Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.

[1]  Michel Verhaegen,et al.  Sensor Fault Diagnosis of Wind Turbines for Fault Tolerant , 2008 .

[2]  Mattias Krysander,et al.  Combining AI, FDI, and Statistical Hypothesis-Testing in a Framework for Diagnosis , 2003 .

[3]  Damiano Rotondo,et al.  Fault Tolerant Control of the Wind Turbine Benchmark using Virtual Sensors/Actuators , 2012 .

[4]  H. Sorenson.  Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.

[5]  Bo Wahlberg,et al.  A Bayesian approach to fault isolation - Structure estimation and inference , 2007 .

[6]  Silvio Simani,et al.  Adaptive Fault–Tolerant Control Design Approach for a Wind Turbine Benchmark , 2012 .

[7]  Ervin Bossanyi,et al.  Wind Turbine Control for Load Reduction , 2003 .

[8]  Peter Fogh Odgaard,et al.  Fault tolerant control of wind turbines: a benchmark model , 2009 .

[9]  Peter Fogh Odgaard,et al.  Results of a wind turbine FDI competition , 2012 .

[10]  Vicenç Puig,et al.  Adaptive threshold generation in robust fault detection using interval models: time‐domain and frequency‐domain approaches , 2012 .

[11]  Mattias Nyberg,et al.  Automated design of an FDI system for the wind turbine benchmark , 2012 .

[12]  Michèle Basseville,et al.  Detection of Abrupt Changes: Theory and Applications. , 1995 .

[13]  Silvio Simani,et al.  Active actuator fault‐tolerant control of a wind turbine benchmark model , 2014 .

[14]  Peter Fogh Odgaard,et al.  Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach , 2012 .

[15]  Abdul Qayyum Khan,et al.  Observer-based FDI Schemes for Wind Turbine Benchmark , 2011 .

[16]  Mogens Blanke,et al.  SATOOL - A SOFTWARE TOOL FOR STRUCTURAL ANALYSIS OF COMPLEX AUTOMATION SYSTEMS 1,2 , 2006 .

[17]  Vicenç Puig,et al.  Passive Robust Fault Detection of Dynamic Processes Using Interval Models , 2008, IEEE Transactions on Control Systems Technology.

[18]  Takao Nishiumi,et al.  Hydraulic Control Systems: Theory And Practice , 2016 .

[19]  Peter Fogh Odgaard,et al.  Observer Based Detection of Sensor Faults in Wind Turbines , 2009 .

[20]  Ron J. Patton,et al.  An FTC Approach to Wind Turbine Power Maximisation Via T-S Fuzzy Modelling and Control , 2012 .

[21]  Jianfei Dong,et al.  Data Driven Fault Detection and Isolation of a Wind Turbine Benchmark , 2011 .

[22]  Jakob Stoustrup,et al.  Robust and fault-tolerant linear parameter-varying control of wind turbines , 2011 .

[23]  Peter J Seiler,et al.  Wind Turbine Fault Detection Using Counter-Based Residual Thresholding , 2011 .

[24]  Peter Fogh Odgaard,et al.  An evaluation of fault tolerant wind turbine control schemes applied to a benchmark model , 2014, 2014 IEEE Conference on Control Applications (CCA).

[25]  Bo Wahlberg,et al.  A bayesian approach to fault isolation - Structure estimation and inference , 2006 .

[26]  Vicenç Puig,et al.  Fault diagnosis and fault tolerant control using set-membership approaches: Application to real case studies , 2010, Int. J. Appl. Math. Comput. Sci..

[27]  Sami Othman,et al.  Support Vector Machines for Fault Detection in Wind Turbines , 2011 .

[28]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[29]  Vicenç Puig,et al.  Fault Diagnosis of Wind Turbines using a Set-membership Approach , 2011 .

[30]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[31]  V. Puig,et al.  Non-linear set-membership identification approach based on the Bayesian framework , 2015 .

[32]  Damiano Rotondo,et al.  FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors , 2014 .

[33]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2006 .

[34]  Jakob Stoustrup,et al.  Active and passive fault-tolerant LPV control of wind turbines , 2010, Proceedings of the 2010 American Control Conference.

[35]  Qi Zhang,et al.  Fault Detection and Isolation of the Wind Turbine Benchmark: an Estimation-based Approach , 2011 .

[36]  Peter Fogh Odgaard,et al.  Fault-Tolerant Control of Wind Turbines: A Benchmark Model , 2009, IEEE Transactions on Control Systems Technology.

[37]  Dobrila Christian,et al.  Fault Tolerant Wind Turbine Control , 2007 .