Design of CEDnet Coverage Optimization Algorithm Based on Grey Clustering

The accuracy of traditional network coverage algorithm is greatly reduced in the power transmission range with large area, wide area and relatively complex geographical environment. At the same time, with the gradual expansion of the scale of CEDnet, the computational time complexity of existing CED...

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Bibliographic Details
Published in2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE) pp. 1405 - 1411
Main Authors Xu, Huichen, Hua, Weitao, Wang, Dayang, He, Muxin, Bai, Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.09.2023
Subjects
CEDnet
Clustering algorithms
Grey clustering
Heuristic algorithms
Optimize
Power system reliability
Power transmission
Production
Safety
Soft switching
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Summary:The accuracy of traditional network coverage algorithm is greatly reduced in the power transmission range with large area, wide area and relatively complex geographical environment. At the same time, with the gradual expansion of the scale of CEDnet, the computational time complexity of existing CEDnet coverage optimization algorithms is high. Based on this, this paper designs a CEDnet coverage optimization algorithm based on grey clustering. According to the normal distribution theory, the proposed algorithm updates and tracks the gain value, which ensures that the dynamic data can cover the whole area in the process of realtime change, and reduces the distortion rate on the premise of reducing the offset rate. The feasibility of the communication network coverage optimization algorithm is verified by experiments, and the results all verify the feasibility of this method. The experimental results show that the CEDnet coverage optimization using this algorithm has high efficiency and low error, and the clustering accuracy can reach 94.98%. This shows that the proposed network coverage optimization algorithm has strong adaptability and more accurate network coverage results.
DOI:10.1109/EPEE59859.2023.10352023