Applications of Health Monitoring to Wind Turbines Michael H. AZARIAN, Ranjith S.R. KUMAR, Nishad PATIL, Anshul SHRIVASTAVA, Michael G. PECHT Center for Advanced Life Cycle Engineering (CALCE), Univ. of Maryland, College Park, MD 20742, U.S.A.,
[email protected],
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ABSTRACT With rising oil prices, the depletion of fossil fuel sources and changing geo‐political environment, there is heavy investment worldwide in the development of alternate sources of energy. Wind provides a widely available, non‐polluting, renewable source of energy which can help to meet the ever growing demand. The success of wind energy as an alternative to fossil fuels hinges on the extent to which wind turbines can provide a dependable and cost‐effective source of power. Statistics show that the longest downtime for wind turbines is due to failures of the gearbox, followed by the electrical system and the control system. Wear‐out failure of bearings caused the longest average downtime among the gearbox failures, most of which demanded a complete change of the gearbox or bearings. The circuitry used for conversion and transfer of power in wind turbines consists of components such as power semiconductor devices (e.g., IGBTs, MOSFETs, diodes), as well as transformers, inductors, and capacitors. Insulated gate bipolar transistors (IGBTs) are known to exhibit latch‐up or parametric degradation due to aging. Liquid aluminum electrolytic capacitors have been responsible for numerous costly and potentially hazardous equipment failures. The availability of wind energy generation equipment can be improved by implementing techniques for health monitoring, anomaly detection, and prognostics. Successful identification of degraded components is critical to preventing deterioration of power quality or a potentially catastrophic short circuit. This paper provides an overview of PHM (prognostics and health management) strategies for critical components of wind energy systems. Specific examples from health monitoring studies of IGBTs, electrolytic capacitors, and bearings provide an illustration of how these techniques can lead to real‐time fault detection and life time estimation in wind turbines, enabling condition‐based maintenance and reduction in life‐cycle costs. Keywords: wind turbine, power electronics, gearbox and bearings
1.
INTRODUCTION
Wind energy is a renewable energy source which is experiencing rapid growth as the power generation industry looks for alternatives to fossil fuels. Wind energy can be seen as an indirect form of solar energy which is constantly being replenished by pressure differences arising from the uneven heating of the earth's surface by solar radiation. This energy is harnessed by wind turbines which convert wind into electrical energy by the rotation of an aerodynamic rotor connected to an electrical generator. It is estimated that there is a global wind energy potential of between 20,000 TWh and 50,000 TWh of electricity each year, and that wind energy will account for 4.5% to 12.3% of the total global electricity demand by 2020 (GWEC 2010). The driving forces for adoption of wind energy include reduced dependence on imported fuel, reduction in CO2 emissions resulting in improved air quality, and economic development as a result of remote power availability leading to increased investments and job growth. In 2009, wind turbines generated 340 TeraWatt hours of electricity, which constituted 2% of the total global electricity demand. The turnover of the wind sector worldwide reached US $70 billion in the year 2009, compared with US$50 billion in 2008, a 40% increase. China and the United States accounted for 62% of new installed capacity; China’s installed capacity grew by 113% and the US by 39% in 2009 (WWEA 2009). Some obstacles to wind energy adoption are complaints over its visual impact and the potential for bird and bat deaths (Paraschivoiu 2002). Wind turbines are also believed to conflict with radar systems (Perry & Biss 2007), which is estimated to have prevented the installation of over 9,000 megawatts worth of wind capacity in the United States. However, the adoption and growth of power generation using wind
has been faster than for other renewable sources such as solar and tidal because wind power generation technologies have advanced more rapidly. Recent developments in design have led to higher capacity wind turbines which are better able to meet power demands. These turbines require larger foundations and better cabling infrastructure (Wilkinson & Tavner 2006), prompting wind turbine operators to favor offshore locations for these high capacity turbines. However offshore wind turbines present their own set of challenges, such as reduced accessibility, especially during winter periods and rough weather conditions, and the high cost of specialized personnel and equipment, such as seaborne cranes, for carrying out maintenance operations. A failure of a wind turbine can be defined as the termination of the ability of an assembly or sub‐assembly to perform its required function (Ribrant & Bertling 2007). Early detection of faults in wind turbines can increase availability in multiple ways: reducing the lead time for replacement of components, providing greater flexibility in scheduling personnel and equipment for maintenance operations, and enabling shutdown of equipment before a small fault can develop into a catastrophic failure. All of these benefits help to minimize disruptions in the production of electric power. Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods for assessing the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk (Cheng et al. 2010). Statistical surveys have shown that the electrical system in wind turbines caused the highest frequency of failures. The longest downtime due to failure of a component was attributed to that of the gearbox of the wind turbine (Ribrant & Bertling 2007). This paper provides an overview of PHM strategies for three of the components which are critical to reliability and availability of wind turbines: insulated gate bipolar transistors (IGBTs), electrolytic capacitors, and the gearbox and bearings. In section 2, the typical failures encountered in wind turbines are discussed. Section 3 consists of a brief review of relevant condition monitoring and fault detection approaches. In section 4, PHM methodologies developed for the three critical components for wind turbines are presented along with sample results from studies performed by the authors. Finally, in section 5 there is a discussion of the current state of PHM implementation in wind turbines and future areas of research.
2.
WIND TURBINE FAILURES
In a study on wind power plants in Sweden it was determined that electrical system failures, sensor failures and control system failures outnumbered mechanical failures (Ribrant & Bertling 2007). A 10 year study on wind power plants from Germany also indicated that electrical failures outnumber mechanical failures (Amirat et al. 2009). Failures in these plants occurred more frequently in the electrical and control systems than in mechanical systems such as the gearbox. Another study was performed comparing the cause of failure in variable‐speed and fixed‐speed wind turbines (Wilkinson et al. 2007). This study showed that fixed speed turbines have predominantly mechanical failures while variable speed turbines have electrical failures (Lu et al. 2009). For fixed speed turbines, failures tended to occur in the drive train, which includes the main‐shaft, bearings, gearbox, rotor brake, blades and generator. For variable‐speed wind turbines, the failure rates of control electronics, sensors and electrical systems were higher than drive train failures. Based on field failure data, a failure modes and effects analysis (FMEA) of wind turbines was reported (Arabian‐Hoseynabadi et al. 2010). The FMEA assigns a risk priority number (RPN) based on the severity and occurrence each type of failure. A ranking order is obtained based on the RPN number. The FMEA study reported the failures of the rotor and blade assembly to be the most likely to cause high severity failures followed by electrical systems such as the generator and controls. A study on the failures and repair times for 235 small wind turbines was reported (Kühn 2007). The frequency of electrical failures is seen to be higher than mechanical failures which are consistent with other studies. However, the downtime of a wind turbine is significantly longer after a mechanical failure in comparison to electric failures.
3.
CONDITION MONITORING AND DIAGNOSTICS
Mechanical failures result in longer wind turbine downtimes than electrical failures. Additionally, repairs of mechanical systems are more expensive than electrical systems. Thus, a majority of condition monitoring and diagnosis studies on wind turbines have been focused on mechanical systems.
3.1
Gearbox
Gearbox failures occur due to gear tooth damage and bearing failures. Several approaches for fault detection have been employed for the gearbox. The most common approach is to monitor gearbox vibration using accelerometers and use vibration analysis approaches to detect faults. Accelerometer readings can be integrated to obtain the velocity and displacement of components. Tracking the displacement allows one to detect faults in the components. The accelerometer data can also be used for analysis of the frequency spectrum. Faults in the bearing will have distinct spectral signatures that can be identified. Techniques such as wavelet analysis (Huang et al. 2008)(Yang et al. 2009), fast Fourier transforms (FFT) (Hatch 2004), and neural networks have been used for vibration analysis (Rafiee et al. 2007). Acoustic emission has been shown to give earlier warning of failure compared to vibration analysis (Loutas et al. 2009). However acoustic emission techniques require a high sampling rate and they may not be a cost‐effective solution to gearbox fault detection. It has also been proposed to employ gearbox oil analysis in conjunction with vibration analysis (Ebersbach et al. 2006) . Gearbox oil is analyzed for wear debris in a process also known as ferrography. In wear debris analysis, the quantity, size distribution, morphology and color of wear debris is determined. As wear particles have distinctive characteristics, a representative sample of the lubrication fluid of a gearbox can provide information on the wear modes, wear sources, and wear phases present in the machine. Some disadvantages of wear debris analysis are that it is time consuming, requires an expensive laboratory set up, and involves human expert inspection of the wear debris samples.
3.2
Rotor
The accumulation of ice, dirt, and moisture can cause rotor imbalance and aerodynamic asymmetry. One method of detecting faults is by monitoring power characteristics of the wind turbine. The relation between wind speed and active power output of a wind turbine provides information about the overall rotor condition. In one implementation of the approach, wind speed was monitored and the power was calculated. The power characteristic curve was developed which is the plot of power vs. wind speed. Thresholds for fault detection were established based on the power characteristic curve. Rotor faults were detected as a result of shifts of the power characteristic curves (Caselitz & Giebhardt 2005).
3.3
Generator
Bearing failures are said to account for 40% of generator failures. The breakdown of the insulation of the stator is also known to cause generator failures. Additional failures can occur as a result of open or short circuits in the windings of the rotor or stator. One implementation of fault diagnosis of generators involved creating an artificial short in one of the phases of the generator by the addition of an inductance or resistance. The stator and rotor currents were monitored for fault signatures. It was found that the shorts induced additional frequency components in the frequency spectrum of the stator and rotor currents. This study showed how monitoring the line stator and rotor currents helped in identifying the presence of an unbalance in a stator and rotor phase (Popa et al. 2003). Machine current signal analysis was thus proposed as a solution to diagnose turn‐to‐turn faults as well as inductive and resistive unbalance in stator and rotor phases.
3.4
Power electronics and electronic controls
Power electronics and electronic controls account for 1% of wind turbine cost but cause more than 10% of the failures. The cost of power electronics is higher in variable speed turbines compared to fixed speed turbines. Incidentally, electronic failures are greater in variable speed turbines compared to fixed speed turbines. A large portion of power electronics system failures are caused by defects and failures of the semiconductor devices in the power electronics circuits (Lu et al. 2009). There have been several diagnostic approaches developed for power electronic devices, especially IGBTs, for open‐circuit, short‐ circuit and gate driven faults in three phase converter systems (Lu & Sharma 2009). The diagnostic approaches include artificial neural networks, wavelet analysis, Bond graph methods, and pattern recognition methods used on system level current and voltage parameters that exhibit fault signatures. All the approaches listed are for detection of a faulty IGBT in the system rather than detecting degradation in the IGBTs before the faults occur. Once a faulty IGBT is detected the time for protective action before converter failure is on the order of micro to milliseconds. The time criticality of these faults
has led to the view that fault detection and diagnostic methods for these semiconductor devices should be implemented as protection functions instead of monitoring functions.
3.5
Blades
Factors associated with the environment, such as erosion, temperature, humidity, icing, and insects, can damage blades by increasing their surface roughness. This leads to loss of energy capture efficiency. Fault diagnostics of blades has been implemented by strain measurement techniques such as fiber‐optic Bragg grating and acoustic emission ( Lu et al. 2009). An approach using wavelet transforms was demonstrated that provided a qualitative assessment of blade damage (Tsai et al. 2006). Another approach used for blade diagnostics was to measure the power spectral density at the generator terminals (Jeffries et al. 1998). The power spectral density analysis allows for detection of small physical changes in the blades. This technique requires measurements of current and voltage at the generator terminal. As these parameters are monitored during turbine operation, this approach does not require additional sensors.
3.6
Hydraulic controls
Hydraulic controls are used to control the pitch of the rotor blades. The blade pitch is changed during normal operation to maximum energy extraction. Alternatively in periods of high winds, the blade pitch is changed to slow the rotation and prevent turbine damage. Faults of hydraulic systems include air trapping in the hydraulic fluid thereby affecting the blade pitch response to a control signal. Leakage of the hydraulic fluid also causes similar problems. Suggested diagnostic approaches for hydraulic systems are based on monitoring the control signal of the hydraulic pitch system to determine signatures that allow for detection of hydraulic system faults (Kong & Wang 2007).
3.7
System level
System level fault detection and prediction is a challenging task. There have been several modeling approaches presented that offer possible solutions. These approaches include using Petri Nets (Rodriguez et al. 2008), physics‐based models, and sensor based networks. A qualitative physics‐based approach for an intelligent maintenance system for wind turbines was proposed that included a design methodology based on re‐configuration to achieve self‐maintained wind turbines (Echavarria et al. 2007). Another study presented a preliminary framework for fault detection in wind turbines (Zaher & McArthur 2007). The framework proposed the development of an anomaly detection module that combines temperature data from wind turbines along with the power characteristic curve (wind speed and power output) using neural networks with a supervised learning phase.
4.
PHM IMPLEMENTATION STRATEGIES
Prognostics and health management approaches are either model‐based, data‐driven or a fusion of the two approaches. The model‐based approaches take into account the physical processes and interactions between components in the system. These approaches to PHM use mathematical representations to incorporate a physical understanding of the system, and include both system modeling and physics‐of‐ failure (PoF) modeling. Prognosis of remaining useful life (RUL) is carried out based on knowledge of the processes causing degradation and leading to failure of the system. The data‐driven approaches use statistical pattern recognition and machine‐learning to detect changes in parameter data, thereby enabling diagnostic and prognostic measures to be calculated. Anomalies and trends or patterns are detected in data collected by in situ monitoring to determine the state of health of a system. The trends are then used to estimate the time to failure of the system.
4.1
PHM of IGBTs
PHM using the data driven approach was implemented on insulated gate bipolar transistors (IGBTs). IGBTs are the devices of choice for power conversion circuits in wind turbines due to their low conduction loss and high power handling capabilities.
4.1.a IGBT Aging Power cycling aging tests were performed on discrete IGBT devices manufactured by International Rectifier. The IGBTs were repeatedly switched under a resistive load with a predefined frequency, duty cycle, gate voltage and collector‐emitter voltage. The device temperature increased with switching as a result of conduction and switching losses. When the temperature rose beyond a pre‐set level Tmax,
device switching was stopped. Switching was resumed again when the temperature fell below a set value of Tmin. This process was continued until device failure. In‐situ measurement of the gate‐emitter voltage, collector‐emitter voltage, collector‐emitter current and package temperature was performed during the test. The on‐state collector‐emitter current (ICE(ON)) was observed to reduce with aging and the on‐state collector‐emitter voltage (VCE(ON)) increased as shown in Figure 1. ICE(ON) Ice(on) VCE(ON) Vce(on)
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ICE(ON) (A)
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Aging Time (Minutes)
Figure 1: IGBT ICE(ON) and VCE(ON) vs. aging time
4.1.b Anomaly Detection The Mahalanobis distance approach was implemented to detect anomalies in the IGBTs. Mahalanobis distance (MD) is a distance measure that is used in applications such as anomaly detection, pattern recognition and process control. MD was calculated using VCE(ON) and ICE(ON) parameters. To implement the MD approach, VCE(ON) and ICE(ON) data at the mean aging temperature were partitioned into healthy data and test data. The initial observations (approximately the initial five minutes of the test) were classified as healthy data. The entire set of observations was used as test data. The MD was transformed to a normal distribution using a Box‐Cox transform. The transformed healthy MD data was used to define a detection threshold. The mean (µ) and standard deviation (σ) of the transformed healthy MD values were used to obtain 3σ bounds about the mean using the healthy data. The upper bound (µ+3σ) was used as the detection threshold for anomaly detection since increasing MD values indicate degradation in the IGBT. The test MD data was then transformed using the Box‐Cox transform. The time when the transformed test MD data crossed the threshold was considered as the time when the anomaly was first detected. The anomaly in the IGBT in this example was detected at 11.8 minutes as shown in Figure 2. 15
Transformed MD
13 11 9 Transformed MD crosses threshold
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Transformed MD threshold
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Figure 2: Anomaly detection of an IGBT using MD
4.1.c Failure Prediction using Particle Filters A particle filter (PF) approach was used to predict IGBT failure. As the PF requires models developed from known system behavior (empirical models) as well as system measurements for state estimation,
these were developed from the IGBT test data. A system model was developed for the IGBTs based on the on‐state collector‐emitter voltage VCE(ON). A 2nd order least squares regression of the VCE(ON) degradation curve was obtained from the aging experiments. The system model obtained from the aging data was then used for RUL estimation of the remaining IGBT devices. For the purpose of RUL estimation, the system model was used with a 20% increase in the VCE(ON) as the failure threshold. The remaining useful life estimate for the IGBT evaluated at the detection time of 11.8 minutes was found to be 20.8 minutes while the actual failure occurred at 21.4 minutes resulting in a prediction error of 2.6%. The state estimation and prediction for the IGBT is shown in Figure 3. The RUL probability distribution is represented using a mixture of Gaussians of the particle distribution at the predicted failure time.
Figure 3: Failure prediction of the IGBT using particle filters
4.2.
PHM of Electrolytic Capacitors
Liquid aluminum electrolytic capacitors are widely used in power electronics circuits, and have been responsible for numerous costly and potentially hazardous equipment failures. Health monitoring offers one approach for prevention of such failures. Successful identification of degraded capacitors requires an understanding of the mechanisms by which these components are likely to fail, and the selection and monitoring of parameters that provide a sensitive measure of the progress of these mechanisms. Prediction of remaining life is possible when failure models or trends are well established and can be applied to data from health monitoring.
4.2.a Failure Mechanisms and Models Physics‐of‐failure based analysis of electronic products involves evaluation of reliability based on relevant failure mechanisms and failure models. However, it is not always feasible to assign failure mechanisms to a product due to its architectural complexity or insufficient knowledge of the actual application environment. In situations like these, a product’s remaining life can be determined by analyzing parameters that indicate the product’s performance (Nie et al. 2007)(Albella et al. 1984). By analyzing trends within the data, precursors can be derived from one or more parameters that anticipate the changes that the product will experience in time. With the appropriate selection of precursors, one can extract features from the product that can be used to better describe the current and future state of health of the product. Liquid aluminum electrolytic capacitors have a finite life but their life times are measured in decades at room temperature. Accelerated testing is performed to estimate life at elevated stress conditions, and the results of this testing are used to generate the rated or expected life time. The model presented below is frequently employed for making this extrapolation in life values (United Chemi‐Con Inc 1995). It expresses a rule of thumb that life doubles for every ten degrees Kelvin reduction of the usage temperature below the rated temperature.
L2 =2 L1
T1 −T2 10
(1)
where, T1, T2 are the capacitor temperatures (K); and L1, L2 are the life at temperatures T1 and T2. This model is a somewhat oversimplified expression of a life model based on thermally activated processes such as electrolyte evaporation. The relationship between life time and temperature embodied in the model arises from an Arrhenius relationship with a specific activation energy over a specific, narrow temperature range. Errors are inherent in the simplification with deviations from any of the implicit assumptions. For example, changes in capacitor construction or electrolyte formulation may alter the activation energy, and application of the model over a wide range of temperatures (e.g., more than 30 or 40K) will invalidate the accuracy of the linearization. An alternate model has been proposed which is an extension of an Arrhenius Law, and defines the change in equivalent series resistance (ESR) over time for a capacitor subjected to a constant high temperature. The linear inverse model for computing ESR is given by (Lahyani et al. 1998): ESR0/ESRt = (1 – k.t.e(‐4700/T))
(2)
where: ESRt = the ESR value at time = t; ESR0 = the initial ESR value; T = the temperature in units of Kelvin at which the capacitor is aged; t = the operating time; and k = a constant which depends on the design and the construction of the capacitor. This model has the advantage of explicitly incorporating a term which expresses the thermally activated nature of the degradation process. Although it lumps together multiple independent mechanisms, such as the volatilization of the electrolyte and the diffusion of the vapor through the various leakage paths associated with the rubber seal, it provides greater fidelity to the underlying physical processes which cause the ESR to rise over time due to thermal stress.
4.2.b PHM Strategies for Electrolytic Capacitors Implementation of PHM for electrolytic capacitors begins with the identification of failure precursor parameters which show trends in the behavior with the degradation of the system health. Monitoring failure precursor parameters is more challenging in the case of electrolytic capacitors because not all the parameters are directly measurable in an active circuit and some parameters require special equipment for monitoring as well as disruption of normal operation for the measurement of these parameters. Some of the failure precursor parameters which pose these challenges include capacitance, dissipation factor, equivalent series resistance (ESR), physical deformation such as bulging (due to an increase in internal pressure), insulation resistance, core temperature, electrolyte degradation and the amount of electrolyte remaining inside the electrolytic capacitor. Research at CALCE has enabled the development of a technique for the calculation of the actual core temperature using only the ambient temperature and the case temperature, both of which can be measured quite simply with thermocouples. Gradual changes to the core temperature reflect loss of electrolyte and increases of ESR, which are key elements of the most common mode of capacitor failure. Figure 4 below is a sample plot from an accelerated stress test of a liquid electrolytic capacitor. It shows the temperature and ripple current through the capacitor, which became bloated after 3.5 hours and failed as an open circuit. The temperature rose as the pressure built up until the electrolyte escaped. If this component, which failed in a common manner for these capacitors, were in the power circuit of a wind turbine, monitoring the temperature of the capacitor could have provided a precursor to the failure. Prognostics and health management (PHM) enables real‐time reliability estimation for electronics. Carefully selected parameters can be used to characterize the health of a liquid aluminum electrolytic capacitor in a power circuit. Monitoring of these parameters during accelerated tests or online/offline life tests can help to detect faults in the early stages of degradation. Early detection and analysis can lead to better prediction and end of life estimates by tracking and modeling the degradation process. Early detection can also help in avoiding catastrophic failures.
Figure 4 Health Monitoring Results of Liquid Electrolytic Capacitor during Accelerated Testing
4.3.
PHM of Gear Boxes and Bearings
Health monitoring of gear boxes and bearings allows one to detect the early stages of degradation, assess the severity of the degradation, estimate the time to reach a pre‐defined threshold, and make key maintenance decisions based on knowledge of the health and remaining life of the system. Studies on the accelerated testing of bearings have led to the development of PHM algorithms which can analyze data from multiple sensors used to monitor the same component. Figure 5 shows an example from a study in which the Mahalanobis distance (MD) was calculated from the data collected from two sensors: an accelerometer, which measures vibration, and an acoustic emission transducer, which measures the elastic stress waves generated in the bearing during operation.
Figure 5 Health monitoring using multiple sensor approach
These studies have shown that the early detection of failure, which is a primary requirement for the implementation of PHM, can be achieved using the combination of parameters which have been extracted from the signals of multiple sensors. The trend in parameters extracted only from the accelerometer signal showed an increasing trend only after 65% of the life time had been reached, whereas the Mahalanobis distance parameter, which is extracted from a combination of both the accelerometer and the acoustic emission sensor signals, indicated an increasing trend after about 30% of the bearing life was reached in the accelerated stress tests. The use of mathematical algorithms to extract parameters from a combination of sensors can be helpful in the implementation of PHM in wind turbine gearboxes and bearings, which can help wind turbine operators in planning maintenance operations well in advance of the occurrence of an actual failure of the component.
5.
SUMMARY AND CONCLUSIONS
Advances in power semiconductor device technology have allowed greater deployment of variable speed turbines to maximize energy production (Carlin et al. 2003). However, variable speed turbine failures are more often caused by failures in the power electronics than by failures of the mechanical systems. Therefore, diagnostic and prognostic approaches are needed for wind turbine power electronics to reduce life cycle costs. The case study reported in this paper demonstrates a data driven PHM approach using Mahalanobis distance and particle filters which can successfully detect anomalies and predict the remaining useful life of discrete IGBTs. Condition monitoring and fault detection approaches for mechanical systems have long been the subject of development efforts and have shown good performance in the field. Most of the current techniques which are being studied and implemented for gearboxes and bearings in wind turbines require the continuous collection of sensory data and real‐time signal processing algorithms for the effective estimation of their health status. These techniques are affected by anomalous data arising from the highly variable loading conditions of the rotors. Algorithms which can make intelligent decisions based on other sensory data, such as wind speed, strain measurements for estimating the bending moment on the structure as well as the blades, and torque measurements, along with the analysis of vibration‐based signals, can reduce the sensitivity to anomalies in the data. This approach can improve decision‐making for turbine utilization and maintenance. Multi‐parameter analysis along with a correlation analysis of these parameters can help in the effective implementation of PHM strategies for the health monitoring of gearbox and bearings in wind turbines. Research carried out by the authors has especially been motivated by the fact that the combination of multiple parameters extracted from different sensors monitoring the same component can be helpful in the detection of early signs of degradation, which is a priority in the implementation of PHM. The sample results presented in this paper for electrolytic capacitors, IGBTs and bearings illustrate potential solutions for the implementation of PHM in wind turbines for these critical components.
ACKNOWLEDGEMENTS The authors would like to thank the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the more than 100 companies and organizations that support its research annually. The authors are grateful to Gilbert Haddad, Hyunseok Oh, and Dr. Diganta Das for their valuable inputs and suggestions for the improvement of this paper.
REFERENCES Albella, J.M., Gomez‐Aleixandre, C. & Martinez‐Duart, J.M., 1984. Dielectric characteristics of miniature aluminium electrolytic capacitors under stressed voltage conditions. Journal of Applied Electrochemistry, 14(1), 9‐14. Amirat, Y. et al., 2009. A brief status on condition monitoring and fault diagnosis in wind energy conversion systems. Renewable and Sustainable Energy Reviews, 13(9), 2629‐2636. Arabian‐Hoseynabadi, H., Oraee, H. & Tavner, P., 2010. Failure Modes and Effects Analysis (FMEA) for wind turbines. International Journal of Electrical Power & Energy Systems, 32(7), 817‐824. Carlin, P.W., Laxson, A.S. & Muljadi, E.B., 2003. The History and State of the Art of Variable‐Speed Wind Turbine Technology. Wind Energy, 6(2), 129‐159. Caselitz, P. & Giebhardt, J., 2005. Rotor Condition Monitoring for Improved Operational Safety of Offshore Wind Energy Converters. Journal of Solar Energy Engineering, 127(2), 253‐261. Cheng, S., Azarian, M.H. & Pecht, M.G., 2010. Sensor Systems for Prognostics and Health Management. Sensors, 10(6), 5774‐5797. Ebersbach, S., Peng, Z. & Kessissoglou, N., 2006. The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques. Wear, 260(1‐2), 16‐24. Echavarria, E., Tomiyama, T. & Bussel, G.J.W.V., 2007. Fault Diagnosis approach based on a model‐based reasoner and a functional designer for a wind turbine. An approach towards self‐maintenance. Journal of Physics: Conference Series, 75, 012078.
GWEC, 2010. Global Wind Energy Outlook 2010. Available http://www.gwec.net/fileadmin/documents/Publications/GWEO%202010%20final.pdf.
at:
Hatch, C., 2004. Improved wind turbine condition monitoring using acceleration enveloping. Orbit, 58–61. Huang, Q. et al., 2008. Application of wavelet neural networks on vibration fault diagnosis for wind turbine gearbox. In Proceedings of the 5th International Symposium on Neural Networks: Advances in Neural Networks, Part II, 313–320. Jeffries, W., Chambers, J. & Infield, D., 1998. Experience with bicoherence of electrical power for condition monitoring of wind turbine blades. Vision, Image and Signal Processing, IEE Proceedings Vision, Image Signal Processing, 145(3), 141‐148. Kong, Y. & Wang, Z., 2007. Modelling and Analysing the Hydraulic Variable‐pitch Mechanism for a Variable‐speed Wind Turbine. Wind Engineering, 31(5), 341‐352. Kühn, P., 2007. Big Experience with Small Wind Turbines–235 Small Wind Turbines and 15 Years of Operational Results. In Proc. EWEC. Lahyani, A. et al., 1998. Failure Prediction of Electrolytic Capacitors During Operation of a Switchmode Power Supply. IEEE Transactions on Power Electronics, 13(6), 1199‐1207. Loutas, T. et al., 2009. Condition monitoring of a single‐stage gearbox with artificially induced gear cracks utilizing on‐line vibration and acoustic emission measurements. Applied Acoustics, 70(9), 1148‐1159. Lu, B. & Sharma, S., 2009. A Literature Review of IGBT Fault Diagnostic and Protection Methods for Power Inverters. IEEE Transactions on Industry Applications, 45(5), 1770‐1777. Lu, B. et al., 2009. A review of recent advances in wind turbine condition monitoring and fault diagnosis. In Power Electronics and Machines in Wind Applications, 2009. PEMWA 2009. IEEE. Power Electronics and Machines in Wind Applications, 2009. PEMWA 2009. IEEE. pp. 1‐7. Nie, L., Azarian, M. H., Keimasi, M., Pecht, M., 2007. Prognostics of ceramic capacitor temperature‐ humidity‐bias reliability using Mahalanobis distance analysis. Circuit World, 33(3), 21‐28. Paraschivoiu, I., 2002. Wind turbine design: with emphasis on Darrieus concept, Presses inter Polytechnique. Perry, J. & Biss, A., 2007. Wind Farm Clutter Mitigation in Air Surveillance Radar. IEEE Aerospace and Electronic Systems Magazine, 22(7), 35‐40. Popa, L. et al., 2003. Condition monitoring of wind generators. In Industry Applications Conference, 2003. Conference Record of the 38th IAS Annual Meeting, 3, 1839‐1846. Rafiee, J. et al., 2007. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 21(4), 1746‐1754. Ribrant, J. & Bertling, L., 2007. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997‐2005. In IEEE Power Engineering Society General Meeting, 2007, 1‐8. Rodriguez, L. et al., 2008. Application of latent nestling method using Coloured Petri Nets for the Fault Diagnosis in the wind turbine subsets. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2008, 767‐773. Tsai, C.‐S., Hsieh, C.‐T., & Huang, S.‐J., 2006. Enhancement of damage‐detection of wind turbine blades via CWT‐based approaches. IEEE Transactions on Energy Conversion, 21(3), 776‐781. United Chemi‐Con Inc, 1995. Understanding aluminum electrolytic capacitors, United Chemi‐Con. Wilkinson, M.R. & Tavner, P.J., 2006. Condition monitoring of wind turbine drive trains. In Proceedings of ICEM'06. Wilkinson, M., Spinato, F. & Tavner, P., 2007. Condition Monitoring of Generators & Other Subassemblies in Wind Turbine Drive Trains. In IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2007, SDEMPED 2007, 388‐392. WWEA, 2009. World Wind Energy Report 2009. Available http://www.wwindea.org/home/images/stories/worldwindenergyreport2009_s.pdf.
at:
Yang, W., Tavner, P. & Wilkinson, M., 2009. Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. Renewable Power Generation, IET, 3(1), 1‐11. Zaher, A. & McArthur, S., 2007. A Multi‐Agent Fault Detection System for Wind Turbine Defect Recognition and Diagnosis. In Power Tech, 2007 IEEE Lausanne. Power Tech, 2007 IEEE Lausanne. pp. 22‐27.
