A Novel Salp Swarm Optimization MPP Tracking Algorithm for the Solar Photovoltaic Systems under Partial Shading Conditions

2019 ◽  
Vol 29 (01) ◽  
pp. 2050017
Author(s):  
S. Krishnan ◽  
K. Sathiyasekar
Keyword(s):  
Tracking Algorithm ◽  
Gray Wolf ◽  
Solar Photovoltaic ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Panel ◽  
Whale Optimization ◽  
Simulation Results ◽  
Power Point

To extract the maximum solar power from the photovoltaic (PV) panel/array with the high conversion efficiency under partial shading condition (PSC), this paper discusses a new and an efficient maximum power point (MPP) tracking algorithm. The proposed algorithm is based on the bio-inspired salp swarm optimization (SSO), and the algorithm forecasts the global MPP (GMPP) with the fast convergence to GMPP and high tracking efficiency. The SSO algorithm thus reduces the computational burden as encountered in whale optimization algorithm (WOA), and gray wolf optimization (GWO) algorithm discussed in the various literatures. The modeling and simulation of the proposed SSO algorithm are done with the help of Matlab/Simulink software to validate the effectiveness to locate the MPP during PSCs. The simulation results prove that the proposed SSO algorithm exhibits a high PV power output with the tracking efficiency of more than 95% at the faster convergence rate to GMPP. The SSO algorithm is experimentally verified on the conventional boost converter under different shading conditions.

Global Maximum Power Point Tracking of Solar Photovoltaic Strings under Partial Shading Conditions Using Cat Swarm Optimization Technique

2021 ◽  
Vol 13 (19) ◽  
pp. 11106
Author(s):  
T. Nagadurga ◽  
P. V. R. L. Narasimham ◽  
V. S. Vakula
Keyword(s):  
Optimization Technique ◽  
Maximum Power ◽  
Global Maximum ◽  
Solar Photovoltaic ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Power Point

The power versus voltage curves of solar photovoltaic panels form several peaks under fractional (partial) shading conditions. Traditional maximum output power tracking (MPPT) techniques fail to achieve global peak power at the output terminals. The proposed Cat Swarm Optimization (CSO) method intends to apply MPPT techniques to extract the global maxima from the shaded photovoltaic systems. CSO is a robust and powerful metaheuristic swarm-based optimization technique that has received very positive feedback since its emergence. It has been used to solve a variety of optimization issues, and several variations have been developed. The CSO-based maximum power tracking technique can successfully tackle two major issues of the PV system during shading conditions, including random oscillations caused by conventional tracking techniques and power loss. The proposed techniques have been extensively used in comparison to conventional algorithms like the Perturb and the Observe (P and O) technique. The main objective is to achieve a tracking speed for extracting the Maximum Power Point (MPP) from the solar Photovoltaic (PV) system under fractional shading conditions by using CSO. Modeling of the solar photovoltaic array in the MATLAB/Simulink platform comprises a photovoltaic module, a switching converter (Boost Converter), and the load. The PSO and CSO techniques are applied to the PV module under different weather conditions. The PSO algorithm is compared to the CSO algorithm according to simulation results, revealing that the CSO algorithm can provide better accuracy and a faster tracking speed.

scholarly journals An Intelligent Maximum Power Point Using a Fuzzy Log Controller under Severe Weather Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Khaled Bataineh
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Weather Condition ◽  
Weather Conditions ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Panel ◽  
Simulation Results ◽  
Power Point

This study is aimed at providing a comparison between fuzzy systems and convectional P&O for tracking MPP of a PV system. MATLAB/Simulink is used to investigate the response of both algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. Under partial shading on a PV panel, multipeaks appeared in P-V characteristics of the panel. Simulation results showed that a fuzzy controller effectively finds MPP for all weather condition scenarios. Furthermore, simulation results obtained from the FLC are compared with those obtained from the P&O controller. The comparison shows that the fuzzy logic controller exhibits a much better behavior.

scholarly journals An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 953
Author(s):  
Ali M. Eltamaly
Keyword(s):  
Maximum Power Point Tracking ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

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