Transformer Health Index estimation using Orthogonal Wavelet Network

The Health Index represents a practical tool that combines the results of operating observations, field inspections, and site and laboratory testing to manage the asset and prioritize investments in capital and maintenance plans. In this paper, the Orthogonal Wavelet Network is used to estimate transformer Health Index using various transformer test results. The idea is to build a tool that can be used to assign a representative health index to each transformer based on various transformer test results. The main purpose of this research is to develop a reliable health index for substation transformers. The proposed transformer health index will provide assessment of the transformer health condition which will be useful for maintenance, ensuring optimum transformer performance, increased efficiency and also increased expected life time.

[1]  V. Vittal,et al.  Risk Assessment for Transformer Loading , 2001, IEEE Power Engineering Review.

[2]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[3]  F. Rivas-Davalos,et al.  An alternative method for estimating mean life of power system equipment with limited end-of-life failure data , 2009, 2009 IEEE Bucharest PowerTech.

[4]  V. I. Kogan,et al.  Failure analysis of EHV transformers , 1988 .

[5]  Ilaria Losa,et al.  Regulation of continuity of supply in the electricity sector and cost of energy not supplied , 2009 .

[6]  Jiwen Dong,et al.  Time-series prediction using a local linear wavelet neural network , 2006, Neurocomputing.

[7]  Jaime Román Úbeda,et al.  Sequential simulation applied to composite system reliability evaluation , 1992 .

[8]  Wenyuan Li,et al.  Risk Assessment Of Power Systems: Models, Methods, and Applications , 2004 .

[9]  Yili Hong,et al.  Prediction of remaining life of power transformers based on left truncated and right censored lifetime data , 2009, 0908.2901.

[10]  Yichuang Sun,et al.  Wavelet neural network approach for fault diagnosis of analogue circuits , 2004 .

[11]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..

[12]  W. J. McNutt,et al.  Insulation thermal life considerations for transformer loading guides , 1992 .

[13]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[14]  Peng Wang,et al.  Operational reliability assessment of power systems considering condition-dependent failure rate , 2010 .

[15]  Wenyuan Li,et al.  Evaluating mean life of power system equipment with limited end-of-life failure data , 2004 .

[16]  Roy Billinton,et al.  Reliability Evaluation of Engineering Systems , 1983 .

[17]  Chengrong Li,et al.  Data Requisites for Transformer Statistical Lifetime Modelling—Part I: Aging-Related Failures , 2013, IEEE Transactions on Power Delivery.

[18]  G. Mazzanti,et al.  Bayesian reliability estimation based on a Weibull stress-strength model for aged power system components subjected to voltage surges , 2006, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[20]  Wenyuan Li,et al.  A Probabilistic Analysis Approach to Making Decision on Retirement of Aged Equipment in Transmission Systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[21]  Wang Fan,et al.  An approach to power transformer asset management using health index , 2009, IEEE Electrical Insulation Magazine.

[22]  S. Skuletic,et al.  Reliability assessment of composite power systems , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[23]  Lin Cheng,et al.  A Hybrid Conditions-Dependent Outage Model of a Transformer in Reliability Evaluation , 2009, IEEE Transactions on Power Delivery.