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

Lou, Jianlou; Xiao, Jian; Xu, Jianliang

doi:10.14257/astl.2016.121.85

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Figure 1 shows SPE chart of PCA. The control limit with confidence level = 0.003, which can be calculated by (4). From Figure 1, the abnormally can be detected from the 411th sample time.…”

Section: Data Feature Extractionmentioning

confidence: 99%

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DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Zhou

Wang

Gao

2017

Journal of Control Science and Engineering

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Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.

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“…Figure 1 shows SPE chart of PCA. The control limit with confidence level = 0.003, which can be calculated by (4). From Figure 1, the abnormally can be detected from the 411th sample time.…”

Section: Data Feature Extractionmentioning

confidence: 99%

“…With the rapid development of modern industrial technology, reliability, maintainability, and security of largescale system have widely received attention [1][2][3][4]. Abnormal detection, root cause identification, and RUL prediction are the stages for efficient system monitoring.…”

Section: Introductionmentioning

confidence: 99%

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Zhou

Wang

Gao

2017

Journal of Control Science and Engineering

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

Cited by 2 publications

References 14 publications

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

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

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