Research on maximum power point tracking of photovoltaic power generation based on improved hybrid optimization...

Area of Science:

• Renewable Energy Systems
• Optimization Algorithms
• Electrical Engineering

Background:

• Photovoltaic (PV) arrays exhibit complex power-voltage (P-V) curves with multiple peaks due to environmental factors.
• Traditional Gravitational Search Algorithm (GSA) struggles with local optima and suboptimal performance in Maximum Power Point Tracking (MPPT).

Purpose of the Study:

• To enhance MPPT performance for photovoltaic arrays under dynamic and static shading conditions.
• To develop a more robust and accurate optimization algorithm for solar energy systems.

Main Methods:

• An improved algorithm integrating Particle Swarm Optimization (PSO) to optimize GSA parameters.
• Incorporation of a dynamic gravitational constant for PSO parameter adjustment and Levy flight for global search enhancement.
• Simulink modeling and simulation were employed to validate the algorithm's effectiveness.

Main Results:

• The enhanced algorithm demonstrates superior speed and stability in identifying the maximum power point compared to traditional methods.
• Effective tracking of the maximum power point under both static and dynamic shading scenarios was achieved.
• Alleviation of oscillation and improved accuracy in power output prediction were observed.

Conclusions:

• The proposed hybrid PSO-GSA algorithm offers a significant advancement in MPPT for photovoltaic systems.
• This improved method provides a more reliable solution for maximizing energy harvest from solar arrays, especially under challenging environmental conditions.