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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 29, NO. 1, MARCH 2014

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Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests Joon-Young Park, Jae-Kyung Lee, Ki-Yong Oh, and Jun-Shin Lee

Abstract—A novel power curve monitoring method for wind turbines was developed to prevent a turbine failure in a wind farm. Compared with the existing methods, this algorithm automatically calculates the power curve limits for power curve monitoring, even when a considerable number of abnormal data are included in wind speed-output power data measured at a wind turbine. In addition, the proposed algorithm automatically generates an alarm message when the wind speed–power data measured at the wind turbine deviate from the power curve limits, particularly considering their degree of deviation from the power curve limits and the cases when the measured data hover between the Warning Zones and the Alarm Zones. We confirmed its effectiveness through its field tests. Index Terms—Alarm generation, condition monitoring, fault data queue, power curve, turbine monitoring, wind turbine. Fig. 1.

Types of power curves that appear in various failure cases [2].

Fig. 2.

Overall procedures of the power curve monitoring method.

I. INTRODUCTION HIS paper deals with the power curve monitoring algorithms that can monitor changes in the power curve and can report measured data outside normal operating conditions in real time. The power curve, which shows a wind turbine’s output graph to the wind speed input, is the official performance indicator of the wind turbine that has to be guaranteed by the turbine manufacturer [1]. As seen in Fig. 1, however, the output power deviates from the normal power curve, when a failure in the turbine occurs due to the following such as overrating, pitch malfunction, wind speed underreading, downrating, dirt or bugs on blades, icing on blades and so on [2]. Used alongside condition monitoring, such evidence can strengthen the case for preventive maintenance, and in the absence of condition monitoring, such evidence may be the first indication that something is wrong [3]. From this observational result, power curve monitoring methods have been noted for like condition monitoring systems [4], [5]. For such a purpose, SgurrEnergy’s sgurrtrend performance monitoring software provides data vi-

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Manuscript received February 14, 2013; revised July 5, 2013 and November 1, 2013; accepted December 3, 2013. Date of publication January 9, 2014; date of current version February 14, 2014. This paper presents the results of “A Study on the Demonstration Project for 2.5 GW Offshore Wind Farm at the Southern Part of Yellow Sea” project, supported by the New & Renewable Energy Program of the Korea Institute of Energy Technology Evaluation and Planning grant funded by the Korea government Ministry of Knowledge Economy (No. 2011T100100307). Paper no. TEC-00087-2013. J.-Y. Park, J.-K. Lee and J.-S. Lee are with the Future Technology Laboratory, KEPCO Research Institute, Korea Electric Power Corporation, Daejeon 305380, Korea (e-mail: [email protected]; [email protected]; [email protected]). K.-Y. Oh is with the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2125 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TEC.2013.2294893

sualization through a range of metrics that support trained personnel in the rapid assessment of a wind farm’s performance, and its experience of reviewing the performance of wind farms suggests that improvements of 1–3% in revenue are readily attainable [3]. Recently, OptiFarm, which is a commercialized wind farm management tool of Overspeed GmbH & Co. KG, offers monitoring of the power curve that detects and reports changes in the curve and measured data outside normal operating conditions [6]. However, there has been not much research on this issue. Fig. 2 shows the overall procedures of a power curve monitoring method largely consisting of the Learning Phase and the Monitoring Phase. As for existing studies on the Learning Phase, there was a research case at Institute for Solar Energy Supply Technology (ISET) where 5-min average values of measured data were classified with a bin width of 0.5 m/s wind speed and Alarm Limits were set by obtaining the class average value and the standard deviation in each bin class [7]. However, the proposed method sets the limits to be suitable only for stall-controlled turbines, and therefore is difficult to apply to pitch-controlled turbines which are currently the mainstream of MW-class wind turbines.

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Fig. 4. Problem cases when simple alarm generation algorithms are used. (a) Case 1. (b) Case 2.

Fig. 3. Cases of power curve limit setting by previous methods. (a) ISET [7]. (b) Intelligent Systems Laboratory [8].

The representative power curve monitoring method is the nonparametric model approach using a data mining algorithm developed by the Intelligent Systems Laboratory at the University of Iowa [8]. The Intelligent Systems Laboratory tested various data mining algorithms for power curve estimation; from these, the k-nearest neighbors (k-NN) model showed the best performance. In addition, the residual control chart for setting power curve limits was presented. However, the accuracy of the k-NN model used by the presented data mining method can be greatly decreased by abnormal data; in addition, since k-NN have to be assigned through computing the distances of the new data from whole learning data whenever new data are added, total calculation time increases with the amount of learning data. One of the most difficulties in applying power curve monitoring methods is that the input data of power curve limit-setting algorithms should not include abnormal data as many as possible so as to obtain satisfactory power curve limits. However, in case of irregular energy sources such as wind power, there is no guarantee that only normal data are generated after their installation. Thus, for real-world application, abnormal data have

to be removed from measurement data, which take tremendous time and effort. Next, when looking at existing studies on the Monitoring Phase, related studies have been limited to simple level of generating an alarm message when measurement data deviate from the power curve limits; the ISET in Germany has a study case in which an alarm message is generated if the Alarm Limits have been exceeded three consecutive times and then subsequently an Alarm Limit is exceeded for the fourth time [7]. Such a simple algorithm causes many problems in real-world applications. For example, when the measured data move in the following sequence as shown in Fig. 4(a): Normal Zone (within Warning Limits) → Warning Zone (between Warning and Alarm Limits) → Alarm Zone (where Alarm Limits have been exceeded) → Alarm Zone → Normal Zone → Alarm Zone no alarm message is created since the same type of limit (one of Warning or Alarm Limits) has not been exceeded three consecutive times if the existing algorithm is used. However, the measurement data’s trajectory shows that there is a problem with the wind turbine currently. That is to say, there is no research yet for cases in which, as in Fig. 4(a), the same type of limits has not been exceeded three consecutive times but moves between zones over Warning Limits three consecutive times and whether warning or alarm message should be generated in such cases. In the case of Fig. 4(b), two cases are illustrated together; although the right case is a more serious alarm generation situation than the left case, the current simple alarm message generation identifies both as the same situation. That is, the existing algorithm only considers whether the measured data have exceeded the limit, but does not consider the amount of limit deviation which shows the extent the limit was exceeded, as an algorithm parameter. To facilitate the actual field application of the developed power curve monitoring method, in this study, new algorithms were developed with the problems of existing algorithms per each phase solved. In addition, the developed power curve monitoring method was actually applied on a MW class wind turbine to show its effectiveness by proving the algorithm’s fault diagnostics reliability.

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Fig. 7. Calculation of the average of the powers per each sorted bin (displayed on bin’s center).

Fig. 5.

Automatic power curve limit calculation algorithm.

Fig. 6. Example of input data to algorithm (showing the wind turbine’s output characteristics according to wind speed).

II. DEVELOPMENT OF AUTOMATIC POWER CURVE LIMIT CALCULATION ALGORITHM A. Developed Algorithm Fig. 5 shows the overall procedures of the automatic power curve limit calculation algorithm developed in this study. A detailed description of each algorithm stage, using the wind speed–power data in Fig. 6, which was measured at an actual wind turbine, is presented next. The first stage sorts input data using a variable speed bin; it sorts the measured speed–power data based on the speed bin to prepare for the second stage where the power’s average and

standard deviation are calculated per each bin. For example, when the speed bin’s width is 1 m/s, input data with the range of “1.5 m/s ≤ wind speed < 2.5 m/s” are sorted into the same bin of 2 m/s. The speed bin’s width (unit: m/s) is calculated as follows: 1 . Bin width = Number of iterations of overall algorithm loop (1) If the bin’s width is determined according to (1), the width starts at 1 m/s and narrows to “1/2 m/s, 1/3 m/s . . .” with each iteration of the algorithm. Such a variable speed bin is used for the following reasons. 1) A wide bin is used initially to reduce the impact of abnormal data whenever possible since a large amount of abnormal input data may be included initially. 2) As can be seen in Fig. 6, torque control of the generator dominates the entire system characteristics at low wind speeds and gradually increases the energy capture in proportion to the wind speed. But the wind turbine characteristics due to pitch control dominate the entire system characteristics near the rated wind speed so that the output is regulated rapidly. Thus, to reflect such characteristics of wind turbines, bins with narrower width have to be used. In the second stage, the power’s average and standard deviation of each sorted bin are obtained. Here, the average power values per bin are used as the inputs of the next stage where the power curve is estimated through interpolation; the standard deviation values of each bin’s power are used for determining whether to terminate the entire loop at a later stage. The third stage estimates the power curve using interpolation. The average power per bin calculated at the previous stage is used as the input for interpolation. In this study, the cubic (third order) B-Spline interpolation [9], which shows good performance, was used; however, various interpolation methods can be used for estimating the power curve. Fig. 8 shows the result of applying cubic B-Spline interpolation method on the power average per bin of Fig. 7. The fourth stage searches for the optimal power curve limits including only normal data whenever possible while excluding

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Fig. 8.

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Estimation of power curve using the interpolation method.

abnormal data by moving the estimated power curve left/right or up/down. That is, only normal data are selected as the new input data for the next algorithm loop whenever possible by excluding abnormal data from the existing input data. First, the obtained power curve of Fig. 8 is moved left/right to search for the optimal upper and lower power curve limits that include only normal data whenever possible. Here, the upper and lower limits refer to the power curve limits that will be located above and below the estimated power curve, respectively. Here, the following inequality determines whether the optimum upper/lower power curve has been obtained at the current stage; the power curve is moved left/right by Δυ repetitively until the following equation is satisfied. In the implementation example of Fig. 10, Δυ of 0.1 m/s was used: PDLi − PDLi−1 < βshift (i > 1).

(2)

Here, i is the number of the limit search algorithm’s iterations; the percentage of data within limits (PDL) is defined as follows in unit of percent:

Fig. 9. (a) Percentage of data within upper and lower power curve limits per left/right movement distance and (b) variation of that percentage (right).

PDLi  Number of data existing between upper & lower limits = Total number of input data  × 100 . i

βshift is a constant that determines whether the optimal upper and lower power curve limits have been determined at this stage; in the implementation examples of Figs. 9 and 10, 1% was used. Fig. 9 illustrates the percentage of data within upper and lower power curve limits per left/right movement distance and the variation of that percentage; Fig. 10 shows the resulting optimal upper and lower power curve limits. Next, while moving the upper and lower power curve limits obtained at Fig. 10 up and down, respectively, the optimal upper and lower power curve limits including only normal data whenever possible are searched. In the same way, the following inequality has to be satisfied if the optimal upper and lower power curve limits have been obtained at the current stage; the power curve is moved up and down repetitively by ΔP . In the

Fig. 10. Optimal upper and lower power curve limits obtained through left/right movement search of power curve.

example of Fig. 12, ΔP of 5 kW was used: PDLi − PDLi−1 < γoffset (i > 1).

(3)

Here, the definitions of i and PDL are the same as in (2). γoffset is a constant that determines whether the optimum

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Fig. 12. Optimum upper and lower power curve limits obtained from power curve limit up/down movement search.

Fig. 11. (a) Percentage of data within upper and lower power curve limits per upper/lower movement distance (left) and (b) variation of that percentage (right).

Fig. 13. Implementation example of algorithm according to this study (that is applied to input data of Fig. 6).

data through the variable speed bin: upper and lower power curve limits have been identified at the current stage; 0.05% was used in the example of Figs. 11 and 12. Fig. 11 illustrates the percentage of data within the upper and lower power curve limits per up/down movement distance and the variation of said percentage; Fig. 12 shows the finally obtained optimum upper and lower power curve limits. In the aforementioned application example, right/left search was performed before up/down search; however, it is also possible to search for the optimum upper/lower power curve limits in the reverse order. The fifth stage extracts only data existing within set upper/lower power curve limits as new input data to exclude abnormal data from input data. The last stage identifies whether to terminate the entire algorithm loop. To do this, the average of the power standard deviation per bin obtained at the second stage is calculated and compared with the average of the power’s standard deviation values per bin from the previous overall algorithm loop. That is, if the following inequality is satisfied, the entire algorithm loop is terminated; if not, it returns to the first stage of sorting input

(Average of power’s standard deviation values per bin)k · · · (Average of power’s standard deviation values per bin)k −1 < αlo op .

(4)

Here, k (k > 1) refers to the number of iterations of the entire algorithm loop; αlo op is the constant for identifying whether to terminate the automatic power curve limit calculation algorithm. In this example, 1 was used as αlo op . B. Application Examples Fig. 13 shows the final result of applying this study’s automatic power curve calculation algorithm on the input data example of Fig. 6. The entire algorithm loop was terminated at the fourth iteration; we can see that it successfully calculates the power curve limits. Fig. 14(a) shows the original input dataset measured from another MW class wind turbine; compared to Fig. 6, we can see that more abnormal data are included in the input data. Fig. 14(b) to (f) depicts the power curve limits (denoted by upper and lower

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Fig. 14. Another implementation example of developed algorithm. (a) Original input dataset. (b) Iteration number = 1. (c) Iteration number = 2. (d) Iteration number = 3. (e) Iteration number = 4. (f) Iteration number = 5.

dotted lines) obtained from each iteration of the algorithm loop and new input data for the next iteration loop. As the entire process of the algorithm loop repeatedly goes on, abnormal data are gradually excluded from the original input dataset, which is achieved by setting only data existing within obtained power curve limits as new input data for the next iteration loop. The entire algorithm loop was terminated after five iterations as a result of applying this study’s algorithm. It can be seen that the power curve limit is calculated successfully despite abundant abnormal data.

III. DEVELOPMENT OF ALARM MESSAGE GENERATION ALGORITHM A. Developed Algorithm The power curve limit calculation algorithm developed in the previous section automatically calculates the Warning Limits shown in Fig. 4 that separates the Normal Zone from the Warning Zone. The Alarm Limits separating the Warning Zone from the Alarm Zone usually use a constant multiple of the Warning Limits as follows; in this study, δ was set to 2:

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TABLE I RESULTS OF APPLYING THIS STUDY’S ALGORITHM IN FIG. 4(a)

Fig. 15.

Fault data queue.

Fig. 16.

Definition of parameters included in fault data queue.

Alarm Limits = Warning Limits × δ.

(5)

The study of this section is about developing an algorithm that automatically generates an alarm message if the wind speed– power data measured at the wind turbine exceed the aforementioned power curve limits, which is the algorithm for the Monitoring Phase where power curve monitoring is actually being performed. The alarm message generation algorithm for power curve monitoring will be explained in detail later. To solve the problems occurring when the simple alarm message generation is applied to various situations in the actual field, the fault data queue as in Fig. 15 is proposed and used in this study. The fault data queue consists of three fault datasets; each fault dataset consists of the error distance of measured power from the power curve center Pi , the Warning Limit value at measured speed Wi , and the Alarm Limit value at measurement speed Ai . Here, the “estimated power curve” of Fig. 16 used when calculating Pi refers to the “power curve estimated by using the interpolation method” at the third phase of the previous section’s automatic power curve limit calculation algorithm. The alarm message generation algorithm using fault data queue is as follows. 1) The fault data queue has a FIFO data structure just like a generic queue: First In First Out data structure. 2) If the measurement data enter the Warning Zone or the Alarm Zone, the fault dataset calculated by the relevant data is entered into the fault data queue. 3) If new fault datasets are entered while three fault datasets in the fault data queue are already saturated, the oldest fault dataset is deleted and new fault dataset is entered. 4) If the measured data enter the Normal Zone, the oldest fault dataset is deleted from the fault data queue. 5) If the fault data queue is filled with fault datasets or new fault dataset is entered into an existing full fault data queue,

the following alarm message identification algorithm is performed. a) Calculate the following fault Index F : F = P1 + P2 + P3 . b) If “F ≥ A1 + A2 + A3 ,” then an alarm message is generated. c) If “W1 + W2 + W3 ≤ F < A1 + A2 + A3 ,” then a warning message is generated. 6) If the fault data queue is emptied completely, that is, if all data are deleted, currently active warning or alarm message is released. For the input data used as the input of the alarm message generation algorithm, 10-min average values of measured data are used in this study. As can be seen in the aforementioned study’s algorithm, the alarm message is generated fastest when three fault data are measured consecutively. In addition, unlike the existing algorithms that do not generate an alarm message even if measured data move in the following direction sequentially as in the example of Fig. 4(a), the alarm message generation mechanism generates an alarm message as in Table I to notify that a problem has occurred at the wind turbine.

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Fig. 17. Example of actual application of alarm message generation algorithm according to this study. (a) Measured wind speed-output power data (10-min average). (b) Result of algorithm application.

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Fig. 18. Analysis example on actual application of the alarm message generation algorithm. (a) Measured wind speed-output power data (10-min average). (b) Result of Algorithm application.

B. Application Examples Fig. 17(a) illustrates the 10-min average data of wind speed– output measured while operating a 2-MW class wind turbine for three months; Fig. 17(b) shows the alarm message results (Alarm level 1 denotes Warning, Alarm level 2 Alarm) of applying the alarm message generation algorithm according to this study. Among the aforementioned data and the results of application, this section explains one representative case of algorithm application. Fig. 18 shows the analysis result on the cases, among the results of Fig. 17 shown previously, where alarm and warning messages were generated during data no. 11 556–11 625 according to this study’s algorithm. The circular points in Fig. 18(a) show the data during data number range of 11556–11625 that contributed to the alarm message generation. Fig. 18(a) and (b) shows that alarm and warning messages occurred as the measurement data left the power curve limits. Verification of turbine status by checking the 1-s data stored in the wind turbine’s supervisory control and data acquisition (SCADA) system showed that errors were continuously generated while alarm messages occurred. As a result, we can confirm that the alarm message generation algorithm according to this study has properly generated alarm and warning messages.

Fig. 19.

System configuration for field tests.

IV. FIELD TESTS A wind turbine of the 22 MW YeongHeung wind farm in Korea was selected as the target for field tests of the developed monitoring method. Fig. 19 shows the whole system configuration for the field tests. The nacelle anemometer was used for wind speed measurement, and output power was measured by the current transformer and the potential transformer in the inverter. The measured data were sent to the SCADA system

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TABLE II COMPARISON OF THE RESULTS OF POWER CURVE MONITORING METHOD APPLICATION AND TURBINE CONTROLLER’S ALARM

through the turbine controller. Then, the power curve monitoring algorithm embedded in the SCADA system was performed by using these data. As shown in Fig. 17(a), the 10-min average values of the wind speed–power data measured from 2011/11/24 12:00 to 2012/02/24 12:00 (total of 12 644 data) were used as the algorithm input data for verification. The dotted and solid lines of Fig. 17(a) show the Warning Limits and Alarm Limits, respectively. These lines were set by applying the developed automatic power curve monitoring limit calculation algorithm according to the aforementioned measurement data. Fig. 17(b) shows the result of warning and alarm messages generated by the alarm message generation algorithm. As shown in Fig. 17(b), the developed power monitoring method generated a total of 21 alarm messages and 12 warning messages during this period. To check whether the developed monitoring method has properly generated the alarm messages, a comparative analysis on all the “Trip Alarm (Alarm Level 1)” messages generated by the chosen turbine’s controller during the same period was performed. In this analysis, alarm messages that occurred during similar time interval are considered to have occurred from the same cause and bundled into one Event. Table II shows a final summary of results from “Fault Analysis Reliability Verification by Case” on 21 events, excluding low wind speed and nonoperating time below 30 min. Applying the power curve monitoring method and comparing with the turbine controller’s alarm message generation functionality led to the following conclusions.

1) Regarding the characteristics of algorithm, the power curve monitoring method excludes low wind speed intervals and nonoperating time below 30 min. However, these cases were caused by light turbine failures that could be recovered immediately. 2) As a result of applying the developed power curve monitoring method, a total of 26 alarm messages occurred. On the other hand, the turbine controller generated only 21 alarm messages (including two instances of delayed controller alarm generation). 3) The result of analysis on 1-s data showed that from the 26 times above, the turbine was actually in fault condition 25 times (error generation status). 4) In the single case where a turbine failure did not occur (Event 13), the power curve monitoring method generated a warning message two hours before the largest failure occurred; therefore, we believe it provides reliable alarm generation. 5) When the turbine controller generated a Trip Alarm, the power curve monitoring method generated an alarm message with 100% probability. Summarizing these conclusions, the developed power curve monitoring method showed a fault analysis reliability of more than 100% (actually, 26/21×100%) relative to the turbine controller. That is, it can be concluded that the power curve monitoring method generates alarm messages more reliably than the turbine generator; the usefulness of the developed power

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curve monitoring method could be verified through actual field application on a MW class wind turbine.

Jae-Kyung Lee received the B.S. degree from Kyungpook National University, Daegu, Korea, in 2002, and the M.S. degree from the Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 2004, both in electrical engineering. He is currently a Senior Researcher at the Future Technology Laboratory, KEPCO Research Institute, Daejeon. His research interests include the development of high performance robot control, hazardous robot systems, and supervisory control and data acquisition/condition monitoring systems for wind

V. CONCLUSION This paper presented a novel power curve monitoring method. The automatic power curve limit calculation algorithm automatically calculates the power curve limit even if many faulty data are included in wind speed–power data, measured from a wind turbine in addition to normal data, so the power curve monitoring method can be easily applied in the field. The alarm message generation algorithm of this study generates alarm messages in due consideration of the extent of deviation from the limit and movement of measurement data between Warning Zone and Alarm Zone. Therefore, if the power curve monitoring method of this study is applied to actual wind turbine status monitoring, the operational efficiency, reliability, and economic feasibility can be maximized since failures in a wind turbine’s overall system can be rapidly recognized and handled. The power curve monitoring method developed through this study has been applied to the CMS for the YeongHeung wind farm since January 2013 and will be used as the key monitoring algorithm of the SCADA system for the 2.5 GW Offshore Wind Farm at the Southern Part of Yellow Sea in Korea.

turbines.

Ki-Yong Oh received the B.S. degree from Hanyang University, Seoul, Korea, in 2005, and the M.S. degree from the Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 2006, both in mechanical engineering, and is currently working toward the Ph.D. degree in mechanical engineering from the University of Michigan, Ann Arbor, MI, USA. He is a Senior Researcher at the Future Technology Laboratory, KEPCO Research Institute, Daejeon. His research interests are in the area of energy systems, more specifically, active control and fault diagnosis/prognosis of wind turbines, wind resource assessment, and dynamic characterization of the energy storage system.

REFERENCES [1] E. Hau, Wind Turbines: Fundamentals, Technologies, Application, Economics, 2nd ed. Berlin, Germany: Springer, 2005. [2] Z. Zhang, “Power curve: Concepts and applications,” Intelligent Systems Laboratory, The University of Iowa, Iowa City, USA, 2013. [3] D. McLaughlin, P. Clive, and J. McKenzie, “Staying ahead of the wind power curve: SCADA data can aid monitoring,” Renewable Energy World, vol. 13, pp. 61–68, Apr. 13, 2010. [4] M. Sheehy, “Wind energy: Optimising operations,” Eng. J., vol. 64, no. 6, pp. 237–239, 2010. [5] A. Verma and A. Kusiak, “Predictive analysis of wind turbine faults: A data mining approach,” in Proc. Ind. Eng. Res. Conf., Reno, Navada, May 19–23, 2011, pp. 1–9. [6] [Online]. Available: http://overspeed.de/gb/media/flyer_optifarm.pdf [7] P. Caselitz and J. Giebhardt, “Rotor condition monitoring for improved operational safety of offshore wind energy converters,” J. Sol. Energy Eng., vol. 127, pp. 253–261, 2005. [8] A. Kusiak, H. Zheng, and Z. Song, “On-line monitoring of power curves,” Renewable Energy, vol. 34, pp. 1487–1493, 2009. [9] M. T. Heath, Scientific Computing: An Introductory Survey, 2nd ed. New York, NY, USA: McGraw-Hill, 2002.

Joon-Young Park received the B.S. degree in electrical engineering in 1995, and the M.S. and Ph.D. degrees in mechanical engineering from the Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 1997 and 2004, respectively. He is currently a Senior Researcher at the Future Technology Laboratory, KEPCO Research Institute, Daejeon, and a Project Leader of “Development of Condition Monitoring System for Thermal and Wind Power Plant Complex” project. His research interests include the robust control of nonlinear systems, the robot systems for the electric power industry as well as supervisory control and data acquisition/condition monitoring systems for wind turbines.

Jun-Shin Lee received the B.S. degree in electrical engineering from Seoul National University, Seoul, Korea, in 1985, and the M.S. and Ph.D. degrees in mechanical engineering from the Korea Advanced Institute of Science and Technology, Daejeon, Korea, in 1988 and 1995, respectively. He is currently a Principal Researcher at the Future Technology Laboratory, KEPCO Research Institute, Daejeon. His research interests include vibration control of pipelines built in the nuclear power plants and development of predictive monitoring-algorithms for wind turbines.