scholarly journals Improved PSO: A Comparative Study in MPPT Algorithm for PV System Control under Partial Shading Conditions

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2035
Author(s):  
Wafa Hayder ◽  
Emanuele Ogliari ◽  
Alberto Dolara ◽  
Aycha Abid ◽  
Mouna Ben Hamed ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control Method ◽  
Particle Swarm ◽  
Climatic Conditions ◽  
System Control ◽  
Pv System ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Ipso Algorithm

This paper deals with the implementation and analysis of a new maximum power point tracking (MPPT) control method, which is tested under variable climatic conditions. This new MPPT strategy has been created for photovoltaic systems based on Particle Swarm Optimization (PSO). The novel Improved Particle Swarm Optimization (IPSO) algorithm is tested in several simulations which have been implemented in view of the various system responses such as: voltage, current, and power. The performances of the proposed IPSO algorithm have been completed and compared with results of well-established methods adopted in the literature showing a higher accuracy.

A Novel Combined Particle Swarm Optimization and Genetic Algorithm MPPT Control Method for Multiple Photovoltaic Arrays at Partial Shading

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Liqun Liu ◽  
Chunxia Liu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Large Scale ◽  
Control Method ◽  
Particle Swarm ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

The output characteristics of multiple photovoltaic (PV) arrays at partial shading are characterized by multiple steps and peaks. This makes that the maximum power point tracking (MPPT) of a large scale PV system becomes a difficult task. The conventional MPPT control method was unable to track the maximum power point (MPP) under random partial shading conditions, making the output efficiency of the PV system is low. To overcome this difficulty, in this paper, an improved MPPT control method with better performance based on the genetic algorithm (GA) and adaptive particle swarm optimization (APSO) algorithm is proposed to solve the random partial shading problem. The proposed genetic algorithm adaptive particle swarm optimization (GAAPSO) method conveniently can be used in the real-time MPPT control strategy for large scale PV system, and the implementation of the collect circuit is easy to gain the global peak of multiple PV arrays, thereby resulting in lower cost, higher overall efficiency. The proposed GAAPSO method has been experimentally validated by using several illustrative examples. Simulations and experimental results demonstrate that the GAAPSO method provides effective, fast, and perfect tracking.

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.

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