Title :
Component Reliability Modeling of Distribution Systems Based on the Evaluation of Failure Statistics
Author :
Zhang, Xiang ; Gockenbach, Ernst
Author_Institution :
Univ. of Hanover, Hanover
fDate :
10/1/2007 12:00:00 AM
Abstract :
The procedures of asset-management have been developed into a central element of network operation and planning in liberalised electric markets of power supply for the present. The method of asset management considers all relevant life cycle cost related to the network equipment and provides strategies for reinvestment, maintenance and fault elimination. However, the method requires practical information about the available failure statistic from distribution networks, as well as about the reliability evaluation of equipment installed in distribution network. For the quantitative evaluation we collect failure record data of distribution networks in the special failure statistic. Furthermore, this paper represents the first attempt at modeling the component reliability for representative electrical components due to electrical stress, mechanical stress, temperature and time, which takes general aging mechanisms of insulating materials into consideration. Thus the proposed models provide reliability estimates, e.g. failure probability and failure rate for distribution systems. These models can be not only parameterized with a great deal of statistical data but also determined by aging tests and breakdown tests being available for the probabilistic assessment. Our results imply that the assessment approach for component reliability will motivate a need for reasonable and accurate data at an early decision-making stage in future deregulation of electric power market.
Keywords :
fault diagnosis; life cycle costing; power distribution reliability; power markets; statistical analysis; asset-management; component reliability modeling; distribution systems; failure probability; failure statistics; fault elimination; liberalised electric market derregulation; life cycle cost; network operation; quantitative evaluation; Aging; Asset management; Costs; Power supplies; Power system modeling; Power system planning; Power system reliability; Statistical distributions; Stress; Testing;
Journal_Title :
Dielectrics and Electrical Insulation, IEEE Transactions on
DOI :
10.1109/TDEI.2007.4339478