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Description:
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New maintenance techniques for circuit breakers are studied in this dissertation by proposing a probabilistic maintenance model and a new methodology to assess circuit breaker condition utilizing its control circuit data . A risk -based decision approach is proposed at system level making use of the proposed new methodology , for optimizing the maintenance schedules and allocation of resources .
This dissertation is focused on developing optimal maintenance strategies for circuit breakers , both at component and system level . A probabilistic maintenance model is proposed using similar approach recently introduced for power transformers . Probabilistic models give better insight into the interplay among monitoring techniques , failure modes and maintenance techniques of the component . The model is based on the concept of representing the component life time by several deterioration stages . Inspection and maintenance is introduced at each stage and model parameters are defined . A sensitivity analysis is carried to understand the importance of model parameters in obtaining optimal maintenance strategies . The analysis covers the effect of inspection rate calculated for each stage and its impact on failure probability , inspection cost , maintenance cost and failure cost . This maintenance model is best suited for long -term maintenance planning . All simulations are carried in MATLAB and how the analysis results may be used to achieve optimal maintenance schedules is discussed .
A new methodology is proposed to convert data from the control circuit of a breaker into condition of the breaker by defining several performance indices for breaker assemblies . Control circuit signal timings are extracted and a probability distribution is fitted to each timing parameter . Performance indices for various assemblies such as , trip coil , close coil , auxiliary contacts etc . are defined based on the probability distributions . These indices are updated using Bayesian approach as the new data arrives . This process can be made practical by approximating the Bayesian approach calculating the indices on -line . The quantification of maintenance is achieved by computing the indices after a maintenance action and comparing with those of previously estimated ones .
A risk -based decision approach to maintenance planning is proposed based on the new methodology developed for maintenance quantification . A list of events is identified for the test system under consideration , and event probability , event consequence , and hence the risk associated with each event is computed . Optimal maintenance decisions are taken based on the computed risk levels for each event .
Two case studies are presented to evaluate the performance of the proposed new methodology for maintenance quantification . The risk -based decision approach is tested on IEEE Reliability Test System . All simulations are carried in MATLAB and the discussions of results are provided . |