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.

Rajasekar N

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

Sudhakar Babu T

Priya K

Balasubramanian K.

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

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

2015 Annual IEEE India Conference (INDICON)

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

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.

Fulltext

Energy Procedia

Solar PV Modelling and Parameter Extraction Using Artificial Immune System

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.

Application of Modified Particle Swarm Optimization for Maximum Power Point Tracking 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 Typeset2015 Annual IEEE India Conference (INDICON)
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Open Access	0