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A transformer winding deformation detection method based on the analysis of leakage inductance changes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The detection of transformer winding deformation caused by short-circuit current is of great significance to the realization of condition based maintenance. Considering the influence of environment and measurement errors, an online deformation detection method is proposed based on the analysis of leakage inductance changes. First, the operation expressions are derived on the basis of the equivalent circuit and the leakage inductance parameters are identified by the partial least squares regression algorithm. Second, the amount of the leakage inductance samples in a detection time window is determined using the Monte Carlo simulation thought, and then the samples in the confidence interval are obtained. Last, a criteria is built by the mean value changes of the leakage inductance samples and the winding deformation is detected. The online detection method considers the random fluctuation characteristics of the leakage inductance samples, adjust the threshold value automatically, and can quantify the change range to assess the severity. Based on the field data, the distribution of the leakage inductance samples is analyzed to obey the normal function approximately. Three deformation experiments are done by different sub-winding connections and the detection results verify the effectiveness of the proposed method.
Rocznik
Strony
333--346
Opis fizyczny
Bibliogr. 19 poz., rys., wykr., wz.
Twórcy
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
autor
  • Jiangsu Electric Power Company Research Institute, Nanjing 211103, China
Bibliografia
  • [1] Xu J., Wang J., Gao F., Shu H.C., A survey of condition based maintenance technology for electric power equipments. Power System Technology 24(8): 48-52 (2000).
  • [2] Huang J.H., Quan L.S., Current status and development of condition-based maintenance of highvoltage electric power equipment in substation. Automation of Electric Power System 25(16): 56-61 (2001).
  • [3] Zhang H.Y., Zhu S.L., Zhang Y. et al., Research and implementation of condition-based maintenance technology system for power transmission and distribution equipments. Power System Technology 33(13): 70-73 (2009).
  • [4] Bagheri M., Naderi M.S., Blackburn T., Advanced transformer winding deformation diagnosis: moving from off-line to on-line. IEEE Transactions on Dielectrics and Electrical Insulation 19(6): 1860-1870 (2012).
  • [5] Ma H.Z., Geng Z.H., Chen K. et al., A new fault diagnosis method for power transformer winding deformation based on vibration. Automation of Electric Power Systems 37(6): 89-95 (2013).
  • [6] Xu J., Shao Y.Y., Wang F.H. et al., Comparative research on behavior of vibration frequency response analysis and frequency response analysis in detection of transformer winding deformation. Power System Technology 35(6): 213-218 (2011).
  • [7] Gawrylczyk K.M., Banaszak S., Modeling of frequency response of transformer winding with axial deformations. Archives of Electrical Engineering 63(1): 5-17 (2014).
  • [8] Mehdi B., Mehdi V., Ebrahim R., Comparison of transfer functions using estimated rational functions to detect winding mechanical faults in transformers. Archives of Electrical Engineering 61(1): 85-99 (2012).
  • [9] Ebrahim R., Stefan T., Fault diagnosis of actual large-power high-voltage winding using transfer function method. Archives of Electrical Engineering 60(3): 269-281(2011).
  • [10] Secue J., Mombello E., Cardoso C.V., Review of sweep frequency response analysis - SFRA for assessment winding displacements and deformation in power transformers. Revista IEEE America Latina 5(5): 321-328 (2007).
  • [11] William G., Hurley D.J., Calculation of leakage inductance in transformer windings. IEEE Transactions on Power Electronics 9(1): 121-126 (1994).
  • [12] Li P., Zhang B.H., Hao Z.G., Chu Y.L., Actuality and prospect of transformer winding deformation monitoring based on electric characteristic. Electric Power Automation Equipment 26(2): 28-32 (2006).
  • [13] Hao Z.G., Zhang B.H., Li P., Gao J., Wang Q., Study on on-line detection of transformer winding deformation based on parameter identification of leakage inductance. High Voltage Engineering 32(11): 67-70 (2006).
  • [14] Deng X.L., Xiong X.F., Gao L., Fu Y., Chen Y.J., Method on on-line monitoring of transformer winding deformation based on parameter identification. Proceedings of the CSEE 34(28): 4950-4958 (2014).
  • [15] Wang M.H., Bao J., Qi X., Zhang Z., Online estimation of transmission line parameters based on PMU measurements. Automation of Electric Power Systems 34(1): 25-27 (2010).
  • [16] Chen J., Yan W., Lu J.G. et al., A robust transformer parameter estimation method considering multi-period measurement random errors. Automation of Electric Power Systems, 35(2): 28-33 (2011).
  • [17] Wang M.H., Qi X., Niu S.Q., Han F.K., Online estimation of transformer parameters based on PMU measurements. Automation of Electric Power Systems 35(13): 61-65 (2011).
  • [18] Wang W.S., Ding J., Zhao Y.L., Zhang X.M., Study on the long term prediction of annual electricity consumption using partial least square regressive model. Proceedings of the CSEE 23(10): 17-21 (2003).
  • [19] Mao L.F., Jiang Y.C., Long R.H. et al., Medium- and long- term load forecasting based on partial least squares regression analysis. Power System Technology 32(19): 71-77 (2008).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f4f419ba-31e1-4375-9efc-e831c84509c3
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