Research Papers

Filter-Based Fault Diagnosis of Wind Energy Conversion Systems Subject to Sensor Faults

[+] Author and Article Information
Jianhua Zhang

State Key Laboratory of Alternate Electrical Power
System With Renewable Energy Sources,
North China Electric Power University,
Beijing 102206, China

Jing Xiong

Zhuhai Power Supply Bureau,
Zhuhai 519000, China

Mifeng Ren

College of Information Engineering,
Taiyuan University of Technology,
Taiyuan 030024, China
e-mail: renmifeng@126.com

Yuntao Shi

Key Lab of Field Bus and
Automation of Beijing,
North China University of Technology,
Beijing 100144, China

Jinliang Xu

The Beijing Key Laboratory of New
and Renewable Energy,
North China Electric Power University,
Beijing 102206, China

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 28, 2015; final manuscript received December 21, 2015; published online April 1, 2016. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 138(6), 061008 (Apr 01, 2016) (10 pages) Paper No: DS-15-1195; doi: 10.1115/1.4032827 History: Received April 28, 2015; Revised December 21, 2015

The operational reliability of wind energy conversion systems (WECSs) has attracted a lot of attention recently. This paper is concerned with sensor fault detection (FD) and isolation problems for variable-speed WECSs by using a novel filtering method. A physical model of WECS with typical sensor faults is first built. Due to the non-Gaussianity of both wind speed and measurement noises in WECSs, an improved entropy optimization criterion is then established to design the filter for WECSs. Different from previous entropy-filtering results, the generalized density evolution equation (GDEE) is adopted to reveal the relationship among the estimation error, non-Gaussian noises, and the filter gain. The sensors FD and isolation algorithms are then obtained by evaluating the decision rule based on the residual signals generated by the filter. Finally, simulation results show that the sensor faults in WECSs can be detected and isolated effectively by using the proposed method.

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Amirat, Y. , Benbouzid, M. E. H. , Al-Ahmara, E. , Bensakerb, B. , and Turri, S. , 2009, “ A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems,” Renewable Sustainable Energy Rev., 13(9), pp. 2629–2636. [CrossRef]
Spinato, F. , Tavner, P. J. , van Bussel, G. J. W. , and Koutoulakos, E. , 2009, “ Reliability of Wind Turbine Subassemblies,” IET Renewable Power Gener., 3(4), pp. 387–401. [CrossRef]
Frank, P. M. , and Ding, S. X. , 1997, “ Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems,” J. Process Control, 7(6), pp. 403–424. [CrossRef]
Basseville, M. , and Nikiforov, I. , 2002, “ Fault Isolation for Diagnosis: Nuisance Rejection and Multiple Hypothesis Testing,” Annu. Rev. Control, 26(2), pp. 189–202. [CrossRef]
Van Eykeren, L. , and Chu, Q. P. , 2014, “ Sensor Fault Detection and Isolation for Aircraft Control Systems by Kinematic Relations,” Control Eng. Pract., 31, pp. 200–210. [CrossRef]
Rahme, S. , and Meskin, N. , 2015, “ Adaptive Sliding Mode Observer for Sensor Fault Diagnosis of an Industrial Gas Turbine,” Control Eng. Pract, 38, pp. 57–74. [CrossRef]
Carminati, M. , Ferrari, G. , Grassetti, R. , and Sampietro, M. , 2012, “ Real-Time Data Fusion and MEMS Sensors Fault Detection in an Aircraft Emergency Attitude Unit Based on Kalman Filtering,” IEEE Sens. J., 12(10), pp. 2984–2992. [CrossRef]
Liu, J. , Xu, D. , and Yang, X. , 2008, “ Sensor Fault Detection Invariable Speed Wind Turbine System Using H − / H ∞ Method,” 7th World Congress on Intelligent Control and Automation, WCICA 2008, pp. 4265–4269.
Wei, X. , and Liu, L. , 2010, “ Fault Detection of Large Scale Wind Turbine Systems,” 5th International Conference on Computer Science and Education (ICCSE), Hefei, China, Aug. 24–27, pp. 1299–1304.
Odgaard, P. F. , and Stoustrup, J. , 2009, “ Unknown Input Observer Based Scheme for Detecting Faults in a Wind Turbine Converter,” 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, Spain, pp. 161–166.
Chen, W. , Ding, S. X. , Haghani, A. , Naik, A. , Khan, A. Q. , and Yin, S. , 2011, “ Observer-Based FDI Schemes for Wind Turbine Benchmark,” 18th IFAC World Congress, Milan, Italy, Aug. 28–Sept. 2, pp. 7073–7078.
Kamal, E. , Aitouche, A. , Ghorbani, R. , and Bayart, M. , 2012, “ Robust Fuzzy Fault-Tolerant Control of Wind Energy Conversion Systems Subject to Sensor Faults,” IEEE Trans. Sustainable Energy, 3(2), pp. 231–241. [CrossRef]
Rothenhagen, K. , and Fuchs, F. , 2009, “ Doubly Fed Induction Generator Model-Based Sensor Fault Detection and Control Loop Reconfiguration,” IEEE Trans. Ind. Electron., 56(10), pp. 4229–4238. [CrossRef]
Shashoa, N. A. A. , Kvascev, G. , Marjanovic, A. , and Djurovic, Z. , 2013, “ Sensor Fault Detection and Isolation in a Thermal Power Plant Steam Separator,” Control Eng. Pract., 21(7), pp. 908–916. [CrossRef]
Wei, X. , Verhaegen, M. , and van Engelen, T. , 2010, “ Sensor Fault Detection and Isolation for Wind Turbines Based on Subspace Identification and Kalman Filter Techniques,” Int. J. Adapt. Control Signal Process., 24(8), pp. 687–707.
Luo, H. , Ding, S. , Haghani, A. , Hao, H. , Yin, S. , and Jeinsch, T. , 2013, “ Data-Driven Design of KPI-Related Fault-Tolerant Control System for Wind Turbines,” American Control Conference (ACC), Washington, DC, June 17–19, pp. 4465–4470.
Guo, L. , and Wang, H. , 2005, “ Fault Detection and Diagnosis for General Stochastic Systems Using B-Spline Expansions and Nonlinear Filters,” IEEE Trans. Circuits Syst. I: Regular Pap., 52(8), pp. 1644–1652. [CrossRef]
Li, T. , and Guo, L. , 2009, “ Optimal Fault-Detection Filtering for Non-Gaussian Systems Via Output PDFs,” IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum., 39(2), pp. 476–481. [CrossRef]
Ren, M. F. , Zhang, J. H. , Fang, F. , Hou, G. L. , and Xu, J. L. , 2013, “ Improved Minimum Entropy Filtering for Continuous Nonlinear Non-Gaussian Systems Using a Generalized Density Evolution Equation,” Entropy, 15(7), pp. 2510–2523. [CrossRef]
Kasem, A. H. , El-Saadany, E. F. , El-Tamaly, H. H. , and Wahab, M. A. A. , 2008, “ An Improved Fault Ride-Through Strategy for Doubly Fed Induction Generator-Based Wind Turbines,” IET Renewable Power Gener., 2(4), pp. 201–214. [CrossRef]
Karimi, S. , Gaillard, A. , Poure, P. , and Saadate, S. , 2009, “ Current Sensor Fault-Tolerant Control for WECS With DFIG,” IEEE Trans. Ind. Electron., 56(11), pp. 4660–4670. [CrossRef]
Lescher, F. , Zhao, J. Y. , and Borne, P. , 2006, “ Switching LPV Controllers for a Variable Speed Pitch Regulated Wind Turbine,” IMACS Multiconference on Computational Engineering in Systems Applications, Beijing, China, Oct. 4–6, Vol. 2, pp. 1334–1340.
Boukhezzar, B. , and Siguerdidjane, H. , 2010, “ Comparison Between Linear and Nonlinear Control Strategies for Variable Speed Wind Turbines,” Control Eng. Pract., 18(12), pp. 1357–1368. [CrossRef]
Principe, J. C. , Xu, D. , and Fisher, J. , 2000, “ Information Theoretic Learning,” Unsupervised Adaptive Filtering, Vol. I, S. Haykin , ed., Wiley, New York, pp. 265–319.
Li, J. , and Chen, J. B. , 2010, Stochastic Dynamics of Structures, Wiley, Singapore.
Zhang, J. H. , Chu, C. C. , Munozb, J. , and Chen, J. H. , 2009, “ Minimum Entropy Based Run-to-Run Control for Semiconductor Processes With Uncertain Metrology Delay,” J. Process Control, 19(10), pp. 1688–1697. [CrossRef]
Mao, X. R. , 1997, Stochastic Differential Equations and Their Applications, Horwood Publishing, Chichester, UK.
Bououden, S. , Chadli, M. , Filalia, S. , and El Hajjajib, A. , 2012, “ Fuzzy Model Based Multivariable Predictive Control of a Variable Speed Wind Turbine: LMI Approach,” Renewable Energy, 37(1), pp. 434–439. [CrossRef]


Grahic Jump Location
Fig. 1

Structure of the wind power system

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Fig. 2

Structure of sensor FD and isolation

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Fig. 3

Wind speed and measurement noise

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Fig. 4

(a) Performance index, (b) state estimation errors, and (c) filter gain

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Fig. 5

PDFs of the state estimation errors

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Fig. 6

(a) Residuals and (b) decision function of the sensor 1 fault

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Fig. 7

(a) Residuals and (b) decision function of the sensor 2 fault

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Fig. 8

(a) Residuals and (b) decision function of the sensor 3 fault



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