A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis
Affected by meteorological conditions, terrains, locations and specific designs, wind turbines exhibit significant uncertainties and disparities in wind energy input, which makes it difficult to predict their output power. This paper aims to enhance operational control balance in wind turbines and a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2024-04-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.02.002 |
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| _version_ | 1849224989198254080 |
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| author | ZHANG Jiayou YAN Yibing WEN Kun HU Kaikai CHEN Gang |
| author_facet | ZHANG Jiayou YAN Yibing WEN Kun HU Kaikai CHEN Gang |
| author_sort | ZHANG Jiayou |
| collection | DOAJ |
| description | Affected by meteorological conditions, terrains, locations and specific designs, wind turbines exhibit significant uncertainties and disparities in wind energy input, which makes it difficult to predict their output power. This paper aims to enhance operational control balance in wind turbines and advance more sophisticated and intelligent control at wind farms. Utilizing the autoregressive integrated moving average (ARIMA) model, a component of time series analysis, this study analyzed time series data related to the micro wind speeds of wind turbines, and examined their correlation and randomness. The study results culminated in time series modeling to represent micro wind speeds of wind turbines, which facilitated the subsequent wind speed prediction trials. Through employing the algorithm developed for micro wind speed prediction of individual wind turbines at wind farms, the proposed approach provides supporting data for the vortex-induced vibration resistance, grid connection preparation, prevention of operational risks including load impacts, and precise control, establishing a framework for performance balance across wind turbines at wind farms, refined management including service life, and efficient operation and maintenance. |
| format | Article |
| id | doaj-art-e0c1f8014f0845488f53b95071355bf1 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-e0c1f8014f0845488f53b95071355bf12025-08-25T06:48:13ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272024-04-01121856102302A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series AnalysisZHANG JiayouYAN YibingWEN KunHU KaikaiCHEN GangAffected by meteorological conditions, terrains, locations and specific designs, wind turbines exhibit significant uncertainties and disparities in wind energy input, which makes it difficult to predict their output power. This paper aims to enhance operational control balance in wind turbines and advance more sophisticated and intelligent control at wind farms. Utilizing the autoregressive integrated moving average (ARIMA) model, a component of time series analysis, this study analyzed time series data related to the micro wind speeds of wind turbines, and examined their correlation and randomness. The study results culminated in time series modeling to represent micro wind speeds of wind turbines, which facilitated the subsequent wind speed prediction trials. Through employing the algorithm developed for micro wind speed prediction of individual wind turbines at wind farms, the proposed approach provides supporting data for the vortex-induced vibration resistance, grid connection preparation, prevention of operational risks including load impacts, and precise control, establishing a framework for performance balance across wind turbines at wind farms, refined management including service life, and efficient operation and maintenance.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.02.002wind turbineswind speed predictiontime series analysisnon-stationaryautoregressive integrated moving average(ARIMA) model |
| spellingShingle | ZHANG Jiayou YAN Yibing WEN Kun HU Kaikai CHEN Gang A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis Kongzhi Yu Xinxi Jishu wind turbines wind speed prediction time series analysis non-stationary autoregressive integrated moving average(ARIMA) model |
| title | A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis |
| title_full | A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis |
| title_fullStr | A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis |
| title_full_unstemmed | A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis |
| title_short | A Modeling Method for Micro Wind Speed Prediction of Wind Turbines Based on Time Series Analysis |
| title_sort | modeling method for micro wind speed prediction of wind turbines based on time series analysis |
| topic | wind turbines wind speed prediction time series analysis non-stationary autoregressive integrated moving average(ARIMA) model |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.02.002 |
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