Harnessing of maximum power from solar PV with the aid of maximum power point tracking (MPPT) methods is of significant importance as it contributes to better utilization of the system. Amidst the conventional MPPT methods, hill climbing (HC) and incremental conductance methods are widely recognized but they yield maximum power only under constant irradiation and utterly fail when exposed to conditions of varying irradiation levels. Besides these, they exhibit wide power fluctuations even under steady state along with poor transient characteristics under partial shading conditions which is quite probable. Therefore, a recently developed optimization technique namely, fireworks algorithm is utilized for global MPPT. Extensive simulations are performed for constant irradiation as well as partial shading condition. The obtained results are compared with existing methods to highlight the superiority of the method used in this work. © Springer India 2016.

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

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

Fireworks Algorithm-Based Maximum Power Point Tracking for Uniform Irradiation as Well as Under Partial Shading Condition

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

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

The double diode model for photovoltaic (PV) modules is currently less adopted than one-diode model because of the difficulty in the extraction of its seven unknown parameters IPV,I01,I02, Rs, Rp, a1 and a2, which is a serious inverse problem. This paper proposes application of the Fireworks Algorithm (FWA) for the accurate identification of these unknown parameters in such a way to solve effectively this modeling problem. In particular, firstly, the FWA has been comprehensively tested with two different technologies of Mono-Crystalline (SM55 &amp; SP70) and Multi-Crystalline (Kyocera200GT) PV modules. In addition, further statistical and error analysis for three different panels are exclusively carried out to validate the suitability of proposed methodology. The results of proposed algorithm are benchmarked with popular Genetic Algorithm and Particle Swarm Optimization (PSO) methods. Fitness convergence curves or FWA method for SM55, SP70 and Kyocera200GT produce very less objective function as 2.2498E−07, 2.85765E−08 and 4.0075E−08 respectively. This illustrates the wise and accurate validation of FWA method. Calculated curve-fit via FWA in agreement to datasheet curve strongly suggest the FWA can constitute the core of suitable optimization code for two diode PV parameter extraction. © 2016 Elsevier Ltd

Solar Energy

Parameter extraction of two diode solar PV model using Fireworks algorithm

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 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 TypesetAdvances in Intelligent Systems and Computing Artificial Intelligence and Evolutionary Computations in Engineering Systems
Publisher	Data powered by TypesetSpringer India
ISSN	2194-5357
Open Access	0