Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators
1
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria
2
Department of Electrical and Electronics Engineering, Nisantasi University, 34398 Sarıyer, İstanbul, Turkey;
Submission date: 2022-12-11
Final revision date: 2023-02-01
Acceptance date: 2023-02-16
Online publication date: 2023-02-17
Publication date: 2023-02-17
Corresponding author
Diagnostyka 2023;24(1):2023110
KEYWORDS
TOPICS
ABSTRACT
This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.
REFERENCES (38)
1.
Gao Z, Liu X. An Overview on fault diagnosis, prognosis and resilient control for wind turbine systems. Processes. 2021;9:300. https://doi.org/10.3390pr90203....
2.
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. Renew Energy. 2019;133:620–635. https://doi.org/10.1016/j.rene....
3.
Fekik A, Habibi H, Simani S. Fault diagnosis and fault tolerant control of wind turbines: An overview. Energies. 2022;15:7186. https://doi.org/10.3390/ en15197186.
4.
Zhu Y, Zhu C, Tan J, Song C, Chen D, Zheng J. Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion. Renewable Energy. 2022;200:1023-1036. https://doi.org/10.1016/j.rene....
5.
Pandit R, Astolfi D, Hong J, Infield D, Santos M. SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends, Wind Engineering. 2022:1-20. http://doi:10.1177/0309524X221....
6.
Simani S, Farsoni P, Castaldi P. Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques. Sustainable Energy, Grids and Networks. 2015;1:45–52 http://dx.doi.org/10.1016/j.se....
7.
Zhang Z, Wang K. Wind turbine fault detection based on SCADA data analysis using ANN. Advance Manuf. 2014;2:70–78. http://doi 10.1007/s40436-014-0061-6.
8.
Simani S, Castaldi P. Intelligent fault diagnosis techniques applied to an offshore wind turbine system. Appl. Sci. 2019;9:783. https://doi:10.3390/app9040783.
9.
Zhu H, Liu J, Zhu H, Lu D, Wang Z. A novel wind turbine fault detection method based on fuzzy logic system using neural network construction method. IEEE International Conference on Industrial Application of Artificial Intelligence, IAAI, 2021. https://doi.org/10.1016/j.ifac...
10.
Odgaard PF, Stoustrup J, Kinnaert M. Fault tolerant control of wind turbines- a benchmark model. 7th IFAC symposium on fault detection, supervision and safety of technical processes. 2009:155-160. https://doi.org/10.3182/201208....
11.
Odgaard PF, Stoustrup J, Kinnaert M. Fault-tolerant control of wind turbines: A benchmark model. IEEE Transactions on Control Systems Technology. 2013;21(4):1168–1182. https://doi.org/10.3182/200906....
12.
Saci A, Cherroun L, Hafaifa A, Mansour O. Effective fault diagnosis method for the pitch system, drive train and the generator with converter in a wind turbine system. Electrical Engineering. 2022;104(4)”1967-1983. https://doi.org/10.1007/s00202....
13.
Borja-Jaimes V, Adam-Medina M, López-Zapata BY, Valdés LG, Pachecano LC, Coronado ME. Sliding mode observer-based fault detection and isolation approach for a wind turbine benchmark. Processes. 2022;10:54. https://doi:10.3390/pr10010054.
14.
Laouti N, Othman S, Alamir M, Othman NS. Combination of model-based observer and support vector machines for fault detection of wind turbines. International Journal of Automation and Computing, 2014;11:274-287. https://doi.org/10.1007/s11633....
15.
Fernandez-Canti RM, Blesa J, Tornil-Sin S, Puig V. Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach. Annual Rev. in Control. 2015;40:59-69. https://doi.org/10.1016/j.arco....
16.
Kościelny JM, Bartyś M, Sztybe A. Diagnosing with a hybrid fuzzy–Bayesian inference approach, Engineering Applications of Artificial Intelligence. 2021;104:104345. https://doi.org/10.1016/j.enga....
17.
Liu Y, Ferrari R, Wu P, Jiang X, Li S, Wingerden JW. Fault diagnosis of the 10mw floating offshore wind turbine benchmark: a mixed model and signal-based approach. Renew. Energy. 2021;164:391-406. https://doi.org/10.1016/j.rene....
18.
Biazar D, Khaloozadeh H, Siahi M. Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation. IET Renew. Power Gener. 2022;16:1623–1635. https://doi.org/10.1049/rpg2.1....
19.
Wang Z, Gao SL, Yao L, Qi X, Zhang J. Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping. Knowledge-Based Systems. 2021;228:107276. https://doi.org/10.1016/j.knos....
20.
Li Y, Jiang W, Zhang G, Shu L. Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data. Renewable Energy. 2021;171:103-115. https://doi.org/10.1016/j.rene....
21.
Chen W, Qiu Y, Feng Y, Li Y, Kusiak A. Diagnosis of wind turbine faults with transfer learning algorithms, Renewable Energy. 2021;163:2053-2067. https://doi.org/10.1016/j.rene....
22.
Chang Y, Chen J, Qu C, Pan T, et al. Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels. Renewable Energy. 2020;153:205-213. https://doi.org/10.1016/j.rene....
23.
Zemali Z, Cherroun L, Hafaifa A, Hadroug N. Fault diagnosis structure based on Kalman filter for the pitch system of a wind turbine process. 2nd Algerian Symposium on Renewable Energy and Materials ASREM2022. 2022.
24.
Teng J, Li C, Feng Y, Yang T, Zhou R, Sheng Z. adaptive observer based fault tolerant control for sensor and actuator faults in wind turbines. Sensors. 2021;21: 8170. https:// doi.org/10.3390/s21248170.
25.
Jlassi J, et al., Multiple open-circuit faults diagnosis in back-to-back converters of PMSG drives for wind turbine systems. IEEE Transactions on Power Electronics. 2015;30(5). https://doi: 10.1109/TPEL.2014.2342506.
26.
Cho S, Choi M, Gao Z, Moan T. Fault detection and diagnosis of a blade pitch system in a Floating Wind Turbine based on Kalman Flters and Artificial Neural Networks. Renew. Energy. 2021;169:1-13. https://doi.org/10.1016/j.rene....
27.
Kim D, Lee D. Fault parameter estimation using adaptive fuzzy fading Kalman filter. Applied Sciences. 2019;9:3329. http://doi:10.3390/app9163329.
28.
Ye M, Zhang J, Yang J. Bearing fault diagnosis under time-varying speed and load conditions via observer-based load torque analysis. Energies. 2022;15:3532. https://doi.org/10.3390/en1510....
29.
Horváth Z, Molnárka G. Design Luenberger observer for an Electromechanical Actuator, Acta Technica Jaurinensis. 2014;7(4):328-343. https://doi:10.14513/actatechj....
30.
Jia Q, Wu L, Li H. Robust actuator fault reconstruction for Takagi-Sugeno fuzzy systems with time-varying delays via a synthesized learning and Luenberger observer. International J. of Control, Automation and Systems. 2021;9(2):799-809. http://dx.doi.org/10.1007/s125....
31.
Ortega R, Praly L, Aranovskiy S, Yi B, Zhang. On dynamic regressor extension and mixing parameter estimators: Two Luenberger observers interpretations. Automatica. 2018;95:548–551. https://doi.org/10.1016/j.auto....
32.
Kumar V, Jerome EK, Ayyappan S. Comparison of four state observer design algorithms for MIMO system,. Archives of Control Sciences. 2013;23(LIX):131-144. https://doi.org/10.2478/acsc-2....
33.
Boutat D, Zheng G. Observer design for nonlinear dynamical systems, lecture notes in control and information sciences. Springer Cham. 2021:487. https://doi.org/10.1007/978-3-....
34.
Nail B, Kouzou A, Hafaifa A, Hadroug N, Puig V. A robust fault diagnosis and forecasting approach based on Kalman filter and interval type-2 fuzzy logic for efficiency improvement of centrifugal gas compressor system. Diagnostyka. 2019,20(2):57-75. https://doi.org/10.29354/diag/....
35.
Ben Djoudi H.CH, Hafaifa A, Djoudi D, Guemana M. Fault tolerant control of wind turbine via identified fuzzy models prototypes, Diagnostyka. 2020; 21(3): 3-13. https://doi.org/10.29354/diag/....
36.
McKinnon C; Carroll J, McDonald A, Koukoura S, Plumley C. Investigation of isolation forest for wind turbine pitch system condition monitoring using SCADA data. Energies. 2021;14:6601. https://doi: 10.3390/en14206601.
37.
Tang M, Yi J, Wu H, Wang Z. Fault detection of wind turbine electric pitch system based on IGWO-ERF. Sensors 2021;21:6215. https://doi: 10.3390/s21186215.
38.
Tang M, Peng Z, Wu H. Fault detection for pitch system of wind turbine-driven doubly fed based on IHHO-LightGBM. Appl. Sci. 2021;11:8030. https://doi: 10.3390/app11178030.
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