Adaptive PSO MPPT Algorithm for Solar PV System under Partial Conditions

Abstract

The rapid growth of the global population has significantly increased the demand for electricity, prompting extensive research into sustainable and renewable energy sources. Among these, solar photovoltaic (PV) systems have gained considerable attention due to their availability, cleanliness, and scalability. A solar cell exhibits a nonlinear current–voltage (I–V) characteristic with a unique operating point known as the Maximum Power Point (MPP), where the system achieves its highest power output. However, this point continuously shifts due to variations in irradiance, temperature, and inherent cell parameters. To minimize the cost of solar- generated electricity and enhance overall system efficiency, real-time Maximum Power Point Tracking (MPPT) is essential. The performance of GSA is evaluated and compared against another widely adopted evolutionary optimization technique, Particle Swarm Optimization (PSO). The comparative analysis aims to determine the effectiveness, response time, and accuracy of both algorithms in tracking the MPP under dynamic environmental conditions.

Introduction

The text discusses the application of optimization algorithms, particularly the Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), for improving Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. GSA, inspired by Newtonian gravity, treats candidate solutions as interacting masses and demonstrates strong optimization capability with fast convergence and low oscillation. PSO, inspired by swarm behavior, adjusts particle positions based on individual and collective experiences, offering simplicity and adaptability but slower convergence under dynamic conditions.

MPPT is crucial for PV systems to maximize power output under varying environmental conditions such as irradiance, temperature, and shading. Conventional methods like Perturb & Observe (P&O) and Incremental Conductance face limitations in rapidly changing conditions, which modern optimization algorithms address. The study models PV systems in MATLAB Simulink, integrating PSO and GSA algorithms to dynamically track the maximum power point by controlling a DC–DC converter. Hardware implementation includes a PV panel, microcontroller, sensors, boost converter, battery/load, and display for real-time monitoring.

Experimental results show that GSA outperforms PSO in terms of faster convergence, less oscillation, and higher stabilized power output, making it a robust choice for MPPT in dynamic solar environments. PSO remains valuable for its simplicity and flexibility but may require careful tuning to avoid local optima. Future work includes real-time implementation and further refinement of adaptive MPPT strategies to enhance performance under partial shading and other variable conditions.

Conclusion

In summary, the application of Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA) in Maximum Power Point Tracking (MPPT) systems provides a meaningful comparison between these two optimization approaches. Both algorithms exhibit distinct advantages and limitations when applied to the task of maximizing power extraction in photovoltaic systems. This comparison emphasizes the importance of selecting an appropriate optimization technique based on the specific design requirements and operating conditions of the MPPT system. Among the studied methods, the implementation of GSA demonstrates promising potential for improving the performance of MPPT control strategies The outcomes of this study are expected to contribute to further research in the area of MPPT optimization for renewable energy systems. With the increasing demand for efficient solar energy utilization, future investigations may focus on developing hybrid optimization techniques, improving algorithm parameters, and evaluating the scalability and adaptability of these methods for different PV system configurations. Such advancements could significantly enhance the efficiency and reliability of solar energy harvesting technologies.

References

[1] A Comprehensive Survey on Gravitational Search Algorithm by Esmat Rashedi, Elaheh Rashedi, Hossein Nezamabadi-pour: https://www.sciencedirect.com/science/art icle/am/pii/S2210650217303577 [2] A Gentle Introduction to Particle Swarm Optimization by Adrian Tam on October 12, 2021https://machinelearningmastery.co m/a-gentle-introduction-to-particle- swarm- optimization/ [3] Maximizing photovoltaic system power output with a master-slave strategy for parallel inverters by Mohamed Zaki , Ahmed Shahin , Saad Eskander , Mohamed A. Elsayes , Vladimír Bureš : https://www.sciencedirect.com/science/art icle/pii/S2352484723016128 [4] A maximum power point tracking method for PV system with improved gravitational search algorithm by Ling- Ling Li , Guo-Qian Lin , Ming-Lang Tseng, Kimhua Tan , Ming K. Lim https://www.sciencedirect.com/science/art icle/abs/pii/S156849461830036X

Copyright

Copyright © 2026 Dr. R. Arulmurugan, Anusuya V, Kavya S, Maneesha M, Srimathi S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.