Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0001-8216-9464
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0001-6016-6342
Number of Authors: 32017 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 10, no 2, article id 184Article in journal (Refereed) Published
Abstract [en]

Structures vibrate with their natural frequencies when disturbed from their equilibrium position. These frequencies reduce when an additional mass accumulates on their structures, like ice accumulation on wind turbines installed in cold climate sites. The added mass has two features: the location and quantity of mass. Natural frequencies of the structure reduce differently depending on these two features of the added mass. In this work, a technique based on an artificial neural network (ANN) model is proposed to identify added mass by training the neural network with a dataset of natural frequencies of the structure calculated using different quantities of the added mass at different locations on the structure. The proposed method is demonstrated on a non-rotating beam model fixed at one end. The length of the beam is divided into three zones in which different added masses are considered, and its natural frequencies are calculated using a finite element model of the beam. ANN is trained with this dataset of natural frequencies of the beam as an input and corresponding added masses used in the calculations as an output. ANN approximates the non-linear relationship between these inputs and outputs. An experimental setup of the cantilever beam is fabricated, and experimental modal analysis is carried out considering a few added masses on the beam. The frequencies estimated in the experiments are given as an input to the trained ANN model, and the identified masses are compared against the actual masses used in the experiments. These masses are identified with an error that varies with the location and the quantity of added mass. The reason for these errors can be attributed to the unaccounted stiffness variation in the beam model due to the added mass while generating the dataset for training the neural network. Therefore, the added masses are roughly estimated. At the end of the paper, an application of the current technique for detecting ice mass on a wind turbine blade is studied. A neural network model is designed and trained with a dataset of natural frequencies calculated using the finite element model of the blade considering different ice masses. The trained network model is tested to identify ice masses in four test cases that considers random mass distributions along the blade. The neural network model is able to roughly estimate ice masses, and the error reduces with increasing ice mass on the blade.

Place, publisher, year, edition, pages
2017. Vol. 10, no 2, article id 184
Keywords [en]
artificial neural network; ice mass; detection; wind turbine blade; natural frequency
National Category
Applied Mechanics Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-61885DOI: 10.3390/en10020184ISI: 000395469200038Scopus ID: 2-s2.0-85014095862OAI: oai:DiVA.org:ltu-61885DiVA, id: diva2:1072923
Projects
Wind power in cold climates
Funder
Swedish Energy Agency
Note

Validerad; 2017; Nivå 2; 2017-02-15 (andbra)

Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2018-07-10Bibliographically approved

Open Access in DiVA

fulltext(2562 kB)134 downloads
File information
File name FULLTEXT01.pdfFile size 2562 kBChecksum SHA-512
36e5fa7da3945e46de7e8f3842de9b191ca2c10f895b42ed5b185dafabef55cd5cffe3df7c0a642dc119153cb3ccaefc43d994140a353b4ead64a7493c3fd6b6
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Gantasala, SudhakarLuneno, Jean-ClaudeAidanpää, Jan-Olov
By organisation
Product and Production Development
In the same journal
Energies
Applied MechanicsOther Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 134 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 495 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf