Budgets and maintenance programs in fossil power plants are frequently set based on engineering judgment rather than a Probabilistic Risk Assessment (PRA). Fossil power plants seldom use PRA due to a lack of practical processes, especially when field reliability data are not readily available. In the absence of field data, using general industry data may not be right for conducting PRA for a given organization as the operation and maintenance conditions significantly vary between organizations. To have a successful PRA, an organization needs to use its own field data, and have an efficient process for its organization. Studies have suggested using field data provides a robust model and accurate results. Use of Reliability Block Diagrams for Reliability, Availability, and Maintainability analysis is not new. However, applications of these techniques are new to fossil power plants. In this paper, we propose a practical process by integrating several existing reliability techniques for fossil plants to apply PRA. The process was tested at many power plants, and the results aligned well with the actual values. The methodology outlined in this paper is a forward-looking tool for managers to predict system reliability, and proactively develop maintenance plans and budgets.

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With the fuel prices going up and many states mandating use of more renewable energy, a number of utilities are forced to convert some of their base loaded units to cycling operation. This change in operation requires a departure from the standard maintenance practices established for a given unit. This includes changes to Preventative Maintenance (PM), Predictive Maintenance (PdM), Planning and Scheduling and Key Performance Indicators (KPIs). When a unit is cycled — either minimum load to maximum load or two shift operation — it goes through stress cycles and its expected life decreases relative to the severity of cycling. When a decision is made to cycle a base loaded unit, the impact of the cycling has to be analyzed and the PM and PdM procedures need to be modified in order to maintain the expected life of the components. Cycling affects different components to different degrees and appropriate inspection and maintenance schedules need to be developed. The Key Performance Indicators (KPIs) have to be modified to monitor the effectiveness of the inspections and maintenance performed on the equipment. For example, Maintenance Basis Violation (MBV) is an important KPI for a cycling unit. Similarly, more attention has to be paid to the Planning and Scheduling activities as there are many uncertainties in the availability of the unit for preventative maintenance. The paradigm of performing PMs on a time basis should be changed to a throughput or hours of operation basis. This paper reviews the impact and severity of different cycling modes on a unit, vulnerability of common components adapting to the new mode, and discusses — in general terms — the required changes which need to be made in the inspection and maintenance practices. Also the paper reviews various KPIs that can be put in place for monitoring the impact of these procedures.