Modelling and estimating performance for PV module under varying operating conditions independent of reference condition

Existing methods for parameter identification and performance estimation under varying operating conditions are accomplished in two steps: first, the model parameters are extracted under the prescribed reference condition; second, the model parameters under varying operating conditions are calculated based on the dependence of parameters on environmental conditions. There are two disadvantages for existing methods: the results are affected by the selection of reference condition, and the irradiance and temperature dependence of parameters are non-uniform for different types PV modules. This paper studies the effect of the reference condition selection and proposes a novel method for parameter identification and performance estimation under varying operating conditions. In the proposed method, a set of improved transformation equations are modified by considering both irradiance and temperature dependence for all parameters in single-diode model (SDM). Since the improved transformation equations are independent of reference condition, the effect of the selection of reference condition is eliminated. The coefficients of the improved transformation equations are extracted through guaranteed convergence particle swarm optimization technology from measured I-V curves under different operating conditions. The efficiency and accuracy of proposed method is validated by amount of experimental data under different irradiance and temperature conditions for different types of PV modules. The variations of SDM's parameters by the proposed method have been investigated thoroughly and shown to be in accordance with the physical behavior of the PV devices. The proposed method can be further employed to simulate the performance of a PV module under real operating conditions and exhibits great accuracy and suitability.

Automated summary

Existing methods for parameter identification and performance estimation have two disadvantages. This paper proposes a novel method that eliminates the effect of reference condition selection and achieves high efficiency and accuracy through considering the dependence of parameters on environmental conditions.