Equivalent modeling and multi-parameter coupling optimization for DFIG-based wind farms considering SSO mode

Abstract

As a low-carbon and environmentally friendly renewable energy source, wind power has been globally recognized as the best solution to achieve energy saving and emission reduction and promote low-carbon economic growth. With the increase of wind power penetration, wind power has a great impact on sub-synchronous state stability and dynamic characteristics of the grid-connected system. Aiming at the fact that the correlation between clustering indexes and sub-synchronous oscillation (SSO) mode and the difference of the contribution to the clustering results are seldom considered in the current equivalent modeling of doubly-fed induction generator (DFIG)-based wind farm, this paper proposes a clustering method based on the index dimension reduction and weighted fuzzy C-means (WFCM) clustering algorithm. Besides, for the SSO study of the grid-connected system without sufficiently considering the coupling effects between controller parameters, a multi-parameter coupling optimization design strategy combining orthogonal experiment method (OEM) and response surface method is proposed. Firstly, the dominant variables of SSO mode of the DFIG-based wind farm connected to weak grid by series compensation system are taken as the initial clustering indexes. After dimension reduction by principal component analysis, the WFCM algorithm is utilized to cluster the wind farm. Then, the proportional and integral coefficients of the grid-side controller, rotor-side controller and phase-locked loop are optimized to achieve the simultaneous optimization of the SSO characteristics and dynamic characteristics of the system. Finally, the interaction between control parameters and the influence degree and trend on the system performance are quantitatively evaluated, and the optimal parameter combination is obtained. The proposed strategy can mitigate SSO more effectively while improving anti-interference than the particle swarm optimization based on OEM.