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Optimum Tuning of Photovoltaic System Via Hybrid Maximum Power Point Tracking Technique

M. Nisha1,*, M. Germin Nisha2

1 Department of EEE, Lourdes Mount College of Engineering and Technology, Mullanganavilai, Tamil Nadu, 629195, India
2 Department of EEE, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, 629003, India

* Corresponding Author: M. Nisha. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1399-1413.


A new methodology is used in this paper, for the optimal tuning of Photovoltaic (PV) by integrating the hybrid Maximum Power Point Tracking (MPPT) algorithms is proposed. The suggested hybrid MPPT algorithms can raise the performance of PV systems under partial shade conditions. It attempts to address the primary research issues in partial shading conditions in PV systems caused by clouds, trees, dirt, and dust. The proposed system computes MPPT utilizing an innovative adaptive model-based approach. In order to manage the input voltage at the Maximum PowerPoint, the MPPT algorithm changes the duty cycle of the switch in the DC-DC (Direct Current-Direct Current) converter (MPP). Temperature as well as fluctuations in output power will induce alteration in PV panel operating current and voltage. MPPT is gaining a lot of interest as a key optimization sector to solve this optimization problem in PV systems. A hybrid optimization approach is utilized to produce a combination of the Cuckoo Search-Perturb & Observe (CS-PO) and incremental conductance-particle swarm optimization (IC-PSO) algorithms. After measuring the voltage and current from the solar system, this optimization computes the output power. The IC-PSO optimization achieves Maximum PowerPoint Tracking with increased efficiency of 99.5%. The proposed optimization techniques are established in the MATLAB Simulink program to validate its efficiency.


Cite This Article

M. Nisha and M. Germin Nisha, "Optimum tuning of photovoltaic system via hybrid maximum power point tracking technique," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1399–1413, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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