Article: MICPO: a modified crested porcupine optimiser with dynamic balancing for superior PV parameter accuracy and convergence Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.48 pp.88 - 112 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Qiang Wang; Yaoduo Ya; Zhenghui Li; Jiahao Wang

Addresses: Hubei Provincial Engineering Research Center of Intelligent Energy Technology, Yichang 443002, China ' College of Electrical and New Energy, China Three Gorges, Yi-chang, 443002, China ' College of Electrical and New Energy, China Three Gorges, Yi-chang, 443002, China ' College of Electrical and New Energy, China Three Gorges, Yi-chang, 443002, China

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.

Keywords: PV cell parameter identification; single-diode and double-diode model; modified improved crested porcupine optimisation; MICPO; algorithm; dynamic balancing framework.

DOI: 10.1504/IJICT.2026.153546

International Journal of Information and Communication Technology, 2026 Vol.27 No.48, pp.88 - 112

Received: 29 Jan 2026
Accepted: 02 Mar 2026
Published online: 13 May 2026 *

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

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