What question did this study set out to answer?

This study aims to enhance the accuracy and convergence of photovoltaic model parameter identification using a modified optimiser.

May 16, 2026Open Access

MICPO: a modified crested porcupine optimiser with dynamic balancing for superior PV parameter accuracy and convergence

Key Points

This study aims to enhance the accuracy and convergence of photovoltaic model parameter identification using a modified optimiser.
Proposed a modified improved crested porcupine optimiser (MICPO) with a dynamic balancing framework.
Integrated chaotic reverse learning, optimal value-guided search, and polynomial differential learning.
Validated on single-and double-diode PV models and benchmarked against commercial PV modules.
Achieved RMSE of 9.8602E-04, demonstrating state-of-the-art accuracy.
Demonstrated faster and more stable convergence compared to traditional methods.
MICPO showed superior generalisation across varying conditions in CEC2017 benchmark.

Abstract

Accurate photovoltaic (PV) model parameter identification is crucial for reliable simulation and maximum power point tracking (MPPT).To address common metaheuristic shortcomings like sensitivity to initialisation and premature convergence, this study proposes a modified improved crested porcupine optimiser (MICPO) featuring a dynamic balancing framework.MICPO integrates chaotic reverse learning, optimal value-guided search, and polynomial differential learning to maintain a robust global-local search balance.Validated on single-and double-diode models, MICPO achieves state-of-the-art accuracy (e.g., RMSE of 9.8602E-04) with faster, more stable convergence.Its superior generalisation is further demonstrated on the CEC2017 benchmark and commercial PV modules under varying conditions.Results confirm MICPO as a highly accurate, efficient, and robust solution for practical PV parameter extraction.

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