A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network
Source abstract
Abstract
The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power Transformer Diagnostics and Insulation · Advanced Computational Techniques and Applications · Advanced Sensor and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
State Grid Jinzhong Electric Power Supply Company, Jinzhong 030600, China
State Grid UHV Transformation Co. of SXPC, Taiyuan 030021, China
Funding
State Grid Shanxi Electric Power Company Technology Project
References (24)
Show source-provided references
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