Reliability-Centered Maintenance and Replacement for Transformer
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Deregulated and competitive power market places utilities under high pressure to assure providing power with a satisfactory level of power continuity. This objective entails a high level of reliability which in turn demands a high financial budget for design, operation, and maintenance. Therefore, the need for utilities to balance these factors has been increasing to become the core of a utility's asset management activities. Maintenance is a key aspect of asset management. The main objective of maintenance is to extend the lifetime of equipment and/or reduce the probability of failure. Maintenance activities play an important role in improving system reliability by keeping the condition of a system's equipment within an acceptable level. Generally speaking, technical requirements and budget constraints are the most influential factors in assigning maintenance activities. The most cost-effective maintenance approach is the approach that can sustain a high level of reliability while maintenance cost is minimized. The transformer has a significant role in the power system due to its remarkable effect on the overall level of reliability in addition to its extensive investments in the power grid. Transformer management is comprised of identifying the appropriate type and frequency to maintain the transformer, and the appropriate time to replace the transformer in a cost-effective manner. The essential objective of this thesis is to introduce a novel framework for transformer management. An approach which links maintenance and replacement decisions is presented in this thesis. This approach proposes a methodical decision-making system to determine the optimal time to replace the transformer. Indeed, the proposed approach essentially investigates the cost-effectiveness of replacing the transformer both before and after the lifetime is extended by maintenance. To properly investigate the effect of maintenance, maintenance activities should first be scheduled effectively. Therefore, this approach introduces a maintenance strategy based on reliability-centered maintenance (RCM) concept and genetic algorithm (GA) to optimally schedule maintenance activities. Two replacement studies are conducted: with and without the effect of maintenance. A comparison between replacement studies is discussed in the proposed approach.