Correlation Analysis between Wind Speed/Voltage Clusters and Oscillation Modes of Doubly-Fed Induction Generators

Jierong Miao, Da Xie, Chenghong Gu and Xitian Wang
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Jierong Miao: School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China
Da Xie: School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China
Chenghong Gu: Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
Xitian Wang: School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China

Energies, 2018, vol. 11, issue 9, 1-21

Abstract: Potential machine-grid interactions caused by large-scale wind farms have drawn much attention in recent years. Previous work has been done by analyzing the small–signal modeling of doubly-fed induction generators (DFIGs) to obtain the oscillation modes. This paper, by making use of the metered power data of wind generating sets, studies the correlation between oscillation modes of the DFIG system and influence factors which includes wind speed and grid voltage. After the metered data is segmented, the Prony algorithm is used to analyze the oscillation modes contained in the active power. Then, the relevant oscillation modes are extracted in accordance with the small-signal analysis results. Meanwhile, data segments are clustered according to wind speed and grid voltage. The Apriori algorithm is finally used to discuss the association rules. By training the mass of data of wind generating sets, the inevitable association rules between oscillation modes and influence factors can be mined. Therefore, the prediction of oscillation modes can be achieved based on the rules. The results show that the clustering number quite affects the association rules. When the optimal cluster number is adopted, part of the wind speed/voltage clusters can analyze the certain oscillation modes. The predicted results are quite consistent with the practical data.

Keywords: wind power; oscillation mode; correlation analysis; Apriori algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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