Title: Photovoltaic maximum power point tracking based on IWD-SVM

Authors: Wenqing Zhao; Yayun Meng

Addresses: School of Computer and Control Engineering, North China Electric Power University, Baoding, China ' School of Computer and Control Engineering, North China Electric Power University, Baoding, China

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.

Keywords: IWD model; photovoltaic array; support vector machine; maximum power point tracking; neural network.

DOI: 10.1504/IJSPM.2019.104121

International Journal of Simulation and Process Modelling, 2019 Vol.14 No.5, pp.452 - 463

Received: 23 Aug 2018
Accepted: 10 Mar 2019
Published online: 14 Dec 2019 *

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