A new method based on the concept of voltage band for efficient Distributed Maximum Power Point Tracking (DMPPT) of PV module is proposed. This novel formulation exhibits many advantages including faster convergence, improved tracking efficiency, zero steady state oscillations and simplicity in implementation. The paper unveils the possibility of utilizing a narrow voltage band for effective MPP tracking which has not been discussed so far in the literature. Extensive analysis is done and simulations are carried out in MATLAB/SIMULINK environment. To showcase the superiority of the proposed method, comparison is done with conventional methods for step change as well as gradual change in irradiation. © 2016 Elsevier Ltd.

Sudhakar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Rajasekar N

Sangeetha K

Sudhakar Babu T

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

Sustainable Energy Technologies and Assessments

Increased penetration of wind and solar PV system in Distributed Generation (DG) and isolated micro grid environment necessitates the use of maximum power point tracking method for wind and solar PV resources. Considering the change in environmental conditions and non-linearity, a variety of publications reporting various MPPT algorithms for solar and wind energy systems are put forward in recent times. But the review reports on common MPPT techniques used for solar and wind applications for hybrid power generation have not yet been reported. Hence, in this paper, conventional techniques and artificial intelligent techniques found extensively used in the power generation platform are peerly reviewed and compared. Historical MPPT methods like Perturb &amp; Observe (P&amp;O) / Hill Climbing, Incremental Conductance (INC), Fuzzy and Neural Network methods benchmarked in MPPT province are comprehensively compared in a common platform. In addition to the common existing techniques, recent swarm intelligence and bio-inspired techniques in solar PV and sensor less adaptive techniques in wind MPPT are also been reviewed provided for quality assessment. Finally an economic analysis is arrived for MPPT methods based on (i) Capacity utilization factor (ii) Cost (iii) Energy Savings (iv) payback period (v) Income generated and (vi) stability. © 2017 Elsevier Ltd

Renewable and Sustainable Energy Reviews

Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review

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 extraction of maximum power from solar photovoltaic (PV) using Maximum Power Point Tracking (MPPT) methods is a promising research area in the recent past. Many methods including conventional methods, such as Hill Climbing and Incremental Conductance, and methods based on neural network, Fuzzy logic and bio-inspired algorithms, were proposed for MPPT application. However, all these methods suffer from drawbacks such as slower convergence, reduced power output, predominant steady state oscillations, larger memory requirement, and complex structure. Hence, in this paper an attempt is made to enhance existing Particle Swarm Optimization technique by emphasizing proper initial value selection. The key features of this method include the ability to track the global peak power accurately under partial shading conditions with almost zero steady state oscillations, faster dynamic response, and easy implementation. Simulations are carried out for different shading patterns and the results obtained are compared with existing methods. Further, simulation results are validated via experimental values. © 2016 AIP Publishing LLC.

Fulltext

Journal of Renewable and Sustainable Energy

Voltage band based improved particle swarm optimization technique for maximum power point tracking in solar photovoltaic system

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

This paper proposes modified Particle Swarm Optimization (PSO) for maximum power point tracking in solar PV system. MPPT methods are used for extraction of maximum power from PV panels; since the non-linear characteristics of panel hinders the power output from the panel. Earlier proposed methods like HC, Incremental Conductance suffers from steady state and lower efficiency. 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 DC-DC CUK Converter and simulation results are compared with Incremental Conductance and Hill Climbing methods. © 2014 IEEE.

2014 International Conference on Advances in Electrical Engineering (ICAEE)

Maximum power point tracking using modified PSO with CUK Converter

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.

Journal	Data powered by TypesetSustainable Energy Technologies and Assessments
Publisher	Data powered by TypesetElsevier BV
ISSN	2213-1388
Open Access	0