Robust Global Adaptive Asymptotic Performance in Power Systems with Indefinite Control Parameters

Uncertainties in large-scale multimachine power systems can significantly affect stability, efficiency, and transient performance, particularly under dynamic disturbances and parameter variations. This paper presents a robust adaptive control strategy to address uncertainties in control inputs of interconnected power systems. By employing the Nussbaum function, the proposed method effectively handles time-varying indefinite control coefficients arising in complex system dynamics. The control framework integrates error-dependent tracking, prescribed performance-based barrier functions, and adaptive parameter estimation laws to ensure consistent and bounded performance under uncertainties. A super-twisting algorithm embedded within a backstepping structure enhances disturbance rejection while reducing chattering, and a filter-based design mitigates recursive errors inherent to the backstepping approach. Stability of the overall system is rigorously established using Lyapunov-based analysis. Simulation studies and real-time implementation on an RTDS platform using the IEEE 39-bus, 10-machine system demonstrate improved transient performance, faster tracking, and enhanced robustness compared to existing control schemes. Simulation and real-time results on the IEEE 39-bus system demonstrate that the proposed method achieves approximately 0.5–1% reductions in settling time, 5–6% improvements in tracking accuracy, and 8–10% reductions in oscillation amplitude compared to existing controllers. Furthermore, the scheme enhances disturbance rejection capability and maintains stable operation under severe parameter uncertainties, confirming its superiority in terms of transient performance and robustness.