Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of Particle Swarm Optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems. © 2016 IEEE.

Sivasubramanian A

Electronics

School of Electronics Engineering

Chennai Campus

Malarvizhi E

Kamala J

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

Evaluation of particle swarm optimization algorithm in photovoltaic applications

2016 10th International Conference on Intelligent Systems and Control (ISCO)

Solar Power is the potential alternative to other sources of energy due to its pure and readily available nature. Solar power wattage is not always wonted or constant. It is due to its unaccustomed multi-peak power voltage characteristics exhibited by a variable swing in environmental conditions and shading effects. Swarm Intelligence has been playing a vital role in optimization problem owing to its intelligibility and directness. This article presents an Inertia Modified Particle Swarm Optimization (IM-PSO) algorithm for photovoltaic power extraction. The proposed method helps to keep track of the maximum power point in lesser effort thereby ensuring proper maximum power. The algorithm is modeled and simulated in MATLAB/SIMULINK environment. The results prove it especial to other traditional techniques. © 2019 IEEE.

2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)

Implementation of Enhanced Global Search Particle Swarm Optimization Algorithm for Photovoltaic Systems

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

International Journal of Electrical Power & Energy Systems

An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

2014 International Electrical Engineering Congress (iEECON)

Improved particle swarm optimization algorithm using average model on MPPT for partial shading in PV array

IEEE Transactions on Power Electronics

Theoretical and Experimental Analysis of an MPP Detection Algorithm Employing a Single-Voltage Sensor Only and a Noisy Signal

Analysis and Optimization of Maximum Power Point Tracking Algorithms in the Presence of Noise

Journal	Data powered by Typeset2016 10th International Conference on Intelligent Systems and Control (ISCO)
Publisher	Data powered by TypesetIEEE
Open Access	No