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| An algorithm of abnormal data reconstruction based on RISE-FALL-feature of the wind speed and support vector machine |
| DOI：10.7667/PSPC171181 |
| Key Words:wind power abnormal data reconstruction SVM RISE-FALL-feature of the wind speed |
| Author Name | Affiliation | E-mail | | YANG Mao | Modern Power System Simulation Control & Renewable Energy Technology, Jilin Province Key Laboratory Northeast Electric Power University, Jilin 132012, China | | | ZHAI Guanqiang | Modern Power System Simulation Control & Renewable Energy Technology, Jilin Province Key Laboratory Northeast Electric Power University, Jilin 132012, China | 374421104@qq.com | | LI Dayong | State Grid Jilin Electric Power Co., Ltd., Tonghua Power Supply Company, Tonghua 130022, China | | | SU Xin | College of Science, Northeast Electric Power University, Jilin 132012, China | | | ZHAI Yucheng | Changchun Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Changchun 130600, China | |
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| Abstract:The historical power data of wind turbine is the important foundation for the study of wind power. However, amounts of data collected from wind farms usually contain abnormal data, which has adverse effects on the wind power prediction. First, the wind speed-power correspondence of historical data is studied, and the abnormal data is identified and eliminated. The influence of RISE-FALL-feature of the wind speed on the power is analyzed, and the SVM data reconstruction model is established. A data reconstruction model is improved based on the RISE-FALL-feature of the wind speed and the output characteristics of the correlation wind turbine. Taking the measured data of wind turbine as an example, the simulation results show that the method described in this paper can effectively identify and reconstruct the abnormal data. This work is supported by National Key Research and Development project of China (No. 2018YFB0904200). |
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