scholarly journals Enhanced Grey Wolf Optimizer based MPPT Algorithm of PV system under Partial Shaded Condition

2017 ◽  
Vol 6 (3) ◽  
pp. 203 ◽  
Author(s):  
Santhan Kumar Cherukuri ◽  
Srinivasa Rao Rayapudi
Keyword(s):  
Maximum Power ◽  
Global Maximum ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-212

scholarly journals A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2521
Author(s):  
Alfredo Gil-Velasco ◽  
Carlos Aguilar-Castillo
Keyword(s):  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Computational Resources

There are multiples conditions that lead to partial shading conditions (PSC) in photovoltaic systems (PV). Under these conditions, the harvested energy decreases in the PV system. The maximum power point tracking (MPPT) controller aims to harvest the greatest amount of energy even under partial shading conditions. The simplest available MPPT algorithms fail on PSC, whereas the complex ones are effective but require high computational resources and experience in this type of systems. This paper presents a new MPPT algorithm that is simple but effective in tracking the global maximum power point even in PSC. The simulation and experimental results show excellent performance of the proposed algorithm. Additionally, a comparison with a previously proposed algorithm is presented. The comparison shows that the proposal in this paper is faster in tracking the maximum power point than complex algorithms.

scholarly journals Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 2656
Author(s):  
Ahmed G. Abo-Khalil ◽  
Walied Alharbi ◽  
Abdel-Rahman Al-Qawasmi ◽  
Mohammad Alobaid ◽  
Ibrahim M. Alarifi
Keyword(s):  
Firefly Algorithm ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Opposition Based Learning ◽  
Power Point Tracking ◽  
Convergence Process ◽  
Power Point

This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using an opposition-based learning firefly algorithm (OFA) that improves the performance of the Photovoltaic (PV) system both in the uniform irradiance changes and in partial shading conditions. The firefly algorithm is based on fireflies’ search for food, according to which individuals emit progressively more intense glows as they approach the objective, attracting the other fireflies. Therefore, the simulation of this behavior can be conducted by solving the objective function that is directly proportional to the distance from the desired result. To implement this algorithm in case of partial shading conditions, it was necessary to adjust the Firefly Algorithm (FA) parameters to fit the MPPT application. These parameters have been extensively tested, converging satisfactorily and guaranteeing to extract the global maximum power point (GMPP) in the cases of normal and partial shading conditions analyzed. The precise adjustment of the coefficients was made possible by visualizing the movement of the particles during the convergence process, while opposition-based learning (OBL) was used with FA to accelerate the convergence process by allowing the particle to move in the opposite direction. The proposed algorithm was simulated in the closest possible way to authentic operating conditions, and variable irradiance and partial shading conditions were implemented experimentally for a 60 [W] PV system. A two-stage PV grid-connected system was designed and deployed to validate the proposed algorithm. In addition, a comparison between the performance of the Perturbation and Observation (P&O) method and the proposed method was carried out to prove the effectiveness of this method over the conventional methods in tracking the GMPP.

A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systems

2018 ◽  
Vol 7 (1) ◽  
pp. 66-85 ◽  
Author(s):  
Afef Badis ◽  
Mohamed Habib Boujmil ◽  
Mohamed Nejib Mansouri
Keyword(s):  
High Efficiency ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Conventional Methods ◽  
Power Point

This article concerns maximizing the energy reproduced from the photovoltaic (PV) system, ensured by using an efficient Maximum Power Point Tracking (MPPT) process. The process should be fast, rigorous and simple for implementation because the PV characteristics are extremely affected by fast changing conditions and Partial Shading (PS). PV systems are popularly known to have many peaks (one Global Peak (GP) and several local peaks). Therefore, the MPPT algorithm should be able to accurately detect the unique GP as the maximum power point (MPP), and avoid any other peak to mitigate the effect of (PS). Usually, with no shading, nearly all the conventional methods can easily reach the MPP with high efficiency. Nonetheless, they fail to extract the GP when PS occurs. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are simulated and compared to the conventional methods (Perturb & Observe) under the same software.

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