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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Ampere-Maxwell's Law: Problem-Solving01:17

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

Gwo-Ruey Yu1,2, Yong-Dong Chang3, Weng-Sheng Lee1

  • 1Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan.

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|April 26, 2024
PubMed
Summary

An improved quantum-behavior particle swarm optimization (IQPSO) enhances maximum power point tracking (MPPT) in photovoltaic systems. This new method offers superior accuracy and speed compared to existing algorithms, even under partial shading.

Keywords:
improved quantum-behavior particle swarm optimizationmaximum power point trackingphotovoltaic generation systems

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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.