results of the contributions to the competition on wind turbine fault detection and isolation...
Post on 18-Dec-2015
216 Views
Preview:
TRANSCRIPT
RESULTS OF THE CONTRIBUTIONS TO THE COMPETITION ON WIND TURBINE FAULT DETECTION AND ISOLATIONPresented by Peter Fogh Odgaard*At Wind Turbine Control Symposium at Aalborg University 28th-29th November 2011*kk-electronic a/s, Denmark, peodg@kk-electronic.comContributions from: Stoustrup, J., Kinnaert, M., Laouti, N., Sheibat-Othman, N., Othman, S., Zhang, X., Zhang, Q., Zhao, S., Ferrari, R. M., Polycarpou, M. M., Parisini, T., Ozdemir, A., Seiler, P., Balas, G., Chen, W., Ding, S., Sari, A., Naik, A., Khan, A., Yin, S., Svard, C. & Nyberg, M.
Outline
• Motivation• FDI/FTC Benchmark Model and Competition• Description of Selected Contributions• Results of the Selected Contributions• Planned Continuations• Is the Objectives meet?
Motivation
• Increased reliability is of high important in order to minimize cost of energy of wind turbines.
• Fault Detection and isolation (FDI) and Fault Tolerant Control (FTC) are some of the important solutions in obtaining this.
Objective
• The benchmark model1 and competition should:– To attract attention from Academia to the FDI & FTC
problem on wind turbines.– Provide a platform some how relating to wind turbines
which all can use, and which can be used for comparisons.
– A part of showing the potential of FDI and FTC in Wind Turbines.
1 Odgaard, P.; Stoustrup, J. & Kinnaert, M. Fault Tolerant Control of Wind Turbines – a benchmark model Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009, 155-160
FDI/FTC Benchmark
• A generic 4.8MW wind turbine is used.
Blade & Pitch System
Drive TrainGenerator & Converter
Controller
Model Details
• Pitch actuators Second order transfer function with constraints for each blade. Close loop system.
• Converter First order transfer function with constraints. Close loop system.
• Drive train modeled with a 3 state model. Including inertias from generator and rotor.
• Simple Cp curve based aerodynamic model• Sensors modeled by band limited random noise blocks.
Wind Speed Input
Faults
• Sensor Faults– 1m1 fixed to 5 deg. 2000s-2100s– 2m2 scaled with a factor of 1.2. 2300s-2400s– 3m1 fixed to 10 deg. 2600s-2700s– r,m1 fixed to 1.4 rad/s. 1500s-1600s– r,m2 scaled with 1.1 and g,m1 scaled with 0.9. 1000s-1100s
Faults (II)
• Actuator Faults– Hydraulic pressure drop in pitch actuator 2. Abrupt
changed actuator dynamics. 2900s-3000s.– Increased air content in hydraulic oil in pitch actuator 3.
Slowly changing actuator dynamics. 3500s-3600s.– Offset on with 100 Nm. 3800s- 3900s.
• System Faults– Changed dynamics of drive train 4100s-4300s
Faults III
• Seven additional tests were performed with time shifted fault occurrences, resulting in other point of operations for the faults.
FDI Requirements
• Requirements to detection times– Sensors 10Ts– Converter 3Ts– Hydraulic oil leakage 8Ts– Air in oil 100Ts
• Requirement to interval between false positive detections – 100000 samples, and three successive detections are accepted.
• All faults should be detected.
Gausian Kernel Support Vector Machine solution2
• This scheme is based on a Support Vector Machine build on a Gaussian kernel.
• In this design a vector of features is defined for each fault containing 2-4 relevant measurements, filtered measurements or combinations of these.
• Data with and without faults were used for learning the model for FDI of the specific faults, based on this the vectors, kernel were found.
2 Laouti, N., Sheibat-Othman, N. & Othman, S., Support Vector Machines for Fault Detection in Wind Turbines Proceedings of IFAC World Congress 2011, 2011, 7067-7072
Estimation Based solution3
• A fault detection estimator is designed to detect faults, and an additional bank of N isolation estimators are designed to isolate the faults.
• The estimators used for fault detection and isolation are designed based on the provided models including model parameters.
• Each isolation estimator is designed based on a particular fault scenario under consideration.
3 Zhang, X., Zhang, Q., Zhao, S., Ferrari, R. M., Polycarpou, M. M. & Parisini, T.Fault Detection and Isolation of the Wind Turbine Benchmark: An Estimation-Based Approach. Proceedings of IFAC World Congress 2011, 2011, 8295-8300
Up-Down Counter solution4
• Up-down counters are used in this solution for decision of fault detection and isolation based on residuals for each of the faults.
• The fault detection and isolation residuals are based on residuals obtained by physical redundancy, parity equations and different filters.
• Up-down counters based decisions depends on discrete-time dynamics and amplitude of the residuals.
4 Ozdemir, A., Seiler, P. & Balas, G. Wind Turbine Fault Detection Using Counter-Based Residual Thresholding Proceedings of IFAC World Congress 2011, 2011, 8289-8294
Combined Observer and Kalman Filter solution5
• A diagnostic observer based residual generator is used for the faults in the Drive Train, in which the wind speed also is considered as a disturbance. It is decoupled from the disturbance and optimal.
• A Kalman filter based scheme is designed for the other two subsystems.
• GLR test and cumulative variance index are used for fault decision.
• Filter banks are used for fault isolation.
5 Chen, W., Ding, S., Sari, A., Naik, A., Khan, A. & Yin, S. Observer-based FDI Schemes for Wind Turbine Benchmark Proceedings of IFAC World Congress 2011, 2011, 7073-7078
General Fault Model solution6
• An automatic generated solution for FDI. • Main steps in the design are:
– Generate a set of potential residual generators.– Select the most suitable residual – Design the diagnostic tests for the selected set of
residual generators are designed. • A comparison between the estimated probability
distributions of residuals is used for diagnostic tests and evaluated with current and no-fault data.
6 Svard, C. & Nyberg, M. Automated Design of an FDI-System for the Wind Turbine Benchmark Proceedings of IFAC World Congress 2011, 2011, 8307-8315
Results Simulation – Fault 1
Fault # GKSV EB UDC COK GFM
1 Td:Mean 0.02s, Min 0.02s, Max 0.02sFd:Mean 0, Min 0, Max 0
Td:Mean 0.02s, Min 0.01s, Max 0.02sFd:Mean 0, Min 0, Max 0 MD:Mean 3%, Min 0% Max 20%
Td:Mean 0.03s, Min 0.02s, Max 0.03sFd:Mean 0, Min 0, Max 0
Td:Mean 10.32s, Min 10.23s, Max 10.33sFd:Mean 0.89, Min 0, Max 1
Td:Mean 0.04s, Min 0.03s, Max 0.04sFd:Mean 0, Min 0, Max 0
Results Simulation – Fault 2
Fault # GKSV EB UDC COK GFM
2 Td:Mean 47.24s, Min 3.23s, Max 95.09sFd:Mean 0, Min 0, Max 0MD:Mean 56%, Min 0% Max 100%
Td:Mean 44.65s, Min 0.63s, Max 95.82sFd:Mean 22, Min 16, Max 28 MD:Mean 56%, Min 0% Max 100%
Td:Mean 69.12s, Min 7.60s, Max 95.72sFd:Mean 0, Min 0, Max 0MD:Mean 67%, Min 0% Max 100%
Td:Mean 19.24s, Min 3.43s, Max 49.93sFd:Mean 0.97, Min 0, Max 5
Td:Mean 13.70s, Min 0.38s, Max 25.32sFd:Mean 3.08, Min 1, Max 18
Results Simulation – Fault 3
Fault # GKSV EB UDC COK GFM
3 Td:Mean 0.02s, Min 0.02s, Max 0.02sFd:Mean 0, Min 0, Max 0
Td:Mean 0.54s, Min 0.51s, Max 0.76sFd:Mean 4, Min 1, Max 11 MD:Mean 3%, Min 0% Max 20%
Td:Mean 0.04s, Min 0.03s, Max 0.10sFd:Mean 0, Min 0, Max 0MD:Mean 3%, Min 0% Max 20%
Td:Mean 10.35s, Min 1.54s, Max 10.61sFd:Mean 1.42, Min 1, Max 4
Td:Mean 0.05s, Min 0.03s, Max 0.06sFd:Mean 1.61, Min 1, Max 5
Results Simulation – Fault 4
Fault # GKSV EB UDC COK GFM
4 Td:Mean 0.11s, Min 0.09s, Max 0.18sFd:Mean 0, Min 0, Max 0
Td:Mean 0.33s, Min 0.27s, Max 0.44sFd:Mean 0, Min 0, Max 0
Td:Mean 0.02s, Min 0.02s, Max 0.02sFd:Mean 1, Min 1, Max 8
Td:Mean 0.18s, Min 0.03s, Max 0.46sFd:Mean 2.31, Min 0, Max 5
Td:Mean 0.10s, Min 0.03s, Max 0.34sFd:Mean 3.36, Min 1, Max 18
Results Simulation – Fault 5
Fault # GKSV EB UDC COK GFM
5 Td:Mean 25.90s, Min 1.24s, Max 87.49sFd:Mean 0, Min 0, Max 0MD:Mean 3%, Min 0% Max 20%
Td:Mean 0.01s, Min 0.01s, Max 0.01sFd:Mean 117, Min 95, Max 142
Td:Mean 2.96s, Min 0.38s, Max 21.08sFd:Mean 0.75, Min 0, Max 3
Td:Mean 31.32s, Min 1.54s, Max 91.13sFd:Mean 0.26, Min 0, Max 2MD:Mean 14%, Min 0% Max 40%
Td:Mean 9.49s, Min 0.56s, Max 17.18sFd:Mean 2.42, Min 1, Max 18
Results Simulation – Fault 6
Fault # GKSV EB UDC COK GFM
6 MD:Mean 100%, Min 100% Max 100%
Td:Mean 11.31s, Min 0.06s, Max 55.27sFd:Mean 2, Min 0, Max 20
Td:Mean 11.81s, Min 0.53s, Max 55.72sFd:Mean 22, Min 15, Max 25
Td:Mean 23.80s, Min 0.33s, Max 64.95sFd:Mean 0.03, Min 0, Max 3
Td:Mean 15.52s, Min 0.02s, Max 61.13sFd:Mean 3.67, Min 1, Max 37
Results Simulation – Fault 7
Fault # GKSV EB UDC COK GFM
7 MD:Mean 100%, Min 100% Max 100%
Td:Mean 26.07s, Min 3.33s, Max 52.66sFd:Mean 1.8, Min 1, Max 5
Td:Mean 12.93s, Min 2.86s, Max 51.08sFd:Mean 2, Min 1, Max 4
Td:Mean 34.00s, Min 17.22s, Max 52.93sFd:Mean 0, Min 0, Max 0
Td:Mean 31.70s, Min 0.61s, Max 180.70sFd:Mean 1.25, Min 1, Max 5
Results Simulation – Fault 8
Fault # GKSV EB UDC COK GFM
8 Td:Mean 0.01s, Min 0.01s, Max 0.01sFd:Mean 0, Min 0, Max 0 MD:Mean 97%, Min 0% Max 100%
Td:Mean 0.01s, Min 0.01s, Max 0.01sFd:Mean 0, Min 0, Max 0 MD:Mean 97%, Min 0% Max 100%
Td:Mean 0.02s, Min 0.02s, Max 0.02sFd:Mean 0, Min 0, Max 0 MD:Mean 97%, Min 0% Max 100%
Td:Mean 0.01s, Min 0.01s, Max 0.01sFd:Mean 0, Min 0, Max 0 MD:Mean 97%, Min 0% Max 100%
Td:Mean 7.92s, Min 7.92s, Max 7.92sFd:Mean 0, Min 0, Max 0 MD:Mean 97%, Min 0% Max 100%
Planned Continuations
• Competition Part II – FTC – 2 invited sessions proposals submitted to IFAC Safeprocess 2012
• An extended version of this benchmark model by merging it with FAST. Planning a invited session on this for ACC 2013. Details and model available in primo 2012. With Kathryn Johnson
• Competition Part III (2013) & Part IV (2014) on a simple wind farm model with faults. Details and model available in primo 2012. With Jakob Stoustrup
Is the Objective Meet?
• Yes!– Higher than expected interest in the FDI and FTC parts
of the competition.– General interest in the problem and benchmark model.
• We hope to continue the momentum of this interest into the new initiatives.
top related