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

Lou, Jianlou; Xiao, Jian; Xu, Jing

doi:10.14257/ijca.2016.9.3.37

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…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

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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.

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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

Self Cite

View full text Add to dashboard Cite

show abstract

Control chart monitoring of wind turbine generators using the statistical inertia of a wind farm average

et al. 2018

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Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data

et al. 2018

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Ageing of a wind turbine and its components is inevitable. It will affect the reliability and power generation of the turbine over time. Therefore, performing the ageing assessment of wind turbines is of significance not only to optimize the operation and maintenance strategy of the wind turbine but also to improve the management of a wind farm. However, in contrast to the significant number of research on wind turbine condition monitoring and reliability analysis, little effort was made before to investigate the ageing led performance degradation issue of wind turbines over time. To fill such a technology gap, four SCADA-based wind turbine ageing assessment criteria are proposed in this paper for measuring the ageing resultant performance degradation of the turbine. With the aid of these four criteria, a reliable information fusion based wind turbine ageing assessment method is developed and verified in the end using the real wind farm SCADA data. It has been shown that the proposed method is effective and reliable in performing the ageing assessment of a wind turbine.

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

Abstract: Abstract

Cited by 4 publications

References 15 publications

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

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

Control chart monitoring of wind turbine generators using the statistical inertia of a wind farm average

Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data

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