Photovoltaic maximum power point tracking based on IWD-SVM. (9th December 2019)

• Record Type: Journal Article
• Authors: Zhao, Wenqing; Meng, Yayun
• Is Part Of: International journal of simulation and process modelling. Volume 14:Number 5(2019)
• Page Start: 452
• Page End: 463
• Publication Date: 2019-12-09
• Subjects: IWD model -- photovoltaic array -- support vector machine -- maximum power point tracking -- neural network; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

Photovoltaic system maximum power point tracking (MPPT) has great potential for improvement of power generation. To optimise MPPT, this paper presents a prediction model based on an intelligent water drops optimisation support vector machine (IWD-SVM) for maximum power point working voltage. The Intelligent Water Drops (IWD) algorithm is used to optimise the penalty factor and kernel function parameters of the SVM, thus improving the training efficiency of the learning machine. Based on the optimisation algorithm, the SVM is used to model the PV array, the prediction results are compared to verify the accuracy and effectiveness of the IWD-SVM model. In addition, the IWD-SVM model is compared with the traditional neural network prediction results, which further verifies the validity of the proposed IWD-SVM model.