A new cast-resin transformer thermal model based on recurrent neural networks

• Autorzy: Azizian D. , Bigdeli M.

Identyfikatory

DOI

10.1515/aee-2017-0002

• Języki publikacji: EN

Abstrakty

EN

Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.

Słowa kluczowe

EN

cast-resin transformer; dynamics; recurrent neural network; thermal modeling

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2017
• Tom: Vol. 66, nr 1
• Strony: 17--28
• Opis fizyczny: Bibliogr. 28 poz., rys., wz.

Twórcy

autor

Azizian D.

• Department of Electrical Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran

autor

Bigdeli M.

• Department of Electrical Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran

Bibliografia

• [1] Azizian D., Bigdeli M., Faiz J., Design optimization of cast-resin transformer using nature inspired algorithms, Arabian Journal for Science and Engineering, vol. 41, no. 9, pp. 3491-3500 (2016).
• [2] Rahimpor E., Azizian D., Analysis of temperature distribution in cast-resin dry-type transformers, Electrical Engineering, vol. 89, no. 4, pp. 301-309 (2007).
• [3] Pierce L.W., An investigation of the temperature distribution in cast-resin transformer windings, IEEE Transaction on Power Delivery, vol. 7, no. 2, pp. 920-926 (1992).
• [4] Lee M., Abdullah H.A., Jofriet J.C., Patel D., Fahrioglu M., Air temperature effect on thermal models for ventilated dry-type transformers, Journal of Electrical Power System Research, vol. 81, no. 3, pp. 783-789 (2011).
• [5] Eslamian M., Vahidi B., Eslamian A., Thermal analysis of cast-resin dry-type transformers, Journal of Energy Conversion and Management, vol. 52, no. 7, pp. 2479-2488 (2011).
• [6] Dianchun Z., Jiaxiang Y., Zhenghua W., Thermal field and hottest spot of the ventilated dry-type transformer, IEEE 6th International Conference on Properties and Applications of Dielectric Materials, Xi’an, China (2000).
• [7] Cho H.G., Lee U.Y., Kim S.S., Park Y.D., The temperature distribution and thermal stress analysis of pole cast resin transformer for power distribution, IEEE Conference International Symposium on Electrical Insulation, Boston, USA (2002).
• [8] Azizian D., Windings temperature prediction in split-winding traction transformer, Turkish Journal of Electrical Engineering and Computer Science, vol. 24, no. 4, pp. 3011-3022 (2016).
• [9] Jian H., Lin C., Zhang S.Y., Transformer real-time reliability model based on operating conditions, Journal of Zhejiang University, vol. 8, no. 3, pp. 378-383 (2007).
• [10] Randakovic Z., Feser K.A., New method for the calculation of hot-spot temperature in power transformers with ONAN cooling, IEEE Transactions on Power Delivery, vol. 18, no. 4, pp. 1284-1292 (2003).
• [11] Susa D., Palola J., Lehtonen M., Hyvärinen M., Temperature rises in an OFAF transformer at OFAN cooling mode in service, IEEE Transactions on Power Delivery, vol. 20, no. 4, pp. 2517-2525 (2005).
• [12] Susa D., Lehtonen M., Nordman H., Dynamic Thermal Modeling of Distribution Transformers, IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 1919-1929 (2005).
• [13] Taghikhani M.A., Power transformer top oil temperature estimation with GA and PSO methods, Energy and Power Engineering, vol. 4, no. 1, pp. 41-46 (2012).
• [14] Ghareh M., Sepahi L., Thermal modeling of dry-transformers and estimating temperature rise, International Journal of Electrical, Computer, Electronics and Communication Engineering, vol. 2, no. 9, pp. 1789-1790 (2008).
• [15] Azizian D., Bigdeli M., Firuzabad M.F., A dynamic thermal based reliability model of cast-resin dry-type transformers, International Conference on Power System Technology, Hangzhou, China (2010).
• [16] Azizian D., Bigdeli M., Cast-resin dry-type transformer thermal modeling based on particle swarm optimization, 6th International Workshop on Soft Computing Applications, Timisoara, Romania (2014).
• [17] Azizian D., Bigdeli M., Application of heuristic methods for dynamic thermal modeling of castresin transformer, International Journal of Advanced Intelligence Paradigms, vol. 8, no. 1, pp. 288-302 (2016).
• [18] Baboo S.S., and Shereef I.K., An efficient weather forecasting system using artificial neural network, International Journal of Environmental Science and Development, vol. 1, no. 4, pp. 321-326 (2010).
• [19] Moreno C.J.G., Using neural networks for simulating and predicting core-end temperatures in electrical generators: power uprate application, World Journal of Engineering and Technology, vol. 3, no. 1, pp. 1-14 (2015).
• [20] De S.S., Debnath A., Artificial neural network based prediction of maximum and minimum temperature in the summer monsoon months over India, Applied Physics Research, vol. 1, no. 2, pp. 37-44 (2009).
• [21] He Q., Si J., Tylavsky D.J., Prediction of top-oil temperature for transformers using neural networks, IEEE Transactions on Power Delivery, vol. 15, no. 4, pp. 1205-1211 (2000).
• [22] Assunção T.C.B.N., Silvino J.L., Resende P., Transformer top-oil temperature modeling and simulation, World Academy of Science, Engineering and Technology, vol. 2, no. 10, pp. 1115-1120 (2008).
• [23] Alias A.M., George A., Francis A., Neural network based temperature prediction, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 1, pp. 103-110 (2013).
• [24] Smith B.A., McClendon R.W., Hoogenboom G., Improving air temperature prediction with artificial neural networks, International Journal of Computer, Control, Quantum and Information Engineering, vol. 1, no. 10, pp. 3100-3107 (2007).
• [25] IEC Std. 60076-12, IEC Loading Guide for Dry-Type Power Transformers, (2008).
• [26] ANSI/IEEE C57.96, IEEE Guide for Loading Dry-Type Distribution and Power Transformers, (April 1989).
• [27] IEC Std. 60529, IEC Degree of Protection Provided by Enclosures, (1989).
• [28] Gao Y., Er M.J., NARMAX time series model prediction: feed-forward and recurrent fuzzy neural network approaches, Fuzzy Sets and Systems, vol. 150, no. 2, pp. 331-350 (2005).

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).