scholarly journals An Enhanced Cuckoo Search Algorithm Fitting for Photovoltaic Systems’ Global Maximum Power Point Tracking under Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7210
Author(s):  
Ehab Mohamed Ali ◽  
Ahmed K. Abdelsalam ◽  
Karim H. Youssef ◽  
Ahmed A. Hossam-Eldin

The output power against voltage curve of the photovoltaic system changes its characteristics under partial shading conditions because of using bypass diodes. These bypass diodes are connected across the PV modules inside the string to avoid hotspot formation in the shaded PV modules. Therefore, the output curve has multiple power peaks with only one Global Max Power Point. The classical Maximum Power Point Tracking algorithms may fail to track that Global Max Power. Several soft computing algorithms have been proposed to improve tracking efficiency with different optimization principles. In this paper, an Improved Cuckoo Search Algorithm has been proposed to increase the tracking speed with minimum output power oscillation. The proposed algorithm avoids spreading the initial particles among the whole curve to predict shading pattern, but it reduces the exploration area after each iteration to compensate for the algorithm’s randomness. The proposed algorithm was compared with other methods by simulation using MATLAB/Simulink program and with practical experiments under the same operating conditions. The comparison showed that the proposed algorithm overcomes the other methods’ drawbacks and concurrently minimizes the convergence time, power oscillation, and system power losses.

Author(s):  
Taufik Hidayat ◽  
Mohammad Zaenal Efendi ◽  
Farid Dwi Murdianto

The problems of using solar panels include the power and efficiency that can be achieved by solar panels during conditions where the surface of the solar panel is covered by shadows, because the performance of the solar panels is affected by the amount of sunlight received and the temperature of the solar panels. Then, a solution appears to overcome the problem, called Maximum Power Point Tracking or a technique to get the maximum output power from solar panels. Initially, MPPT worked with conventional methods, one of which was Perturb and Observe. Furthermore, the MPPT method on solar panels continues to develop to solve problems during partial shade conditions. The development of this conventional method is called the metaheuristic method, an example of which is the Cuckoo Search Algorithm method implemented in this research. This method is characterized by the Levy Flight equation in generating duty cycle values so that it can reach the maximum peak power of solar panels. The system built in this research is also supported by the highly efficient Interleaved Boost converter. Based on simulation results show that the power that can be generated by the MPPT Cuckoo Search Algorithm is higher than the MPPT Perturb and Observe, which is 121.23 W compared to 72.38 W.


2019 ◽  
Vol 162 ◽  
pp. 117-126 ◽  
Author(s):  
Mohamed I. Mosaad ◽  
M. Osama abed el-Raouf ◽  
Mahmoud A. Al-Ahmar ◽  
Fahd A. Banakher

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 953
Author(s):  
Ali M. Eltamaly

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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