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Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Use of SCADA Data for Failure 

Detection in Wind Turbines

K. Kim, G. Parthasarathy, O. Uluyol, 

and W. Foslien

Honeywell

S. Shuangwen and P. Fleming

National Renewable Energy Laboratory

Presented at the 2011 Energy Sustainability Conference and Fuel 

Cell Conference

Washington, D.C.

August 7-10, 2011

Conference Paper

NREL/CP-5000-51653

October 2011
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Proceedings of 2011 Energy Sustainability Conference and Fuel Cell Conference

August 7-10, 2011, Washington DC, USA

USE OF SCADA DATA FOR FAILURE DETECTION IN WIND TURBINES

Kyusung Kim

Honeywell

Golden Valley, MN, USA

Girija Parthasarathy

Honeywell

Golden Valley, MN, USA

Onder Uluyol

Honeywell

Golden Valley, MN, USA

Wendy Foslien

Honeywell

Golden Valley, MN, USA

Shuangwen Sheng

NREL

Golden, CO, USA

Paul Fleming

NREL

Golden, CO, USA

ABSTRACT

High operations and maintenance costs for wind turbines 

reduce their overall cost effectiveness. One of the biggest 

drivers of maintenance cost is unscheduled maintenance due to 

unexpected failures. Continuous monitoring of wind turbine 

health using automated failure detection algorithms can 

improve turbine reliability and reduce maintenance costs by 

detecting failures before they reach a catastrophic stage and by 

eliminating unnecessary scheduled maintenance. A SCADAdata based condition monitoring system uses data already 

collected at the wind turbine controller. It is a cost-effective 

way to monitor wind turbines for early warning of failures and

performance issues. In this paper, we describe our exploration 

of existing wind turbine SCADA data for development of fault 

detection and diagnostic techniques for wind turbines. We used 

a number of measurements to develop anomaly detection 

algorithms and investigated classification techniques using 

clustering algorithms and principal components analysis for 

capturing fault signatures. Anomalous signatures due to a 

reported gearbox failure are identified from a set of original 

measurements including rotor speeds and produced power.

INTRODUCTION

Among the challenges, noted in the DOE-issued report 

‘20% Wind Energy by 2030’ [1], are improvement of wind 

turbine performance and reduction in operating and 

maintenance costs. After the capital costs of commissioning 

wind turbine generators, the biggest costs are operations, 

maintenance, and insurance [1-3]. Reducing maintenance and 

operating costs can considerably reduce the payback period and 

provide the impetus for investment and widespread acceptance 

of this clean energy source.

Maintenance costs can be reduced through continuous, 

automated monitoring of wind turbines. Wind turbines often 

operate in severe, remote environments and require frequent 

scheduled maintenance. Unscheduled maintenance due to 

unexpected failures can be costly, not only for maintenance 

support but also for lost production time. In addition, as wind 

turbines age, parts fail, and power production performance 

degrades, maintenance costs increase as a percentage of 

production. Monitoring and data analysis enables conditionbased rather than time-interval-based maintenance and 

performance tune-ups. Experience from other industries shows 

that condition monitoring detects failures before they reach a 

catastrophic or secondary-damage stage, extends asset life, 

keeps assets working at initial capacity factors, enables better 

maintenance planning and logistics, and can reduce routine 

maintenance.

Traditionally, condition monitoring systems for wind 

turbines have focused on the detection of failures in the main 

bearing, generator, and gearbox, some of the highest cost 

components on a wind turbine [4–6]. Two widely-used methods 

are vibration analysis and oil monitoring [4, 5, 7, 8]. These are 

standalone systems that require installation of sensors and 

hardware. A supervisory control and data acquisition (SCADA) 

-data based condition monitoring system uses data already 

being collected at the wind turbine controller and is a costeffective way to monitor for early warning of failures and

performance issues.

In this paper, we describe our exploration of existing wind 

turbine SCADA data for development of fault detection and 

diagnostic techniques. Our ultimate goal is to be able to use 

SCADA-recorded data to provide advance warning of failures 

or performance issues. For the work described here, we used 

data from the Controls Advanced Research Turbine 2 (CART2) 

at the National Wind Technology Center (NWTC) at the 

National Renewable Energy Laboratory (NREL). A number of 

measurements from the turbine are used to develop anomaly

detection algorithms. Classification techniques such as 

clustering and principal components analysis were investigated 
2

for capturing fault signatures. The developed algorithms were

tested with data from a failed gearbox incident. The next two 

sections give a survey of failure modes and the current state of 

condition monitoring in wind turbines. We then describe the 

wind turbine and data used for this work, followed by a 

description of the detection algorithm approaches, results, 

discussion, and concluding remarks.

FAILURE MODES IN WIND TURBINES

A condition-based monitoring (CBM) system must be 

designed to provide maximum benefit for its cost. Since not 

every failure can be detected ahead of time nor prevented, it 

makes sense to target the failures that are most costly to repair. 

After reviewing publications [9–15] that provide survey data on 

wind turbine field failures, we summarized the failure rates of 

wind turbine subsystems. Our emphasis was on learning which 

subsystems were most costly to repair, not just the frequency of 

failures. While the cost of repair or replacement information is 

not available in the surveys, we could use downtime as an 

indirect indicator of cost and effort to repair. Downtime 

provides an additional measure of lost revenue due to failure. 

We determined the ranks of the subsystems by failure rate and 

downtime per turbine per year, by summarizing all surveys 

reviewed above. Fig. 1 is a graphical representation of the 

average failure rate and downtime. Note that in the surveys and 

Fig. 1, gearbox failures are separated from other drivetrain

component failures.

FIG. 1. LITERATURE REVIEW SUMMARY OF FAILURE RATE 

AND DOWNTIME/TURBINE/YEAR

Some surveys present downtime as downtime/

turbine/year, others present it as downtime/failure. We present 

the information as downtime/turbine/year, because it captures 

frequency of failure as well as downtime per failure. We find 

that although large subsystems such as gearboxes fail 

infrequently, these failures result in the most downtime per 

turbine, per year. Anecdotal comments from wind farm owners 

and operators suggest that gearbox failures dominate the cause 

for concern. However, as seen in Fig. 1, other failures are not 

far behind. A monitoring system that provides detection 

coverage for multiple top failure modes will be valuable in a 

maintenance system. With this objective, we explore the use of 

SCADA data for fault detection and diagnostics.

CONDITION MONITORING IN WIND TURBINES

Most of the literature about condition monitoring of wind 

turbines focuses on gearboxes and other drivetrain components

[16–18]. This is consistent with the conclusions from the survey 

of wind turbine failure modes, where the gearbox failures

contribute to the most costly repairs. Vibration-based 

approaches and oil monitoring techniques are the most mature, 

having been applied to large rotating machinery in other 

domains. Some are being applied to wind turbines at a research 

or pilot scale [5]. One comprehensive study is being conducted 

at NREL, using both dynamometer and field turbines as testing 

platforms [5]. Other techniques that are being developed in a

lab or pilot setting are acoustic emission sensors, wavelet 

analysis for vibration signatures, and electrical current and 

power signatures [5, 19]. In some cases, SCADA data, mainly 

temperature (bearing or generator winding), have been used 

along with vibration data for fault detection [6, 19]. Operating 

data also is used to normalize the vibration or temperature data

[6, 20]. Zaher [2] presented a method to use SCADA data for 

anomaly detection based on neural network models of normal 

operating modes. Use of SCADA data for fault diagnostics and 

prognostics algorithms in wind turbines is not as mature as in 

other industries, such as process and aerospace, where the use 

of condition-based maintenance is more widespread.

WIND TURBINE DIAGNOSTICS WITH SCADA DATA

SCADA or operating data of equipment has been used in 

other industries for accurate and timely detection, diagnostics 

and prognostics of failures and performance problems [22–27]. 

For example, in turbine engine diagnostics, failures such as 

turbine degradation, compressor bleed band failure, fuel supply 

system faults, combustion liner burn-through, and in-range 

sensor faults can be automatically detected with appropriate 

diagnostic algorithms. SCADA data provides a rich source of 

continuous time observations, which can be exploited for 

overall turbine performance monitoring. With appropriate 

algorithms, performance monitoring can be matured into 

individual component fault isolation schemes.

Performance is described in the context of the underlying 

process physics of the equipment—in this case, the wind 

turbine. The wind turbine converts wind kinetic energy into 

useful electrical energy. As turbine components deteriorate, the 

efficiency with which wind energy is converted to electrical 

energy decreases and the performance of the turbine decreases. 

Performance degradation can indicate problems such as blade 

aerodynamic degradation due to leading and trailing edge 

losses, dirt or ice buildup on blades, loss due to drivetrain 

misalignment, friction caused by bearing or gear faults, 

generator winding faults, or even pitch control system 
3

degradation. The functional elements of performance 

monitoring are shown in Fig. 2. A performance parameter is 

first computed based on sensor measurements; this parameter 

could be raw sensor values, sensor values corrected for 

environmental conditions, residuals with respect to a wind 

turbine model, component efficiency or aerodynamic 

parameters. Anomaly detection uses one or more such parameters to test whether the wind turbine is behaving within 

normal bounds. If the root cause of the anomaly is further 

classified as a particular component failure, this provides 

diagnosis. Additional elements involve predictive trending and 

prognostics, wherein parameters or fault indicators are trended, 

and time to failure is projected.

FIG. 2. SCADA DATA BASED MONITORING

Anomaly detection can be performed with a series of 

techniques that range from simple threshold checks to complex 

statistical analyses. Here, we focus on anomaly and fault 

detection methods for analyzing sensor data from indivi-dual

wind turbines. Sensor data used in algorithm development and 

the approaches are described in the next sections.

DESCRIPTION OF CART TURBINE AND DATA

The Controls Advanced Research Turbine 2 (CART2) is a 

modified Westinghouse

Fig 

3

WWG-0600 machine rated at 600 

kW. It is located at the NWTC at NREL in Colorado and was 

installed to test new control schemes for power and load 

regulation. In its original configuration, the WWG-0600 uses a 

synchronous generator, fluid coupling, and hydraulic collective

pitch actuation. To enable advanced control research for 

variable speed wind turbines, the CART2 has been retrofitted 

with an induction generator, rigid coupling, and individual

electromechanical pitch actuators. The rotor runs upwind of the 

tower and consists of two blades and a teetering hub [27]. As a 

research turbine, it is outfitted with many more sensors (see 

) than would normally be installed on commercial turbines, 

including pressure transducers, torque transducers, strain 

gauges, thermometers, position encoders, accelerometers, 

anemometers, wind vanes, and power, current, and voltage 

meters. The output of each of these sensors is recorded at the 

control rate of 100 Hz. Eighty-eight measurements are stored in 

ten-minute blocks including: pitch angles, shaft torque, oil 

temperature and pressure, yaw positions, wind speeds and 

 Westinghouse® is a registered trademark of Westinghouse Electric 

Corporation.

directions, ambient temperature, 12 vibrations, tower loads, 

generator power/current/voltage, and other control signals.

FIG. 3. CART2 SENSOR LOCATIONS [33]

NON-LINEAR PCA

The sensor data contain information about the state of the 

system being observed as well as redundant information and 

noise. Principal component analysis (PCA) is often used to 

extract useful and non-redundant information from the sensor 

data. PCA is a linear data analysis method that can produce 

orthogonal principal directions along which the data exhibit the 

largest variances. Because it is limited to capturing linear 

relationships, an extension to the nonlinear domain may be considered. An auto-associative neural network (AANN) approach 

provides one of the ways of implementing nonlinear PCA.

An AANN processes the input data through five sequential 

layers, one input layer, three hidden layers composed of a 

mapping layer, a bottleneck layer, and a de-mapping layer, and 

one output layer, as shown in Fig. 4, to produce an output that 

is identical to the input at the final layer [21]. This type of 

network learns an approximation of the identity mapping 

between the inputs and the outputs. The network contains a 

bottleneck at its third layer that compresses the information, 

allowing the network to learn a correlation model for the input 

data.
4

FIG. 4. AANN WITH THREE HIDDEN LAYERS, PLUS INPUT 

AND OUTPUT LAYERS

The network develops an internal representation of the 

data. As the data is mapped from one layer to another, linear or 

nonlinear transfer functions are used. The internal (compressed) 

representation obtained at the bottleneck layer through a 

nonlinear transfer function corresponds to nonlinear principal 

components of the input data [29]. The network is trained with 

data from normal operation. When presented with a set of new 

inputs, it can then produce a small set of principal components 

that can be trended or otherwise processed to detect changes in 

the underlying nonlinear dynamics of the system.

To map the changes seen in the principal components into 

anomaly detection and fault isolation indicators, the Q statistic 

and Hotelling T2 statistics are computed. The Q statistic is a 

measure of the amount of variation not captured by the linear or 

nonlinear PCA model while the T2 statistic is a measure of the 

variation in the model.

CLUSTERING TECHNIQUES— SELF-ORGANIZING 

FEATURE MAPS

Clustering algorithms are methods for dividing a set of n

observations into g groups, called clusters, so that members of 

the same group are more alike than members of different 

groups. When class labels of observations are available,

supervised learning techniques are used to learn the 

observations; otherwise, unsupervised learning is appropriate.

In unsupervised learning, no target outputs are available, so the 

free parameters, such as weights and biases in a neural network, 

are modified only in response to network inputs. They 

categorize the input patterns into a number of classes.

A self-organizing feature map (SOFM) is a type of 

unsupervised clustering algorithm that forms neurons located 

on a regular grid, usually in one or two dimensions [30]. The 

cluster representatives that are the neurons in the layer of a 

SOFM are initially assigned at random, in some suitable 

distribution according to a topology function, that dictates the 

structure of the map. SOFM can detect regularities and 

correlations in its input and adapt its future responses to that 

input by learning to classify input vectors. Based on the 

competitive learning process, the neurons become selectively 

tuned to input patterns so that neurons physically near each 

other in the neuron layer respond to similar input vectors. Since 

the health condition (normality or failure) for each data point is 

not available in the field, SOFMs are particularly suited to find 

patterns in the data, without target class labels. Fig. 5 illustrates 

the training and operating steps in using the SOFM technique

[31].

FIG. 5. FAULT DIAGNOSIS USING SOFM

DEVELOPMENT APPROACH

In our work, wind turbine data is segmented according to 

the wind turbine operating regime (Fig. 6). Different control 

schemes govern different regimes [32]. In order to differentiate 

real fault signatures from similar operational signatures, data is 

partitioned and signatures are compared from the same 

operating regimes. Our focus is on data from operating Region 

2 in Fig. 6.

Data preprocessing also includes modification of the 

sampling rate. The original CART2 data was collected at the 

sampling rate of 100 Hz, as CART2 is used mainly to test 

control schemes. Although such high frequency data contains 

richer information enabling superior fault detection, we downsampled the original data set to 1 Hz. Down-sampling may be 

used because the fault signatures are still observable from the 

lower sampled data. Also, a main objective of this work is to 

use SCADA data for CBM purposes, and the sampling 

frequency of SCADA data is much slower, typically one sample 

per minute.

FIG. 6. WIND TURBINE OPERATION REGION

A gearbox-related failure was reported for CART2. We 

obtained the data measured around the time of failure along 

Training Data

(normal and

fault data)

Training Step

Implementation

Weights

Data

(actual or 

residuals)

Cluster 

Number

Fault

Diagnosis

SOFM

SOFM

Normal

Drivetrain

Faults

Gearbox 

Faults

Fault-Cluster 

Number Map

Cluster Number Associated With

Normal and Fault Conditions
5

with data sets collected before and after the failure. The data 

sets were analyzed to explore patterns in the data. Trends in 

individual measurements, different combinations of individual 

measurements, and their relationships to other measurements 

were analyzed. Fig. 7 and Fig. 8 show results of some of this 

analysis. In both figures, the circles represent faulty data and 

the Xes represents normal, baseline data. Fig. 7 (Fault 

Indications A) shows the variations in the ratio of high-speed 

shaft (HSS) torque to HSS speed versus the power level. This 

figure shows a clear distinction between the fault and the 

baseline, especially at relatively lower power levels (< 400 

kW).

FIG. 7. OBSERVED FAULT INDICATIONS A

FIG. 8. OBSERVED FAULT INDICATIONS B

The variation of the ratio of HSS speed to LSS speed 

versus power level is shown in Fig. 8 (Fault Indications B). In 

this case, the distinction between the fault and the baseline can 

still be observed, although the indication is not as strong as in

Fig. 8—the variation of the speed ratio from fault data is much 

larger across every power level and overlaps with the variation 

from the baseline data.

Based on these observations, fault detection and isolation 

algorithms are developed using PCA and SOFM. The alarms 

are generated based on three indicators: two indicators from 

PCA based on Q-stat and T2

, and one indicator from the SOFM

based on the clusters. During the training stage, two training 

sets were prepared. One set was composed of power, HSS 

torque, and HSS speed, and the other set was composed of 

power, HSS torque, HSS speed, and LSS speed. The first set 

captures the fault indication observed from Fig. 7, and the 

second set captures the fault indications observed from Fig. 7

and Fig. 8.

The data set used to train the AANN was prepared to 

ensure that it represented the widest possible operation range 

within the Region 2, defined earlier. In fact, the training/testing 

data set consists of data collected from operations over a period 

of two years. The training/test data set for the AANN is 

composed of baseline data only. AANN training is done with 

varying numbers of nodes at the bottleneck, mapping, and demapping layers. The number of nodes at the bottleneck layer is 

always smaller than the number of nodes at the mapping and 

input layers. Having trained the model based on the two sets 

described above, we found that the AANN trained with the first 

set performed better, and resulted in fewer false alarms with

more successful detection. We believe this occurs because the 

wider spread of the speed ratio, from the fault data shown in 

Fig. 7, makes the fault and normal data less separable. The 

better performance of the model trained with the first data set 

also is observed when training the SOFM.

The only difference between the training set used for 

SOFM and the training set used for AANN is that the SOFM 

training set must include the fault data. Thus, the SOFM 

training data set is an augmentation of the AANN training set,

with the addition of the fault data. The SOFM training involves 

varying the number of nodes.

RESULTS AND DISCUSSION

Fig. 9 shows the Q-stat and T2 values obtained from the 

AANN using the training data set, which represents the 

baseline. The horizontal line represents the threshold indicating 

anomalies when the Q-stat or T2 of a sample rises above the 

limit. Based on the variability seen in the training set, the 

thresholds are set at the six sigma level for Q-stat and the three 

sigma level for T2

, respectively.
6

FIG. 9. NON-LINEAR PCA OUTPUT FROM TRAINING SET

The result from another test data set that was not part of the 

training data is shown in Fig. 10. This data set covers all 

seasons and includes no reported incidents.

FIG. 10. NON-LINEAR PCA OUTPUT FROM TEST DATA 

(NOT USED IN TRAINING PROCESS)

A portion of the data samples cross the threshold of Q-stat. 

This portion corresponds to the unknown anomalies. Even 

though no confirmed incidents occurred in that time frame, this 

indicates samples that do not fit the non-linear PCA model 

developed from the training data set. With the current threshold 

set at the six sigma level from the training set, there will be 

triggers for any other anomalies that are not covered by the 

training set. A specific event we want to detect is a gearbox 

failure incident that occurred in CART2. The Q-stat values 

from the samples (Fig. 11) obtained during the incident show 

very large values compared to those from the training or other 

test data set (Fig. 9 and Fig. 10). These large values enable us to 

set the threshold at a much higher level so that the alarm is 

triggered only for the target failure and less sensitive to other 

unknown anomalies.

The result from the data collected on the day when the 

gearbox failure occurred is shown in Fig. 11. Both Q-stat and 

T2 display large values. The samples of Q-stat or T2 values 

exceeding the threshold indicate that they are not representative 

of the data used to develop the AANN, which corresponds to 

the baseline condition.

FIG. 11. NON-LINEAR PCA OUTPUT FROM FAILURE CASE

Results from the data collected before the failure are 

shown in Fig. 12 and Fig. 13.

FIG. 12. NON-LINEAR PCA OUTPUT 40 DAYS BEFORE THE 

FAILURE
7

FIG. 13. NON-LINEAR PCA OUTPUT 17 DAYS BEFORE THE 

FAILURE

The data obtained 40 days before the failure is shown in 

Fig. 12, and Fig. 13 is the data obtained 17 days before the 

failure. A few samples cross the thresholds but the indication is 

not strong or persistent, as shown in Fig. 11. These days were 

chosen because they were closest to the failure and had data

that was available to us. The log book indicates that the failure 

was suspected 14 days before it occurred. Unfortunately, data 

for the days leading up to the failure were not available. 

Additional analysis on the consistency of the alarms and lead 

time for early failure detection can be done only when 

additional data leading up to a failure becomes available. 

Tuning thresholds early to maximize accurate detection and to 

minimize false positives also can be done with additional data.

The result from the SOFM training is shown in Fig. 14.

FIG. 14. SOFM OUTPUT FROM TRAINING SET

As mentioned above, the SOFM training set includes not 

only sufficient baseline data but also the fault data. The baseline 

data are shown as xes and include the data from the samples to 

around 3500. The data marked with os and corresponding to the 

samples after 3500 are the fault data. The y-axis represents the 

cluster number. A clustering system of 28 clusters is obtained 

after the training; the y-axis shows the cluster number from 1 to 

28. Notice that no baseline samples correspond to clusters 17, 

18, and 21 and only the samples from the fault data correspond 

to these clusters. This indicates that clusters 17, 18, and 21 are 

formed based on the fault data only; thus, they represent the 

fault condition. Some clusters are not observed in the baseline 

data samples because baseline data samples are obtained from 

various operating conditions. The remaining clusters 

correspond to the baseline data and represent the normal 

condition. Depending on the separation between the fault 

baseline data, the fault or baseline conditions can be 

represented by multiple clusters.

Based on this information, the cluster numbers are mapped 

into the normal and fault conditions. The faulty data set as 

shown in Fig. 15.

FIG. 15. SOFM OUTPUT FROM FAULT CASE AFTER 

MAPPING THE CLUSTERS INTO CONDITIONS

The samples shown in Fig. 15 correspond to the last 3.5 

minutes of the data marked in Fig. 14. This illustrates how an 

alarm based on this diagnostic technique might function in 

practice. Although SOFM can isolate faults, it is trained in only 

one failure mode, as that is the only failure history available. If 

other failure modes are observable from the same set of 

parameters, a SOFM can demonstrate its isolation capability by 

forming other clusters that correspond to new failure modes. In 

that case, two different alarms indicating two different failure 

modes will be generated. Fig. 16 shows the result from the data 

closest to the failure day among the data available. As noted 

earlier, no fault is indicated yet. The comparison of alarms 

triggered by three different indicators is shown in Fig. 17—two 

indicators from AANN based on Q-stat and T2 and one 

indicator from SOFM based on the clusters. The alarms are 

consistent, i.e. when T2 triggers the other two also trigger. 

Compared to T2

, earlier detection is possible from Q-stat and 

SOFM. The three indicators seem to complement each other 
8

well. For the brief periods, when one of the indicators does not 

trigger, the others provide a fault detection signal consistent 

with the indications in the neighboring time points. This finding 

shows that the three indicators presented in this research form a 

good set to include in a fusion scheme for increased robustness 

across indicators and along the time axis during a failure 

progression.

FIG. 16. SOFM OUTPUT BEFORE THE FAILURE AFTER 

MAPPING THE CLUSTERS INTO CONDITIONS

FIG. 17 COMPARISON OF ALARMS FROM THREE 

INDICATORS

SUMMARY

In this work, we presented an approach for fault detection 

using available SCADA-data from wind turbines. Systematic 

analysis of data indicated clear distinctions between fault and 

no-fault conditions in relationships among several parameters. 

These distinctions in relationships were exploited in the 

development of automated fault diagnostics algorithms. Two 

approaches, PCA and SOFM, were used in the algorithms. Both 

approaches are successful in producing persistent indicators 

that are well-distinguished from those generated based on nofault data. Future work in maturing the approaches involves 

testing and tuning the algorithms, using additional data from 

the same and other wind turbines, and developing a fusion 

scheme for increased robustness and early fault detection.

ACKNOWLEDGMENTS

The support for this work by the U.S. Department of 

Energy under Award Number DE-EE0001368 is gratefully 

acknowledged.

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