Explicit Lambert W-Function Reformulation and Improved Harris Hawks Optimization for Robust Photovoltaic Parameter Identification

Abstract

Precise extraction of photovoltaic parameters is essential for reliable I-V characteristic modeling across varying irradiance and temperature conditions. Nonetheless, this remains a challenging task because of the multimodal and transcendental characteristics of diode current equations. This study proposes an Improved Harris Hawks Optimization algorithm, reformulating single and double-diode photovoltaic models through an explicit Lambert W-function, thereby eliminating the need for iterative current calculations. This method utilizes a stochastic habitat relocation mechanism and adaptive logarithmic-exponential search operators to improve the algorithm’s diversity and convergence stability by balancing exploration and exploitation. The algorithm is evaluated using root mean square error, convergence analysis, and Wilcoxon rank-sum testing against state-of-the-art algorithms, and is validated on benchmark photovoltaic datasets. The findings demonstrate that the proposed algorithm offers superior accuracy, expedited convergence, and reduced root mean square error variance. Consequently, this method is an effective and precise approach for photovoltaic performance forecasting, maximum power point tracking design, and fault diagnosis.