scholarly journals Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions

2020 ◽  
Vol 10 (13) ◽  
pp. 4575 ◽  
Author(s):  
Gajendra Singh Chawda ◽  
Om Prakash Mahela ◽  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Tomonobu Senjyu
Keyword(s):  
Particle Swarm Optimization ◽  
Active Power ◽  
Operating Conditions ◽  
Maximum Power ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Incremental Conductance ◽  
Global Power ◽  
Maximum Power Tracking ◽  
Photo Voltaic

In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.

Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

2018 ◽  
Vol 9 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Thanikanti Sudhakar Babu ◽  
J. Prasanth Ram ◽  
Tomislav Dragicevic ◽  
Masafumi Miyatake ◽  
Frede Blaabjerg ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Solar Pv ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Extraction ◽  
Pv Array

scholarly journals Photovoltaic maximum power point tracking under dynamic partial shading changes by novel adaptive particle swarm optimization strategy

2019 ◽  
Vol 42 (1) ◽  
pp. 104-115 ◽  
Author(s):  
Ali M Eltamaly ◽  
Mamdooh S Al-Saud ◽  
Ahmed G Abokhalil ◽  
Hassan MH Farh
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Array ◽  
Power Point

Maximum power point tracker (MPPT) is vital device in the Photovoltaic (PV) system because it can increase the generated power considerably. Partial shading conditions (PSCs) on the PV array generates many peaks in the P-V curve of PV array. Metaheuristic techniques like particle swarm optimization (PSO) have the ability to track the global peak (GP) at any operating conditions. PSO technique can track the GP but once the shading pattern (SP) changes, the value and location of the new GP will change and may PSO cannot catch the new GP because all particles are busy around the previous GP. This problem is classified into two conditions: the first condition if the GP change its location and value suddenly, the second condition occurs when the GP changes its value gradually and still in same place. The first problem is solved by reinitializing the particles. The second problem is solved using a new adaptive strategy that has not been treated or adopted in any literature before. The results obtained prove the superiority of the new proposed strategy in always catching GP in dynamic change PSCs.

scholarly journals Proportional-Integral Controllers Performance of a Grid-Connected Solar PV System with Particle Swarm Optimization and Ziegler–Nichols Tuning Method

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2516
Author(s):  
Klemen Deželak ◽  
Peter Bracinik ◽  
Klemen Sredenšek ◽  
Sebastijan Seme
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Open Loop ◽  
Limited Area ◽  
Solar Pv ◽  
Pv System ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Tuning Method

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

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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