Development of Fatigue Load Prediction Algorithm for Wind Turbines

Jungchul Choi; Eunkuk Son; Gwangse Lee; Minsang Kang; Jinjae Lee; Sungmok Hwang; Sail Park

doi:10.7836/kses.2021.41.4.093

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2021 Vol.41, Issue 4 Preview Page Next Page

Research Article

Development of Fatigue Load Prediction Algorithm for Wind Turbines 풍력발전시스템 하중예측 알고리즘 개발

30 August 2021. pp. 93-105

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Abstract

Continuous fatigue information is essential for the structural health monitoring (SHM) of wind turbines. Faults, such as sensor failure, data loss, and cable disconnection, can result in a total loss of SHM. To avoid such a malfunction, machine learning algorithms and polynomial curve fitting are suggested to predict the missing fatigue data from the otherwise known measurement data. Artificial neural networks showed the best prediction performance. Decision trees and regularized linear regression are also powerful alternatives.

Keywords

Structural health monitoring

Condition-based maintenance

Machine learning

Artificial neural network

키워드

구조건전성 모니터링

상태기반정비

기계 학습

인공 신경망

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Information

Publisher :Korean Solar Energy Society
Publisher(Ko) :한국태양에너지학회
Journal Title :Journal of the Korean Solar Energy Society
Journal Title(Ko) :한국태양에너지학회 논문집
Volume : 41
No :4
Pages :93-105
Received Date : 2021-04-12
Revised Date : 2021-05-07
Accepted Date : 2021-06-02
DOI :https://doi.org/10.7836/kses.2021.41.4.093

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

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