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

Frontiers in Energy Research

Ziqiang Bi¹  Minming Gu¹  Jianmin Ban¹  Xinyu Pan² 

[1] School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China;School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China;The Suzhou Smart City Research Institute, Suzhou University of Science and Technology, Suzhou, China;

关键词: photovoltaic module;    probabilistic model;    relevance vector machine;    cuckoo search;    simulation;   

DOI  :  10.3389/fenrg.2021.610405

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY