Electrical Characteristics Estimation of Photovoltaic Modules via Cuckoo Search—Relevant Vector Machine Probabilistic Model

Abstract

This work presents an optimized probabilistic modeling methodology that facilitates the modeling of photovoltaic (PV) modules with measured data over a range of environmental conditions. The method applies cuckoo search to optimize kernel parameters, followed by electrical characteristics estimation via relevance vector machine. Unlike analytical modeling techniques, the proposed cuckoo search-relevance vector machine (CS-RVM) takes advantages of no required knowledge of internal PV parameters, more accurate estimation capability and less computational effort. A comparative study has been done among the electrical characteristics predicted by back-propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), Villalva's model, relevance vector machine (RVM), and the CS-RVM. Experimental results show that the proposed CS-RVM provides the best prediction in most scenarios.

Chaotic honey badger algorithm for single and double photovoltaic cell/module

“…Recently, a grouped beetle antennae search (GBAS) algorithm has been proposed to effectively extract the unknown parameters of the single, double, and triple diode s PV models (Sun et al, 2021). The cuckoo search-relevance vector machine (CS-RVM) has been introduced for providing a PV model with measured data over a range of environmental conditions (Ban et al, 2021). The Peafowl optimization algorithm has been reported for identifying the double and triple-diode PV models.…”