Abstract
Insulation structure changes, operational state variations, and internal defects in a 750 kV autotransformer can cause changes in the capacitance of the transformer windings, resulting in asymmetrical capacitance parameters of the three-phase windings and unbalanced foundation voltage on the low-voltage side winding bus. This paper offer a capacitance state evaluation fashion for 750 kV transformer windings based on BP neural network. By constructing a simulation model of a three-phase 750 kV autotransformer and considering the actual range of variation in winding capacitance parameters, a measurement dataset of unbalanced voltages on the low-voltage winding is obtained. The unbalanced voltage of the low-voltage winding and the winding capacitance are selected as input and output datasets, respectively. Based on BP and PSO-BP neural networks, transformer winding capacitance state evaluation models are established and trained. The capacitance state of the windings is evaluated and verified through simulation experiments exploitation unbalanced voltage data from a certain 750 kV transformer. The verification consequence show that the PSO-BP neural network model has better forecasting accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cheng, J., Zhong, Y., Lan, X.: Analysis and treatment of the inner fault in a 750 kV transformer. High Volt. Apparatus 52(02), 200–204 (2016). (in Chinese)
Lv, J., Zhang, H., Wang, X., et al.: Discussion on preload voltage time in 750 kV transformer long-duration induced AC voltage test. High Volt. Apparatus 48(12), 98–104 (2012). (in Chinese)
Liu, H., Chen, C., Ju, Y., et al.: Characteristics analysis and suppression strategy of voltage unbalance in areas. Autom. Electr. Power Syst. 45(14), 132–139 (2021). (in Chinese)
Li, J., Zeng, X., Yu, K., et al.: Suppression method of unbalanced overvoltage in distribution network based on zero-sequence voltage regulation and control. Autom. Electr. Power Syst. 44(20), 121–126 (2020). (in Chinese)
Wang, X., **an, R., Chen, L., et al.: Analysis and repair of a 35kV transformer internal insulation fault. Transformer 58(07), 71–74 (2021). https://doi.org/10.19487/j.cnki.1001-8425.2021.07.014.(inChinese)
Liu, Q., Deng, J., Wu, H., et al.: Simulation and application of voltage unbalance on low-voltage side of 500kV transformer. Transformer 55(10), 36–40 (2018). https://doi.org/10.19487/j.cnki.1001-8425.2018.10.008.(inChinese)
Zhu, M., Li, K.: Calculation method and application of three-phase unbalance in low voltage distribution network. Electr. Meas. Instrum. 56(02), 41–46 (2019). https://doi.org/10.19753/j.issn1001-1390.2019.02.006.(inChinese)
Liu, Y., **a, X., Yi, M., et al.: Research on energy balance of a flexible and direct converter terminal under unbalanced grid voltage. Power Syst. Protect. Control 48(12), 71–79 (2020). (in Chinese)
Ding, R., Wang, G., Liu, H., et al.: Research on three-phase unbalanced load compensation scheme of distribution networks. Power Syst. Clean Energy 34(04), 22–28 (2018). (in Chinese)
Guo, L., Wen, D., Zhang, G., et al.: A novel compensation method for voltage unbalance between single-phase winding and ground of 750 kV autotransformers. Power Syst. Clean Energy 37(12), 56–63 (2021). (in Chinese)
Yang, W., Pu, C., Yang, K., et al.: Short-term fault prediction method for a transformer based on a CNN-GRU combined neural network. Power Syst. Protect. Control 50(06), 107–116 (2022). https://doi.org/10.19783/j.cnki.pspc.210783.(inChinese)
Yuan, J., Xu, P., Li, L., et al.: Prediction of transformer oil-paper insulation aging based on BP neural networks with the chicken swarm optimization algorithm. J. Electr. Power Sci. Technol. 35(04), 33–41 (2020). https://doi.org/10.19781/j.issn.1673-9140.2020.04.005
**e, L., Qiu, W., Li, Z., et al.: Prediction model of dissolved gas in transformer oil based on variational modal decomposition and recurrent neural network with gated recurrent unit. High Volt. Eng. 48(02), 653–660 (2022). https://doi.org/10.13336/j.1003-6520.hve.20201808. (in Chinese)
Yang, Z., Zhou, Q., Zhao, Y., et al.: Prediction of interfacial tension of transformer oil based on artificial neural network and multi-frequency ultrasonic testing technology. High Volt. Eng. 45(10), 3343–3349 (2019). https://doi.org/10.13336/j.1003-6520.hve.20190924036. (in Chinese)
Acknowledgements
This research was supported by the National Natural Science Foundation of China (52367017) and the State Grid Corporation of China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Bei**g Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Ma, Z., Zhang, H., Wang, H., Lu, Z., Li, X., Lu, Z. (2024). Capacitance State Evaluation of 750 kV Autotransformer Windings Based on BP Neural Network. In: Yang, Q., Li, Z., Luo, A. (eds) The Proceedings of the 18th Annual Conference of China Electrotechnical Society. ACCES 2023. Lecture Notes in Electrical Engineering, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-97-1420-9_77
Download citation
DOI: https://doi.org/10.1007/978-981-97-1420-9_77
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1419-3
Online ISBN: 978-981-97-1420-9
eBook Packages: EngineeringEngineering (R0)