSO like fast convergence, better dynamic performance, easy implementation and high efficiency. To evaluate the proposed method simulation result of PV panel, Siemens S75 is provided for three different shaded conditions. © 2017 The Authors. Published by Elsevier Ltd.

EL-PSO based MPPT for Solar PV under Partial Shaded Condition

Partial shading of PV arrays is one of the most discussed and worked upon problem in the field of solar photovoltaic as it reduces the output power and exhibits multiple peaks in the PV and IV characteristics. As, a result the modules have to be reconfigured to get a maximum power output. This paper 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 proposed technique utilizes, standard deviation genetic algorithm (SDGA) as an optimization tool, which gives the final connection matrix for the new electrical interconnection which extracts the maximum power from the PV array. This is done to obtain uniform shade dispersion throughout the panel. The proposed method has been tested and simulated in Matlab-Simulink environment under partial shading conditions. Results of the simulation show that the proposed reconfiguration technique exhibit superior results as compared to the traditional TCT interconnection scheme. © 2017 The Authors. Published by Elsevier Ltd.

Solar PV array reconfiguration using the concept of Standard deviation and Genetic Algorithm

Surmount demand requirement in electrical energy has given a provision of integrating renewable energy to the three-phase grid-connected system. In particular, the penetration of distributed generation system has contributed more in electrical power generation. But the system connected produces adverse effects in grid due to the unpredictable atmospheric conditions. To handle such condition, in this research article, PV system is considered under study and a new two-stage CPG-P&amp;O MPPT method is used for interfacing PV with grid systems. In addition, a wide range of feed-in power to grid capacity at inverter level is briefly addressed. Further, the algorithm is designed to reduce the increased thermal stresses in switch and to get rid of the grid overshoot and power loss. The desired objective was acknowledged by the simulation results by ensuring delivery of optimal performance. © Springer Nature Singapore Pte Ltd. 2017.

Advances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems

Dynamic Performance Enhancement of Three-Phase PV Grid-Connected Systems Using Constant PowerGeneration (CPG)

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.

Bacterial Foraging Algorithm based solar PV parameter estimation

Comparison between conventional methods and GA approach for maximum power point tracking of shaded solar PV generators

Renewable and Sustainable Energy Reviews

A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition

IEEE Transactions on Power Electronics

An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation

2012 IEEE International Conference on Industrial Technology

Ant colony system based PI maximum power point tracking for stand alone photovoltaic system

Application of Modified Particle Swarm Optimization for Maximum Power Point Tracking under Partial Shading Condition

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