Hybrid Ai-Driven Intelligent Monitoring and Control for Photovoltaic-Storage-Dc-Flexible Microgrid With Reliability Enhancement

Addressing the reliability and efficiency optimization requirements of photovoltaic-storage-DC-flexible (PSDF) microgrid systems, this paper proposes an intelligent monitoring and control platform based on hybrid artificial intelligence methods. A novel intelligent algorithm combining ARIMA-LSTM hybrid prediction model with adaptive control is innovatively designed, integrating particle swarm optimization techniques to construct multi-source coordinated control strategies. Through hierarchical distributed architecture and dual-layer intelligent control, the system operational reliability and energy utilization efficiency are significantly enhanced. The upper-layer hybrid prediction model formulates optimal power allocation strategies, while the lower-layer adaptive algorithm achieves real-time DC bus voltage stabilization control. Validation based on a 21-node simulation platform demonstrates that compared with conventional control methods, the proposed hybrid intelligent algorithm significantly improves system reliability indicators: voltage deviation reduced by 68% and regulation time shortened by 67%, while efficiency indicators are substantially enhanced: photovoltaic consumption rate reaches 94.2% and response speed improves to 85ms, providing an intelligent solution for reliable and efficient operation of microgrids.