- Astolfi, D. ; Castellani, F. Editorial on the special issue “wind turbine monitoring through operation data analysis”. 2022 Energies. 15 3664-
Paper not yet in RePEc: Add citation now
- Bangalore, P. ; Letzgus, S. ; Karlsson, D. ; Patriksson, M. An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox. 2017 Wind Energy. 20 1421-1438
Paper not yet in RePEc: Add citation now
Bi, R. ; Zhou, C. ; Hepburn, D.M. Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. 2017 Renew. Energy. 105 674-688
Byrne, R. ; Astolfi, D. ; Castellani, F. ; Hewitt, N.J. A study of wind turbine performance decline with age through operation data analysis. 2020 Energies. 13 2086-
- Canova, F. Methods for Applied Macroeconomic Research. 2007 Princeton University Press: Princeton, NJ
Paper not yet in RePEc: Add citation now
- Chen, P. ; Lin, Z. ; Xie, Z. ; Qu, C. Real-time yaw-misalignment calibration and field-test verification of wind turbine via machine learning methods. 2024 Mech. Syst. Signal Process.. 208 -
Paper not yet in RePEc: Add citation now
Dao, P.B. A CUSUM-based approach for condition monitoring and fault diagnosis of wind turbines. 2021 Energies. 14 3236-
Dao, P.B. Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data. 2022 Renew. Energy. 185 641-654
- Dao, P.B. Condition monitoring of wind turbines based on cointegration analysis of gearbox and generator temperature data. 2018 Diagnostyka. 19 63-71
Paper not yet in RePEc: Add citation now
Dao, P.B. On cointegration analysis for condition monitoring and fault detection of wind turbines using SCADA data. 2023 Energies. 16 2352-
Dao, P.B. On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines. 2022 Appl. Energy. 318 -
- Dao, P.B. ; Staszewski, W.J. Cointegration and how it works for structural health monitoring. 2023 Measurement. 209 -
Paper not yet in RePEc: Add citation now
- Dao, P.B. ; Staszewski, W.J. ; Barszcz, T. ; Uhl, T. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. 2018 Renew. Energy. 116 107-122
Paper not yet in RePEc: Add citation now
Dickey, D.A. ; Fuller, W.A. Likelihood ratio statistics for autoregressive time series with a unit root. 1981 Econometrica. 49 1057-1072
Emerson, J. Cointegration analysis and the choice of lag length. 2007 Appl. Econ. Lett.. 14 881-885
Engle, R.F. ; Granger, C.W.J. Cointegration and error-correction: representation, estimation and testing. 1987 Econometrica. 55 251-276
- Gao, Z. ; Liu, X. An overview on fault diagnosis, prognosis and resilient control for wind turbine systems. 2021 Processes. 9 300-
Paper not yet in RePEc: Add citation now
- Guo, P. ; Infield, D. ; Yang, X. Wind turbine generator condition-monitoring using temperature trend analysis. 2012 IEEE Trans. Sustain. Energy. 3 124-133
Paper not yet in RePEc: Add citation now
Hatemi-J, A. ; Hacker, R.S. Can the LR test be helpful in choosing the optimal lag order in the VAR model when information criteria suggest different lag orders?. 2009 Appl. Econ.. 41 1121-1125
- Khan, P.W. ; Byun, Y.C. A review of machine learning techniques for wind turbine's fault detection, diagnosis, and prognosis. 2024 Int. J. Green Energy. 21 771-786
Paper not yet in RePEc: Add citation now
Kusiak, A. ; Verma, A. Analyzing bearing faults in wind turbines: a data-mining approach. 2012 Renew. Energy. 48 110-116
- Latiffianti, E. ; Sheng, S. ; Ding, Y. Wind turbine gearbox failure detection through cumulative sum of multivariate time series data. 2022 Front. Energy Res.. 10 -
Paper not yet in RePEc: Add citation now
- LeSage, J.P. Applied Econometrics Using MATLAB. 1998 Department of Economics, University of Toledo:
Paper not yet in RePEc: Add citation now
- Letzgus, S. Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models. 2020 Wind Energy Science. 5 1375-1397
Paper not yet in RePEc: Add citation now
Liew, V.K.S. Which lag length selection criteria should we employ?. 2004 Econ. Bull.. 3 1-9
- Maddala, G. ; Kim, I. Unit Roots, Cointegration, and Structural Change. 1998 Cambridge University Press: Cambridge
Paper not yet in RePEc: Add citation now
Meyer, A. Multi-target normal behaviour models for wind farm condition monitoring. 2021 Appl. Energy. 300 -
Morrison, R. ; Liu, X. ; Lin, Z. Anomaly detection in wind turbine SCADA data for power curve cleaning. 2022 Renew. Energy. 184 473-486
- Murgia, A. ; Verbeke, R. ; Tsiporkova, E. ; Terzi, L. ; Astolfi, D. Discussion on the suitability of SCADA-based condition monitoring for wind turbine fault diagnosis through temperature data analysis. 2023 Energies. 16 620-
Paper not yet in RePEc: Add citation now
- Perez-Sanjines, F. ; Peeters, C. ; Verstraeten, T. ; Antoni, J. ; Nowé, A. ; Helsen, J. Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence. 2023 Mech. Syst. Signal Process.. 185 -
Paper not yet in RePEc: Add citation now
Pozo, F. ; Vidal, Y. ; Salgado, Ó. Wind turbine condition monitoring strategy through multiway PCA and multivariate inference. 2018 Energies. 11 749-
- Qadri, B.A. ; Ulriksen, M.D. ; Damkilde, L. ; Tcherniak, D. Cointegration for detecting structural blade damage in an operating wind turbine: an experimental study. 2020 En : Pakzad, S. Dynamics of Civil Structures. Springer: Cham
Paper not yet in RePEc: Add citation now
- Qiao, L. ; Zhang, Y. ; Wang, Q. Fault detection in wind turbine generators using a meta-learning-based convolutional neural network. 2023 Mech. Syst. Signal Process.. 200 -
Paper not yet in RePEc: Add citation now
- Ren, H. ; Liu, W. ; Shan, M. ; Wang, X. A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning. 2019 Measurement. 148 -
Paper not yet in RePEc: Add citation now
- Salameh, J.P. ; Cauet, S. ; Etien, E. ; Sakout, A. ; Rambault, L. Gearbox condition monitoring in wind turbines: a review. 2018 Mech. Syst. Signal Process.. 111 251-264
Paper not yet in RePEc: Add citation now
- Schlechtingen, M. ; Santos, I.F. ; Achiche, S. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description. 2013 Appl. Soft Comput.. 13 259-270
Paper not yet in RePEc: Add citation now
Schwert, W. Test for unit roots: a Monte Carlo investigation. 1989 J. Bus. Econ. Stat.. 7 147-159
Sims, C.A. Macroeconomics and reality. 1980 Econometrica. 48 1-49
Staffell, I. ; Green, R. How does wind farm performance decline with age?. 2014 Renew. Energy. 66 775-786
Stetco, A. ; Dinmohammadi, F. ; Zhao, X. ; Robu, V. ; Flynn, D. ; Barnes, M. ; Keane, J. ; Nenadic, G. Machine learning methods for wind turbine condition monitoring: a review. 2019 Renew. Energy. 133 620-635
Sun, S. ; Wang, T. ; Yang, H. ; Chu, F. Condition monitoring of wind turbine blades based on self-supervised health representation learning: a conducive technique to effective and reliable utilization of wind energy. 2022 Appl. Energy. 313 -
- Sun, X. ; Xue, D. ; Li, R. ; Li, X. ; Cui, L. ; Zhang, X. ; Wu, W. Research on condition monitoring of key components in wind turbine based on cointegration analysis. 2019 IOP Conf. Ser. Mater. Sci. Eng.. 575 -
Paper not yet in RePEc: Add citation now
Wang, P. ; Li, Y. ; Zhang, G. Probabilistic power curve estimation based on meteorological factors and density LSTM. 2023 Energy. 269 -
- Wang, T. ; Han, Q. ; Chu, F. ; Feng, Z. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review. 2019 Mech. Syst. Signal Process.. 126 662-685
Paper not yet in RePEc: Add citation now
- Wilcoxon, F. Individual comparisons by ranking methods. 1945 Biometrics Bull.. 1 80-83
Paper not yet in RePEc: Add citation now
- Xiang, L. ; Wang, P. ; Yang, X. ; Hu, A. ; Su, H. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism. 2021 Measurement. 175 -
Paper not yet in RePEc: Add citation now
Xiang, L. ; Yang, X. ; Hu, A. ; Su, H. ; Wang, P. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. 2022 Appl. Energy. 305 -
- Xu, M. ; Li, J. ; Wang, S. ; Yang, N. ; Hao, H. Damage detection of wind turbine blades by Bayesian multivariate cointegration. 2022 Ocean Eng.. 258 -
Paper not yet in RePEc: Add citation now
- Zaher, A. ; McArthur, S.D.J. ; Infield, D.G. ; Patel, Y. Online wind turbine fault detection through automated SCADA data analysis. 2009 Wind Energy. 12 574-593
Paper not yet in RePEc: Add citation now
- Zhan, J. ; Wu, C. ; Ma, X. ; Yang, C. ; Miao, Q. ; Wang, S. Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation. 2022 Mech. Syst. Signal Process.. 174 -
Paper not yet in RePEc: Add citation now
- Zhang, K. ; Tang, B. ; Deng, L. ; Liu, X. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. 2021 Measurement. 179 -
Paper not yet in RePEc: Add citation now
- Zhang, Y. ; Liu, W. ; Wang, X. ; Shaheer, M.A. A novel hierarchical hyper-parameter search algorithm based on greedy strategy for wind turbine fault diagnosis. 2022 Expert Syst. Appl.. 202 -
Paper not yet in RePEc: Add citation now
- Zhu, J. ; Yoon, J.M. ; He, D. ; Bechhoefer, E. Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. 2015 Wind Energy. 18 1131-1149
Paper not yet in RePEc: Add citation now
- Zivot, E. ; Wang, J. Modeling Financial Time Series with S-PLUS. 2006 Springer: New York
Paper not yet in RePEc: Add citation now