Self-adaptive single-diode model parameter identification under small mismatching conditions

Abstract

Current model-based methods for monitoring photovoltaic (PV) modules typically rely on the single-diode model (SDM) or its variants, assuming uniform operating conditions across the module. However, these ideal conditions are difficult to realize in real-world applications due to partial shading, soiling, degradation, and other phenomena. This paper proposes a 7-parameter self-adapting Double SDM model (D-SDM) to enhance the accuracy and reliability of parameter identification in PV modules under real operating conditions. A robust methodology based on evolutionary algorithms is proposed to estimate the parameters of the D-SDM, directly from the I–V characteristic of a PV module, applicable in both uniform and mismatched scenarios. The proposed methodology also includes a robust fitting error calculation that only considers the section of the I-V curve where all the cells operate with positive voltage. The methodology is validated using experimental and simulated I–V curves across various mismatching patterns, demonstrating the superior stability and reliability of the proposed method, which can be used for PV system monitoring and diagnosis in complex conditions.