Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression

Jian Ping Sun; Lin Tao Hu

doi:10.4028/www.scientific.net/AMR.347-353.2337

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Characteristic Simulation of Wind Turbine Based on an Induction Motor
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The Modeling and Simulation of Wind Turbines and the Design of Pitch Control System
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Dynamic Response Analysis of Large Wind Turbine Blade Based on Davenport Wind Speed Model
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Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression
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Research on Active Noise Control in Power Transformer
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Study on the Photocatalytic Degradation of Ammonia Nitrogen in Aquaculture Wastewater by Sn4+-Doped Nano-TiO2 under UV Irradiation
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Determination of Doxycycline Residues in Milks and Content in Serum, Tablet and Injection Samples with Self-Ordered Ring Fluorescence Microscopic Imaging Technique
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 347-353Application of Status Monitoring of Wind Turbines...

Application of Status Monitoring of Wind Turbines Based on Relevance Vector Machine Regression

Abstract:

Based on the single kernel function relevance vector machine(RVM) models，a multiple load-forecasting model has been established and simulated with several compound kernel functions, including Gauss kernel, Laplace, linear compounded by Gauss and Laplace, Gauss and polynomial kernel. Each model gained comparatively reasonable results in simulation .Moreover, multi linear-compound kernel RVMs performed better than single kernel RVMs in terms of most evaluating indicators, which prove that RVM is an appropriate machine learning method in monitoring status of components of wind turbines.

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Periodical:

Advanced Materials Research (Volumes 347-353)

Pages:

2337-2341

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.347-353.2337

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Online since:

October 2011

Authors:

Jian Ping Sun, Lin Tao Hu

Keywords:

Kernel Function, Linear-Compound Kernel Function, Load Prediction, Relevance Vector Machine, Wind Turbine (WT)

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