A New Prognostic Approach for Hydro-generator Stator Windings
Significant improvements in hydro-generator diagnostics were achieved, in the past decades, by using continuous online measurements and a number of periodic tests. In recent years, the diagnostic raw data has been converted into more useful information by way of integrated diagnostic systems that used expert knowledge. For example, an integrated methodology for hydro-generator diagnostics was developed at Hydro-Québec’ s research institute (IREQ) using a Web-based application. This comprehensive diagnostic system gives the degradation state of generator stator winding insulation by using a portfolio of diagnostic tools. Combining the results leads to a health index ranging from 1 (good condition) to 5 (worst condition). This system is used by Hydro-Québec’s power plant managers as well as technical support and maintenance engineers in the context of condition-based maintenance (CBM). The next step of development is to add new prognostic-related features. This involves automatic identification of active failure mechanisms, root cause analysis and estimation of the stage of advancement of any active mechanism. These characteristics form the basis of predictive maintenance and support the optimization of maintenance strategies.
The approach is based on a number of causal trees (the failure mechanisms) formed by the combination of sequential physical degradation states that ultimately lead to a failure mode. Each combination of sequential physical states is unique and defines a particular failure mechanism. Failure mechanism analysis was followed by identification of all symptoms (diagnostics measurements, observations) with their respective thresholds defining each physical state.
This paper presents the development of a prognostic approach where the modeling of failure mechanisms is combined with observable symptoms from our diagnostic system for the identification of active failure mechanisms.
How to Cite
Prognostic, hydro-generator, stator winding, predictive maintenance, failure mechanisms, symptoms, physical state
Hudon, C., Bélec, M. & Nguyen N.D. (2009). Innovative web system for condition-based maintenance of generators”, Proc. IEEE Electrical Insulation Confrence (EIC), pp. 234–245.
Anders, G.J., Endrenyi, J., Ford, G.L. & Stone, G.C. (1990). A probabilistic model for evaluating the remaining life of electrical insulation in rotating machines, IEEE Trans. on Energy Conversion, Vol. 5, no.4, pp.761-7.
Sim, S.H. & Endrenyi, J. (1988). Optimal preventive Maintenance with repair, IEEE Trans. on Reliability, Vol. 37, No. 1, pp. 92-6.
Welte, T.M. (2009). Using state diagrams for modeling maintenance of deteriorating systems, IEEE Trans. on Power Systems, Vol. 24, No.1, pp.58-66.
Endrenyi, J., Arboreshid, S., Allan, R. N., Anders, G.J., Asgarpoor, S., Billingtonn, R., Chowdhury, N., Dialynas, E.N., Fipper, M., Fletcher, R.H., Grigg, C., McCalley, J., Meliopoulos, S.,
Mielnik, T.C., Nitu, P., Rau, N., Reppen, N.D., Salvaderi, L., Schneider, A. & Singh Ch.. (2001). The present status of maintenance strategies and the impact of maintenance on reliability, IEEE Trans. on Power Systems, Vol. 16, No.4, pp. 638- 46.
International Standards Organization (ISO) (2012). Condition Monitoring and Diagnostic of Machines – general guidelines on data interpretation and diagnostic techniques, In ISO, ISO13379-1:2012, (p.24). Genève, Switzerland: International Standards Organization.
Byington, C.S., Roemer, M.J., Gallie, T. (2002). Prognostic enhancements to diagnostic systemes for improved condition-based maintenance, Proc. IEEE Aerospace conf., Vol 6., pp. 2815-24.
Nguyen, D.N. & Yelle, R. (2001). Analyse des modes de défaillance et de leurs effets sur les alternateurs : Rapport de synthèse, Report IREQ-2001-173.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.