TY - GEN
T1 - Two-stage deep learning-based wind turbine condition monitoring using SCADA data
AU - Afrasiabi, Shahabodin
AU - Afrasiabi, Mousa
AU - Parang, Benyamin
AU - Mohammadi, Mohammad
AU - Kahourzade, Solmaz
AU - Mahmoudi, Amin
PY - 2020/12/16
Y1 - 2020/12/16
N2 - This paper proposes a two-stage data-driven methodology in condition monitoring (CM) of wind turbines (WTs). In the first stage, a fast and powerful network, namely a parallel generative adversarial network (PGAN) is proposed to resolve the problem of limited available information by generating artificial data. In the second stage, a robust deep network is designed based on a one-module Gabor filter oriented convolutional neural network and reformulation of a new loss function, namely robust deep Gabor network (RDGN). The experimental dataset of 3MW wind turbines in Ireland is used to verify the effectiveness of the proposed method and demonstrate the superiority of the proposed two-stage method in comparison with several state-of-the-art methods in terms of accuracy and reliability.
AB - This paper proposes a two-stage data-driven methodology in condition monitoring (CM) of wind turbines (WTs). In the first stage, a fast and powerful network, namely a parallel generative adversarial network (PGAN) is proposed to resolve the problem of limited available information by generating artificial data. In the second stage, a robust deep network is designed based on a one-module Gabor filter oriented convolutional neural network and reformulation of a new loss function, namely robust deep Gabor network (RDGN). The experimental dataset of 3MW wind turbines in Ireland is used to verify the effectiveness of the proposed method and demonstrate the superiority of the proposed two-stage method in comparison with several state-of-the-art methods in terms of accuracy and reliability.
KW - Condition Monitoring (CM)
KW - Parallel generative adversarial network (PGAN)
KW - Robust deep Gabor network (RDGN)
KW - Wind turbine (WT)
UR - http://www.scopus.com/inward/record.url?scp=85103874510&partnerID=8YFLogxK
U2 - 10.1109/PEDES49360.2020.9379393
DO - 10.1109/PEDES49360.2020.9379393
M3 - Conference contribution
AN - SCOPUS:85103874510
T3 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
BT - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020
PB - Institute of Electrical and Electronics Engineers
CY - Jaipur, India
T2 - 9th IEEE International Conference on Power Electronics, Drives and Energy Systems
Y2 - 16 December 2020 through 19 December 2020
ER -