A new MPPT mechanism based on multi-verse optimization algorithm tuned FLC for photovoltaic systems.

Area of Science:

• Renewable Energy Systems
• Solar Photovoltaics
• Control Systems Engineering

Background:

• Maximum Power Point Tracking (MPPT) is crucial for solar Photovoltaic (PV) systems to maximize energy yield.
• Nonlinear power-voltage characteristics and changing environmental conditions (irradiance, temperature) complicate accurate MPP tracking.
• Existing MPPT techniques may struggle with rapid environmental fluctuations.

Purpose of the Study:

• To develop and evaluate an advanced MPPT technique for solar PV systems.
• To improve tracking accuracy and response time under dynamic irradiance and temperature variations.
• To combine the strengths of Fuzzy Logic Control (FLC) with swarm intelligence for optimized MPPT.

Main Methods:

• A novel MPPT technique integrating a Fuzzy Logic Controller (FLC) with the Multi-Verse Optimization (MVO) algorithm.
• The MVO algorithm is employed to optimize the scaling factors of the FLC for precise MPP tracking.
• The solar PV system and the proposed MPPT method were modeled and simulated in MATLAB/Simulink.

Main Results:

• The proposed MVO-FLC MPPT technique demonstrated efficient tracking of the Maximum Power Point (MPP).
• The system exhibited a quick response time compared to conventional FLC-based MPPT and Perturb and Observe (P&O) methods.
• Performance was validated under various conditions: uniform, abruptly changing, and ramp with abruptly changing irradiance, alongside temperature variations.

Conclusions:

• The integrated MVO-FLC approach offers a robust and efficient solution for MPPT in solar PV systems.
• This technique significantly enhances the performance of PV systems under challenging and dynamic environmental conditions.
• The proposed method provides a superior alternative to traditional MPPT algorithms for maximizing solar energy harvesting.