Open Access

ARTICLE

A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines

Xiyang Li^1,2, Bin Cheng^1,2, Hui Zhang^1,2,*, Xianghan Zhang¹, Zhi Yun¹

1 College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China
2 Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, 832003, China

* Corresponding Author: Hui Zhang. Email:

Energy Engineering 2021, 118(6), 1869-1886. https://doi.org/10.32604/EE.2021.015542

Received 26 December 2020; Accepted 21 May 2021; Issue published 10 September 2021

Abstract

With the continuous increase in the proportional use of wind energy across the globe, the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consideration for research. Therefore, it is crucial to accurately analyze the thickness of icing on wind turbine blades, which can serve as a basis for formulating corresponding control measures and ensure a safe and stable operation of wind turbines in winter times and/or in high altitude areas. This paper fully utilized the advantages of the support vector machine (SVM) and back-propagation neural network (BPNN), with the incorporation of particle swarm optimization (PSO) algorithms to optimize the parameters of the SVM. The paper proposes a hybrid assessment model of PSO-SVM and BPNN based on dynamic weighting rules. Three sets of icing data under a rotating working state of the wind turbine were used as examples for model verification. Based on a comparative analysis with other models, the results showed that the proposed model has better accuracy and stability in analyzing the icing on wind turbine blades.

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