With the large-scale integration of renewable energy through power electronic inverters,modern power systems are gradually transitioning to low-inertia systems. Grid-forminginverters are prone to power overshoot and frequency deviation when facing externaldisturbances, threatening system stability. Existing methods face two main challenges indealing with complex disturbances: neural-network-based approaches have high computationalburdens and long response times, while traditional linear algorithms lack sufficientprecision in adjustment, leading to inadequate system response accuracy and stability. Thispaper proposes an innovative coordinated adaptive control strategy for virtual inertia anddamping. The strategy utilizes a Radial Basis Function neural network for the adaptiveregulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm.This approach provides refined inertia regulation while maintaining computationalefficiency, optimizing the rate of change in frequency and frequency deviation. Simulationresults demonstrate that the proposed control strategy significantly outperforms traditionalmethods in improving system performance. In the active power reference variationscenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by66.7%, and the system recovery time is shortened. In the load variation scenario, frequencyovershoot is reduced by approximately 3.6%, and the maximum frequency deviation isreduced by approximately 26.9%. In the composite disturbance scenario, the frequencypeak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by35%, and the power response improves by 23.3%. These results indicate that the proposedmethod offers significant advantages in enhancing system dynamic response, frequencystability, and power overshoot suppression, demonstrating its substantial potential forpractical applications.
Zheng et al. (Mon,) studied this question.