Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine

doi:10.19595/j.cnki.1000-6753.tces.180561

	Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine
	Lin, Jie; Wu, Butuo; Chen, Wei
刊名	Diangong Jishu Xuebao/Transactions of China Electrotechnical Society
	2018-08-30
卷号	33 页码:205-212
关键词	Anomaly detection Data acquisition Data handling Forecasting Hidden Markov models Signal processing Speed Statistics Stochastic systems Wind power Data acquisition system Deep boltzmann machines Empirical Mode Decomposition Outlier identification Pre-processing method Prediction accuracy Pretreatment methods Wind speed sequences
ISSN号	10006753
DOI	10.19595/j.cnki.1000-6753.tces.180561
英文摘要	To improve the availability and accuracy of data acquisition system of existing wind power plant, this study puts forward the adaptive detection pretreatment method of abnormal wind speed value based on the deep Boltzmann machine (DBM), empirical mode decomposition (EMD) and hidden Markov model (HMM) combination algorithm. Due to the random variability of wind speed sequences, the DBM prediction method is adopted to excavate the potential characteristics of abnormal wind speed value, and get the residual sequences reflecting the anomaly wind speed value. In order to further improve the detection accuracy and reduce the system error interference, the EMD method is adopted to capture the characteristics of bulky errors of the residual sequences. With the help of the Dual stochastic process of HMM algorithm, the abnormal wind speed points are adaptively detected and eliminated, thereby avoid difficulty in accurate outlier identification of the traditional threshold detection method. Finally, in order to get a complete sequence of wind speed, weighted bi-directional ARMA algorithm is taken to revise the data of detected abnormal points. RBF prediction results verify that preprocessing can improve the quality of wind speed. The proposed method, compared with traditional wavelet outlier detection method, is more accurate in identification and further improves the prediction accuracy of short-term wind speed. © 2018, Electrical Technology Press Co. Ltd. All right reserved.
语种	中文
出版者	Chinese Machine Press
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/113822]
专题	电气工程与信息工程学院
作者单位	School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Lin, Jie,Wu, Butuo,Chen, Wei. Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine[J]. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society,2018,33:205-212.
APA	Lin, Jie,Wu, Butuo,&Chen, Wei.(2018).Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine.Diangong Jishu Xuebao/Transactions of China Electrotechnical Society,33,205-212.
MLA	Lin, Jie,et al."Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine".Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 33(2018):205-212.