Condition-based hazard rate estimation and optimal maintenance scheduling for electrical transmission system
JIANG, YONG (2006) Condition-based hazard rate estimation and optimal maintenance scheduling for electrical transmission system. PhD thesis, Iowa State University.
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The effectiveness of expending maintenance resources can vary dramatically depending on the target and timing of the maintenance activities. The objective of the work to develop a method of allocating economic resources and scheduling maintenance tasks among bulk transmission system equipment, so as to optimize the effect of maintenance with respect to the mitigation of component failure consequences. Techniques including condition-based failure rate estimation of electric transmission system components, analysis of failure consequences in power system, probabilistic modeling and risk assessment, and optimization are integrated in the work. Hidden Markov model is a good tool to estimate instantaneous status for deteriorating components. The maintenance selection and scheduling approach for bulk transmission equipment is based on the cumulative long-term risk caused by failure of each piece of equipment. This approach not only accounts for equipment failure probability and equipment damage, but it also accounts for the outage consequence in term of system related security problems. Various types of maintenance activities are studied and their relationship to the failure modes and system security improvement are investigated. An optimizer is developed to select and schedule the maintenance for large networks with various types of resource constraints, together with methods of resource reallocation. Finally, a strategy of incorporating maintenance activities among different transmission owners is developed. The objective of our work is to allocate resources economically and strategically so as to provide best performance of maintenance for electrical transmission system. These strategies can also be applied to problems inherent to resource intensive asset management in many similar types of infrastructures such as gas pipelines, airlines, and telecommunications.
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