Open AccessResearch on Load Balancing Dispatching Method of Power Network Based on Cloud Computing
Author(s) -
Dongmei Bin,
Tong Yu,
Xin Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/2/022047
Subject(s) - load balancing (electrical power) , computer science , cloud computing , scheduling (production processes) , grid , distributed computing , load management , real time computing , big data , reliability engineering , mathematical optimization , data mining , engineering , electrical engineering , geometry , mathematics , operating system
With the continuous development of big data era and cloud computing(CC) technology, there are still a series of problems in the planning performance and related algorithms of CC grid, such as unbalanced grid load and uneven grid spatial distribution. However, with the continuous development of social economy and the continuous improvement of people’s living standards and quality, the scale of power grid construction is also expanding, the load distribution of power grid is unbalanced, and the power distribution capacity is insufficient. In order to effectively analyze the load balance and scheduling of power grid, CC technology can accurately improve the accuracy of data analysis and processing. In the process of network load balancing and distribution, the use of CC can not only improve the scheduling efficiency, but also improve the quality and speed of work. Aiming at the problem of poor scheduling performance, this paper proposes a fuzzy iterative algorithm and applies it to CC environment. Plan load balancing for the current network. The experimental process of the algorithm is introduced in detail, the balanced distribution parameters of the load data are extracted, and the load model is established. Realize and improve the network load balance distribution. Experimental results show that the proposed method can improve the load balancing configuration of power grid, improve the management efficiency of power grid, and has good experimental performance.