Fast Convergence Modified Cuckoo Search Algorithm to Pursue String PV Modules Maximum Power Point under Partial Shading Conditions

Author(s):  
Ahmed A. Hossam-Eldin ◽  
Ahmed K. Abdelsalam ◽  
Karim H. Youssef ◽  
Ehab Mohamed Ali
Author(s):  
Ahmed Ibrahim ◽  
Raef Aboelsaud ◽  
Sergey Obukhov

This paper presents a cuckoo search (CS) algorithm for determining the global maximum power point (GMPP) tracking of photovoltaic (PV) under partial shading conditions (PSC). The conventional methods are fail to track the GMPP under PSC, which decrease the reliability of the power system and increase the system losses. The performance of the CS algorithm is compared with perturb and observe (P&O) algorithm for different cases of operations of PV panels under PSC. The CS algorithm used in this work to control directly the duty cycle of the DC-DC converter without proportional integral derivative (PID) controller. The proposed CS model can track the GMPP very accurate with high efficiency in less time under different conditions as well as in PSC.


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.


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

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.


Sign in / Sign up

Export Citation Format

Share Document