Indoor distribution transformers oil temperature prediction using new electro‐thermal resistance model and normal cyclic overloading strategy: an experimental case study

Taheri, Ali Asghar; Abdali, Ali; Rabiee, Abbas

doi:10.1049/iet-gtd.2020.0457

Cited by 16 publications

(2 citation statements)

References 23 publications

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“…If the order of values of the time series changes, the results of the output of model are various. 7 Traditional prediction methods such as moving average method and exponential average method are used for time series analysis. However, deep learning methods, particularly deep neural networks (DNN), are increasingly employed in various fields, including time series prediction tasks.…”

Section: Introductionmentioning

confidence: 99%

“…A new model based on fundamental heat transport theory was suggested by Ali Taheri and others. 7 The model uses an electro-thermal resistance model to estimate the thermal behavior of the top oil of interior distribution transformers (E-TRM). By assigning thermal resistance to each three-dimensional heat transfer channel, the thermal resistance network is constructed.…”

Section: Introductionmentioning

confidence: 99%

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Power transformer oil temperature prediction based on empirical mode decomposition-bidirectional long short-term memory

Chen

Meng

et al. 2023

Journal of Algorithms & Computational Technology

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Power transformers are crucial components of power transmission and transformation networks. Their operational status has a direct impact on the reliability of power supply systems. As such, the security and stability of power systems depend heavily on the state of transformers within them. The oil temperature of a transformer is a critical indicator of its working condition. Accurately and rapidly predicting transformer oil temperature is therefore of significant practical importance for ensuring the safe and effective operation of power systems. To address this prediction problem, this article proposes a transformer oil temperature prediction method based on empirical mode decomposition-bidirectional long short-term memory (EMD-BiLSTM). The time series of oil temperature is first cleaned before being processed. Next, the EMD algorithm is used to decompose the time series into relatively stable components. The BiLSTM neural network is then utilized to predict the complex nonlinear long-term series. The proposed method is evaluated using the open data set Electricity Transformer Temperature (ETT)-small. Experimental results show that the EMD-BiLSTM model outperforms traditional LSTM, BiLSTM, EMD-BP, and Wavelet Transform-Bidirectional Long Short-Term Memory (WT-BiLSTM) methods, demonstrating that it is an effective and accurate prediction method for transformer oil temperature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%