A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System under Partial Shading Condition

2012 ◽  
pp. 1-1 ◽  
Author(s):  
K. Ishaque ◽  
Z. Salam
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Maximum Power Point Tracker ◽  
Power Point

scholarly journals Application of mutant particle swarm optimization for MPPT in photovoltaic system

2020 ◽  
Vol 19 (2) ◽  
pp. 600
Author(s):  
Thom Thi Hoang ◽  
Thi Huong Le
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Operating Efficiency ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

<span>The P –V characteristic of a photovoltaic system (PVs) is non-linear and de-pends entirely on the extreme environmental condition, thus a large amount PV energy is lost in the environment. To enhance the operating efficiency of the PVs, a maximum power point tracking (MPPT) controller is normally equipped in the system. This paper proposes a new mutant particle swarm optimization (MPSO) algorithm for tracking the maximum power point (MPP) in the PVs. The MPSO-based MPPT algorithm not only surmounts the steady-state oscillation (SSO) around the MPP, but also tracks accurately the optimum power under different varying environmental conditions. To demonstrate the effectiveness of the proposed method, MATLAB simulations are implemented in three challenging scenarios to the PV system, including changing irradiation, load variation and partial shading condition (PSC). Furthermore, the obtained results are compared to some of the con-ventional MPPT algorithms, such as incremental conductance (INC) and clas-sical particle swarm optimization (PSO) in order to show the superiority of the proposed approach in improving the efficiency of PVs. </span>

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%.

scholarly journals Efficiency Improvement of Photovolataic by Using Maximum Power Point Tracking Based on Particle Swarm Optimization Algorithm Under Normal and Partial Shading

2018 ◽  
Vol 2 (2) ◽  
pp. 71
Author(s):  
Machmud Effendy ◽  
Khusnul Hidayat ◽  
Wahyu Dianto Pramana
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Power Point Tracking ◽  
Particle Swarm ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Photovoltaic (PV) is a device which is capable to converts solar irradiance into Direct Current (DC) electricity energy. To increase the power result of PV, it needs a method to track the Maximum Power Point(MPP) which is usually called Maximum power Point Tracking(MPPT). So that, the power result increased with low cost. The purpose of this research is to conduct MPPT modeling by Particle Swarm Optimization (PSO). The proposed method is implemented in DC to DC converter. This research used SEPIC converter. The purpose of using SEPIC converter is in order the output of current and voltage have smallest ripple. The modelling system is conducted by using MATLAB 2016b software to find out performance of PSO and SEPIC converter. The evaluation of PSO and SEPIC converter performance has been done. The simulation result shows that the proposed system has been working very well. The PSO has good accurateness in tracking and capable to to track the power produced by PV with velocity around ±4,2 seconds when in conditions STC, ±9,2 seconds when in conditions partial shading, despite a fluctuating irradiance change. While in SEPIC converter is able to reach efficiency of ≥ 80%. 

scholarly journals PARTICLE SWARM OPTIMIZATION WITH INDIVIDUALLY BIASED PARTICLES FOR RELIABLE AND ROBUST MAXIMUM POWER POINT TRACKING UNDER PARTIAL SHADING CONDITIONS

2020 ◽  
Vol 17 (2) ◽  
pp. 128-137
Author(s):  
K. Baktybekov ◽  
◽  
◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Power Point Tracking ◽  
Particle Swarm ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Efficient power control techniques are an integral part of photovoltaic system design. One of the means of managing power delivery is regulating the duty cycle of the DC to DC converter by various algorithms to operate only at points where power is maximum power point. Search has to be done as fast as possible to minimize power loss, especially under dynamically changing irradiance. The challenge of the task is the nonlinear behavior of the PV system under partial shading conditions. Depending on the size and structure of the photovoltaic panels, PSC creates an immense amount of possible P-V curves with numerous local maximums - requiring an intelligent algorithm for determining the optimal operating point. Existing benchmark maximum power point tracking algorithms cannot handle multiple peaks, and in this paper, we offer an adaptation of particle swarm optimization for the specific task.

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.

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