Determination of transformer health condition using artificial neural networks

This paper presents a method to estimate a transformer health condition based on diagnostic tests. A feed forward artificial neural network (FFANN) is used to find the health index of the transformer. The health index is used to find the health condition of the transformer. The training of the FFANN is done using real measurements of 59 working transformers. The testing of the trained neural network performance is done using real data for 29 working transformers. The performance evaluation of the trained FFANN shows that the trained neural network is reliable in finding the health condition of any working transformer.

[1]  Alan Emsley,et al.  Degradation of cellulosic insulation in power transformers. Part 3: effects of oxygen and water on ageing in oil , 2000 .

[2]  R. Piercy,et al.  An Approach to Determine the Health Index of Power Transformers , 2008, Conference Record of the 2008 IEEE International Symposium on Electrical Insulation.

[3]  Gary C. Stevens,et al.  A new approach to condition assessment and lifetime prediction of paper and oil used as transformer insulation , 2001, ICSD'01. Proceedings of the 20001 IEEE 7th International Conference on Solid Dielectrics (Cat. No.01CH37117).

[4]  D. Hughes,et al.  Condition based risk management (CBRM) — enabling asset condition information to be central to corporate decision making , 2005 .

[5]  Ben Coppin,et al.  Artificial Intelligence Illuminated , 2004 .

[6]  N. Dominelli,et al.  Equipment health rating of power transformers , 2004, Conference Record of the 2004 IEEE International Symposium on Electrical Insulation.

[7]  Magdy M. A. Salama,et al.  Asset management techniques for transformers , 2010 .

[8]  S. Miletić.  Asset condition assessment by health index benchmarking , 2009 .

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

[10]  T. Hjartarson,et al.  Predicting Future Asset Condition Based on Current Health Index and Maintenance Level , 2006, ESMO 2006 - 2006 IEEE 11th International Conference on Transmission & Distribution Construction, Operation and Live-Line Maintenance.

[11]  M.K. Pradhan,et al.  Criteria for estimation of end of life of power and station transformers in service , 2004, The 17th Annual Meeting of the IEEE Lasers and Electro-Optics Society, 2004. LEOS 2004..

[12]  I. Hohlein,et al.  Aging of cellulose at transformer service temperatures. Part 1: Influence of type of oil and air on the degree of polymerization of pressboard, dissolved gases, and furanic compounds in oil , 2005, IEEE Electrical Insulation Magazine.

[13]  E. Gulski,et al.  Monitoring and Diagnostic of Transformer Solid Insulation , 2002, IEEE Power Engineering Review.