Back to Search Start Over

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

Authors :
Yubin Lin
Jiyu Li
Xiaofei Ruan
Xiaoyu Huang
Jinbo Zhang
Source :
PeerJ Computer Science, Vol 9, p e1632 (2023)
Publication Year :
2023
Publisher :
PeerJ Inc., 2023.

Abstract

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

Details

Language :
English
ISSN :
23765992
Volume :
9
Database :
Directory of Open Access Journals
Journal :
PeerJ Computer Science
Publication Type :
Academic Journal
Accession number :
edsdoj.4cf79e9e40dd4895ae895352842f642d
Document Type :
article
Full Text :
https://doi.org/10.7717/peerj-cs.1632