Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems
Abstract
:1. Introduction
2. Wind Turbine Mathematical Modeling
3. Results
3.1. Adaptive Iterative Learning Approach
- (A1)
- The angle pitch dynamic is limited. That is, there exists a positive constant , such that for all .
- (A2)
- (A3)
- The pitch angle command is bounded. That is, there exists a positive constant , such that for all .
3.2. Data Fusion Design
3.3. Fault Detection Algorithm
4. Numerical Simulations
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Hall, D.L.; Llinas, J. An introduction to multisensor data fusion. Proc. IEEE 1997, 85, 6–23. [Google Scholar] [CrossRef] [Green Version]
- White, F.E. Data Fusion Lexicon; Joint Directors of Laboratories (JDL), Technical Panel for C3; NCCOSC Research and Development Center: San Diego, CA, USA, 1991. [Google Scholar]
- Varshney, P.K. Multisensor data fusion. Electron. Commun. Eng. J. 1997, 9, 245–253. [Google Scholar] [CrossRef]
- Dong, X.L.; Gabrilovich, E.; Heitz, G.; Horn, W.; Murphy, K.; Sun, S.; Zhang, W. From data fusion to knowledge fusion. arXiv 2015, arXiv:1503.00302. [Google Scholar] [CrossRef] [Green Version]
- Castanedo, F. A review of data fusion techniques. Sci. World J. 2013, 2013, 704504. [Google Scholar] [CrossRef] [PubMed]
- Lau, B.P.L.; Marakkalage, S.H.; Zhou, Y.; Hassan, N.U.; Yuen, C.; Zhang, M.; Tan, U.X. A survey of data fusion in smart city applications. Inf. Fusion 2019, 52, 357–374. [Google Scholar] [CrossRef]
- Biancolillo, A.; Boqué, R.; Cocchi, M.; Marini, F. Chapter 10—Data Fusion Strategies in Food Analysis. In Data Fusion Methodology and Applications; Cocchi, M., Ed.; Data Handling in Science and Technology Series; Elsevier: Amsterdam, The Netherlands, 2019; Volume 31, pp. 271–310. [Google Scholar] [CrossRef]
- Panicker, M.; Mitha, T.; Oak, K.; Deshpande, A.M.; Ganguly, C. Multisensor data fusion for an autonomous ground vehicle. In Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India, 9–11 June 2016; pp. 507–512. [Google Scholar] [CrossRef]
- Vavrinsky, E.; Subjak, J.; Donoval, M.; Wagner, A.; Zavodnik, T.; Svobodova, H. Application of Modern Multi-Sensor Holter in Diagnosis and Treatment. Sensors 2020, 20, 2663. [Google Scholar] [CrossRef] [PubMed]
- Vervliet, N.; De Lathauwer, L. Chapter 4—Numerical Optimization-Based Algorithms for Data Fusion. In Data Fusion Methodology and Applications; Cocchi, M., Ed.; Data Handling in Science and Technology Series; Elsevier: Amsterdam, The Netherlands, 2019; Volume 31, pp. 81–128. [Google Scholar] [CrossRef]
- Cariou, V.; Jouan-Rimbaud Bouveresse, D.; Qannari, E.; Rutledge, D. Chapter 7—ComDim Methods for the Analysis of Multiblock Data in a Data Fusion Perspective. In Data Fusion Methodology and Applications; Cocchi, M., Ed.; Data Handling in Science and Technology Series; Elsevier: Amsterdam, The Netherlands, 2019; Volume 31, pp. 179–204. [Google Scholar] [CrossRef]
- Xia, Y.; Leung, H. Performance analysis of statistical optimal data fusion algorithms. Inf. Sci. 2014, 277, 808–824. [Google Scholar] [CrossRef]
- Ruiz, M.; Mujica, L.E.; Alferez, S.; Acho, L.; Tutiven, C.; Vidal, Y.; Rodellar, J.; Pozo, F. Wind turbine fault detection and classification by means of image texture analysis. Mech. Syst. Signal Process. 2018, 107, 149–167. [Google Scholar] [CrossRef] [Green Version]
- Acho Zuppa, L. A quick fault detection system applied to pitch actuators of wind turbines. Renew. Energy Power Qual. J. 2020, 18, 13–17. [Google Scholar] [CrossRef]
- Vidal, Y.; Tutivén, C.; Rodellar, J.; Acho, L. Fault diagnosis and fault-tolerant control of wind turbines via a discrete time controller with a disturbance compensator. Energies 2015, 8, 4300–4316. [Google Scholar] [CrossRef] [Green Version]
- Pujol-Vazquez, G.; Acho, L.; Gibergans-Báguena, J. Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems. Energies 2020, 13, 2861. [Google Scholar] [CrossRef]
- Bristow, D.A.; Tharayil, M.; Alleyne, A.G. A survey of iterative learning control. IEEE Control Syst. Mag. 2006, 26, 96–114. [Google Scholar]
- Tayebi, A. Adaptive iterative learning control for robot manipulators. Automatica 2004, 40, 1195–1203. [Google Scholar] [CrossRef]
- Acho, L. Iterative learning control for homing guidance design of missiles. Def. Technol. 2017, 13, 360–366. [Google Scholar] [CrossRef] [Green Version]
- Chaaban, R.; Ginsberg, D.; Fritzen, C.P. Structural load analysis of floating wind turbines under blade pitch system faults. In Wind Turbine Control and Monitoring; Springer: Cham, Switzerland, 2014; pp. 301–334. [Google Scholar]
- Nguyen, P.; Nguyen, N. An intelligent parameter determination approach in iterative learning control. Eur. J. Control 2021, 61, 91–100. [Google Scholar] [CrossRef]
- Gu, P.; Tian, S. Iterative learning control with high-order internal model for first-order hyperbolic systems. ISA Trans. 2021, in press. [Google Scholar] [CrossRef] [PubMed]
- Qiang, H.; Lin, Z.; Zou, X.; Sun, C.; Lu, W. Synchronizing non-identical time-varying delayed neural network systems via iterative learning control. Neurocomputing 2020, 411, 406–415. [Google Scholar] [CrossRef]
- Blackwell, M.; Tutty, O.; Rogers, E.; Sandberg, R. Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors. Int. J. Control 2016, 89, 55–68. [Google Scholar] [CrossRef] [Green Version]
- Inthamoussou, F.A.; Bianchi, F.D.; De Battista, H.; Mantz, R.J. Gain Scheduled H∞ Control of Wind Turbines for the Entire Operating Range. In Wind Turbine Control and Monitoring; Springer: Cham, Switzerland, 2014; pp. 71–95. [Google Scholar]
- Nazir, M.; Khan, A.Q.; Mustafa, G.; Abid, M. Robust fault detection for wind turbines using reference model-based approach. J. King Saud-Univ.-Eng. Sci. 2017, 29, 244–252. [Google Scholar] [CrossRef] [Green Version]
Scenario | Abbreviation |
---|---|
No fault | H |
High air oil content | |
Hydraulic leakage | |
Pump wear |
Scenario | Parameter (rad/s) | Parameter |
---|---|---|
H | 11.11 | 0.6 |
5.73 | 0.45 | |
3.42 | 0.9 | |
7.27 | 0.74 |
Name | Value |
---|---|
0.5 | |
T | sec |
0 | |
n | 1000 |
Case | m | |
---|---|---|
H | 810.14 () | 1 |
1732.83 | 2.13 | |
2921.09 | 3.60 | |
1238.93 | 1.52 |
Case | Regression Parameter | |
---|---|---|
H () | 848.30 | 1 |
(m) | 1800.57 | 2.12 |
(m) | 3053.26 | 3.59 |
(m) | 1295.67 | 1.52 |
Case | Regression Parameter | |
---|---|---|
H () | 162.42 | 1 |
(m) | 273.60 | 1.68 |
(m) | 739.94 | 4.55 |
(m) | 287.80 | 1.77 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Acho, L.; Pujol-Vázquez, G. Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems. Sensors 2021, 21, 8437. https://doi.org/10.3390/s21248437
Acho L, Pujol-Vázquez G. Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems. Sensors. 2021; 21(24):8437. https://doi.org/10.3390/s21248437
Chicago/Turabian StyleAcho, Leonardo, and Gisela Pujol-Vázquez. 2021. "Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems" Sensors 21, no. 24: 8437. https://doi.org/10.3390/s21248437
APA StyleAcho, L., & Pujol-Vázquez, G. (2021). Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems. Sensors, 21(24), 8437. https://doi.org/10.3390/s21248437