Abstract
Capacitive voltage transformer (CVT) is an essential power measurement equipment in the power grid, which generates errors in long-term operation. Therefore, it is necessary to quantify the measurement performance of CVT and predict its measurement deterioration trend. This study proposes a measurement performance index to characterize the ratio error of CVT and a trend prediction method for CVT measurement deterioration based on double Gaussian model-KAN fusion. First, approximation and detail coefficients are formed after multilayer wavelet transform on the secondary side voltage of CVT. The maximum approximate coefficient is selected from the approximate coefficients, and the State of Performance (SOP) representation ratio error of CVT is calculated using the maximum approximate coefficient. Then, a Variational Modal Decomposition Mean Difference (VMD-MD) method is proposed to decompose the SOP sequence of CVT in multiple layers. The residual decomposed from the SOP sequence is used to characterize the deterioration trend of SOP, and the double Gaussian model is used to model and predict it. The Intrinsic Mode Functions (IMFs) decomposed from the SOP sequence are used to characterize the deterioration fluctuation of SOP, and the KAN algorithm is used to predict it. Finally, all the predicted results are added to represent the deterioration trend of CVT. Using CVTs and SWCVT-3 CVT online test system of China Electric Power Research Institute, the three-phase voltage data with increasing ratio error are collected, and the proposed double Gaussian model-KAN fusion method is tested. During the experiment, the ratio error of CVT was characterized effectively by SOP, and the proposed double Gaussian model-KAN fusion method could accurately predict the CVT SOP deterioration trend.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
References
Ushakov, V. Y., Mytnikov, A. V. & Rakhmonov, I. U. High-voltage Equipment of Power systems[J] (Springer, 2023). Power systems https://doi.org/10.1007/978-3-031-38252-9.
Beheshti Asl, M., Fofana, I. & Meghnefi, F. Review of various sensor technologies in monitoring the condition of power transformers[J]. Energies 17 (14), 3533 (2024).
Faifer, M., Toscani, S. & Ottoboni, R. Electronic combined transformer for power-quality measurements in high-voltage systems[J]. IEEE Trans. Instrum. Meas. 60 (6), 2007–2013 (2011).
Borghei, M. & Ghassemi, M. Insulation materials and systems for more-and all-electric aircraft: A review identifying challenges and future research needs[J]. IEEE Trans. Transp. Electrification. 7 (3), 1930–1953 (2021).
Zhang, Y. et al. An online detection method for capacitor voltage transformer with excessive measurement error based on multi-source heterogeneous data fusion[J]. Measurement 187, 110262 (2022).
Ameli, A. et al. An auxiliary framework to mitigate measurement inaccuracies caused by capacitive voltage transformers[J]. IEEE Trans. Instrum. Meas. 71, 1–11 (2022).
Zhou, F. et al. Capacitive voltage transformer measurement error prediction by improved long short-term memory neural network[J]. Energy Rep. 8, 1011–1021 (2022).
Li, Z. et al. Research into an online calibration system for the errors of voltage Transformers based on open–closed capacitor[J]. Energies 11 (6), 1455 (2018).
Zhang, Z. et al. Monitoring the metering performance of an electronic voltage transformer on-line based on cyber-physics correlation analysis[J]. Meas. Sci. Technol. 28 (10), 105015 (2017).
Chen, B. et al. Measurement error Estimation for capacitive voltage transformer by insulation parameters[J]. Energies 10 (3), 357 (2017).
Freiburg, M., Sperling, E. & Predl, F. Capacitive voltage transformers-electrical performance and effective diagnostic measures[C]. In 2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, pp. 20–23. (2016).
de Andrade Reis, R. L., Neves, W. L. A. & Lopes, F. V. Coupling capacitor voltage Transformers models and impacts on electric power systems: A review[J]. IEEE Trans. Power Delivery. 34 (5), 1874–1884 (2019).
Meng, Z. et al. Research on the reliability of capacitor voltage Transformers calibration results[J]. Measurement 146, 770–779 (2019).
Zhang, W. et al. Online measurement of capacitor voltage transformer metering errors based on GRU and MTL[J]. Electr. Power Syst. Res. 221, 109473 (2023).
Liu, J. & Geng, G. Fault prediction for power plant equipment based on support vector regression[C]. In 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2: 461–464. (2015).
Sun, L. et al. Real-time power prediction approach for turbine using deep learning techniques[J]. Energy 233, 121130 (2021).
Wang, Q., Bu, S. & He, Z. Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN[J]. IEEE Trans. Industr. Inf. 16 (10), 6509–6517 (2020).
Zhang, C. et al. Monitoring the ratio error drift of CVTs connected to the same phase along with KDE-PCA[C]. In 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 1–6. (2019).
Zhao, X. & Wei, H. Error Evaluation method of capacitive voltage transformer based on improved principal component analysis [C]. J. Phys. Conf. Ser. IOP Publishing 2625(1), 012041 (2023).
Zhang, Z. et al. Evaluating the metering error of electronic Transformers on-line based on vn-mwpca[J]. Measurement 130, 1–7 (2018).
Zhang, C., Li, H. & Chen, Q. Detection of the ratio error drift in CVT considering AVC[J]. Measurement 138, 425–432 (2019).
Zhang, C. et al. An analog circuit fault diagnosis approach based on improved wavelet transform and MKELM[J]. Circuits Syst. Signal. Process., : 1–32. (2022).
Bakka, H. et al. Non-stationary Gaussian models with physical barriers[J]. Spat. Stat. 29, 268–288 (2019).
Vaca-Rubio, C. J. et al. Kolmogorov-arnold networks (kans) for time series analysis[J]. (2024). arxiv preprint arxiv:2405.08790.
Zhao, S., Zhang, C. & Wang, Y. Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network[J]. J. Energy Storage. 52, 104901 (2022).
Zhang, C. et al. Battery SOH Estimation method based on gradual decreasing current, double correlation analysis and GRU[J]. Green. Energy Intell. Transp. 2 (5), 100108 (2023).
Hu, W. et al. Integrated method of future capacity and RUL prediction for Lithium-Ion batteries based on CEEMD‐Transformer‐LSTM. Model. Energy Sci. Eng. 12 (11), 5272–5286 (2024).
Azimi-Pour, M., Eskandari-Naddaf, H. & Pakzad, A. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly Ash self-compacting concrete[J]. Constr. Build. Mater. 230, 117021 (2020).
Acknowledgements
This study was supported by Young Talent Initiation Program of State Grid Corporation of China (522201250026-618-RC).
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This work was conceived by Bolun Du and Yinglong Diao. Data was collected and analyzed by Feng Zhou. Xiaodong Yin and Shuai Yang helped to revise manuscript and proposed constructive opinions. All authors contributed to the article and approved the submitted version.
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Du, B., Diao, Y., Zhou, F. et al. Trend prediction method for capacitive voltage transformer measurement deterioration based on double Gaussian model-KAN fusion. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35455-z
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DOI: https://doi.org/10.1038/s41598-026-35455-z
