This project presents a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm designed to improve the efficiency of solar photovoltaic (PV) systems. It focuses on implementing an intelligent optimization technique in MATLAB to accurately track the Maximum Power Point (MPP) under varying environmental conditions such as fluctuating solar irradiance and temperature.

Conventional MPPT methods, including Perturb and Observe (P&O) and Incremental Conductance (INC), often experience limitations such as steady-state oscillations and slow response during rapidly changing weather conditions. To address these challenges, this project utilizes PSO, a population-based metaheuristic algorithm inspired by swarm intelligence and bird flocking behavior, to efficiently locate the global maximum power point of a PV array.

The system model integrates a PV array with a DC-DC converter, where the PSO algorithm dynamically adjusts the converter duty cycle to ensure maximum power extraction. Compared to traditional techniques, the proposed PSO-based MPPT demonstrates faster convergence speed, reduced oscillations, and higher tracking accuracy.

This work provides a robust and scalable framework for researchers, students, and engineers interested in renewable energy systems, intelligent control, and solar power optimization. The model can also be extended for real-time hardware implementation, making it highly relevant for advanced energy applications and smart grid technologies.

Key Features

• MATLAB/Simulink implementation of PV system model

• Intelligent MPPT using Particle Swarm Optimization

• Fast convergence to global maximum power point

• Reduced steady-state oscillations

• Performance evaluation under varying irradiance and temperature

• Comparison capability with conventional MPPT methods

• Suitable for research and educational purposes

Applications

• Solar energy optimization

• Smart grid and renewable energy systems

• Microgrid control research

• Power electronics education

• Real-time PV system control development

Keywords

Particle Swarm Optimization, MPPT, Photovoltaic System, Renewable Energy, MATLAB Simulation, DC-DC Converter, Intelligent Control, Solar Power Optimization

The high penetration of power electronic converters into dc microgrids may cause the constant power load stability issues, which could lead to large voltage oscillations or even system collapse. On the other hand, dynamic performance should be satisfied in the control of power electronic converter systems with small overshoot, less oscillations, and smooth transient performance. This article proposes an offset-free model predictive controller for a dc/dc buck converter feeding constant power loads with guaranteed dynamic performance and stability. First, a receding horizon optimization problem is formulated for optimal voltage tracking. To deal with the unknown load variation and system uncertainties, a higher order sliding mode observer is designed and integrated into the optimization problem. Then an explicit closed-loop solution is obtained by solving the receding horizon optimization problem offline. A rigorous stability analysis is performed to ensure the system large signal stability. The proposed controller achieves optimized transient dynamics and accurate tracking with simple implementation.

Reference:

Yan, Huaicheng, Xuping Zhou, Hao Zhang, Fuwen Yang, and Zheng-Guang Wu. “A novel sliding mode estimation for microgrid control with communication time delays.” IEEE Transactions on Smart Grid 10, no. 2 (2017): 1509-1520.

Lin, Pengfeng, Chuanlin Zhang, Jinyu Wang, Chi Jin, and Peng Wang. “On autonomous large-signal stabilization for islanded multibus DC microgrids: A uniform nonsmooth control scheme.” IEEE Transactions on Industrial Electronics 67, no. 6 (2019): 4600-4612.

Reference paper:

Jain, Shivam, and Yogesh V. Hote. “Design of improved nonlinear active disturbance rejection controller for hybrid microgrid with communication delay.” IEEE Transactions on Sustainable Energy 13, no. 2 (2022): 1101-1111.