풍력발전시스템 하중예측 알고리즘 개발
Development of Fatigue Load Prediction Algorithm for Wind Turbines
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초록
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.
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.
참고문헌 (12)
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