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2 2025

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Construction of intelligent transformer anomaly detection model based on LSTM combined with optical flow features

Zhao Shuzong, Pattanadech Norasage

Diagnostyka

2025

Vol. 26, No. 1

art. no. 2025115

Transformers are essential for the transmission and distribution of electricity, but due to changes in load and the influence of the working environment, various faults may occur in transformers. To accurately and quickly detect faults in transformers and conduct effective fault diagnosis and equipment maintenance, this study solves the problems of data imbalance and temporal data in transformers by introducing a long short-term memory network with fatigue factors. In addition, a fusion model is ultimately constructed by combining the recursive all-pair field transformation streamer method to achieve more accurate and robust optical flow estimation in the model. The experiment indicated that the maximum accuracy of the predicted values combined with the model was around 95% and the minimum was around 35%. Compared to other models, the maximum accuracy of actual values was around 80% and the minimum accuracy was better at 10%. In the application experiment, the frequency of insulation faults was the least obvious, with only 10 faults. The resistance fault was evident, with a total of 100 faults. The combined model could well reflect the fluctuation of fault current and the collection of fault numbers by different sensors. Therefore, the proposed model has high accuracy, good precision, and outstanding application effects, which can provide new ideas for constructing intelligent transformer anomaly detection models.

Power transformer fault warning combining support vector machine and improved grey wolf optimization algorithm

Zhao Shuzong, Pattanadech Norasage

Archives of Electrical Engineering

2025

Vol. 74, nr 1

191--208

To optimize the parameter setting of the support vector machine and improve the classification performance and computational efficiency of power transformer fault diagnosis, this study proposes an improved grey wolf optimization algorithm. By optimizing the global search and local optimization capabilities of the grey wolf algorithm and combining them with stacked denoising autoencoders, a new power transformer fault warning model is constructed. Firstly, the grey wolf optimization algorithm is optimized through four strategies: elite reverse learning, nonlinear control parameters, Lévy flight, and particle swarm optimization, which improve its global search and local optimization capabilities. Secondly, the stacked denoising autoencoder is utilized to extract high-level features of fault data, and the improved GWO algorithm and SVM are combined to complete fault classification. The results indicated that the proposed diagnostic model achieved a diagnostic accuracy of 0.979, a recall rate of 0.986, and an F1 value of 0.983 in benchmark performance testing. In practical applications, the average fault diagnosis accuracy of this model could reach up to 99.21%, and the average diagnosis time was only 0.08 s. The developed power transformer fault warning model can provide an efficient and reliable technical solution for fault diagnosis in the power system.