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Access Type
WSU Access
Date of Award
January 2024
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Industrial and Manufacturing Engineering
First Advisor
Murat Yildirim
Abstract
Maintenance scheduling and management play a crucial role in the operation andmaintenance (O&M) policies of power systems, significantly affecting their total expenditures [2, 3]. For example, 20%-25% of the total levelized cost per kWh of wind turbines can be linked to maintenance operations [4]. The harsh and continuously changing operating environments lead to degradation of generation assets within these systems, which require maintenance interventions to maintain or restore their normal conditions [5]. Uncontrolled degradation can lead to frequent outages, unexpected failures, and lower system reliability. In complex and interconnected power systems, asset outages and failures can further disrupt production and service processes, cause plant shutdowns, and, in extreme cases, lead to disasters [6]. Therefore, it is essential to balance the focus on reducing maintenance costs with ensuring system reliability [7]. The growing size and operational uncertainties of power systems add to the challenges of establishing proper O&M regimes. The variability in renewable assets’ production capacity, electricity demand, and market prices significantly contribute to the impact of maintenance downtime on the overall performance of these systems. To minimize the impact of maintenance downtime on the system, maintenance decisions should adapt to changes in units’ health conditions and operational requirements. To tackle these challenges, various maintenance management policies have been proposed. Preventive maintenance policies are designed to reduce failure instances, especially for critical components, by conducting maintenance actions at predetermined intervals. Time-based (periodic) maintenance (TBM) is the most common preventive maintenance strategy in the energy industry, in which units are maintained based on a calendar time or machine run-time [8, 9]. Maintenance intervals are obtained through manufacturer recommendations, engineering expertise, and field observations [10,11]. Maintenance intervals are generally established by collecting failure time data of assets of similar types and using them to develop population-based failure distributions. These population-based failure distributions are then used to predict the time of failure for specific assets within the power system.
Recommended Citation
Fallahi, Farnaz, "Integrating Sensor-Driven Degradation Analytics Into Operations And Maintenance In Energy Systems" (2024). Wayne State University Dissertations. 3944.
https://digitalcommons.wayne.edu/oa_dissertations/3944
