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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.

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