Research on elimination method of abnormal power data of wind turbine
Wind speed-power curve is widely used in power prediction, condition monitoring and fault diagnosis of wind turbine. Its main construction method is to fit the supervisory control and data acquisition (SCADA) data. However, due to wind abandonment, power limitation, instrument failure and other fact...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-02-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220331001&flag=1&journal_id=dcyyben&year_id=2025 |
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| author | YANG Xinyue JING Bo MEI Zhigang QIAN Zheng |
| author_facet | YANG Xinyue JING Bo MEI Zhigang QIAN Zheng |
| author_sort | YANG Xinyue |
| collection | DOAJ |
| description | Wind speed-power curve is widely used in power prediction, condition monitoring and fault diagnosis of wind turbine. Its main construction method is to fit the supervisory control and data acquisition (SCADA) data. However, due to wind abandonment, power limitation, instrument failure and other factors, some abnormal power data exist in SCADA data. In order to ensure the accuracy and reliability of fitting results, these abnormal data should be eliminated first. In this paper, a method for eliminating abnormal data of wind turbine is proposed. Firstly, the quantile method is used to eliminate the discrete points far from the normal data. Then, K-means clustering method and improved time series method are combined to eliminate the central accumulation points. Finally, the combination method of quantile method and density-based spatial clustering of applications with noise (DBSCAN) clustering method is used to eliminate the discrete points close to the normal data. In this paper, the quantile method, the basic time series method and the method in this paper are compared and tested by using the simulation data set and the measured data set respectively. The results show that the proposed method is optimal and has a good effect on eliminating both the middle accumulation points and discrete points. |
| format | Article |
| id | doaj-art-43a47d3c795647fb83c465634accfa30 |
| institution | OA Journals |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-43a47d3c795647fb83c465634accfa302025-08-20T02:02:09ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-02-01622768210.19753/j.issn1001-1390.2025.02.0101001-1390(2025)02-0076-07Research on elimination method of abnormal power data of wind turbineYANG Xinyue0JING Bo1MEI Zhigang2QIAN Zheng3Beihang University, Beijing 100191, ChinaBeihang University, Beijing 100191, ChinaChina Power Hua Chuang Electricity Technology Research Co., Ltd., Suzhou 215000, Jiangsu, ChinaBeihang University, Beijing 100191, ChinaWind speed-power curve is widely used in power prediction, condition monitoring and fault diagnosis of wind turbine. Its main construction method is to fit the supervisory control and data acquisition (SCADA) data. However, due to wind abandonment, power limitation, instrument failure and other factors, some abnormal power data exist in SCADA data. In order to ensure the accuracy and reliability of fitting results, these abnormal data should be eliminated first. In this paper, a method for eliminating abnormal data of wind turbine is proposed. Firstly, the quantile method is used to eliminate the discrete points far from the normal data. Then, K-means clustering method and improved time series method are combined to eliminate the central accumulation points. Finally, the combination method of quantile method and density-based spatial clustering of applications with noise (DBSCAN) clustering method is used to eliminate the discrete points close to the normal data. In this paper, the quantile method, the basic time series method and the method in this paper are compared and tested by using the simulation data set and the measured data set respectively. The results show that the proposed method is optimal and has a good effect on eliminating both the middle accumulation points and discrete points.http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220331001&flag=1&journal_id=dcyyben&year_id=2025wind power generationabnormal data eliminationclustering methodquantile |
| spellingShingle | YANG Xinyue JING Bo MEI Zhigang QIAN Zheng Research on elimination method of abnormal power data of wind turbine Diance yu yibiao wind power generation abnormal data elimination clustering method quantile |
| title | Research on elimination method of abnormal power data of wind turbine |
| title_full | Research on elimination method of abnormal power data of wind turbine |
| title_fullStr | Research on elimination method of abnormal power data of wind turbine |
| title_full_unstemmed | Research on elimination method of abnormal power data of wind turbine |
| title_short | Research on elimination method of abnormal power data of wind turbine |
| title_sort | research on elimination method of abnormal power data of wind turbine |
| topic | wind power generation abnormal data elimination clustering method quantile |
| url | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220331001&flag=1&journal_id=dcyyben&year_id=2025 |
| work_keys_str_mv | AT yangxinyue researchoneliminationmethodofabnormalpowerdataofwindturbine AT jingbo researchoneliminationmethodofabnormalpowerdataofwindturbine AT meizhigang researchoneliminationmethodofabnormalpowerdataofwindturbine AT qianzheng researchoneliminationmethodofabnormalpowerdataofwindturbine |