Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components
Different types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining. However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers. To address this problem, an unsupervised outlier det...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8620681/ |
| _version_ | 1819179472120709120 |
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| author | Zexian Sun Hexu Sun |
| author_facet | Zexian Sun Hexu Sun |
| author_sort | Zexian Sun |
| collection | DOAJ |
| description | Different types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining. However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers. To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed. First, the characteristics of the outliers in supervisory control and data acquisition data caused by different reasons are analyzed. Then, the SDAE is utilized to extract features by training the original data. Furthermore, the density-grid-based clustering method is applied to achieve the clustering results. Window width is added to classify the outliers as isolated outliers, missing data, and fault data according to the duration of abnormal data. The monitoring data of four wind turbines are sampled as the training data to demonstrate the effectiveness of the proposed method. The results show that the proposed model can effectively identify the isolated outliers, missing data, and fault information in the high dimensional data set by unsupervised learning. |
| first_indexed | 2024-12-22T21:58:59Z |
| format | Article |
| id | doaj.art-7cff3f08acbe49f0ac8c2422e38bde0b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:58:59Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7cff3f08acbe49f0ac8c2422e38bde0b2022-12-21T18:11:11ZengIEEEIEEE Access2169-35362019-01-017130781309110.1109/ACCESS.2019.28932068620681Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine ComponentsZexian Sun0https://orcid.org/0000-0003-0702-3347Hexu Sun1School of Artificial Intelligence, Hebei University of Technology, Tianjin, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin, ChinaDifferent types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining. However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers. To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed. First, the characteristics of the outliers in supervisory control and data acquisition data caused by different reasons are analyzed. Then, the SDAE is utilized to extract features by training the original data. Furthermore, the density-grid-based clustering method is applied to achieve the clustering results. Window width is added to classify the outliers as isolated outliers, missing data, and fault data according to the duration of abnormal data. The monitoring data of four wind turbines are sampled as the training data to demonstrate the effectiveness of the proposed method. The results show that the proposed model can effectively identify the isolated outliers, missing data, and fault information in the high dimensional data set by unsupervised learning.https://ieeexplore.ieee.org/document/8620681/Density-grid based clusteringoutlier detectionstacked denoising autoencoderunsupervised learning |
| spellingShingle | Zexian Sun Hexu Sun Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components IEEE Access Density-grid based clustering outlier detection stacked denoising autoencoder unsupervised learning |
| title | Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components |
| title_full | Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components |
| title_fullStr | Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components |
| title_full_unstemmed | Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components |
| title_short | Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components |
| title_sort | stacked denoising autoencoder with density grid based clustering method for detecting outlier of wind turbine components |
| topic | Density-grid based clustering outlier detection stacked denoising autoencoder unsupervised learning |
| url | https://ieeexplore.ieee.org/document/8620681/ |
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