The adverse effects resulting from the intermittent nature of wind energy on power generation and stability is well documented in the literature. More specifically, the impact of power oscillations on doubly fed induction generator (DFIG)-based wind turbines can have drastic effects and even lead to large area blackouts, if not addressed properly. This paper deals with the design and implementation of a probabilistic coordinated approach for power systems with synchronous generators (SGs), DFIGs, and facts devices. The parameters of the power system stabilizers (PSSs) of the SG, power oscillation dampers (POD) of the DFIGs and STATCOM controllers were optimally coordinated using a modified Imperialist Competitive Algorithm (MICA) combined with a probabilistic eigenvalue approach. The proposed approach was implemented to a modified 39-bus New England power system. Its effectiveness in guaranteeing power system's dynamic stability was assessed using time domain analysis, eigenvalue mapping, and robustness analysis under various wind conditions. DFIG's realistic modes of operation, including normal, synchronous, and sub-synchronous modes were considered in the performance analysis. The obtained results were further compared to those of deterministic and uncoordinated approaches. A comparison of the MCA algorithm to other optimization approaches such as particle swarm optimization (PSO), genetic algorithm (GA), and imperialist competitive algorithm (ICA) is also carried out. The simulation results and robustness analysis confirmed the superiority of the proposed probabilistic approach over various other approaches.