SCADA Data Interpretation for Condition-Based Monitoring of Wind Turbines
Transcript of SCADA Data Interpretation for Condition-Based Monitoring of Wind Turbines
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Kesheng Wang
Jan Ola Strandhagen
Dawei TuEditors
Proceedings of
IWAMA 2013
The Third International Workshop of
Advanced Manufacturing and Automation
27 November 2013, Trondheim, Norway
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Preface
IWAMA International Workshop of Advanced Manufacturing and Automation began in
2010 as a joint seminar between SFI Norman (SINTEF and NTNU) and Shanghai Key Laboratoryof Manufacturing Automation and Robotics (Shanghai University). In 2013 IWAMA expanded toinclude academic and industrial experts in the fields of advanced manufacturing and automationworldwide when the University of Manchester and Tongji University joined as co-organizers. AsIWAMA becomes an annual event, we expect more universities and industry sponsors will join theinternational workshop as co-organizers.
Manufacturing and automation have assumed paramount importance and are vital factors forthe economy of a nation and the quality of life. The field of manufacturing and automation isadvancing at a rapid pace and new technologies are also emerging in the field. The challengesfaced by todays engineers are forcing them to keep on top of the emerging trends through
continuous research and development.IWAMA aims at providing a common platform for academics, researchers, practicingprofessionals and experts from industries to interact, discuss trends and advances, and share ideasand perspectives in the areas of manufacturing and automation.
IWAMA 2013 takes place in Trondheim, Norway, 27 November 2013, organized byNorwegian University of Science and Technology, SFI Norman/SINTEF, Shanghai University,University of Manchester and Tongji University. The program is designed to improvemanufacturing and automation technologies for the next generation through discussion of the mostrecent advances and future perspectives, and to engage the worldwide community in a collectiveeffort to solve problems in manufacturing and automation. Participants from different countries,such as Norway, China, UK, Germany, Italy and India attend the workshop.
Manufacturing research includes a focus on the transformation of present factories, towardsreusable, flexible, modular, intelligent, digital, virtual, affordable, easy-to-adapt, easy-to-operate,easy-to-maintain and highly reliable smart factories. Therefore, IWAMA 2013 has mainlycovered 3 sessions in manufacturing engineering:
1. Intelligent Manufacturing Technology2. Intelligent Logistics and Supply Chains3. Intelligent Diagnosis and Prognosis of Wind Turbines
The proceedings of IWAMA2013 provide up-to-date, comprehensive and worldwide state-of-
the-art knowledge of the manufacturing and automation science and engineering. All paperssubmitted to the workshop have been subjected to strict peer-review by at least 2 expert referees.Finally, 28 papers have been selected to be included in the proceedings after a revision process.We hope that the proceedings will not only give the readers a broad overview of the latestadvances, and a summary of the event, but also provide researchers with a valuable reference inthis field.
On behalf of the organization committee and the international scientific committee of IWAMA2013, I would like to take this opportunity to express my appreciation for all the kind support,from the contributors of high-quality keynotes and papers, and all the participants. My thanks areextended to all the workshop organizers and paper reviewers, NTNU and SFI Norman for thefinancial support, and co-sponsors for their generous contribution. Thanks are also given to Quan
Yu, Yi Wang, Cecilia Haskins, Tonje Berg Hamnes, Line Holien, Jorurnn Auth, and Lilan Liu for
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their hard editorial work of the proceedings and arrangement of the workshop. Thank also goes toLasse Postmyr from Tapir Academic Press for producing the proceedings.
Kesheng Wang, Ph.D. Professor of NTNU
Chairman of IWAMA 2013
Trondheim, Norway, 25thOctober, 2013
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Organized and Sponsored by
NTNU(Norwegian University of Science and Technology);
SHU(Shanghai University);
SINTEF(The Foundation for Scientific and Industrial Research)
SFI Norman(Center for Research-based Innovation Norwegian Manufacture Future)UM (The University of Manchester)
TU (Tongji University)
Honorary Chairs:Prof. Minglun Fang
General Chairs:Prof. Kesheng Wang, Prof. Jan Ola Strandhagen and Prof. Dawei Tu
Local Organizing Committee:Prof. Jan Ola Strandhagen, Prof. Kesheng Wang, Prof. Heidi Dreyer, Lars Tore Gellein, Quan Yu,Tonje Berg Hamnes, Associate Prof. Cecilia Haskins, Jorunn Auth
International Program Committee:
Prof. Jan Ola Strandhagen, NorwayProf. Kesheng Wang, NorwayAssociate Prof. Per Schjlberg, NorwayProf. Heidi Dreyer, Norway
Associate Prof. Erlend Alfnes, NorwayLars Tore Gellein, NorwayGeir Vevle, NorwayAssociate Prof. Hirpa L. Gelgele, NorwayAssociate Prof. Wei Deng, NorwayLecturer Yi Wang, UKProf. Wladimir Bodrow, Germany
Prof. Janis Grundspenkis, LatviaProf. Hans Henrik Hvolby, DenmarkProf. Dawei Tu, ChinaProf. Minglun Fang, China
Prof. Tao Yu, ChinaProf. Binheng Lu, ChinaProf. Ming Chen, ChinaProf. Yun Chen, ChinaProf. Xinguo Ming, ChinaProf. Yingjie Yu, ChinaProf. Yayu Huang, China
SecretariatTonje Berg Hamnes (Norway) and Prof. Lilan Liu (China)
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About Editors
Prof. Kesheng Wang holds a Ph.D. in production engineering from the Norwegian
University of Science and Technology (NTNU), Norway. In 1993 he was appointedProfessor at the Department of Production and Quality Engineering, NTNU. He is adirector of the Knowledge Discovery Laboratory (KDL) at IPK, NTNU at present. He isalso an active researcher and serves as a technical adviser in SINTEF. He was electedmember of the Norwegian Academy of Technological Sciences in 2006. He has published17 books, 10 book chapters and over 214 technical peer-reviewed papers in international
journals and conferences. Prof. Wangs current areas of interest are intelligentmanufacturing systems, applied computational intelligence, data mining and knowledgediscovery, swarm intelligence, condition-based maintenance and structured light systemsfor 3D measurements and RFID.
Prof. Jan Ola Strandhagenis a research director of the research center SFI NORMAN atSINTEF. He is also Professor at Department of Production and Quality Engineering, the
Norwegian University of Science and Technology (NTNU). He holds a PhD inProduction Engineering from NTNU (1994). His research has focused on productionmanagement and control, logistics, manufacturing economics and strategies. He hasmanaged and performed R&D projects in close collaboration with a wide variety of
Norwegian companies and participated as researcher and project manager in severalEuropean projects.
Prof. Dawei Tureceived his bachelors degree, masters degree and PhD from ZhejiangUniversity in 1987, 1989 and 1993 respectively. Since 1993 he joined ShanghaiUniversity, and became a professor in 2000. Now he is the Executive Dean of the Schoolof Mechatronics Engineering and Automation, Shanghai University. His researchinterests include optoelectronic detecting, precision mechanics and instrumentation, 3Dvision and vision-based robot servo-control.
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Table of Contents
Preface............................................................................................................................... I
Keynotes
Knowledge Dynamic in Software Applications for Process ControllingWladimir Bodrow ......................................................................................................................... 1
Toward Intelligent Manufacturing: a Label Characters Marking and Recognition
Method for Steel Products with Machine VisionQijie Zhao, Dawei Tu and Peng Cao ............................................................................................ 9
The Development of Intelligent and Integrated RFID (II-RFID) System for
Manufacturing IndustriesKesheng Wang ........................................................................................................................... 25
Responsible and Dynamic LogisticsJan Ola Strandhagen ................................................................................................................... 49
Session IMT: Intelligent Manufacturing Technology
Development and Deployment of ACT System - RFID SolutionsJan Erik Evanger and Fredrik W. Goborg .................................................................................. 53
Assessment of Geometric Tolerance Information as a Carrier of Design Intent to
Manufacturing and InspectionHirpa G. Lemu ........................................................................................................................... 59
Phase to Depth Conversion Algorithm by Using Epipolar Restraint in Object-
adaptive Fringe Projection TechniqueJunzheng Peng, Yingjie Yu, Wenjing Zhou and Mingyi Chen .................................................. 71
Computer Vision Supported by 3D Geometric ModelingSven Fjeldaas ............................................................................................................................. 81
Studies on the Intelligent Manufacturing Technologies of the Fan Blade
Wenhua Zhu, Baorui Li, Bin Bai, Hu Han and Minglun Fang ................................................... 93Virtual Simulation of Production Line for Ergonomics EvaluationMing Chen and Jinfei Liu ......................................................................................................... 113
A Point Cloud Registration Strategy Combining Particle Swarm Optimisation
andIterative Closest Point MethodQuan Yu and Kesheng Wang ................................................................................................... 123
Graph Representation of N-Dimensional SpaceTomasz Kosicki ........................................................................................................................ 135
Study of the Role of Virtual Engineering Technologies in DesignHirpa G. Lemu ......................................................................................................................... 147
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Session ILSC: Intelligent Logistics and Supply Chains
Lean Thinking in Product Development: Looking into Front-load Effects Using aSystem Dynamics ApproachAlemu Moges Belay and Torgeir Welo.................................................................................... 157
Ontology Based Interoperability Solutions within Textile Supply Chain: Review
andAnalysisYi Wang, Rishad Rayyaan and Richard Kennon ..................................................................... 171
A Framework for Real Time Production Planning and ControlEmrah Arica and Daryl J. Powell ............................................................................................. 185
Sources of Complexities in New Product and Process Development ProjectsGiedre Pigagaite, Pedro P. Silva, Bassam A. Hussein ............................................................. 197
Investigating the Fit of Planning Environments and Planning Methods, the Case
of anAutomotive Part ManufacturerPhilipp Spenhoff, Marco Semini, Erlend Alfnes and Jan Ola Strandhagen ............................. 211
Investigating the Applicability of Lean Principles in a Small, One-of-a-kindProduction CompanyLayli Dahir and Daryl Powell .................................................................................................. 221
Introducing Portfolio Maps as Basis for Standardization and Knowledge-basedDevelopmentSren Ulonska and Torgeir Welo ............................................................................................. 235
Incentive Regulation of Banks on Third Party Logistics Enterprises in Principal-
agent-based Inventory FinancingXuehua Sun and Xuejian Chu .................................................................................................. 249
Automation in the ETO Production Situation: The Case of a Norwegian Supplier
of Ship EquipmentBrge Sjbakk, Maria Kollberg Thomassen and Erlend Alfnes .............................................. 259
Competing in the Shell Eco-marathon. Challenges and Lessons LearnedBassam A. Hussein and Fariborz Ali Haidrloo ........................................................................ 271
Tackling the Storage Problem through Genetic Algorithms
Lapo Chirici and Kesheng Wang ............................................................................................. 281
Session IDPWT: Intelligent Diagnosis and Prognosis of Wind Turbines
(WINDSENSE Project)
Evaluation of Thermal Imaging System and Thermal Radiation Detector for
Real-time Condition Monitoring of High Power Frequency ConvertersAnders Eriksen, Dominik Osinski and Dag Roar Hjelme ........................................................ 295
SCADA Data Interpretation for Condition-based Monitoring of Wind TurbinesKesheng Wang, Vishal S. Sharma and Zhenyou Zhang ........................................................... 307
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Wind Turbine Fault Detection Based on SCADA Data Analysis Using ANNZhenyou Zhang and Kesheng Wang ........................................................................................ 323
Models for Lifetime Estimation - An Overview with Focus on Applications to
WindTurbinesThomas M. Welte and Kesheng Wang ..................................................................................... 337
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List of Authors
Alfnes, E. 211, 259
Arica, E. 49, 185
Bai, B. 93Belay, A. M. 157
Bodrow, W. 1
Cao, P. 9
Chen, M. 113
Chen, M. 71
Chirici, L. 281
Chu, X. 249
Dahir, L. 221
Eriksen, A. 295
Evanger, J. E. 53Fang, M. 93
Fjeldaas, S. 81
Goborg, F. W. 53
Haidrloo, F. A. 271
Han, H. 93
Hjelme, D. R. 295
Hussein, B. A. 197, 271
Kennon, R. 171
Kosicki, T. 135
Lemu, H. G. 59, 147Li, B 93
Liu, J. 113
Osinski, D. 295
Peng, J. 71
Pigagaite, D. 197Powell, D. J. 185, 221
Rayyaan, R. 171
Semini, M. 211
Sharma, V. S. 307
Silva, P. P. 197Sjbakk, B. 259
Spenhoff, P. 211
Strandhagen, J. O. 49, 211
Sun, X. 249
Thomassen, M. K. 259Tu, D. 9
Ulonska, S. 235
Wang, K. 25, 123, 281, 307, 323, 337
Wang, Y. 171
Welo, T. 157, 235
Welte, T. M. 337
Yu, Q. 123
Yu, Y. 71
Zhang, Z. 307, 323
Zhao, Q. 9Zhou, W. 71
Zhu, W. 93
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SCADA Data Interpretation for Condition-based Monitoring of
Wind Turbines
Kesheng Wang
1
, Vishal S. Sharma
1, 2
and Zhenyou Zhang
1
1Department of Production and Quality Engineering, Norwegian University andScience and Technology, Norway2Dept. of Industrial and Production Engineering, National Institute of Technology,India
Abstract
Most of the wind turbines have of some kind of CBM devices/systems, which provide the
information about the device to the central data base i.e. SCADA database. Thesedevices/systems make use some kind of data processing techniques/methods in order tolocate faults. Finally all the information keeps on recoding in the SCADA data base. Sothe endeavour in this article is, to review the techniques/methods/algorithms developed,to carry out diagnosis and prognosis of the faults, based upon SCADA data obtained fromwind turbines. The focus here is also on the Computational Intelligence methodsdeveloped.
Keywords
SCADA data, CI, CBM, WT
AbbreviationsBT: Boosting Regression TreeOEM: Original equipment manufacturerCART: Standard classification & Regression treeFMECA: Failure Analysis, Failure Mode Effect and Criticality AnalysisCBM: Condition Based MonitoringREP: Representative TreeCBMS: Condition Based Monitoring SystemDE: Differential evolutionkNN: k Nearest Neighbor NetworkTPM: Total Productive MaintenanceANN: Artificial Neural NetworkWIP: Work in progress
NNE: Neural Network EnsembleES: Evolution StrategyRF: Random ForestRCM: Reliability Centred MaintenanceSCADA: Supervisory Control & Data AcquisitionPSO: Particle Swarm optimization
SVM: Support Vector Machine
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CMMS: Computerised Maintenance and Management SystemsWT: Wind TurbinePCA: Principal Component analysisMLP : Multi-Layer PerceptronOEE: Overall Equipment Effectiveness
CI: Computational IntelligenceFFT: Fast-Fourier transformBT: Bragging TreeTSA: Time synchronous averagingFIS: Fuzzy inference systemANFIS: Adaptive neuro fuzzy inference system
1 Introduction
Renewable energy sources are playing an important role in the global energy mix, as ameans of reducing the impact of energy production on climate change. Wind energy
made its first actual step though simpler wind devices date back thousands of years agowith the vertical axis windmills found at the Persian-Afghan borders around 200 BC andthe horizontal-axis windmills of the Netherlands and the Mediterranean following muchlater (1300-1875 AD). Further evolution and perfection of these systems was performedin the USA during the 19thcentury mainly for pumping water between 1850 and 1970.The first large wind machine to generate electricity of 12 kW was installed in Cleveland,Ohio, in 1888. Toward the end of World War I, use of 25 kW machines throughoutDenmark was widespread. Further development of wind turbines in the USA was inspired
by the aerospace developments, while subsequent efforts in Denmark, France, Germany,and the UK (between 1935 and 1970) showed that large-scale WTs could work.European developments continued after World War II and lot of development have beenmade till date(Kaldellis & Zafirakis, 2011). During the last thirty years, securities ofenergy supply and environmental issues have reheated the interest for wind energyapplications. Also the challenge of wind energy applications is the target of 1000 GW ofwind power by 2030. Further the trend in future is towards producing big and offshoreturbines. The future forecast is indicated that the demand for wind power will increasemany folds in the years to come. The rated capacity and size both will increase in time tocome.
2 Brief Background of WTsA wind turbine is a machine for converting the kinetic energy in wind into mechanicalenergy. Wind turbines are mainly classified into two general types: horizontal axis andvertical axis. A horizontal axis machine has its blades rotating on an axis parallel to theground. A vertical axis machine has its blades rotating on an axis perpendicular to theground. There are a number of available designs for both and each type has certainadvantages and disadvantages. However, compared with the horizontal axis type, veryfew vertical axis machines are available commercially. Further horizontal wind turbinesare also of two types upwind and downwind WTs. In case of upward WT the rotor is inthe front of the unit. In order to keep it oriented into the wind, a yaw mechanism is
needed. Also the extended nacelle is required to position the rotor far enough away from
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the tower, in order to avoid any problems with a blade strike. In a downwind turbine therotor is on the back side of the turbine. The nacelle is designed to seek the wind, thusthere is no need to have a separate yaw mechanism. The flexible blade could be used butthis advantage may also be a disadvantage, as the flexing may cause fatigue to the blades.Tower shadow is problem with downwind machines because the rotor blade is behind the
tower. This may cause turbulence and lead to increased fatigue on the unit. Keeping inview the pros and cons of both the types, mostly upward WTs are used. The various
parts of horizontal wind turbine (Figure 1) and their purpose are indicated: 1: blade (twoor three blades, wind blowing over the blades causes the blades to "lift" and rotate); 2:rotor ( blades and the hub together); 3: pitch (blades are turned out of the wind to keep therotor from turning in winds that are too high or too low to produce electricity); 4: brake(disc brake which can be applied mechanically, electrically, or hydraulically); 5: low-speed shaft (turns at about 30 to 60 rpm); 6: gear box(connect the low-speed shaft to thehigh-speed shaft in order to increase speed from 30 or 60 rpm to about 1200 to 1500 rpmfor producing electricity); 7: generator (induction generator that produces 60-cycle AC
electricity). 8: controller ( starts or stops the WT at wind speeds of about 8 to 16 mph and65 mph respectively; 9: anemometer (measures the wind speed and transmits wind speeddata to the controller); 10: wind vane (measures wind direction and communicates withthe yaw drive to orient the turbine); 11: nacelle (the rotor attaches to the nacelle, whichsits atop the tower and includes the gear box, low and high-speed shafts, generator,controller, and brake, a cover protects the components inside the nacelle); 12: high-speedshaft (drives the generator); 13: yaw drive (upwind turbines face into the wind; the yawdrive is used to keep the rotor facing into the wind as the wind direction changes;downwind turbines don't require a yaw drive); 14: yaw motor (powers the yaw drive); 15:tower (made tubular steel or steel lattice, wind speed increases).
(a) (b)
Fig. 1(a) Parts of Wind Turbine http://www.wwindea.org/technology/ch01/estructura-en.htm, (b): CBMS on WT (Kusiak et al, 2010)
3 Fault Diagnosis and Prognosis
A fault is a physical defect, imperfection or flaw that occurs within the system. This may
cause a failure: the non-performance of some action that is due or expected. Fault
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detection is determination of faults present in a system and time of detection. Faultisolation is determination of kind, location and time of detection of a fault; follows faultdetection. Fault identification is determination of size and time-variant behavior of afault; follows fault isolation and Fault diagnosis is determination of kind, size, locationand time of a fault; follows fault detection, and includes fault isolation and identification
[Donders, 2002]. Prognosis is an approach that combines information on each machinescurrent condition with historical data from machines of the same class, models of the
physics of failure of components and short term projected usage to predict the futureprobability of failure of that individual machine. That is, prognosis gives a probabilisticforecast that is specific to each machine, allowing a strategy that balances the risk ofrunning a machine with damage indications against the lost revenue while waiting formaintenance [Hyers et al, 2006]. Data Mining is a process of extraction of usefulinformation and patterns from huge data. It is also called as knowledge discovery process,knowledge mining from data, knowledge extraction or data /pattern analysis [Ramageri,2010]. Various maintenance practices are have been described and classified, empirically,
into the following approaches [Alsyouf, 2004]: reactive Approach; preventive approach;predictive approach, diagnostic (expert systems) approach ; autonomous approach ; leanapproach ; proactive approach. The ultimate goal of Fault Diagnosis and Prognosis is todecide the appropriate maintenance strategy.
3.1 Fault Diagnosis and Prognosis Systems on WTCondition monitoring can detect faults early and can prevent major breakdowns. This isassociated with significantly decrease in maintenance costs. Furthermore it allows foroptimisation of maintenance schedules, thereby reducing downtime and enhancing
equipment availability, safety and reliability [Entezami et al, 2012]. Garca et al (2012)provided comprehensive review of the CBM of wind turbines, describing the differentmaintenance strategies. In their study they have focused their attention on the sensorstechnology (acquiring and analysing data for fault diagnosis). Hameed et. al (2010) andHameed et al (2009) have also provided significant vital information in the area of CM.On a WT major faults are generated because of the main gearbox, generator, main
bearings, rotor blades and their possibility of failures in terms of percentage are 32, 23, 11and < 10% respectively as stated by the insurer German Lloyd. The CBMS is a tool that
provides information on the status of a component and can also predict an expectedfailure/fault. The possible applications of CBMS on WT are shown in the Figure 1(b).
The fault diagnoses sensors used and techniques employed for CBMS on various parts ofthe WT are summarized in Table 1. [Lu, 2009, Hameed et al, 2009, Hameed et al, 2010,Hyers et al, 2006, Fischer et al, 2012, Liu et al, 2012]. Digital filtering, modelling, signaland spectrum analysis are the main parts of the data processing in CBMS. Data analysisin wind turbines could be based on many of the techniques, few important aresummarised in the Table 2. The next step is to predict the remaining life of thecomponents. So as to adopt a suitable maintenance strategy for WTs [Nadakatti et al,2008].
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Table 1Summary of CBMS on WT
Part of WT Fault Technique Sensor/ Monitoring quantity
Gearbox Gear toothdamages, Bearingfaults
Vibration monitoring & spectrumanalysis, supplemented with
AE sensing detects pitting, cracking
Transducers, velocity sensors,accelerometers, spectral emittedenergy sensors, AE sensors
Oil analysis Temperature,contamination,moisture
Generator Stator, bearing ,rotor inside
Current signature analysis Current measurement
RotorBlades
Creep & corrosion,fatigue, imbalance,roughness
Shearography, Radiography, AEsensing
AE sensors, strain gauges, Fiber bragggrating
Tower andblades
Ultrasonic testingtechniques
time-frequency techniques andwavelet transforms
Ultrasonic sensors, Fiber bragg grating
PitchmechanismYaw systemPowerElectronics/Electricalsystem
Voltage andcurrentanalysis, electricalresistance
Spectrum analysis, eddy current,thermography
Current and voltage measuringequipments
Table 2Summary of sensory signals and signal processing methods [Garca et al, 2012]
Methods Description Applications
Statistical methods Root mean square, peak amplitude Widely used thediagnosis of failuresMaximum value, minimum value, mean, peak to peak, standard
deviation, shape factor, crest factor, impulse factor, definiteintegral, energy ratio and kurtosis.
Trend analysis Particular algorithms power output patterns from WT generators Monitoring pitchmechanisms, poweroutput generators
Filtering methods Least median squares, filtering with and without a classical
statistical method (based on standard deviation)
Vibration filtering
Time-domain analysis Variations in current signals and trends, vibration analysis, oilanalysis, AE analysis
Imbalances betweenthe rotor and the statorphases, faults in therotor windings of thegenerator.
Cepstrum analysis Inverse Fourier transform of the logarithmic power spectrum Gear vibrations
Time synchronousaveraging (TSA)
Waveform signal Cracked gear toothIdentifies source oferror
Fast-Fouriertransform (FFT)
Conversion of a digital signal from the time domain into thefrequency domain, constant speed wind energy converters
Bearings
Amplitude
demodulation
Low-amplitude and low-frequency, periodic signals that might
be masked by other higher energy vibrations
Bearings
Order analysis Out of phase signals can be separated and analysed, suitable forvariable speed wind energy converters.
Overall rotor conditionincluding surfaceroughness, massimbalance andaerodynamicasymmetry.
Wavelet transforms Time-frequency technique ,non-stationary signals Gears, bearings,mechanical systems
Hidden Markovmodels
Classification of patterns in trend analysis Bearings
CI Techniques Computational Intelligent algorithms (Lei, Lin, He, & Kong,2012),(Amjady & Hedayatshodeh, 2012),(Uraikul, Chan, &Tontiwachwuthikul, 2007)
All Systems
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Some existing CBMS are: Gram & Juhl TCM System; DMT WindSafe System;Siemens Monitoring & Safety System; PRFTECHNIK [Wiggelinkhuizen et al, 2008]
4 SCADA Data Based CBM for WT
Supervisory Control and Data Acquisition (SCADA) is an application that collects datafrom a system and send them to a central computer for monitoring and control usages.Current CBM systems essentially provide the necessary sensor and data capturecapability required for monitoring. This monitoring system consists of wireless sensorsthat communicate with an embedded microprocessor mounted on the devices. Its purposeis to allow diagnostic and fault detection algorithms to be deployed down at the sensor /hardware level. This will decrease the volume of data that must be transmitted and storedvia the more traditional centralised SCADA system approach. The SCADA data resideson a server / PC in the form of a database, and the raw data resides on the hard-drive ofthe microprocessor / sensor system mounted on the turbine (Refer Figure 2). A typical
SCADA data is 10 minute averaged data [Zaher and McArthur, 2007]. Thus the collectedand stored SCADA data must then be examined in order to deduce the overall health ofthe turbine as well as its internal components [Uraikul et al, 2007]. An operational windfarm typically generates vast quantities of data. The SCADA data contain informationabout every aspect of a wind farm, from power output and wind speed to any errorsregistered within the system[Kim et al, 2011]. SCADA data may be effectively used totune a wind farm, providing early warning of possible failures and optimising poweroutput across many turbines in all conditions. Typical parameters recorded by SCADA ona WT could be broadly categorized into following types which could be used in faultdiagnosis and prognosis activity: Wind parameters, such as wind speed and wind
deviations; Performance parameters, such as power output, rotor speed, and blade pitchangle; Vibration parameters, such as tower acceleration and drive train acceleration;Temperature parameters, such as bearing temperature and gearbox temperature[Vermaand Kusiak, 2012].
There are many success stories about using SCADA data for CBM. Initially Monostoriet al (2009) discussed a framework for optimizing wind energy systems design, operationand maintenance. They emphasized, as wind turbines have several built-in sensorsmeasuring various physical characteristics during the operation and SCADA systemsserve with huge amount of data. There after Wiggelinkhuizen et al (2008) presented theframework of the EU funded Condition Monitoring for Offshore Wind Farms project.
They studied a small wind farm of five turbines having been instrumented with severalcondition monitoring systems and also with the traditional measurement systems formeasuring mechanical loads and power performance. Data from vibration and traditionalmeasurements, together with data collected by the turbines system control and dataacquisition (SCADA) systems, have been analyzed to assess. They could determinefailures, detect early stage of failure and asses the components health. They developed,applied, and tested several data analysis methods and measurement configurationssuccessfully and conclude that for all types of measurements SCADA data, time series,vibration monitoring could be used for CBM. Zaher and McArthur, 2007 also proposedan idea to use the combination of anomaly detection and data-trending techniques
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encapsulated in a multi-agent framework for the development of a fault detection systemfor WTs.
Fig. 2The SCADA data based CBM system
Sensor 1 Micoprocessor
mounted on device
Sensor 2 MicoprocessorCentralised SCADA
System
SCADA DATA
RAW DATA
SCADA master
10 mins average data
Fault diagnosis
Fault prognosis
Maintenance
DATA MINING HMI
Corroborate
PAST/PRESENT DATA
PRESENT DATA
Operator Workstation
WANRTU
remote location
and/or
PLC
StrategyAlgorithmsand/or
modelsSensor 1 Micoprocessor
mounted on device
Sensor 2 Micoprocessor
ADD ON DATA(AOD)
pre-processing
SCADADATA
AODCM
Add on data
condition monitoring
AODCM
DATA
SCADA data
AOD dataHigh frequency data
ADD ON DATA(AOD)
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Table 4: Summary of papers using SCADA data for CBMS
Faults/outputvariables
Parametersection/algorithms/Classification
Modellingalgorithms/method
Input/output data type Major findings Reference
Power
prediction
PCA MLP,REP
tree , M5Ptree , BT,k-NN
Inputs: wind, speed,
wind direction, outsidetemperature; Outputs:turbine control
k-NN modelling
best amongstudied
[Kusiak, et
al, 2009]
Future statesof the powerdrive
Time series modelfused with thedynamicequations
Data miningalgorithmswith theevolutionarystrategy
Input:wind speed,blade pitch angle,generator torque;Outputs:rotor speedand turbine power
Optimal control toimprove the poweroutput withoutlarge rotor speedchanges.
[Kusiak et al,2009]
Vibrations of awind turbine,performanceevaluation
NN, SVM,CART BT,RF
Input :Torque value,blade pitch angle rate,wind deviation, bladepitch angle, windspeed; Outputs:drivetrain acceleration andtower acceleration
NN algorithm bestamong studied
[ Kusiak andZhang,2010]
Power output CART,BT,SVM,
NN
Radial-basis-
function MLP
Input:current wind
speed, generatorspeed, wind direction,wind directiondifference; Output:blade pitch/ yaw angle
Control approach
generatedoptimized settingsof the blade pitchand yaw angle
[ Kusiak, et
al, 2010]
Wind turbinefailure
PSO, DE,ES
Wind speed differencetests
Detected normaland abnormalstates, PSO bestamong the studied
[Ye et al,2010]
Parametersoptimizing theenergyconversion
Extracts theprocess and thetime seriesmodels fromhistorical SCADAand wind data
NNE,geneticwrapperapproach
Input:Wind conditions,electricity demand;Output:blade, pitchangle, generatortorque.
Intelligent controlsystem smoothedthe power output,generator torque,and rotor speed
[ Kusiak etal, 2010]
WTperformancemonitoring &prognostics
WTperformanceanalyticpower curveanalysis
Power residual Performanceanalytic powercurve analysisseparates out pre-failure data fromother normaloperating data
[Uluyol et al,2011]
Predictturbine faults
NN,NNE,BT,SVM
Wind, performanceVibration andtemperatureparameters
Power curvedetermines thehealth of WT,status/ fault,category
[ Kusiak andLi, 2011]
Identificationand predictionof status
patterns inWT
PCA BT, RF,ripper,k-NN
Power, wind speed,rotor speed, andgenerator speed
A data-miningapproach applied,Random forest
algorithm bestamong studied
[Kusiak andVerma,2011]
vibrations ofWT
modified k-meansclustering
NN,NNE,BT,SVM,randomforest,CART,kNN,NN
SCADA vibration datasampled at 10 sintervals
NNE suitable drivetrain accelerationand the NN modelis most suitable forpredicting toweracceleration.
[Zhang andKusiak,2012]
Carbonbrushes ofgenerator
Chi-squarestatistic, boostingtree algorithm,wrapper(geneticsearch)
Multilayeredperceptron,boostingtree,k-NNSupportvectormachine
Wind , performance,vibration andtemperatureparameters
Drive trainacceleration,generator speed,torque andaerodynamicasymmetry.Boosting tree
algorithm bestamong the studied
[Verma andKusiak,2012]
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5 Data-driven Methods for Analysis of SCADA Data from WTs
Approaches to analyse conditions of WT can be categorized into model-based and datadriven ones. Model-based approaches attempts to incorporate physical models of systeminto estimation of the condition of WT. However, uncertainty due to the assumptions and
simplifications of adopted model may pose significant limitations on this approach.Especially for SCADA data analysis we should use data-driven approaches. Data-driventechniques utilize monitored operational data related to system health. They canbe beneficial when understanding of first principles of system operation is notstraightforward or when the system is so complex that developing an accuratemodel is prohibitively expensive. Data-driven approaches can often be deployedquickly and cheaply, and still provide wide coverage of system behavior. Anadded value of data-driven techniques is their ability to transform highdimensional noisy data into lower dimensional information useful for decision-
making [Dragomir et al., 2007].Pre-processing of SCADA is a must of extraction of useful information and patternsfrom huge data. The various data-driven methods being used for analysis of SCADA datafrom WTs can be divided into two categories: (1) statistical techniques (regressionmethods, ARMA models, etc.) and (2) Computational Intelligence (CI) techniques(Artificial Neural Networks, Fuzzy systems, etc.).We will focus on ComputationalIntelligence techniques in this paper.
5.1 Artificial Neural NetworksANNs can be used for a wide range of applications. They are inspired by the mechanismof the brain and can be classified by different categories as depending upon the learningmechanism or how they are trained (supervised \ unsupervised). ANNs accept input
parameters, process them, and produce output parameters according to a nonlineartransfer function. Some of the key features for NN are their high processing speeds whichare due to their massive parallelism, their proven ability to be trained, to produceinstantaneous and correct responses from noisy or partially incomplete data, and theirability to generalize information over a wide range. These features make them a goodchoice for applying to WTs data analysis. Hofemann et al explored the possibilities toapply ANN not only within a wind park but on turbines located at different sites.
Following the idea to develop a tool to forecast the particular loads of any wind turbine inthe field without the need to install additional measuring systems, a model was developedthat needed only signals contained in the SCADA data as input signals. They trained thenetwork to predict the blade root moments of two wind turbines located at different sites.A supervised ANN was chosen, the load data was the target data of the network and wasused to train the network as well as to verify the made predictions. Parameters as windspeed, wind direction, power output, pitch angle and yaw moment were used as inputdata.
5.2 Fuzzy Systems
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An opportunity for increased transparency and openness of data-driven model is offeredby fuzzy logic methods, which are increasingly proposed in modern diagnosticand prognostic technologies. Based on the principles of Zadehs fuzzy set theory,fuzzy logic provides a formal mathematical framework for dealing with thevagueness of everyday reasoning [Zadeh, 1965]. As opposed to binary reasoningbased on ordinary set theory, within the fuzzy logic framework measurementuncertainty and estimation imprecision can be properly accommodated.
Fuzzy systems are very useful in two general contexts like in situations involvinghighly complex systems whose behaviors are not well understood and in situations wherean approximate, but fast, solution is desired. A further advantage of fuzzy systems is thatexisting expert knowledge can be implemented to improve the approximation by tuning,removing or adding of membership functions and rules.
5.3 Adaptive Neuro-fuzzy Inference SystemHybrid CI model are able to integrate Artificial Neural Network models and Fuzzy Logicsystem models to make a better performance. Fuzzy neural networks have shown to bevery advantageous in dealing with real-world problems. These neuro-fuzzy systemscombine the benefits of these two powerful paradigms into a single capsule. This givesthe ability to accommodate both data and existing expert knowledge about the problemunder consideration. In ANFIS the advantages of NN are combined with FIS. Thereby theFIS is used to set up a set of rules whose membership functions parameters are tuned in atraining phase. Recently Schlechtingen et al (2013), proposed a system for wind turbinecondition monitoring using ANFIS. For this purpose: (1) ANFIS normal behavior modelsfor common SCADA data were developed in order to detect abnormal behavior of thecaptured signals and indicate component malfunctions or faults using the prediction error.They used 33 different standard SCADA signals and described, for which 45 normal
behavior models were developed. The performance of these models was evaluated interms of the prediction error standard deviations to show the applicability of ANFISmodels for monitoring wind turbine SCADA signals. The computational time needed formodel training was compared to NN models showing the strength of ANFIS in trainingspeed. For automation of fault diagnosis FIS were used to analyze the prediction errorsfor fault patterns. The outputs were both the condition of the component and a possibleroot cause for the anomaly. The output was generated by the aid of rules that capture the
existing expert knowledge linking observed prediction error patterns to specific faults.The work was based on continuously measured wind turbine SCADA data from 18turbines of the 2 MW class covering a period of 30 months. The system proposed in thismethod shows a novelty approach with regard to the usage of ANFIS models in thiscontext and the application of the proposed procedure to a wide range of SCADA signals.
5.4 Data Mining ApproachesA considerable work has been carried out in using SCADA data on WT diagnosis and
prognosis by Kusiak and his group. The work carried out by various researchers has beenpresented in Table 4. Kusiak et al (2009) presented various models for monitoring windfarm power output. An evolutionary computation algorithm was used to build a nonlinear
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parametric model to monitor the wind farm performance. The k-NN model producedgood performance for the wind farm operating in normal conditions. In their later studythey [Kusiak et al., 2009] proposed the concept of anticipatory control applied to windturbines. A modified evolutionary strategy algorithm was used to solve a nonlinearconstrained optimization problem. The proposed approach has been tested on the data
collected from a 1.5 MW wind turbine. In a subsequent study Kusiak et al (2010),proposed a framework for optimization of the power produced by wind turbines. Theyproposed control approach generated optimized settings of the blade pitch and yaw angle.They used integration of data mining and evolutionary computation in their approach. Ina later study on SCADA data Kusiak and Zhang (2010) found that vibrations of a windturbine have a negative impact on its performance. Data-mining algorithms were used to
build models with turbine parameters of interest as inputs, and the vibrations of drivetrain and tower as outputs. The performance of each model was thoroughly evaluated
based on metrics widely used in the wind industry. The neural network algorithmoutperforms other classifiers and is considered to be the most promising approach to
study wind turbine vibrations. Ye et al (2010), proposed that with design of wind speeddifference tests to detect both hard failures (cause complete shutdown of the turbine e.g.gear failure) and soft failures (degrade the turbine performance but do not necessarilystop the turbine e.g. anemometer faults). They used PSO based approach to learn fromhistorical data to decide the location and size of the boundary i.e. abnormal state from thenormal state. Further Kusiak et al (2010) presented an intelligent wind turbine controlsystem based on models integrating the following three approaches: data mining, model
predictive control, and evolutionary computation. The results produced by the intelligentcontrol system were found to be better than those of the current wind turbine controlsystem. For turbulent wind, the intelligent control system smoothed the power output,generator torque, and rotor speed without compromising the electricity demand. Uluyol etal (2011) proved that the wind turbine power curve analytic is useful for assessing windturbine performance and generating robust indicators for component diagnostics and
prognostics. This approach makes use of SCADA information and provides easyconfiguration based on process control approaches for condition-based monitoring. Theyused operational regime-based condition indicators that prevented false alarms andincreased the possibility of fault isolation. This approach could also detect slow
performance degradation caused by component wear as well as degradation due to animpending failure. Kusiak and Li (2011) used fault data provided by SCADA system.
They carried out fault prediction at three levels: (1) fault and no-fault prediction; (2) faultcategory (severity); and (3) the specific fault prediction. They used power curve todetermine the health of a wind turbine. The model extraction at level 1 and 3 was
performed using the Neural Network (NN), Neural Network Ensemble (NN Ensemble),the Boosting Tree Algorithm (BTA), and the Support Vector Machine (SVM). Whereasat level 2 they used Neural Network (NN), the Standard Classification and RegressionTree (CART), the Boosting Tree Algorithm (BTA), and the Support Vector Machine(SVM). Furthermore they could successfully predict faults 5-60 min before they occur ateach level. Kusiak and Verma [2011] used an association rule mining algorithm toidentify frequent status patterns of turbine components and systems to predicted using
historical wind turbine data. They explored five data-mining algorithms namely bagging,
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ripper, rotation forest, random forest, and k-nearest neighbor (k-NN, k=10). Theyconcluded that the best predication results were obtained with random forest algorithm.Verma and Kusiak [2012], proposed a methodology to predict the generator brush wornfault in wind turbines. They used SCADA systems information from 27 wind turbines.They used algorithms (Chi-square statistic, Boosting tree algorithm, Wrapper (genetic
search) for parameter selection, by which the ten most important parameters wereidentified. They built the model using four well-known data-mining algorithms, namely,multilayered perceptron (MLP), boosting tree, k-NN (k10), and support vector machine(SVM). These algorithms were studied to evaluate the quality of the models for
predicting generator brush faults and it was found that the boosting tree algorithmprovided the best prediction results. Zhang and Kusiak [2012], proposed three monitoringmodels for detecting abnormal vibration of wind turbines in time domain based onSCADA data. The sampling interval of SCADA data was 10 s, it allows to detectabnormal statuses of wind turbines in the time domain. The data collected includedtraining, test, and error data. They used a modified k-means clustering algorithm to first
vibration monitoring model. The k-means algorithm grouped data into clusters byexamining their similarity. The clusters were then labelled as normal or abnormal statusesof wind turbine vibration based on the error reports of wind turbines. They furtherincorporated the concept of control charts to develop models for monitoring of turbinevibration. They also addressed the detecting of abnormal drive train and tower vibrationof a wind turbine. They compared seven different data-mining algorithms, namely,neural network ensemble (NNE) , neural network (NN) , boosting regression tree (BT) ,support vector machine (SVM) , random forest with regression (RF) , standardclassification and regression tree (CART) , and k nearest neighbor neural network (kNN). Four metrics, the mean absolute error (MAE), standard deviation of absolute error (SDof AE), mean square error (MSE), and the standard deviation of square error (SD of SE),are utilized to evaluate the performance of data-mining algorithms in model extraction.They found that the NNE model is recognized as the most suitable for determining drivetrain acceleration and the NN model is considered to be the most suitable algorithm fordeveloping the model for predicting tower acceleration. Yang et al [2013] in their work
proposed a technique which would interpret the SCADA data collected from windturbines. They developed an effective method for processing raw SCADA data, further
propose an alternative condition monitoring technique based on investigating thecorrelations among relevant SCADA data; and realised the quantitative assessment of the
health condition of a turbine under varying operational conditions. Both laboratory andsite verification tests have been conducted and were found satisfactory.
6 Discussions and Future Challenges
From the literature reviewed it has been demonstrated successfully by researchers that bykeeping track of wind speed and power output parameters, the overall health of theturbine can be supervised. Furthermore SCADA data could be usefully used for CBM ofWTs and locating faults. There has been success in using SCADA data for power
prediction, optimal control settings, performance evaluation, predict turbine faults(predicting drive train acceleration/tower acceleration/gear box failure) and also the
vibrations on a WT. Many AI techniques have been applied including NN, Fuzzy,
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ANFIS, GAs etc [Yang et al,2008, Garcia et al, 2006]. Use of AI model in CBM of windturbines is justifiable as there are many variables involved and it not easy to establishaccurate mathematical model for such kind of complicated systems. It is also proposed touse data fusion techniques for monitoring the health of WTs [Dempsey and Sheng,2013]. Few challenges that need to overcome before SCADA data analysis becomes fully
successful are: SCADA data can differ from turbine to turbine and SCADA data changeswith the operational conditions. So because of this it becomes difficult to differentiate
between a real fault and fake fault which is quite a challenge.More over WT SCADA data are usually 10 min average data, so some of the
information is lost. Thus referring to the research of wind turbine condition monitoring, aframe work is proposed that takes SCADA data and also an add on high frequency datafrom the sensors (some) to diagnose and prognose with both conventional data andSCADA data. After comparison of the two results, the appropriate method can be chosenfor maintenance decision making.
A number of models have been proposed tried and tested[Wilkinson et al, 2013,
Schlechtingen & Ferreira Santos, 2011], however the accuracy of the model dependsupon the careful selection of variables and the quality of the data (free from noise). So
pre-processing of the data plays an important role in the accuracy of the models.Performance of AI based data mining algorithms and CBM algorithms are showing quite
promising results. Hence by using computational intelligence concepts more efficientmodels could be obtained thus enhancing the accuracy and robustness of the model. It is
proposed that Data mining (AI based) and evolutionary computations could be integratedfor building the models for prediction and monitoring.
7 Conclusions
The major outcomes from this review are listed below:
A frame work is proposed for diagnosis and prognosis of CBM of WTs using acombinational approach. These make use of conventional SCADA data and add onhigh frequency data from the sensors. In addition to this it also analyses historicalSCADA data.
Few challenges that need to overcome in SCADA data analysis are: firstly SCADAcan differ from turbine to turbine, secondly SCADA data changes with the operationalconditions. So pre-processing the raw data can equally contribute to the success of the
CBM algorithm itself. Some researchers have successfully demonstrated the use of AI algorithms for SCADA
data analysis. Seeing their performance it is believed that AI models could enhance theaccuracy and robustness of models.
Though the comparison results of various models are mentioned but no one clear cutperfect modelling technique could emerge. So this area remains open for furtherexplorations.
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