基于深层玻尔兹曼机的风电场异常风速值自适应检测预处理方法

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

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Abstract 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.

Key words： Outlier detection wind speed sequence deep Boltzmann machine empirical mode decomposition hidden Markov model

Received: 11 April 2018 Published: 26 September 2018

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TM614

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	Lin Jie
	Wu Butuo
	Chen Wei

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Lin Jie,Wu Butuo,Chen Wei. Adaptive Detection and Preprocessing Method for Abnormal Wind Speed of Wind Farm Based on Deep Boltzmann Machine[J]. Transactions of China Electrotechnical Society, 2018, 33(zk1): 205-212.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180561 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/Izk1/205

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