Co-Adaptive Inertia–Damping Control of Grid-Forming Energy Storage Inverters for Suppressing Active Power Overshoot and Frequency Deviation

With the large-scale integration of renewable energy through power electronic inverters, modern power systems are gradually transitioning to low-inertia systems. Grid-forming inverters are prone to power overshoot and frequency deviation when facing external disturbances, threatening system stability. Existing methods face two main challenges in dealing with complex disturbances: neural-network-based approaches have high computational burdens and long response times, while traditional linear algorithms lack sufficient precision in adjustment, leading to inadequate system response accuracy and stability. This paper proposes an innovative coordinated adaptive control strategy for virtual inertia and damping. The strategy utilizes a Radial Basis Function neural network for the adaptive regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm. This approach provides refined inertia regulation while maintaining computational efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation results demonstrate that the proposed control strategy significantly outperforms traditional methods in improving system performance. In the active power reference variation scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by 66.7%, and the system recovery time is shortened. In the load variation scenario, frequency overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is reduced by approximately 26.9%. In the composite disturbance scenario, the frequency peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by 35%, and the power response improves by 23.3%. These results indicate that the proposed method offers significant advantages in enhancing system dynamic response, frequency stability, and power overshoot suppression, demonstrating its substantial potential for practical applications.