Neural Architecture Search-Assisted Adaptive Damping Framework for Oscillation Suppression in Inverter-Dominated Grid

Abstract

The rise of inverter-based renewable energy in weak-grid systems heightens sub-synchronous resonance (SSR), sub-synchronous control interaction (SSCI), PLL-induced instability, and torsional oscillations, jeopardizing secure system operation. This paper presents a NAS-MOEA-based adaptive damping framework to reduce multi-modal oscillations in a DFIG-integrated modified IEEE 39-bus system under weak-grid conditions. The framework combines NAS-enabled Temporal Convolutional Network (TCN) learning, wide-area PMU feedback, modal stabilization, and many-objective optimization for adaptive oscillation suppression across different operating conditions. Synchronized measurements of rotor speed deviation, PCC voltage oscillations, line current dynamics, PLL angle variation, and DC-link voltage fluctuations are used to create additional damping signals for RSC/GSC and STATCOM control. A small-signal and EMT-based validation framework is developed, accounting for varying short-circuit ratios, renewable penetration levels, wind-speed variations, series compensation levels, and severe transient disturbances. Simulation results show that the NAS-MOEA framework greatly surpasses traditional PI, lead-lag, PSO-based, and DRL-based damping methods. The method boosts the damping ratio from 0.178 to 0.304, a 71.1% increase, and cuts settling time from 8.74 s to 1.86 s, achieving about 78.7% faster stabilization. Significant reductions in oscillation amplitude, ITAE, and control energy occur under severe weak-grid conditions with an SCR as low as 1.5 and series compensation levels reaching 70%. The NAS-TCN architecture boosts oscillation prediction accuracy to 98.2% and cuts inference time by about 31.6%. The results confirm the effectiveness, robustness, and scalability of the NAS-MOEA framework for future inverter-dominated renewable power systems.