scholarly journals Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: A particle swarm optimization technique

2014 ◽  
Vol 6 (2) ◽  
pp. 023102 ◽  
Author(s):  
Mohammadmehdi Seyedmahmoudian ◽  
Saad Mekhilef ◽  
Rasoul Rahmani ◽  
Rubiyah Yusof ◽  
Ali Asghar Shojaei
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Particle Swarm Optimization Technique ◽  
Power Point

Application of Particle Swarm Optimization Technique for the Design of Maximum Power Point Tracking

2013 ◽  
Vol 768 ◽  
pp. 47-56 ◽  
Author(s):  
Rini Venugopalan ◽  
Neeraja Krishnakumar ◽  
R. Sarjila ◽  
N. Rajasekar
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Optimization Technique ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Particle Swarm Optimization Technique ◽  
Power Point

The generation of electricity from solar energy has gained worldwide acceptance and serves as a critical objective for the future due to its abundance and eco-friendly nature. However, the output power extracted from solar PV module varies based on the variation in environmental conditions such as irradiance, temperature, shadow etc. Therefore, Maximum Power Point Tracking (MPPT) algorithms are implemented for the proper utilization of the available photovoltaic energy. This paper proposes an algorithm using Particle Swarm Optimization technique which involves a simple and effective method to calculate the required duty cycle. The key feature of this method is its ability to track the maximum power accurately with almost zero steady state oscillations which in turn improves the performance of the tracking system. The effectiveness of this algorithm has been evaluated under uniform change in environmental conditions in MATLAB/SIMULINK. Moreover, superiority of the proposed method is verified by comparing its results with the conventional algorithms such as Hill Climbing and Incremental Conductance in terms of tracking speed and steady state oscillations.

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