Optimization Configuration Model for Intelligent Measurement Multi-Core Modules Considering “Cloud-Edge-End-Core” Collaboration
The new power system with new energy as the main body gives the low-voltage distribution network (LVDN) a richer connotation, requiring intelligent measurement equipment to have good scalability and collaborative ability. To address these requirements, an optimization configuration model for intelli...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10844280/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The new power system with new energy as the main body gives the low-voltage distribution network (LVDN) a richer connotation, requiring intelligent measurement equipment to have good scalability and collaborative ability. To address these requirements, an optimization configuration model for intelligent measurement multi-core modules considering “cloud-edge-end-core” collaboration is proposed in this paper. Initially, a technical framework for “cloud-edge-end-core” collaboration in intelligent measurement is designed, expanding the conventional “cloud-edge-end” vertical cooperation architecture with multi-core module collaboration and horizontal synergy at the same hierarchical level to enhance the flexibility of data interaction. Then, an optimization configuration model for multi-core modules is formulated, to minimize both the chip configuration costs and the data transmission costs associated with “cloud-edge-end-core” collaboration. The decision variables include the core chip level of the intelligent measurement terminal, the management core chip level for smart meters, and the placement of application configurations. Finally, the effectiveness of the proposed model is verified in the LVDN with 300 users. The performance of the proposed optimization configuration model in different scenarios is compared and the influence of model parameters on the optimization results is analyzed. The results show that the proposed multi-core module optimization configuration model can optimize the selection of intelligent measurement terminal core and smart meter management core according to different application data requirements. It meets the application requirements while minimizing the multi-core module optimization configuration cost and the “cloud-edge-end-core” collaboration cost. |
|---|---|
| ISSN: | 2169-3536 |