Emerging Technologies and Embedded Intelligence in Future Power Systems
The replacement wave around 2030 will create a hybrid power system of old and new technologies of which in particular the latter part will provide eminent opportunities for the implementation of embedded intelligence. However, the investment in smart grids is a difficult decision because it concerns a composition of primary and secondary equipment which have different lifetimes and different levels of robustness. Integration of sensor technology, on/off-line diagnostic systems and advanced ICT solutions enable the monitoring of the health index of the grid and its components, provided a physical model can be devised. From an economical and environmental point of view, there is much to gain by smarter electrical power networks, because in principle they enable us to extend the useful lifetime and to delay large replacement investments. However, the emerging technologies for sensors specifically for high voltage equipment performance, interpretation tools and aging models needed for such smart power networks are still in a premature stage. A few emerging technologies have achieved robustness to some extent. Dedicated techniques for partial discharge detection in high voltages cables and gas-insulated switchgear can predict failures on the basis of incipient dielectric faults. Similarly, dissolved gas monitoring of power transformers to alleviate has also been relatively successful. In this paper, the expectations of the power equipment monitoring will be discussed.
KeywordsOrthogonal Frequency Division Multiplex Smart Grid Partial Discharge Spot Temperature Hybrid Power System
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- 1.J. J. Smit, B.M. Pryor et al., (2003) Cigré Brochure 224, “Emerging Technologies and Material Challenges”.Google Scholar
- 2.J. J. Smit, E. Gulski (2007) Integral Decision Support System for Condition Based Asset Management of Electrical Infrastructures. Proc. World Congress on Engineering Asset Management, Harrogate.Google Scholar
- 3.A. Lou, D.Djairam, H. Nikookar, J.J. Smit (2009 ) Interference in the Wireless Channel. Internal graduation report, Delft University of Technology, Delft.Google Scholar
- 4.G. Bajracharya, T. Koltunowicz, R. R. Negenborn, Z. Papp, D. Djairam, B. D. Schutter, and J. J. Smit (2009) Optimization of maintenance for power system equipment using a predictive health model. in Proceedings of the 2009 IEEE Bucharest Power Tech Conference, Bucharest, Romania.Google Scholar
- 5.G. Bajracharya, T. Koltunowicz, R.R. Negenborn, Z. Papp, D.Djairam, B. de Schutter, J.J. Smit (2009) Optimization of Condition-Based Asset Management using Predictive Health Model. International Symposium on High Voltage 2009, Cape Town, South Africa.Google Scholar