Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests

Park, Joon‐Young; Lee, Jaekyung; Oh, Ki‐Yong; Lee, Jun-Shin

doi:10.1109/tec.2013.2294893

Cited by 64 publications

(51 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, as suggested by the IEC standard, 10-minute averages are use rather than the high frequency data to reduce the noise and the effect of autocorrelation. Data have been filtered for outliers caused by events such as sensor malfunctions, database unavailability or malfunction and icing with the method proposed in [9]. The timestamp of the date is the beginning of the time period.…”

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of power curve monitoring methods

et al. 2017

View full text Add to dashboard Cite

Abstract. Performance monitoring is an important aspect of operating wind farms. This can be done through the power curve monitoring (PCM) of wind turbines (WT). In the past years, important work has been conducted on PCM. Various methodologies have been proposed, each one with interesting results. However, it is difficult to compare these methods because they have been developed using their respective data sets. The objective of this actual work is to compare some of the proposed PCM methods using common data sets. The metric used to compare the PCM methods is the time needed to detect a change in the power curve. Two power curve models will be covered to establish the effect the model type has on the monitoring outcomes. Each model was tested with two control charts. Other methodologies and metrics proposed in the literature for power curve monitoring such as areas under the power curve and the use of statistical copulas have also been covered. Results demonstrate that modelbased PCM methods are more reliable at the detecting a performance change than other methodologies and that the effectiveness of the control chart depends on the types of shift observed.

show abstract

Section: Case Studiesmentioning

confidence: 99%

“…Interesting works have been published lately on various monitoring condition techniques for WT [1,2]. Among the monitoring methods, some authors proposed approaches to monitor the production of a WT using Power Curve Monitoring (PCM) [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of power curve monitoring methods

et al. 2017

View full text Add to dashboard Cite

show abstract

“…At present, the main methods of wind turbine anomaly identification are as follows: To analyses the data distribution under wind speed-power attributes, and the abnormal operation state of the wind turbine is identified by machine learning and control chart [4][5][6]. By analyzing the data distribution of wind speed and power attributes, the power curve is accurately modeled by machine learning method, and the abnormal points are deviated from the normal range of power curve to achieve abnormal recognition [7][8].…”

Section: Introductionmentioning

confidence: 99%

A new Abnormal Identification Method of Wind Turbine Apply for Dealing with Big Data

Lou¹,

Xiao²,

Li³

et al. 2017

Advanced Science and Technology Letters

View full text Add to dashboard Cite

Abstract. In order to solve the problem that the identification method of wind turbine is not suitable for large data environment. In this paper, we propose FSIQUE. Firstly, the data space is divided into dense grid cells and sparse grid cells. Based on the FS-Tree storage structure proposed in this paper, a dense grid cell is stored and the subspace is traversed by this storage structure. Secondly, traversing the connected grid cells in the subspace to find the clusters. Finally, the maximum and minimum coverage of the cluster is calculated. According to the parameter, the clustering is divided into normal data and abnormal data to realize the anomaly recognition. The proposed method is run on the Spark platform and compared with the WPMCLU and DBSCAN methods, has the highest abnormal recognition rate and the runtime is shortest.

show abstract

“…But wind turbines' (WTs') high cost for maintenance and repairing hinders the wind power's more rapid development. However, WT condition monitoring plays an important role in WTs' maintenance and repairing [1][2], which could provide first indication evidence that shows something is wrong [3]. Consequently the economic losses can be avoided by further steps.…”

Section: Introductionmentioning

confidence: 99%

A New Condition Monitoring Method for Wind Turbines Based on Power Curve Model

Lou¹,

Xiao²,

Xu³

2016

Advanced Science and Technology Letters

View full text Add to dashboard Cite

Abstract.A new data-driven condition monitoring method for wind turbines is proposed to prevent a turbine failure in a wind farm. The method works through the power curve model which is built with historical SCADA data. New constraints that established based on Betz' law and RC model are developed for governing the power curve model. Since abnormal data has a strong impact on the power curve model, the Inner-DBSCAN algorithm is proposed to reject it. In addition, we use an edge recognition method for normal data to form the power curve model. Then, the turbine operation condition can be monitored through the model. Its effectiveness through industrial studies is confirmed, and the time cost of building the power curve model is only 1.08s.

show abstract

Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests

Cited by 64 publications

References 2 publications

Comparison of power curve monitoring methods

Comparison of power curve monitoring methods

A new Abnormal Identification Method of Wind Turbine Apply for Dealing with Big Data

A New Condition Monitoring Method for Wind Turbines Based on Power Curve Model

Contact Info

Product

Resources

About