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300W급 Savonius 형 수직축 풍력발전기의 유동소음특성에 관한 수치적 연구
김상현(Sanghyoen Kim),이광세(Gwangse Lee),정철웅(Cheolung Cheong) 한국소음진동공학회 2012 한국소음진동공학회 학술대회논문집 Vol.2012 No.10
In this paper, flow noise characteristics of Savonius-type vertical-axis wind turbines are numerically investigated using hybrid CAA techniques. High frequency harmonics as well as BPF components are identified in the predicted noise spectra from a Savonius wind turbine. As the BPF components belong to infrasound, the higher harmonic components affects human response dominantly. Further analysis is performed to investigate the reason causing the higher frequency harmonic noise by changing operational conditions of a Savonius wind turbine. Based on this result, it is revealed that the frequency of higher harmonic components is determined by the radius of blades and angular velocity of Savonius wind turbine.
최정철(Choi Jungchul),손은국(Son Eunkuk),이광세(Lee Gwangse),강민상(Kang Minsang),이진재(Lee Jinjae),황성목(Hwang Sungmok),박사일(Park Sail) 한국태양에너지학회 2021 한국태양에너지학회 논문집 Vol.41 No.4
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.