Research on Modelling and Parameter Identification Method of Photovoltaic Arrays Based on the Implicit Double Diode Model and Reverse Bias Model

Abstract

The establishment of an accurate photovoltaic (PV) arrays model is instrumental in enhancing the precision of PV performance evaluation and fault diagnosis, which holds significant importance for elevating the level of intelligent operation and maintenance of PV power stations. A modelling approach for PV arrays based on the implicit double diode model (IDDM) and the reverse bias model (RBM) is proposed in this paper, along with a rational and efficient method for the model parameters identification. First, an effective PV array modelling method is proposed for series-connected PV arrays, based on the IDDM and RBM and integrated with the principle of voltage superposition. Then, a two-stage model parameter identification method is introduced. The first stage employs the maximum power point matching (MPPM) method to swiftly calculate the model parameters corresponding to the voltage-current (I-V) characteristics, using these as the initial values for the parameter identification algorithm. The second stage utilises the improved gorilla troops optimizer (IGTO) to achieve precise identification of the model parameters. Ultimately, simulation experiments are conducted to emulate the functionality of the PV array model, with the proposed model achieving a simulation accuracy of 0.0243 A. The parameter identification accuracy of the IGTO reaches 0.0024 A, satisfying the requirements for PV array modelling. This thereby validates the prominent advantages of the model parameter identification method and reflects the application value and prospective development of the PV array model in fault diagnosis.