Accurate identification of fault ride-through (FRT) control parameters is critical for the security and stability of power systems with high wind penetration. To overcome limitations of conventional intelligent optimization algorithms, including their dependency on large-scale iterations and insufficient engineering adaptability, a multi-scenario coordinated parameter identification framework was proposed, integrating electromechanical transient modeling, measurement feature extraction, control logic simplification, and parameter estimation. First, detailed electromechanical transient modeling and measurement analysis were conducted to build identification datasets. The complex FRT control logic in PSD-BPA was then simplified into a dual-mode current/power architecture. A staged identification model incorporating steady-state feature weighting and Huber robust optimization was subsequently developed to ensure compliance with all regulatory metrics. Empirical validation was performed using eight typical fault scenarios from a Guizhou wind farm. The proposed method is demonstrated to maintain simulation deviations at least 18% below standard thresholds in multi-scenario tests, and computational redundancy is significantly reduced compared to traditional algorithms, thus providing an efficient solution for model validation in renewable energy plants.