Noise-like-Signal-Based Sub-Synchronous Oscillation Prediction for a Wind Farm with Doubly-Fed Induction Generators

Abstract

The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm.