DocumentCode :
3009131
Title :
Developing the physical layer of future power grids: Required for automated asset management and renewable energy integration
Author :
Djairam, Dhiradj ; Grizonic, R. ; Zhuang, Qianmeng ; Smit, J.J.
Author_Institution :
High Voltage Technol. & Manage, Delft Univ. of Technol., Delft, Netherlands
fYear :
2012
fDate :
23-27 Sept. 2012
Firstpage :
509
Lastpage :
512
Abstract :
Increased demand for power requires a grid that is equipped to facilitate distributed or large-distance bulk generation. Complying with long term goals, this future grid should be reliable, carbon neutral and sustainable. The main drivers for the upcoming changes in the power grids are the increase of renewable energy production and the aging of the high voltage electrical components. In order to determine the health state of these components, intelligence embedded in the components is required. This includes sensors, communication technology and associated models for interpretation and decision making. To realize an intelligent future grid, it is required that, besides high level smart grids concepts, an actual lower level physical layer is developed. Concrete suggestions will be discussed to implement market ready solutions for predictive health management which can cope with the changing environment of the future grid. Using these suggestions in a case, it is shown that the loss of remaining lifetime can be decreased during a standard 24 hour loading pattern.
Keywords :
decision making; power system management; remaining life assessment; renewable energy sources; smart power grids; automated asset management; decision making; future power grids; intelligence embedded; large-distance bulk generation; physical layer; predictive health management; remaining lifetime; renewable energy integration; smart grids; standard 24 hour loading pattern; Load modeling; Oil insulation; Power transformers; Predictive models; Prognostics and health management; Sensors; Stress; condition monitoring; decision making; diagnostic interpretation; diagnostics; high voltage; power grid intelligence; predictive health model; sensoring;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Condition Monitoring and Diagnosis (CMD), 2012 International Conference on
Conference_Location :
Bali
Print_ISBN :
978-1-4673-1019-2
Type :
conf
DOI :
10.1109/CMD.2012.6416191
Filename :
6416191
Link To Document :
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