A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm
Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a qua...
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Science Press, PR China
2024-12-01
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| Series: | Gaoyuan qixiang |
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| Online Access: | http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00043 |
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| author | Runjin YAO Shuaibing CHENG Qianqian ZHAO Wenlong LI Dong QIAN |
| author_facet | Runjin YAO Shuaibing CHENG Qianqian ZHAO Wenlong LI Dong QIAN |
| author_sort | Runjin YAO |
| collection | DOAJ |
| description | Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability. |
| format | Article |
| id | doaj-art-fb28215d5682443fb1dcf71bcfedd103 |
| institution | OA Journals |
| issn | 1000-0534 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Science Press, PR China |
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| series | Gaoyuan qixiang |
| spelling | doaj-art-fb28215d5682443fb1dcf71bcfedd1032025-08-20T02:35:18ZzhoScience Press, PR ChinaGaoyuan qixiang1000-05342024-12-014361630163810.7522/j.issn.1000-0534.2024.000431000-0534（2024）06-1630-09A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind FarmRunjin YAO0Shuaibing CHENG1Qianqian ZHAO2Wenlong LI3Dong QIAN4Design and Research Institute of Jingke Power Technology Co.， Ltd， Nanjing 210000， Jiangsu， ChinaSchool of Aeronautics， Astronautics and Mechanics， Tongji University， Shanghai 200000， ChinaCarbon neutralization Research Institute， PowerChina Jiangxi Electric Power Construction Co.， Ltd.， Nanjing 210044， Jiangsu， ChinaJiangxi Branch， China Three Gorges new energy （Group） Co.， Ltd.， Nanchang 330038， Jiangxi， ChinaCarbon neutralization Research Institute， PowerChina Jiangxi Electric Power Construction Co.， Ltd.， Nanjing 210044， Jiangsu， ChinaMountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00043quality controlwind speedvariational modal decompositionconvolutional neural networkgate recurrent unit |
| spellingShingle | Runjin YAO Shuaibing CHENG Qianqian ZHAO Wenlong LI Dong QIAN A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm Gaoyuan qixiang quality control wind speed variational modal decomposition convolutional neural network gate recurrent unit |
| title | A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm |
| title_full | A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm |
| title_fullStr | A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm |
| title_full_unstemmed | A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm |
| title_short | A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm |
| title_sort | quality control method based on combination deep learning for measurement data of complex mountain wind farm |
| topic | quality control wind speed variational modal decomposition convolutional neural network gate recurrent unit |
| url | http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00043 |
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