TITLE

Development of Optimal Maintenance Strategies for Offshore Wind Turbine by using Artificial Neural Network

AUTHOR(S)
Hameed, Zafar; Wang, Kesheng
PUB. DATE
June 2012
SOURCE
Wind Engineering;Jun2012, Vol. 36 Issue 3, p353
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Nowadays offshore wind turbines are an emerging area of research in renewable energy fields. They are penetrating in the energy market with a very rapid scale which entails to address their availiblity issues in novel and cost effective way. The maintenance activities for the array of wind turbines in a wind farm are complex in an offshore environment due to access, weather, and logistic issues. A method has been proposed to address the complexity and intricacies in the planning and formulation of maintenance strategies by using neural network. The approach consists of undertaking the clustering analysis of the array of wind turbines and then to segregate the similar wind turbines based on the analysis results by using Self Organizing Map, (SOM) neural network. Then to predict the expected power output of the wind turbines being part of a certain cluster with the help of another Standard Back Propagation (SBP) neural network. Based on the results, a number of implications have been outlined regarding how the results obtained could provide opportunities in optimizing maintenance strategies. Moreover the weather window issues will be addressed coherently to exploit the information of expected power output in advance by using SBP neural network. So based on the proposed method, the link among the condition of the components, clusters of wind turbines, prediction of power output and the access levels based on the weather conditions could be established which will provide the sound basis for developing optimal and cost effective maintenance strategies. It is expected that the implementation of the proposed approach will enhance the reliability and availability of the offshore wind turbines at the wind farm level in a better and viable way.
ACCESSION #
78110781

 

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