Multi-Time Scale Based Cloud-Edge Collaborative Allocation Mechanism for Converter Station Operations Algorithms

The high voltage and large current characteristics of Ultra High Voltage (UHV) transmission systems pose increased demands on equipment safety, particularly in terms of real-time operational monitoring and non-real-time optimization. Real-time operational monitoring requires rapid response to unexpected situations, while non-real-time optimization involves complex computational tasks and extensive data processing. Cloud-edge collaborative computing addresses these needs by leveraging the rapid response capabilities of edge computing and the powerful processing capabilities of cloud computing. Edge computing achieves millisecond-level responses through local data processing and model inference, ensuring system safety and stability. Cloud computing handles the development and training of complex models, supporting long-term optimization and trend prediction. Despite the immense potential of cloud-edge collaborative computing in UHV transmission systems, challenges such as data transmission delays, model version control and updates, and collaborative training remain. This study explores methods to optimize cloud-edge collaborative mechanisms to improve system response speed and processing capacity, ensuring the safe, stable, and efficient operation of UHV transmission systems. This provides theoretical support and practical guidance for their intelligent operation and maintenance.