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Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
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Parameters optimization of photovoltaic systems using modified quantum inspired particle swarm method.

Zia Ur Rehman1, Obaid Ur Rehman1, Amr Munshi2

  • 1Department of Electrical Engineering, Sarhad University of Science and IT, Peshawar, Pakistan.

Scientific Reports
|February 9, 2026
PubMed
Summary

Accurately estimating photovoltaic (PV) system parameters is vital for efficiency. A new Modified Quantum-inspired Particle Swarm Optimization (MQPSO) method significantly improves parameter estimation accuracy over standard methods.

Keywords:
Elitism mechanismParameter estimationPhotovoltaicQPSO

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Electrical Engineering

Background:

  • Growing demand for photovoltaic (PV) energy necessitates reliable PV system performance.
  • Accurate estimation of intrinsic PV system parameters is crucial for enhancing system reliability and efficiency.
  • Conventional optimization methods often struggle with the non-linear nature of PV systems, leading to premature convergence.

Purpose of the Study:

  • To address the challenges in estimating PV system parameters due to non-linearity and limitations of conventional methods.
  • To introduce and evaluate an improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithm for PV parameter estimation.
  • To explore the effectiveness of the proposed Modified Quantum-inspired Particle Swarm Optimization (MQPSO) method.

Main Methods:

  • Development of the Modified Quantum-inspired Particle Swarm Optimization (MQPSO) algorithm.
  • Incorporation of dual attractors, an elitism strategy, and local refinement into the QPSO framework.
  • Quantitative analysis and comparison of MQPSO against standard QPSO across three distinct PV models (SDM, DDM, TDM).

Main Results:

  • MQPSO demonstrated superior performance compared to conventional QPSO in estimating PV parameters across all tested models.
  • Significant Root Mean Square Error (RMSE) reductions were achieved: 24.74% for SDM, 59.32% for DDM, and 14.99% for TDM.
  • The enhanced convergence behavior and performance of MQPSO were quantitatively validated.

Conclusions:

  • The proposed MQPSO method offers a more effective and efficient approach for PV parameter estimation.
  • MQPSO overcomes the limitations of conventional optimization techniques, providing improved accuracy and reliability.
  • The study highlights the potential of MQPSO for advancing PV system modeling and performance optimization.