A Novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique

2007 ◽  
Author(s):  
Masafumi Miyatake ◽  
Fuhito Toriumi ◽  
Tsugio Endo ◽  
Nobuhiko Fujii
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Maximum Power Point Tracker ◽  
Photovoltaic Arrays ◽  
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.

scholarly journals Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition

2020 ◽  
Vol 13 (6) ◽  
pp. 241-254
Author(s):  
Anas Kamil ◽  
◽  
Mahmoud Nasr ◽  
Shamam Alwash ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Maximum Power ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

The maximum power point tracking (MPPT) is an essential key to ensure that the photovoltaic (PV) system is operated at the highest possible power generation. This paper presents an efficient MPPT method for the PV system based on an enhanced particle swarm optimization algorithm to track the location of the global maximum power point, whatever its location changes in the search space under all environmental conditions, including the partial shading on strings. In this paper, the formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process. This enhancement can be achieved by utilizing a special time-varying weighting coefficient and removing the effect of some other coefficients in the conventional particle swarm optimization algorithm (PSO) that cause winding of the particles during the online tracking process. Test results verified the accuracy of the proposed method to track the global maximum power point with considering the effect of partial shading condition. The proposed method was also compared with other MPPT methods to verify the superiority of the proposed work. The obtained results reveal that the proposed method is effective to improve the tracking efficiency and reduce the tracking time and the number of iterations for the different irradiances and load conditions. The maximum number of iterations was 11 iteration and the highest tracking time was 0.273s with tracking efficiency of about 99.98%.

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