To address the limited capability of conventional distributed secondary control methods in exploiting multi-node coupling and dynamic information during frequency and voltage restoration in islanded microgrids, this paper proposes a spatiotemporal convolutional network-based distributed secondary restoration control method. First, a microgrid model incorporating droop control, communication topology, and restoration objectives is established, and the restoration problem is formulated as a distributed control task based on local measurements and neighborhood information. Then, spatiotemporal input features integrating frequency and voltage deviations and power information are constructed. A spatiotemporal convolutional network is employed to extract spatial correlation among distributed generators and temporal characteristics during disturbances, thereby generating secondary compensation signals. Furthermore, a hybrid control framework combining a distributed secondary controller with a spatiotemporal compensator is developed to enhance regulation capability while preserving the control structure. Simulation results show that the proposed approach achieves smaller deviations, shorter recovery times, and stronger disturbance rejection under load variations, parameter perturbations, and communication delays. The results indicate that introducing spatiotemporal feature modeling into distributed secondary control reduces transient deviations, shortens recovery time, and improves disturbance rejection capability in the tested microgrid scenarios.

microgrid, distributed secondary control, spatiotemporal convolutional network, cooperative frequency and voltage restoration