Exploration of Transformer Operation and Maintenance Technology and Realization of Transformer Condition Monitoring System

Wang, Lu; Fan, Yin-Qiu; Yang, Xiaoxu; Li, Bin; Li, Wei; Jia, Xue; Wang, Hui; Wang, Gang; Guo, Xia

doi:10.1007/978-981-99-1439-5_75

Cited by 2 publications

(1 citation statement)

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“…This integration provides several crucial benefits that enhance the efficiency, reliability, and safety of the energy grid [1,2,3,6]. Modern accounting systems integrated into substations provide real-time and accurate data on energy consumption [4,5,7]. This detailed information allows for better analysis of usage patterns, helps in identifying peak usage times, and facilitates more efficient load distribution planning [8].…”

Section: Introductionmentioning

confidence: 99%

To evaluate the operational status of the transformer load using a feed-forward neural network for analysis

Khojayorov,

Saidkhodjaev

2023

E3S Web Conf.

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The results of the first transformer load obtained using the FNN neural network in Fig. 5 determined on the basis of the algorithm described in Fig. 4 show that if the dynamics of transformer loads continue at this rate, after 8 years the minimum loads will increase from 0.8 and after 12 years begins to work in the danger zone completely. Taking into account that the coefficient of wear of transformers and the occurrence of minimum loads is equal to 20% according to Fig.1, it can be said that this situation is in a very serious situation. And the maximum value of the load has already reached its maximum point. In this case, it is suggested that the issue of load redistribution in this Kibray 35/6 substation and its distribution networks should be seriously considered or a new transformer should be installed and appropriate switching devices should be selected.

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Section: Introductionmentioning

confidence: 99%