NDLI: Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink

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Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink

Content Provider	IET Digital Library
Author	Malik, Hasmat Mishra, Sukumar
Abstract	In this study, an artificial neural network (ANN) and empirical mode decomposition (EMD) based condition monitoring approach of a wind turbine using Simulink, FAST (fatigue, aerodynamics, structures and turbulence) and TurbSim is presented. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind turbine [i.e. wind turbine generator (WTG)] model is simulated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e. aerodynamic asymmetry, rotor-furl imbalance, tail-furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG output stator current are decomposed into the intrinsic mode functions using EMD method then RapidMiner-based principal component analysis method is used to select most relevant input variables. An ANN model is then proposed to differentiate the normal operating scenarios from five fault conditions. The analysed results proclaim the effectiveness of the proposed approach to identify the different imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG.
Starting Page	889
Ending Page	902
Page Count	14
ISSN	17521416
Volume Number	11
e-ISSN	17521424
Issue Number	Issue 6, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/11/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2015.0382
Journal	IET Renewable Power Generation
Publisher Date	2016-05-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aerodynamics ANN Artificial Neural Network Condition Monitoring EMD Based Condition Monitoring Approach Empirical Mode Decomposition Based Imbalance Fault Diagnosis FAST Fatigue Fatigue, Aerodynamics, Structure And Turbulence Fault Diagnosis Intrinsic Mode Function Mathematical Analysis Neural Computing Technique Neural Nets Normal Operating Scenario Permanent Magnet Generator Permanent Magnet Synchronous Generator Based Wind Turbine PMSG Based Wind Turbine Power Engineering Computing Power Generation Fault Principal Component Analysis RapidMiner-based Principal Component Analysis Method Rotors Simulink Statistics Stator Current Stators Synchronous Generator Synchronous Machine Time Domain Time Domain Analysis TurboGenerator TurbSim Turbulence Wind Power Plant Wind Turbine Wind Turbine Generator WTG Health Assessment
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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