Quantifying the Impact of DFIG Wind Integration on Transient Rotor-Angle Stability Through Node Contribution Mapping

Wind integration involves numerous interacting factors and time-varying transient responses, making it difficult to represent their effects consistently and distinguish their contributions using equipment parameters directly. This study develops a framework linking wind integration factors, nodal channel variables, and system assessment indices. Five nodal channels summarize wind integration effects, and node contribution mapping relates them to the indices to identify dominant channels and their directions of influence across nodes. Five channels represent pre-fault synchronous-generator active power, equivalent inertia, equivalent damping, dynamic synchronizing torque, and reactive power amplification. Batch total least squares converts transient responses into scenario-level equivalent values. The machine synchronization dispersion index (MSDI) and reactive power mismatch recovery rate (QMRR) provide mechanical- and electrical-domain outputs under fixed-fault conditions; QMRR does not require mechanical rotor-angle measurements. A nonlinear mapping multiplies channel factors within each node and sums contributions across nodes, enabling sensitivity-based analysis of dominant channels and their directions of influence. Source comparisons and parameter perturbations in an extended IEEE 9-bus system reveal differences in factor-to-channel effects. Comparisons of the two indices and response trajectories under two fault durations illustrate their assessment behavior. Across 225 identification scenarios in an IEEE 39-bus system, the mean relative fitting errors are 0.0191% for the MSDI and 0.8518% for QMRR, demonstrating in-sample reproduction of index variation by the channel representation and mapping structure. In a representative scenario, the equivalent-inertia and reactive-power-amplification channels have MSDI sensitivity weights of 62.51% and 36.61%, respectively, while the latter has a QMRR weight of 78.15%. The framework connects factor-to-channel effects with channel-to-index contributions for quantitative analysis within the investigated scenario domain.