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Wind Turbine Blade Failure Detection...

Sheppard, Lindsay Elizabeth Jordan.

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Wind Turbine Blade Failure Detection Using Time-series Classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Wind Turbine Blade Failure Detection Using Time-series Classification./
Author:	Sheppard, Lindsay Elizabeth Jordan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	44 p.
Notes:	Source: Masters Abstracts International, Volume: 81-11.
Contained By:	Masters Abstracts International81-11.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27960722
ISBN:	9798644903948

Wind Turbine Blade Failure Detection Using Time-series Classification.
Sheppard, Lindsay Elizabeth Jordan.

Wind Turbine Blade Failure Detection Using Time-series Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 44 p.

Source: Masters Abstracts International, Volume: 81-11.

Thesis (M.S.)--Utica College, 2020.

This item must not be sold to any third party vendors.

Wind power is a renewable and abundant source of energy. Wind farm locations maximize wind power through placement in high-altitude areas and continual improvements in wind turbine blade design. Blades are vital to wind turbine performance, but are susceptible to damages from weather, irregular loading, malfunction, or other catastrophes. Blade damage is associated with a reduction in both turbine lifespan as efficiency as well as increased safety concerns, maintenance costs, and sensor errors. Due to the prohibitive cost of installing physical detectors on blades, data-driven approaches are increasingly popular for detecting blade failures. This study aimed to identify blade failures through analysis of multivariate time series sensor data monitored by a Supervisory Control and Data Acquisition (SCADA) system. Several machine learning and statistical methods were attempted to forecast sensor trends and classify turbines into groups of those with and without probable blade failures. The results suggested that supervised learning techniques such as support vector machines (SVM), long short-term memory (LSTM) and other neural networks with autoencoders may outperform other blade failure classification methods. The study compared linear kernel and radial basis function (RBF) kernels for SVM. A simple LSTM architecture correctly classified approximately two-thirds of turbines with previous failures and forecasted failures up to two days in advance. A dense autoencoder model provided promising results on one studied wind turbine and requires further study into generalization potential. Finally, vector autoregression (VAR) predicted sensor trends with fair accuracy and may be combined with LSTM or other neural networks to improve performance.

ISBN: 9798644903948Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Classification

Wind Turbine Blade Failure Detection Using Time-series Classification.
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245 1 0 $a Wind Turbine Blade Failure Detection Using Time-series Classification.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 44 p.
500 $a Source: Masters Abstracts International, Volume: 81-11.
500 $a Advisor: McCarthy, Michael.
502 $a Thesis (M.S.)--Utica College, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Wind power is a renewable and abundant source of energy. Wind farm locations maximize wind power through placement in high-altitude areas and continual improvements in wind turbine blade design. Blades are vital to wind turbine performance, but are susceptible to damages from weather, irregular loading, malfunction, or other catastrophes. Blade damage is associated with a reduction in both turbine lifespan as efficiency as well as increased safety concerns, maintenance costs, and sensor errors. Due to the prohibitive cost of installing physical detectors on blades, data-driven approaches are increasingly popular for detecting blade failures. This study aimed to identify blade failures through analysis of multivariate time series sensor data monitored by a Supervisory Control and Data Acquisition (SCADA) system. Several machine learning and statistical methods were attempted to forecast sensor trends and classify turbines into groups of those with and without probable blade failures. The results suggested that supervised learning techniques such as support vector machines (SVM), long short-term memory (LSTM) and other neural networks with autoencoders may outperform other blade failure classification methods. The study compared linear kernel and radial basis function (RBF) kernels for SVM. A simple LSTM architecture correctly classified approximately two-thirds of turbines with previous failures and forecasted failures up to two days in advance. A dense autoencoder model provided promising results on one studied wind turbine and requires further study into generalization potential. Finally, vector autoregression (VAR) predicted sensor trends with fair accuracy and may be combined with LSTM or other neural networks to improve performance.
590 $a School code: 1754.
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650 4 $a Computer science. $3 523869
650 4 $a Environmental studies. $3 2122803
653 $a Classification
653 $a Data science
653 $a LSTM
653 $a Machine learning
653 $a SVM
653 $a Time series
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690 $a 0984
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