Duke Energy is a utility with over 60 plants in 7 states, including renewables, coal, simple cycle combustion turbines, combined cycle and integrated gasification combined cycle plants. AVEVA helps Duke Energy centrally monitor power generation assets with predictive asset analytics software.
Goals
Duke Energy is a regulated and non-regulated utility with over 60 plants in 7 states, including coal, simple cycle combustion turbines, combined cycle and integrated gasification combined cycle plants.
Duke Energy’s goal for the Monitoring and Diagnostic center was to optimize asset performance in their drive to operational excellence, while using the best available digital technology.
The solution employed needed to be software and hardware agnostic to accommodate already installed technologies at the different plants, minimizing rip and replace costs
Challenges
Duke Energy serves over 7.2 millions customers with a generating capacity of 58,000 MW. The generating assets include coal, simple cycle combustion turbines, combined cycle and integrated gasification combined cycle plants.
The Monitoring and Diagnostic center uses 5 analysts to monitor generating assets spread geographically around 7 different US states.
M&D Center serves over 87% of Duke generating fleet using over 11,000 models and over 500,000 data points from the different units
Results
Empower people with early warning notification of equipment problems
Optimize assets with low-cost sensors and connectivity for high-fidelity data access enabling predictive maintenance
Improve operations with contextualized insights
Savings of over $34 millions in a single early catch event in 2016.
AVEVA helps Duke Energy centrally monitor power generation assets with predictive asset analytics software
In the following 27-minute presentation, Duke Energy shares the details of its Monitoring & Diagnostics (M&D), and how it helps shape the cultural transition from a reactive to a proactive organization.
Who is Duke Energy?
(Part 1 of 6 | Video: 3.6 minutes)
Duke Energy is an electric utility with an asset fleet that includes coal, natural gas, hydro, and renewables. Watch the video to learn more as to how Duke’s M&D team has deployed predictive analytics technology across 87% of the entire fleet.
What are the profiles of the M&D staff?
(Part 2 of 6 | Video: 2 minutes)
The M&D team consists of three different profiles or skill sets: APR Analysts, Model Builders, and IT Support. The core team of analysts each have 20-30 years of experience in the industry and is knowledgeable of all assets and what the system is trying to tell them.
How does the M&D function support the reliability framework?
(Part 3 of 6 | Video: 2.4 minutes)
The M&D Center at Duke Energy uses EPRI’s reliability framework to design its work processes including how they communicate with the sites. The M&D Center uses many different tools including PRiSM Predictive Asset Analytics for monitor alarming, trending and in-depth analysis.
What is an example of an early warning "catch"?
(Part 4 of 6 | Video: 3.9 minutes)
Notifications of early catch warnings are critical to the success and avoided failures of the system. Learn more about notification tracking, early catch warnings, and avoided costs from the M&D Center.
What were the key success factors of the M&D initiative?
(Part 5 of 6 | Video: 4.17 minutes)
While the M&D Center had started using Predictive Analytics technology since 2004, a catastrophic event became the trigger to gain top management support and help accelerate the program. Watch the video to learn more about what contributed to and what were the M&D Center keys to success.
Where does the journey go from here?
(Part 6 of 6 | Video: 4.15 minutes)
While the M&D Center has gained momentum through its successes, the team is consistently seeking new opportunities for further improvements in reliability through improved sensor and instrumentation infrastructure maintenance, as well as integration of new technologies as they develop.
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