238 iit conf 238

520 views
367 views

Published on

Published in: Technology, Business
1 Comment
0 Likes
Statistics
Notes
  • Be the first to like this

No Downloads
Views
Total views
520
On SlideShare
0
From Embeds
0
Number of Embeds
35
Actions
Shares
0
Downloads
12
Comments
1
Likes
0
Embeds 0
No embeds

No notes for slide
  • dSPACE DS1006 processor
    board
  • The Simulink’s
    model of the DFIG, as well as of the supervise system
    (filter bank, decision unit,...) were translated in C code
    using Matlab Real-Time Workshop then downloaded to the
    test bench. The result of the FDI processus is display on
    LEDs of the dSPACE DS4002 card.
  • 238 iit conf 238

    1. 1. Sensor Fault Diagnosis for Wind Turbine Generators Using Kalman Filter Guided by: Dr. R . Saravana Kumar Professor School of Electrical Engineering(SELECT) VIT University. Tej Enosh. M M.Tech Power Electronics and Drives VIT University.
    2. 2. Outline Introduction Doubly Fed Induction Generator operation(DFIG) Modeling of DFIG Kalman Filter & Filter Bank Generalized observer Scheme & Dedicated Observer Scheme DFIG State Estimation with Kalman Filter Fault detection using Dedicated Observer Scheme In DFIG Modeling of PMSG Fault Detection in PMSG
    3. 3. Objective • To create a model of DFIG • To Identify the Current Sensor Fault in DFIG with Dedicated observer scheme using kalman Filter • To create a state space model for PMSG • To Identify the Current Sensor Fault in PMSG with Dedicated observer scheme using kalman Filter • To Identify the Current Sensor Fault in PMSG with Augmented kalman Filter
    4. 4. Introduction • Wind energy - the fastest-growing source of energy in the world • The doubly feed induction generator (DFIG) is one of the most used drive in wind energy because of its low cost, simplicity of maintenance, reliability • When a fault occurs, it must be detected as soon as possible • The data validation is important in this processes • The control system operates with the information of the system provided by sensors --- can be go through faults.
    5. 5. Cont.. •When a fault occurs, it must be detected as soon as possible, even where all observed signals remain in their allowable limits. •The fault must then be located and its cause identified •This aspect becomes more and more investigated because of the construction of high capacity offshore wind parks.
    6. 6. Problem Identified • The control system operates with the information of the system provided by sensors, which can be subjected to faults • For The isolation of the fault the two following fault scenarios will be used i) multiple but non simultaneous faults scenario ii) simultaneous faults scenario. • The state observer for fault detection and isolation • Filter bank used to estimate the dynamical behaviors of the system in order to detect then to isolate the fault. • Previous Method For Study of Current sensor Fault and Voltage sensor Fault is Luenberger observers method • It has proposed an observer scheme base on Kalman filter to diagnosticate the current sensor fault of a DFIG because of its discrete property
    7. 7. Methodology  Operating principle of a wind turbine using doubly fed induction generator  Modeling of the doubly fed induction generator  The Kalman filter bank based on Generalized Observer Scheme  The Kalman filter bank based on Dedicated Observer Scheme  Validation of simulated DFIG & PMSM for current sensor FDI
    8. 8. Kalman Filter • The Kalman filter uses the dynamical model, the known inputs to that system as well as the measurement (which given by sensors) to estimate the state of the system. • Widely use in automatic filtering as a mathematical technique to extract a signal from noisy measurements. xk+1 = Axk + Buk + wk zk = Hxk + vk A,B,H are matrices of approximated dimension • p(w) ∼ N(0,Q) Q --process covariance noise • p(v) ∼ N(0,R) R-- measurement covariance noise w- Process noise and v- measurement noises
    9. 9. • The implementation of Kalman filter could be divided in two steps. • Prediction step and Correction step. • Prediction Step: • Correction Step: • The diagnostic scheme with Kalman filter is capable to detect the fault but it is unable to locate the fault. • To resolve this problem, a filter bank will be used.
    10. 10. Filter bank for the FDI problem • To Design state observer for fault detection and isolation is a well known problem. • Filter bank used to estimate the dynamical behaviors of the system in order to detect then to isolate the fault. • The first kind of filter bank is Dedicated Observer Scheme (DOS). • The second one, Generalized Observer Scheme (GOS). • Each filter bank is composed by a number of observers, which are supplied with all of the input and different subsets of output of the system. • A Decision unit diagnosticate whether or not faults are presented in the sensors and which one is faulty by comparing the estimated outputs with the measured ones
    11. 11. Generalized Observer Scheme and Dedicated Observer Scheme •Generalized Observer Scheme – Can Detect Single Sensor Fault •Dedicated Observer Scheme (DOS) – Can Detect a Simultaneous Faults GOS The structure of a GOS for a MIMO system DOS In this scheme each observer is driven by a different single output.
    12. 12. Generalized Observer Scheme • Structure of GOS for MIMO System • Supervised System with 4 outputs • ith observer is driven by the input u and all of the output except yi • By this way residual vector rG,i depends on all but the ith fault
    13. 13. Dedicated Observer Scheme • In this observer scheme each observer is driven by a different single output. • Hence ith observer is only sensitive to the failure of yi • Then the residual rD,i represents the failure of the ith sensor. Advantage: It allows to detect and isolate simultaneous faults.
    14. 14. DFIG State Estimation with Kalman Filter
    15. 15. Schema of wind turbine using DFIG
    16. 16. Modeling Of Double Fed Induction Generator • In this work, we consider that the DFIG operates at a fixedspeed • Crotor convertor should be considered as control signals. • The generated power is determined by the currents in the windings of stator and rotor; these currents are to be measured. • The stator voltages are the voltages of the grid as known external inputs. • The model of DFIG was transformed in dq reference frame. • The d-axis is chosen to coincide with stator phase r-axis at t = 0 and • The q-axis leads the d-axis by 90 degree in the direction of rotation.
    17. 17. State-Space Representation of the DFIG
    18. 18. Cont.. Discrete State Space representation of DFIG C & E are the unity 4x4 matrix
    19. 19. FDI of the Current Sensor Faults •For The isolation of the fault the two following fault scenarios will be used •i) multiple but non simultaneous faults scenario •ii) simultaneous faults scenario. Fault detection using Kalman filter •Residual rK obtained from the Kalman filter with no sensor’s failure. •The sensor’s faults are detected. • Fault detection and isolation using Generalized Observer Scheme • Fault detection and isolation using Dedicated Observer Scheme Model in the Loop validation
    20. 20. Residual Without Sensor Fault
    21. 21. Residual With Sensor Fault
    22. 22. Fault Detection Event Number Fault Number Starting Time 1 F1 50 Sec 2 F2 150 Sec 3 F3 250 Sec 4 F4 350 Sec
    23. 23. Fault Detection Event Number Fault Number Starting Time 1 F1 50 Sec 2 F2 150 Sec 3 F3 250 Sec 4 F4 350 Sec
    24. 24. Fault Detection Event Number Fault Number Starting Time 1 F1 50 Sec 2 F2 150 Sec 3 F3 250 Sec 4 F4 350 Sec
    25. 25. Fault Detection Event Number Fault Number Starting Time 1 F1 50 Sec 2 F2 150 Sec 3 F3 250 Sec 4 F4 350 Sec
    26. 26. PMSG State Estimation with Kalman Filter
    27. 27. Overview • Variable Speed operation of Modern wind turbine enables • Optimization of the performance • Reduces the mechanical loading • Delivers various options for active power plant control • Mathematical Modeling of PMSG • Kalman Filter for State estimation in PMSG • Fault detection Using Kalman Filter • Augmented State Kalman Filter for PMSG
    28. 28. Estimation, Fault Diagnosis Architecture mi Ԑ Z PMSG System PMSG System Weights & initial state information Estimator0 Estimator1 Estimator2 EstimatorN Kalman Estimator bank State Estimation & residual generation
    29. 29. Fault Evaluation method Output Sensors Input Computing Of M( kalman gain) Residual Generation Fault detection Residual Decision Making Fault Detection
    30. 30. Mathematical model of PMSG
    31. 31. State Space model of PMSG } – System Model
    32. 32. Residual without current sensor fault Residual Residual 3 2.5 2 2 1.5 1 1 residual,r residual,r 0.5 0 0 -0.5 -1 -1 -1.5 -2 -2 -3 0 20 40 60 80 100 Id 120 140 160 180 200 -2.5 0 20 40 60 80 100 Iq 120 140 160 180 200
    33. 33. Residual with current sensor fault rD,1 rD,2 PMSM idq1 PMSM idq2 0.03 0.8 0.7 0.025 0.6 0.02 0.015 residual,r residual,r 0.5 0.01 0.4 0.3 0.2 0.005 0.1 0 -0.005 0 0 50 100 150 200 -0.1 0 50 100 150 200
    34. 34. Augmented state kalman filter Discretized equation set
    35. 35. Augmented state vector of PMSG A= Augmented State Vector Augmented PMSM model B=
    36. 36. Residual of Augmented state PMSG Without fault Augmented Model Augmented Model 2 3 1.5 2 1 0.5 1 residual-r residual-r 0 -0.5 0 -1 -1 -1.5 -2 -2 -2.5 -3 0 20 40 60 80 100 Id 120 140 160 180 200 -3 0 20 40 60 80 100 Iq 120 140 160 180 200
    37. 37. Conclusion • In this project, problem of current sensor Fault Detection in DFIG and PMSM of wind turbine was treated. • Detection and the isolation of multiple sensor faults was addressed using the Kalman filter bank in a Dedicated observer scheme(DOS). • All the multiple and simultaneous faults is detected and located with the observer scheme. • There is no miss detection. • The employed DOS based FDI processes has shown its capacity to detect and to isolate simultaneous faults
    38. 38. References • H.Chafouk, G.Hoblos, N.Langlois, S.L. Gonidec, and J.Ragot, “Soft computing algorithm to data validation aerospace systems using parity space approach”, Journal of Aerospace Engineering, vol 20, no .3, pp. 165-171, July 2007. • R.Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, 2005 • Bolognani, S.; Oboe, R.; Zigliotto, M., "Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position," Industrial Electronics, IEEE Transactions on , vol.46, no.1, pp.184,191, Feb 1999 • D.H.Trinch and H.Chafouk, “Current sensor fdi by generalized observer scheme for a generator in wind turbine”, in International Conference on Communications, Computing and Control Applications(CCCA11), Hammamet, Tunisia, March 2011 • O.Anaya-Lara, N.Jenkins, J.Ekanayake P.Cartwright, and M.Hughes, Wind Energy Generation-Modelling and Control. John wiley sons, Ltd, 2009
    39. 39. Thank You

    ×