Multidisciplinary Software Developments in a Power Transformers Scenario

Abstract:

Power transformers’ failures carry great costs to electric companies. To diminish this problem in four working 40 MVA transformers, the authors have implemented the measurement system of a failure prediction tool, which is the basis of a predictive maintenance infrastructure. The prediction models obtain their inputs from sensors, whose values must be conditioned, sampled and filtered before feeding the forecasting algorithms. Applying Data Warehouse tech- niques, the models have been provided with an abstraction of sensors the authors have called Virtual Cards. By means of these virtual devices, models have access to clean data, both fresh and historic, from the set of sensors they need. Besides, several characteristics of the data flow coming from the Virtual Cards, such as the sample rate or the set of sensors itself, can be dynamically reconfigured. A replication scheme was implemented to allow the distribution of demanding processing tasks and the remote management of the prediction applications.

• Periodical: Key Engineering Materials (Volumes 293-294)
• Pages: 635-642
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.293-294.635
• Online since: September 2005
• Authors: Perfecto Mariño, Francisco Poza, Santiago Otero, Fernando Machado
• Keywords: Condition Monitoring, Distributed System, Metadata, Sensor

References

[1] G. Babin and C. Hsu: IEEE Transaction on Knowledge and Data Engineering Vol. 8(5) (1996), p.786.

[2] D. Cowan and C. Lucena: IEEE Transactions on Software Engineering Vol. 21(3) (1995), p.229.

[3] B. Devlin: Data warehouse: from architecture to implementation (Addison-Wesley, MA 1997).

[4] W. Inmon: Building the Datawarehouse (John Wiley, 1993).

[5] M. Jackson and P. Zave: IEEE Transactions on Software Engineering Vol. 24(10) (1998), p.831.

[6] T. Johnson: IEEE Transaction on Software Engineering Vol. 21(3) (1995), p.209.

[7] D. Keck and P. Kuehn: IEEE Transactions on Software Engineering Vol. 24(10) (1998), p.779.

[8] H. Kopetz: Real-Time Systems: Design Principles for Distributed Embedded Applications (Kluwer Academic Publishers, Boston 1997).

[9] U. Kulkarni and R. Ramirez: IEEE Transaction on Knowledge and Data Engineering Vol. 9(5) (1997), p.798.

[10] P. Mari˜no, C. Sig¨uenza, J. Nogueira, F. Poza, and M. Dom´ınguez: A reusable distributed software architecture driven by metadata (Proceedings 1999 Asia-Pacific Software Engineering Conference, Takamatsu, Japan 1999).

DOI: 10.1109/apsec.1999.809609

[11] P. Mari˜no, C. Sig¨uenza, J. Nogueira, F. Poza, and M. Dom´ınguez: An event driven software architecture for enterprise-wide data source integration (Proc. Conf. on Information Technology: Coding and Computing, Information Technology, Las Vegas, Nevada 2000).

DOI: 10.1109/itcc.2000.844197

[12] P. Mari˜no, C. Sig¨uenza, J. Nogueira, F. Poza, and M. Dom´ınguez: Metadata driven data paths: Improving data warehouse plumbing (Proceedings 2000 IASTED International Conference on Applied Informatics, Innsbruck, Austria 2000).

[13] H. L. Rivera, J. Garc´ıa-souto, and J. Sanz: IEEE Journal on Selected Topics in Quantum Electronics Vol. 6(5) (2000), p.788.

[14] S. Vranes and M. Stanojevic: IEEE Transactions on Sofware Engineering Vol. 21(3) (1995), p.244.

[15] Iwanitz F. and Lange J: OPC fundamentals, implementation and application (H¨uthig Verlag Heidelberg 2002).