An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.