Maximum Power Point Tracking of Photovoltaic Generation System Using Improved Quantum-Behavior Particle Swarm...

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Computational Intelligence

Background:

• Photovoltaic generation systems (PGSs) require efficient maximum power point tracking (MPPT) for optimal energy harvesting.
• Existing MPPT algorithms, including Quantum-behavior Particle Swarm Optimization (QPSO), can suffer from premature convergence, limiting tracking accuracy and speed.
• Partial shade conditions (PSCs) significantly complicate MPPT by introducing multiple power maxima.

Purpose of the Study:

• To introduce an Improved Quantum-behavior Particle Swarm Optimization (IQPSO) algorithm for enhanced MPPT in PGSs.
• To address the premature convergence issue inherent in QPSO.
• To improve tracking accuracy and reduce tracking time under various operating conditions, including PSCs.

Main Methods:

• Development of the IQPSO algorithm, incorporating adjustments to probability distribution estimation and exponential decay for faster convergence.
• Implementation of IQPSO within an MPPT system utilizing a series buck-boost converter.
• Experimental validation through single-peak, multi-peak, irradiance-changing, and full-day experiments.

Main Results:

• IQPSO demonstrated superior tracking accuracy compared to QPSO, Firefly Algorithm (FA), and Particle Swarm Optimization (PSO).
• IQPSO significantly reduced tracking time, improving overall convergence efficiency.
• The algorithm effectively achieved optimal operation at the maximum power point under both ideal and partial shade conditions.

Conclusions:

• The proposed IQPSO algorithm offers a robust and efficient solution for MPPT in photovoltaic systems.
• IQPSO overcomes the limitations of traditional QPSO, providing enhanced performance in terms of speed and accuracy.
• The findings highlight the potential of IQPSO for improving the reliability and energy yield of solar power generation.