A two-stage subsynchronous oscillation assessment method for DFIG-based wind farm grid-connected system

Abstract

Abstract In the power system, the wind farm based on Doubly-Fed Induction Generator (DFIG) may lead to Subsynchronous Oscillation (SSO), which poses a challenge to the stability of the power grid. In order to accurately evaluate SSO, this paper proposes a new evaluation method. It is divided into two main stages: firstly, the interference level of Phasor Measurement Unit (PMU) data is identified by using the classification model based on Upper Confidence Bound (UCB) and Double Deep Q Network (DDQN). Then, an SSO parameter estimation model based on Local Feature Fusion Transformer (LFF-Transformer) network is designed for data with different interference levels. Experimental results show that the errors of eRMSE-f and EMAPE-F are 0.001 and 0.003 respectively, and the errors of eRMSE-δ and EMAPE-δ are 0.009 and 0.015 respectively. In terms of training and testing time, this method is 90 s and 18 s respectively, which is significantly better than Multi-SVR and Multi-CNN. After application, the frequency deviation decreased from 0.05 to 0.02 Hz, the voltage deviation decreased from 3.5 to 1.5%, the power fluctuation decreased from 10 to 5 MW, the SSO frequency decreased from 1.5 Hz to less than 0.5 Hz, and the SSO damping ratio increased from 0.08 to 0.15. This shows that the proposed method effectively increases the stability of the power grid.

Keywords

• DFIG-based wind farm
• Two-stage SSO assessment method
• Interference level classification
• SSO modal parameter estimation
• UCB-DDQN
• LFF-transformer