A Collaborative Grid-Connected Control Strategy for Heterogeneous Generator Groups Integrating Spatiotemporal Prediction Feedforward

Mobile emergency generators and mobile energy storage clusters are core flexible resources for the rapid recovery of critical loads in post-disaster distribution networks and the enhancement of resilience in isolated microgrids. However, due to the strong random changes in end-load loads, heterogeneous units are prone to problems such as large transient inrush currents, unstable phase-locked loops (PLLs), and reliance on manual synchronization adjustments when connected under load. To address these issues, this paper proposes a multi-timescale smooth grid-connected control architecture that combines data-driven feedforward and physical feedback. The architecture extracts spatiotemporal load features based on a CNN-BiLSTM-Attention model to achieve capacity optimization and baseline allocation. Predicted load voltage drop is converted into feedforward compensation to construct a virtual internal potential for coarse pre-grid connection adjustment. This is then combined with a closed-loop PLL to achieve fine-tuning of phase angle and voltage errors and autonomous decoupling of transient power after grid connection. Simulation and experimental results show that the proposed method suppresses voltage overshoot from 0.3 p.u. to within 0.03 p.u., increases the minimum frequency to above 49.8 Hz, reduces inrush current, and shortens synchronization time by 15.4%, significantly improving the system’s rapid connection and recovery capabilities.