A health grade system against mechanical faults of power transformers has been little investigated compared to those for chemical and electrical faults. This paper thus presents a statistical health grade system against mechanical faults in power transformers used in nuclear power plant sites where the mechanical joints and/or parts are the ones used for constraining transformer cores. Two health metrics—root mean square (RMS) and root mean square deviation (RMSD) of spectral responses at harmonic frequencies—are first defined using vibration signals acquired via in-site sensors on fifty-four power transformers in several nuclear power plants in sixteen months. We then investigate a novel multivariate statistical model, namely copula, to statistically model the populated data of the health metrics. The preliminary study shows that the proposed health metrics and statistical health grade system are feasible to monitor and predict the health condition of the mechanical faults in the power transformers.
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Chakravarti, M., Laha, R.G., & Roy, J., (1967). Handbook of methods of applied statistics, volume I. John Wiley and Sons, p392–394.
Dick, E.P., & Erven, C.C. (1978). Transformer Diagnostic Testing by Frequency Response Analysis. IEEE Transactions on Power Apparatus and Systems, vPAS- 97, n6, p2144–2153.
Fei, S.-W., Liu, C.-L., & Miao, Y.-B. (2009). Support Vector Machine with Genetic Algorithm for Forecasting of Key-gas Ratios in Oil-immersed Transformer. Expert Systems with Applications, v36, n3, p6326–6331.
Fei, S., & Zhang, X. (2009). Fault Diagnosis of Power Transformer based on Support Vector Machine with Genetic Algorithm. Expert Systems with Applications, v36, n8, p11352–11357.
Garcia, B., Burgos, J.C., Alonso, A.M., & Sanz, J. (2005). A Moisture-in-Oil Model for Power Transformer Monitoring-Part II: Experimental Verification. IEEE Transactions on Power Delivery, v20, n2, p1423–1429.
García, B., Member, Burgos, J.C., & Alonso Á .M. (2006). Transformer Tank Vibration Modeling as a Method of Detecting Winding Deformations—Part I: Theoretical Foundation. IEEE Transactions on Power Delivery, v21, n1, p157–163.
García B., Burgos J.C. & Alonso, Á .M. (2006). Transformer Tank Vibration Modeling as a Method of Detecting Winding Deformations—Part II: Experimental Verification. IEEE Transactions on Power Delivery, v21, n1, p164–169.
Gong, L., Liu, C-H., & Zha, X.F. (2007). Model-Based Real-Time Dynamic Power Factor Measurement in AC Resistance Spot Welding with an Embedded ANN. IEEE Transactions on Industrial Electronics, v54, n3, p1442–1448.
Hao, X., & Cai-xin, S. (2007). Artificial Immune Network Classification Algorithm for Fault Diagnosis of Power Transformer. IEEE Transactions on Power Delivery, v22, n2, p930–935.
Hong-Tzer, Y., & Chiung-Chou, L. (1999). Adaptive Fuzzy Diagnosis System for Dissolved Gas Analysis of Power Transformers. IEEE Transactions on Power Delivery, v14, n4, p1342–1350.
Huang, Y. C. (2003). Evolving Neural Nets for Fault Diagnosis of Power Transformers. IEEE Transactions on Power Delivery, v18, n3, p843–848.
IEEE std. C57.104 (2008). IEEE guide for the interpretation of gases generated in oil-immersed transformers.
Ji, S., Luo, Y., & Li, Y. (2006). Research on Extraction Technique of Transformer Core Fundamental Frequency Vibration Based on OLCM. IEEE Transactions on Power Delivery, v21, n4, p1981–1988.
Joe, H. (1997). Multivariate models and dependence concepts: Monographs on Statistics and Applied Probability, vol. 73. Chapman & Hall, London, UK.
Kole, E., Koedijk, K., & Verbeek, M. (2007). Selecting Copulas for Risk Management. Journal of Banking and Finance, v31, n8, p2405–2423.
Lee, W.R., Jung, S.W., Yang, K.H., & Lee, J.S. (2005). A study on the determination of subjective vibration velocity ratings of main transformers under operation in nuclear power plants. In Proceedings of the 12th International Congress on Sound and Vibration (Paper No. 1017), July 11-14, Lisbon, Portugal,.
McArthur, S.D.J., Strachan, S.M., & Jahn, G. (2004). The Design of a Multi-Agent Transformer Condition Monitoring System. IEEE Transactions on Power Delivery, v19, n4, p1845–1852.
Muhamad, N.A., & Ali, S.A.M. (2006). Simulation Panel for Condition Monitoring of Oil and Dry Transformer Using LabVIEW with Fuzzy Logic Controller. Journal of Engineering, Computing & Technology, v14, p187– 193.
Picanço, A.F., Martinez, M.L.B., & Paulo, C.R. (2010). Bragg System for Temperature Monitoring in Distribution Transformers. Electric Power System Research, v80, n1, p77–83.
Pradhan, M. K. (2006). Assessment of the Status of Insulation during Thermal Stress Accelerated Experiments on Transformer Prototypes. IEEE Transactions on Dielectrics and Electrical Insulation, v13, n1, p227–237.
Purkait, P., & Chakravorti, S. (2002). Time and Frequency Domain Analyzes based Expert System for Impulse Fault Diagnosis in Transformers. IEEE Transactions on Dielectrics and Electrical Insulation, v9, n3, p433–445.
Roser, B.N. (1999). An Introduction to Copulas. New York: Springer.
Saha, T. K., & Purkait, P. (2004). Investigation of an Expert System for the Condition Assessment of Transformer Insulation based on Dielectric Response Measurements. IEEE Transactions on Power Delivery, vol. 19, no. 3, p1127–1134.
Saha, T.K. (2003). Review of Modern Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers. IEEE Transactions on Dielectrics and Electrical Insulation, v10, n5, p903–917.
Shin, H.J., & Cho, S. (2006). Response Modeling with Support Vector Machines. Expert Systems with Applications, v30, n4, p746–760.
Su, Q., Mi, C., Lai, L.L., & Austin, P. (2000) A Fuzzy Dissolved Gas Analysis Method for the Diagnosis of Multiple Incipient Faults in a Transformer. IEEE Transactions on Power Systems, v15, n2, p593–598.
Tang, W.H., Wu, Q.H., & Richardson Z.J. (2004). A Simplified Transformer Thermal Model Based on Thermal-Electric Analogy. IEEE Transactions on Power Delivery, v19, n3, p1112–1119.
Wang, M., Vandermaar, A. J., & Srivastava, K. D. (2002) Review of condition assessment of power transformers in service. IEEE Electrical Insulation Magazine, v18, n6, p12–25.
Wang, P., Youn, B.D., & Hu, C. (2010). A generic sensor network design framework based on a detectability measure. ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 15- 18, Montreal, Quebec, Canada.
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