Innovations in fault detection and tolerant control for a wind farm, using Wireless Sensor Networks

The challenges related to the monitoring, diagnosis and control of a wind farm are presented. First, Wireless Sensors Networks are investigated, from an Internet of Things point of view: the most used and recent routing protocols, and the methods which can secure the network against attacks. As the volume of collected information is very large and diverse, big data processing frameworks needed to aggregate and interpret the information are then looked into. Computing paradigms which can ease the storing and the processing of big data, while lowering the costs for power companies are also considered. Wind farm level fault detection methods are used to look for defects, both from the model based perspective, as well as from the signal processing one. Control algorithms which are tolerant to faults are needed for this large system of systems. A quick summary of this new domain is given, even if it still is in an incipient phase. Perspectives on how all the previous components can be integrated with one another, to control a wind farm, are given.

[1]  Radu Dobrescu,et al.  Smart monitoring and controlling of wind turbines farms based on wireless sensors networks , 2012, 2012 1st International Conference on Systems and Computer Science (ICSCS).

[2]  Eric Duviella,et al.  An evolving classification approach for fault diagnosis and prognosis of a wind farm , 2013, 2013 Conference on Control and Fault-Tolerant Systems (SysTol).

[3]  Cosmin Mihai,et al.  Real Time & Power Efficient Adaptive - Robust Control , 2017 .

[4]  Kathryn E. Johnson,et al.  Simulation comparison of wake mitigation control strategies for a two‐turbine case , 2015 .

[5]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[6]  Damiano Rotondo,et al.  Robust fault detection and isolation of wind turbines using interval observers , 2013, 2013 Conference on Control and Fault-Tolerant Systems (SysTol).

[7]  Silvio Simani,et al.  Fault—Tolerant Control of an Offshore Wind Farm via Fuzzy Modelling and Identification , 2015 .

[8]  Jason R. Marden,et al.  Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study , 2016 .

[9]  Davide Astolfi,et al.  Data mining techniques for performance analysis of onshore wind farms , 2015 .

[10]  Felipe D. M. Oliveira,et al.  QoS analysis of routing protocols in wireless sensor networks in the monitoring of wind farms , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[11]  Rahul Gore,et al.  Big Data challenges in smart Grid IoT (WAMS) deployment , 2016, 2016 8th International Conference on Communication Systems and Networks (COMSNETS).

[12]  Yacine Challal,et al.  Information Security in Wireless Sensor Networks , 2009 .

[13]  Abdelkader Bousselham,et al.  The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid , 2015, FNC/MobiSPC.

[14]  Georges Barakat,et al.  Modeling approach and fault index analysis of a voltage-source brushless DC motor , 2016, 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol).

[15]  Yang Xiaomei,et al.  Evolution of wireless sensor network security , 2016, 2016 World Automation Congress (WAC).

[16]  Peter Fogh Odgaard,et al.  Fault tolerant wind farm control — A benchmark model , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[17]  Erwin Jose Lopez Pulgarin,et al.  Data-driven FDI for wind farms using W-SVM , 2016, 2016 IEEE International Symposium on Intelligent Control (ISIC).

[18]  Nigel P. Smart.  Hash Functions, Message Authentication Codes and Key Derivation Functions , 2016 .

[19]  Christophe Aubrun,et al.  LPV model-based fault detection: Application to wind turbine benchmark , 2015, 2015 7th International Conference on Modelling, Identification and Control (ICMIC).

[20]  Silvio Simani,et al.  Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models , 2015, IEEE Transactions on Industrial Electronics.

[21]  Henry Hong,et al.  Model-free active fault-tolerant cooperative control in an offshore wind farm , 2016, 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol).

[22]  Youmin Zhang,et al.  Active Fault Tolerant Control in a Wind Farm with Decreased Power Generation Due to Blade Erosion/Debris Build-Up , 2015 .

[23]  Ramu Kuchipudi,et al.  Latest developments on dynamic key management for dynamic wireless sensor networks , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[24]  Houcine Chafouk,et al.  Fault Detection and Isolation in DFIG Driven by a Wind Turbine , 2015 .

[25]  Chengliang Liu,et al.  Wind turbines abnormality detection through analysis of wind farm power curves , 2016 .

[26]  Dumitru Popescu,et al.  LQG Control of Horizontal Wind Turbines for Blades and Tower Loads Alleviation , 2011 .

[27]  J. W. van Wingerden,et al.  Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model , 2016 .

[28]  Yong Wang,et al.  Big data processing and analysis platform for condition monitoring of electric power system , 2016, 2016 UKACC 11th International Conference on Control (CONTROL).

[29]  Vehbi C. Gungor,et al.  Cloud Computing for Smart Grid applications , 2014, Comput. Networks.

[30]  Ciprian Lupu,et al.  Adaptive - Robust control a computational efficient real time simulation , 2015, 2015 13th International Conference on Engineering of Modern Electric Systems (EMES).

[31]  G. Merlin,et al.  Security and privacy for smart grid using scalable key management , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[32]  Silvio Simani,et al.  Active Fault—Tolerant Control of Offshore Wind Farm Installations , 2015 .

[33]  Suat Özdemir,et al.  A fog computing based smart grid model , 2016, 2016 International Symposium on Networks, Computers and Communications (ISNCC).

[34]  Jorge Sofrony Esmeral,et al.  Data-driven FDI for wind farms using W-SVM , 2016 .

[35]  Jakob Stoustrup,et al.  Fault Detection and Load Distribution for the Wind Farm Challenge , 2014 .

[36]  Vicenç Puig,et al.  Nonlinear set-membership identification and fault detection using a Bayesian framework: Application to the wind turbine benchmark , 2013, 52nd IEEE Conference on Decision and Control.

[37]  Damiano Rotondo,et al.  An Interval NLPV Parity Equations Approach for Fault Detection and Isolation of a Wind Farm , 2015, IEEE Transactions on Industrial Electronics.