Parameters optimization of photovoltaic systems using modified quantum inspired particle swarm method.

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.