EMD-CPO-GRU-based Transformer Oil Temperature Prediction
DOI:
https://doi.org/10.54097/dx4jft92Keywords:
Transformer oil temperature prediction; Empirical Mode Decomposition; Crested Porcupine Optimization; Gated Recurrent Unit; Hybrid prediction model.Abstract
To improve the accuracy of transformer oil temperature prediction and ensure the stability and safety of transformers during operation, this paper proposes an innovative oil temperature prediction method—an EMD-CPO-GRU hybrid model based on Empirical Mode Decomposition (EMD), Crested Porcupine Optimization (CPO) algorithm, and Gated Recurrent Unit (GRU). The method first decomposes the oil temperature data using EMD, effectively extracting the nonlinear and non-stationary characteristics of the signal, thereby providing more representative and effective features for subsequent predictions. Next, the CPO algorithm is applied to optimize the key hyperparameters of the GRU model, establishing efficient CPO-GRU sub-models for each modal component to improve the accuracy and robustness of the prediction model. Finally, the prediction results of each sub-model are weighted and integrated to obtain the final oil temperature prediction value. Experimental results show that the EMD-CPO-GRU model outperforms traditional prediction models and other hybrid models in transformer oil temperature prediction tasks. In terms of prediction accuracy, the EMD-CPO-GRU model achieves significant improvement, fully verifying its effectiveness as an efficient and precise transformer oil temperature prediction method. This approach not only provides a reliable basis for real-time monitoring and fault warning of power transformers but also offers new ideas and solutions for similar time-series prediction problems.
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[1] Jiménez-Navarro M J, Martínez-Ballesteros M, Martínez-Álvarez F, et al. A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting[J]. Journal of Big Data, 2023, 10(1): 80.
[2] He Q, Si J, Tylavsky D J. Prediction of top-oil temperature for transformers using neural networks[J]. IEEE Transactions on Power Delivery, 2000, 15(4): 1205-1211.
[3] Chen H, Meng L, Xi Y, et al. Gru based time series forecast of oil temperature in power transformer[J]. Distributed Generation & Alternative Energy Journal, 2023: 393-412.
[4] Yang L, Chen L, Zhang F, et al. A Transformer Oil Temperature Prediction Method Based on Data-Driven and Multi-Model Fusion[J]. Processes, 2025, 13(2): 302.
[5] Wong C S, Li W K. On a mixture autoregressive model[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000, 62(1): 95-115.
[6] Wang Z, Zhao B, Ji W, et al. Short-term load forecasting method based on GRU-NN model[J]. Automation of Electric Power Systems, 2019, 43(5): 53-58.
[7] Xi Y, Lin D, Yu L, et al. Oil temperature prediction of power transformers based on modified support vector regression machine[J]. International journal of emerging electric power systems, 2023, 24(3): 367-375.
[8] Xiao F, Yang G, Hu W. Research on intelligent diagnosis method of oil temperature defect in distribution transformer based on machine learning[J]. 2019.
[9] Rao W, Zhu L, Pan S, et al. Bayesian network and association rules-based transformer oil temperature prediction[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1314(1): 012066.
[10] Taheri A A, Abdali A, Rabiee A. Indoor distribution transformers oil temperature prediction using new electro‐thermal resistance model and normal cyclic overloading strategy: an experimental case study[J]. IET Generation, Transmission & Distribution, 2020, 14(24): 5792-5803.
[11] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, 454(1971): 903-995.
[12] Boudraa A O, Cexus J C. EMD-based signal filtering[J]. IEEE transactions on instrumentation and measurement, 2007, 56(6): 2196-2202.
[13] Lahmiri S. Comparing variational and empirical mode decomposition in forecasting day-ahead energy prices[J]. IEEE Systems Journal, 2015, 11(3): 1907-1910.
[14] Da Silva I D, Moura M C, Lins I D, et al. Non-stationary demand forecasting based on empirical mode decomposition and support vector machines[J]. IEEE Latin America Transactions, 2017, 15(9): 1785-1792.
[15] McKinley S, Levine M. Cubic spline interpolation[J]. College of the Redwoods, 1998, 45(1): 1049-1060.
[16] Abdel-Basset M, Mohamed R, Abouhawwash M. Crested Porcupine Optimizer: A new nature-inspired metaheuristic[J]. Knowledge-Based Systems, 2024, 284: 111257.
[17] Zang J, Cao B, Hong Y. Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine Optimizer Optimization[J]. Applied Sciences, 2024, 14(11): 4840.
[18] Fan Y, Ma Z, Tang W, et al. Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation[J]. Energies, 2024, 17(14): 3435.
[19] He Q, Li S. Prediction of Subgrade Settlement of High-speed Railway Based on BP Neural Network Optimized by Crested Porcupine[C]//2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024: 356-359.
[20] Dey, R.; Salem, F.M. Gate-variants of gated recurrent unit (GRU) neural networks. In Proceedings of the 2017 IEEE 60th International Mid-West Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA, 6–9 August 2017; IEEE: Piscatway, NJ, USA, 2017;
[21] Fantini D G, Silva R N, Siqueira M B B, et al. Wind speed short-term prediction using recurrent neural network GRU model and stationary wavelet transform GRU hybrid model[J]. Energy Conversion and Management, 2024, 308: 118333.
[22] Tian Q, Luo W, Guo L. Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model[J]. Journal of Water Process Engineering, 2024, 59: 105052.
[23] Qin Q, Wang Z, Li B, et al. The HWAM-EMD-GRU Forecasting Model for Short-Term Passenger Flow in an Airport Light Rail Transit Line[J]. Urban Rail Transit, 2024, 10(2): 178-187.
[24] Zhou H, Zhang S, Peng J, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting[C]//Proceedings of the AAAI conference on artificial intelligence. 2021, 35(12): 11106-11115.
[25] Zhang Y, Lu Y, Zhang W, et al. Big data analysis method based on time series temperature field data of substation transformer[C]//Journal of Physics: Conference Series. IOP Publishing, 2024, 2703(1): 012064.
[26] Fang T, Zheng C, Wang D. Forecasting the crude oil prices with an EMD-ISBM-FNN model[J]. Energy, 2023, 263: 125407.
[27] Liu H, Zhou R, Zhong X, et al. Multi-strategy enhanced crested porcupine optimizer: CAPCPO[J]. Mathematics, 2024, 12(19): 3080.
[28] Liu S, Jin Z, Lin H, et al. An improve crested porcupine algorithm for UAV delivery path planning in challenging environments[J]. Scientific Reports, 2024, 14(1): 20445.
[29] Bandi A, Adapa P V S R, Kuchi Y E V P K. The power of generative ai: A review of requirements, models, input–output formats, evaluation metrics, and challenges[J]. Future Internet, 2023, 15(8): 260.
[30] Lara-Benítez P, Carranza-García M, Riquelme J C. An experimental review on deep learning architectures for time series forecasting[J]. International journal of neural systems, 2021, 31(03): 2130001.
[31] Chen H, Meng L, Xi Y, et al. Power transformer oil temperature prediction based on empirical mode decomposition-bidirectional long short-term memory[J]. Journal of algorithms & computational technology, 2023, 17: 17483026231176196.
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