An improved particle swarm optimization based maximum power point tracking algorithm for PV system operating under partial shading conditions

Solar Energy ◽  
2017 ◽  
Vol 158 ◽  
pp. 1006-1015 ◽  
Author(s):  
G. Dileep ◽  
S.N. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Tracking Algorithm ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
System Operating

scholarly journals Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 623 ◽  
Author(s):  
Muhannad Alshareef ◽  
Zhengyu Lin ◽  
Mingyao Ma ◽  
Wenping Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Accelerated Particle Swarm Optimization ◽  
Power Point

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

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 Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Liping Guo ◽  
Nauman Moiz Mohammed Abdul
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Adaptive Pso ◽  
Power Point ◽  
Fuzzy Adaptive

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

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