Abstract
The frequency response method is one of the most commonly used methods for determining the winding deformation of a power transformer. However, in the actual measurement, the frequency response curve of the test results will generate spiking data due to interference. If this impact cannot be accurately identified and reduced, the availability of the test results will be seriously affected, causing difficulties and even errors to the analysis and judgment of the test. A multidimensional nonuniform Kriging (MNKriging) interpolation algorithm is proposed in this paper to eliminate spiking data in the frequency response curve and improve the accuracy of the test results. The method constructs a nonuniform multidimensional deformation field model by using the optimal weight coefficient combination, and optimizes it with the particle swarm optimization algorithm to extend the Kriging interpolation to the multidimensional nonuniform field space. It has been applied to the interpolation of the transformer frequency response data. The results prove that the method reduces the noise interference to a certain extent, and therefore the points on the frequency response curve of the transformer winding deformation subject to noise interference are well recognized and repaired.
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References
Chen, H.: Preventive Test Method and Diagnostic Technology of Electric Power Equipment. China Science & Technology Press, Beijing (2001)
Lu, Y.: A Temporal-Spatial Geographic Weighted Regression Method Based on Principal Component Analysis. Chinese Academy of Surveying & Mapping (2018)
Chen, H.: Abnormal Operation and Accident Treatment of Power System. China Water & Power Press, Beijing (1999)
Pucci Jr., A.A., Murashige, J.A.E.: Applications of universal kriging to an aquifer study in New Jersey. Groundwater 25(6), 672–678 (1987)
Rouhani, S., Hall, T.J.: Space-time kriging of groundwater data. Geostatistics 2, 639–650 (1989)
Jaquet, O.: Factorial kriging analysis applied to geological data from petroleum exploration. Math. Geol. 21(7), 683–691 (1989)
Bardossy, A., Bogardi, I., Kelly, W.E.: Kriging with imprecise (fuzzy) variograms. II: application. Math. Geol. 22(1), 81–94 (1990)
Nobre, M.M., Sykes, J.F.: Application of Bayesian kriging to subsurface characterization. Can. Geotech. J. 29(4), 589–598 (1992)
Tarboton, K.C., Wallender, W.W., Fogg, G.E., et al.: Kriging of regional hydrologic properties in the western San Joaquin Valley, California. Hydrogeol. J. 3(1), 5–23 (1995)
Piotrowski, J.A., Bartels, F., Salski, A., et al.: Geostatistical regionalization of glacial aquitard thickness in northwestern Germany, based on fuzzy kriging. Math. Geol. 28(4), 437–452 (1996)
Zhou, C., Gao, H., Gao, L., Zhang, W.: Particle swarm optimization. Appl. Res. Comput. 12, 7–11 (2003)
Yang, W., Li, Q.: Overview of Particle Swarm Optimization, no. 05, pp. 87–94. Chinese Engineering Sciences Press, China (2004)
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Wei, L. et al. (2021). Noise Data Removal Method of Frequency Response Curve Based on MNKriging Interpolation Algorithm. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1234. Springer, Cham. https://doi.org/10.1007/978-3-030-51556-0_1
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DOI: https://doi.org/10.1007/978-3-030-51556-0_1
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