Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve exploration capability of NGO technique while evading premature convergence. suggested algorithm, goshawk, (CNGPS), takes advantage algorithm’s global as well method’s powerful local capability. effectiveness recommended CNGPS verified through use mathematical test functions, its results are contrasted with those a conventional other methods. then extract PV parameters, parameter identification defined objective function be minimized difference between estimated experimental data. usefulness extraction evaluated using three distinct models: SDM, DDM, TDM. numerical investigates illustrate that new may produce better optimum solutions outperform previous approaches literature. simulation display novel achieves lowest root mean square error obtains optima than existing methods various solar cells.