The U.S. electric utility industry continues to undergo dramatic and accelerating transformation. Reliability and resiliency are a key focus. A number of important issues including cyber and physical security challenges, aging infrastructure, and low natural gas prices continue to be of concern. Significant advances in technology, and prolonged regulatory uncertainty are also contributing factors.

Electric utilities are now making substantial investment in renewable resources and other technologies needed for renewables integration. This means a reduction in investment in generation assets and an increase in the transmission and distribution grids. There is also increased investment in providing customers with solutions to lower their costs, reduce their carbon footprint and provide control over their energy management.

The transformation ultimately demands significant increases in power plant generation operating capabilities and higher levels of equipment reliability while reducing O&M and capital budgets. Achieving higher levels of equipment reliability, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices:

• Preventative (time-based)

• Predictive (condition-based)

• Reactive (run-to-failure)

• Proactive (combination of 1, 2 and 3 + root cause failure analysis)

Preventive maintenance (PM) is planned maintenance actions taken to ensure equipment is capable of performing its required functions. PM tasks are generally time-based, depending on the availability of condition monitoring data through a predictive maintenance (PdM) program.

Traditionally, PdM is largely performed by maintenance technicians in the field with handheld devices. Resource constraints usually mean that often weeks or even months elapsed between readings on the same piece of equipment. This approach has limitations with data volume, velocity, variety, and veracity.

Significant recent advances in sensor and technology associated with the Industrial Internet of Things (IIoT) have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with analysis and visualization software, provide a comprehensive on-line conditioning monitoring solution that enables both a reduction in time-based PM tasks and also automation of PdM tasks.

This paper describes an approach by Duke Energy to apply smart, connected power plant assets to greatly enhance its fossil generation equipment reliability program and processes. It will outline the value that is currently being realized and will also examine additional opportunities.

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