A Transformer Oil Temperature Prediction Method Based on Data-Driven and Multi-Model Fusion

• Published in: Processes Vol. 13; no. 2; p. 302
• Main Authors: Yang, Lin, Chen, Liang, Zhang, Fan, Ma, Shen, Zhang, Yang, Yang, Sixu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Aging; Artificial intelligence; Back propagation networks; Data processing; Datasets; Feature selection; Genetic algorithms; Inspection; Machine learning; Modelling; Neural networks; Oils & fats; Oxidation; Parameters; Predictions; Search algorithms; Sensors; Support vector machines; Temperature; Transformers
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr13020302

A power transformer is an important part of the power system, and the oil temperature of the transformer is an important state parameter that reflects the operation state of the transformer. The accurate prediction of the oil temperature of the transformer can ensure the safe and stable operation of the transformer. Given the lack of a practical and effective data processing process and the problem that most of the current research is conducted on small-scale ideal datasets, this paper proposes a transformer oil temperature prediction method based on data-driven and multi-model fusion. The method first analyses and processes the actual transformer inspection data; it then uses the multi-model fusion method to model and predict the transformer oil temperature. The base model was trained using the machine learning method, and the secondary learning model was trained using the improved TSSA-BP neural network. The improved sparrow search algorithm (TSSA) was used to optimise the parameters of the BP neural network to improve the convergence accuracy and prediction performance of the model. The transformer data are classified according to cooling mode, operating voltage, and other indicators, and then eight groups of experimental datasets under different actual conditions are constructed for modelling and prediction. The experimental results show that the maximum root mean square error and the mean absolute percentage error of this method on different datasets are 1.0877 and 1.58%, and compared with other prediction methods, the prediction accuracy of this method is better than other methods, which verifies the practicability and feasibility of modelling and predicting for the actual transformer inspection data.