Assessing Under Varying Circumstances the Behavior of Photovoltaic Systems of Maximum Power Point Methods.

Author(s):  
Imad A. Elzein ◽  
Yuri N. Petrenko

In this article an extended literature surveying review is conducted on a set of comparative studies of maximum power point tracking (MPPT) techniques.  Different MPPT methods are conducted with an ultimate aim of how to be maximizing the PV system output power by tracking Pmax in a set of different operational circumstances. In this paper maximum power point tracking, MPPT techniques are reviewed on basis of different parameters related to the design simplicity and or complexity, implementation, hardware required, and other related aspects.

2015 ◽  
Vol 787 ◽  
pp. 227-232 ◽  
Author(s):  
L.A. Arun Shravan ◽  
D. Ebenezer

In recent years there has been a growing attention towards use of solar energy. Advantages of photovoltaic (PV) systems employed for harnessing solar energy are reduction of greenhouse gas emission, low maintenance costs, fewer limitations with regard to site of installation and absence of mechanical noise arising from moving parts. However, PV systems suffer from relatively low conversion efficiency. Therefore, maximum power point tracking (MPPT) for the solar array is essential in a PV system. The nonlinear behaviour of PV systems as well as variations of the maximum power point with solar irradiance level and temperature complicates the tracking of the maximum power point. This paper reviews various MPPT methods based on three categories: offline, online and hybrid methods. Design of a PV system in a encoding environment has also been reviewed here. Furthermore, different MPPT methods are discussed in terms of the dynamic response of the PV system to variations in temperature and irradiance, attainable efficiency, and implementation considerations.


Author(s):  
C. Pavithra ◽  
Pooja Singh ◽  
Venkatesa Prabhu Sundramurthy ◽  
T.S. Karthik ◽  
P.R. Karthikeyan ◽  
...  

Author(s):  
Yan Xiao ◽  
Yaoyu Li ◽  
John E. Seem ◽  
Kaushik Rajashekara

This paper presents a Maximum Power Point Tracking (MPPT) strategy for multi-string photovoltaic (PV) systems using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The multi-string PV system considered is a decentralized control configuration, controlling the voltage reference to each PV module but based on the feedback of the total power at the DC bus. This requires only one pair of voltage and current measurements. The MPPT control problem for such topology of multi-string PV systems features a high input dimension, which can dramatically slow down the searching process for the real-time optimization process involved. The SPSA algorithm is considered in this study due to its remarkable capability of fast convergence for high dimensional search problems endorsed by various applications recently. Simulation study is performed for an 8-string PV system, and experimental study is performed for a 4-string PV system. Good performances are observed for both simulation and experimental results.


Author(s):  
Mohd Asri Jusoh ◽  
Mohammad Faridun Naim Tajuddin ◽  
Shahrin Md Ayob ◽  
Mohd Azrik Roslan

2018 ◽  
Vol 7 (1) ◽  
pp. 66-85 ◽  
Author(s):  
Afef Badis ◽  
Mohamed Habib Boujmil ◽  
Mohamed Nejib Mansouri

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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