[1]LIU Hao,ZHANG Jingchao,MAO Wandeng,et al.Cloud Edge Collaboration Data Interaction Method of Intelligent Converter Station[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):104-110.[doi:10.13705/j.issn.1671-6833.2022.05.017]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
43
Number of periods:
2022 05
Page number:
104-110
Column:
Public date:
2022-08-22
- Title:
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Cloud Edge Collaboration Data Interaction Method of Intelligent Converter Station
- Author(s):
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LIU Hao1; ZHANG Jingchao2; MAO Wandeng1; MA Shiqi3; JIANG Xin3; JIN Yang3
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1.Electric Power Research Institute of State Grid Henan Electric Power Co., Ltd., Zhengzhou 450052, China;
2.State Grid Henan Electric Power Co., Ltd., Zhengzhou 450052, China;
3.School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
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- Keywords:
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power Internet of Things; intelligent converter station; edge Internet of Things agent; cloud-edge collaboration; data interaction
- CLC:
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TN919. 1
- DOI:
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10.13705/j.issn.1671-6833.2022.05.017
- Abstract:
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As a key in the construction of power Internet of Things, intelligent converter station. faces the problem of insufficient data sharing and fails to fully explore the value of data. Therefore, based on cloud-edge collaboration, this study proposed a data interaction method for the construction of intelligent converter station. Firstly, based on edge computing and container, an edge Iot agent model was proposed, which aimed at the shortest time delay. It used the local universal server computing resource allocation strategy based on weight algorithm. On this basis, the cloud-side collaboration strategy was determined to minimize the time delay. It used the unloading strategy of edge computing tasks to cloud based on task size. Finally, taking the converter station monitoring system in Henan province as an example, the oil chromatographic instrument APP is eventually developed through the local data pretreatment and remote data analysis and mining. The data is firstly pretreated by the station edge Iot agent and then uploaded to the remote cloud platform. The integrity of the data transmission rate and accuracy were 100%. The data transmission delay was controlled in 150 ms with the float up and down in 15 ms. The performance meets the requirements of practical engineering application and verifies the accuracy of the proposed scheme. The method proposed in this paper enables professionals to timely and accurately grasp the transformer operation situation in the station, also improves the stability and safety of the converter station operation, and enhances the data value.