proceedings of the asme 2016 power conference power2016
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The Application of Smart, Connected Power Plant Assets for Enhanced Condition Monitoring and Improving Equipment Reliability
Michael Reid Duke Energy
Charlotte, NC, USA
Bernie Cook Duke Energy
Charlotte, NC, USA
ABSTRACT
The U.S. electric utility industry continues to undergo
dramatic change due to a number of key trends and also
prolonged uncertainty. These trends include:
Increasing environmental regulations uncertainty
Natural gas supply uncertainty and price
Economic / decoupling of electricity demand growth from
GDP
Aging coal and nuclear generation fleet / coal retirements
Aging workforce
Increasing distributed energy resources
Increasing customer expectations
The transformation ultimately demands significant
increases in power plant generation operating capabilities (e.g.
flexibility, operating envelop, ramp rates, turn-down etc.) and
higher levels of equipment reliability, while reducing O&M
and capital budgets. Achieving higher levels of equipment
reliability and flexibility, with such tightening budget and
resource constraints, requires a very disciplined approach to
maintenance and an optimized mix of the following
maintenance practices:
Reactive (run-to-failure)
Preventive (time-based)
Predictive (condition-based)
Proactive (combination of 1, 2 and 3 + root cause failure
analysis)
Many U.S. electric utilities with fossil generation have
adopted and implemented elements of an equipment reliability
process consistent with Institute of Nuclear Power Operations
(INPO) AP-913. The Electric Power Research Institute has
created a guideline modeled from the learnings of AP-913, that
consists of six key sub-processes [1]:
1. Scoping and identification of critical components
(identifying system and component criticality)
2. Continuing equipment reliability improvement (establishing
and continuously improving system and component
maintenance bases)
3. Preventive Maintenance (PM) implementation
(implementing the PM program effectively)
4. Performance monitoring (monitoring system and
component performance)
5. Corrective action
6. Life cycle management (long-term asset management)
A significant proportion of Duke Energy’s coal fleet is of an
age where individual components have reached their design
intent end-of-life thereby creating an increased need for
performance monitoring. Until recent times this was largely
performed by maintenance technicians with handheld devices.
This approach does not allow regular data collection for
trending and optimization of maintenance practices across the
fleet.
Significant and recent advances in sensor technology,
microprocessors, data acquisition, data storage, communication
technology, and software have enabled the transformation of
critical power plant assets such as steam turbines, combustion
turbines, generators, transformers, and large balance-of-plant
equipment into smart, connected power plant assets. These
enhanced assets, in conjunction with visualization software,
provide a comprehensive conditioning monitoring solution that
continuously acquires sensory data and performs real time
analysis to provide information and insight. This advanced
condition monitoring capability has been successfully applied
Proceedings of the ASME 2016 Power Conference POWER2016
June 26-30, 2016, Charlotte, North Carolina
POWER2016-59189
1 Copyright © 2016 by ASME
to obtain earlier detection of equipment issues and failures and
is key to improving overall equipment reliability.
This paper describes an approach by Duke Energy to create
and apply smart, connected power plant assets to greatly
enhance its fossil generation continuous condition monitoring
capabilities. It will discuss the value that is currently being
realized and also look at future possibilities to apply big data
and analytics to enhance information, insight, and actionable
intelligence. INTRODUCTION
The “True North” of Duke Energy’s Fossil Hydro
Operations (FHO) is Event Free, Reliable, and Cost Effective
operations. This is becoming increasingly more challenging due
to transformation of the electric utility industry that Utility
Dive summarized recently [2]:
1. Utility business models are changing
2. Utilities becoming more customer-centric
3. Utilities buying into storage
4. Utilities are modernizing the grid
5. Debates over rate design reforms and value of DERs are
heating up
6. Utilities getting in on the solar game
7. Utilities face growing load defection
8. Renewables reaching grid parity
9. Natural gas is growing fast
10. Coal power in decline
The impacts to FHO are far ranging, and probably not yet
fully understood. In the near-term FHO is seeing an aging
fossil fleet →1 and numerous coal plant retirements →2.
Layered on top is an aging workforce and limited O&M, capital
and staff resources.
Operationally, FHO is seeing significant and increasing
challenge too, driven by abundant, cheap natural gas and
increasing amounts of intermittent distributed energy resources.
Today, the coal fleet is being dispatched differently, with many
units seeing increased cycling, lower capacity factors, and a
need for flexible operation.
Across all industries there is considerable technology
advancement. This involves nine foundational technologies [3]:
Autonomous robots, simulation, horizontal and vertical system
integration, the industrial internet of things, cybersecurity, the
cloud, additive manufacturing, augmented reality, and big data
and analytics.
This fundamental change in technology presents an
opportunity to overcome present and future industry challenges
and remain relevant in the energy arena. The opportunity
discussed in this paper is its application to asset condition
monitoring, an essential component of an effective maintenance
strategy and equipment reliability program.
MAINTENANCE STRATEGY AND EQUIPMENT RELIABILITY PROGRAM
Duke Energy uses a holistic approach for effective asset
management that is focused around three key elements: People,
Processes and Technology →3. Duke Energy’s FHO
maintenance strategy, a key component of effective asset
management, seeks an optimal balance between under
maintaining and over maintaining its plant assets, that results in
lowest operating and maintenance costs. A Condition-Based
Maintenance approach →4 is applied that seeks to optimize a
mix of failure-based, preventive, predictive, and proactive
maintenance.
1 Duke Energy’s Fossil Fleet Commission/Retirement History
2 Duke Energy’s Coal Retirement Landscape
Note: Each block within a bar represents a coal plant
2 Copyright © 2016 by ASME
3 Holistic Approach to Asset Improvement
4 Condition-Based Maintenance Approach
The FHO equipment reliability program is applied to
execute the maintenance strategy and is based on EPRI process
guidelines [1] that have been developed over several years in
partnership with the fossil power generation industry.
The programs brings together six key sub-processes →5:
1. Scoping and identification of critical components
(identifying system and component criticality)
2. Continuing equipment reliability improvement (establishing
and continuously improving system and component
maintenance bases)
3. Preventive Maintenance (PM) implementation
(implementing the PM program effectively)
4. Performance monitoring (monitoring system and
component performance)
5. Corrective action
6. Life cycle management (long-term asset management)
5 EPRI’s Equipment Reliability Process [1]
The foundation of the maintenance strategy is to define and
apply system and component criticality criteria such that each
unique system or component is assigned to one of three
classifications:
Classification Description
Critical Function is so vital that all efforts are made to prevent all failures that are known to occur.
Important
Non-critical components that can impact operations and cost-effective methods are used to maintain component health; typically time-based or condition-based monitoring tasks.
Run-to-Failure These components do not impact event free operations and it is therefore acceptable to run to failure.
Another key element to the maintenance strategy is
monitoring system and component health. This is done by a
variety of processes and technologies:
Equipment Assessments (collection of data and analysis
such as vibration, oil analysis, flow, pressure etc.)
Equipment Inspections (major plant equipment such as
boilers, turbines, large balance-of-plant)
Condition monitoring technology applied to directly
measure mechanical, electric or thermodynamic parameters)
On-line Condition monitoring (continuous monitoring of
asset performance through sensor data)
Developments with on-line condition monitoring
technology are making this technology much more affordable
and therefore accessible. This offers the opportunity to replace
manual collection of data with greater focus on analysis →6.
AssetsCritical Station Assets: Turbines / GeneratorsBoilers / Combustion
BOP: Electrical / SwitchyardBOP: Other
People
Processes Technology
3 Copyright © 2016 by ASME
6 Monitoring System and Component Health
ENHANCED CONDITION MONITORING
As mentioned earlier, this is a time of significant
technology advancement. It is referred to by some as Industry
4.0 [4,5] and is described as a fourth wave of technological
transformation, essentially, the fourth industrial revolution →7. 7 Industry 1.0 to Industry 4.0
Industry 4.0 can be summarized by advances in materials
(including nanotechnology), advanced manufacturing
(including additive manufacturing) and learning ability (due to
advances in computing and communications technology).
Schwab describes it thus, "It is characterized by a much more
ubiquitous and mobile internet, by smaller and more powerful
sensors that have become cheaper, and by artificial intelligence
and machine learning" [6].
For the power generation industry the opportunity now
exists to combine sensors, advances in computing, data
acquisitions, data storage and software with critical power plant
assets such as boilers, steam turbines, combustion turbines
generators, transformers, and critical balance-of-plant
equipment. The resulting “smart, connected power plant assets”
→8 have intelligence and connectivity that enable an entirely
new set of functions and capabilities [7].
The technology advances discussed above have created
microelectromechanical systems (MEMS), that are routinely
used in sensors resulting in cheaper and more reliable designs
with advanced capabilities. This offers great opportunity to
expand on-line monitoring capabilities to support condition-
based maintenance, enhance safety and reliability, and improve
asset performance and utilization. A summary of on-line
condition monitoring technologies applied to FHO assets is
presented in →9.
8 Smart, Connected Power Plant Assets
Plant Assets Smart Power Plant
Assets Smart, Connected
Power Plant Assets
Physical components e.g. boilers, steam
turbines, combustion turbines generators,
transformers, and critical balance-of-plant equipment: pumps and
motors etc.
Sensors, microprocessors, data
acquisitions, data storage, controls,
software, embedded operating system,
enhanced user interface etc.
Ports, antennae, protocols enabling
wired / wireless connections with plant asset: one-to-one, one-
to-many, many-to-many.
FHO is currently in the midst of an initiative, working with
EPRI and National Instruments, to establish a platform of
smart, connected critical power plant assets. The architecture of
this platform is outlined in →10. A key piece to the platform is
utilization of wireless communication technology to minimize
cost.
9 On-Line Condition Monitoring Technologies
4 Copyright © 2016 by ASME
10 Smart, Connected Power Plant Asset Platform
MONITORING & DIAGNOSTICS (M&D) CENTER PERFORMANCE
The FHO Monitoring & Diagnostics (M&D) Center is a key
component to the equipment reliability program. Almost all
equipment gives off early warning signals before it fails. These
warning signals can be detected with condition monitoring
technologies and provides time to plan, schedule and make
repairs. This greatly reduces the probability of significant
failure, and also the cost of equipment failure.
On a daily basis the FHO M&D Center receives continuous
data on asset performance and health. Thousands of Advanced
Pattern Recognition (APR) models scan the data and provide
alarm notification when conditions on an asset deviate from
expected behavior. The M&D Center conducts preliminary
investigation, and if the alarm condition checks out, a
Notification is made to the station. A Notification is where an
abnormal condition is detected and there is interaction between
the station and the Monitoring & Diagnostics center to
investigate further. An investigated Notification, that identifies
an equipment issue that requires corrective action, is referred to
as a Find.
Advanced notification of equipment issues through
detection of potential failure symptoms, has substantial benefit,
allowing prevention of a full or partial functional failure, where
the asset fails to perform a required function. This also
potentially allows optimal repair scheduling and minimizes
operational impacts and repair costs.
The smart, connected power plant platform was initiated in
2012 and began providing on-line condition monitoring
capability at the start of 2013. The number of Notifications and
Finds, and the associated trend is outlined →11.
There has been a steady exponential growth in Finds over a
three year period as more sensors have been added to the smart,
connected power plant platform →12.
Cost avoidance savings are a result of early detection of
equipment failure and avoidance of sudden failure that typically
carry a high cost to repair, lost electricity generation and also
potentially leading to safety and/or environmental-related
events.
Assuming equipment failure is discovered at a point P that
occurs before point F on the P-F curve →13, it is possible to
estimate failure cost avoidance by estimating the results of
better managing the risk. In this example provided, if the failure
is discovered at point P2, it can be seen that there is potentially
significant cost difference (Δ$) between early detection and
corrective action, compared to allowing the component run to
failure.
5 Copyright © 2016 by ASME
11 M&D Notifications vs. Finds
12 M&D Find History Trend
More accurate estimation of cost avoidance savings also
considers the use of a Risk Grid →14 to consider both the
probability and impact of likely failure scenarios. The approach
taken uses elements from the methods described by Cook and
Muiter [8] and EPRI [9]. Failure cost avoidance is estimated for three failure
scenarios that span across a likely range of scenarios: minimal,
significant and catastrophic. Probability of occurrence is
assigned to the three scenarios and is based on consideration of
the likely outcome if the M&D Center did not find the problem.
Historical information about specific equipment, impacts of
failure and repair costs are used to provide system and cost
impact information. Once known, the actual cost of the
corrective action is subtracted from the total of the three “most
likely” scenarios.
In the example provided below →15 high vibration was
detected on two bearings of a low pressure steam turbine. The
risk of catastrophic failure is very low because operations
would be alerted to high vibration issues once vibration levels
got to the alarm threshold limits. The likely scenarios in this
case range from minor bearing damage to complete destruction
of the low pressure rotor. In the end, early detection resulted in
a balance shot being applied during the next outage opportunity
that incurred minimal actual cost. 13 P-F Curve: Interval from Potential Failure to Functional Failure
P0 Pending failure detected (Potential Failure)
P1 1-9 months, PdM (predictive maintenance); ultrasound, vibration etc.
P2 1-6 months, PdM; oil analysis
P3 3-12 months, PdM; thermography
P4 5-8 weeks, PM (preventive maintenance)
P5 1-4 weeks, audible noise
P6 1-5 days, heat by touch
P7 0-0 days, smoke
F Actual Failure
Δ$ Differential cost (impact and repair) between equipment functional failure and early detection of failure at P2, and ability to take timely corrective action.
2013
2014
2015
6 Copyright © 2016 by ASME
14 Risk Grid
15 Cost Avoidance Example for Steam Turbine Find
CONCLUSION
The smart, connected power plant platform installed on
Duke Energy’s Fossil Hydro Operations fleet is starting to
show strong promise of value →16, with significant numbers of
Notifications and Finds that have led to significant cost
avoidance →17.
This is very encouraging since there is still much of the
vision →18 to be realized. The vision is to generate and collect
much more data than was previously possible. There will be
much less manual collection of data and analysis will be
automated where possible. FHO is currently transitioning from
installation of hardware to deployment of National Instruments
NI InsightCM™ Enterprise for conditioning monitoring. This is
a software solution that allows maintenance specialist to
manage and visualize data and have critical information in their
hands when they need it. The M&D Center, system owners,
and subject matter experts will have considerably more insight
into the health of critical equipment and can use this to
optimize machine operational and maintenance performance, as
well as increased safety.
16 Value of Smart, Connected Power Plant Assets
Smart, Connected Power Plant Assets Supports FHO "True North"
Event Free Operations
Improved safety through replacing manual collection of data in hazardous areas / conditions.
Increased environmental monitoring capability and frequency in difficult to access areas.
Early detection of equipment issues can avoid significant or catastrophic equipment failure.
Reliable
Early detection of equipment issues allows for optimized time of repair and minimized downtime.
Much more effective identification and troubleshooting of equipment issues due to increased availability of data and enhanced analysis capability. This helps increase output and reduces downtime. Also reduces need to derate units whilst waiting on analysis.
Cost Effective
Cost avoidance through early identification of equipment failure and abnormal operating conditions.
Remote diagnostic analysis saves time and reduces cost.
Increased output and fuel savings due to ability to effectively identify performance related issues.
Increased online monitoring capability without resource / O&M increase.
Reduction of insurance premiums due to enhanced on-line monitoring capabilities.
Workforce Strategy
Predictive maintenance employees spent much less time on manual collection of data and increased time on analysis.
Increasing pressure on resources impresses need for using data and analysis for enhanced decision making.
17 Cost Avoidance History
Minimal Significant Catastrophic Actual
Probability of Occurrence 94.5% 5.0% 0.5%
SYSTEM IMPACT
Replacement Power Cost $/MWh 7 7 7 7
Reduction in Output MW 1,200 1,200 1,200 0
Time Offline Hrs 48 168 1,440 0
Avoided System Costs 381,024 70,560 60,480 0
Total $
O&M IMPACT
Material Cost $ 20,000 250,000 30,000,000 0
Repair Cost $ 0
Labor Cost $ 0
Other $ 5,000 20,000 1,000,000 0
Avoided O&M Costs 23,625 13,500 155,000 0
Total $
Total Avoided Costs $
512,064
704,189
192,125
7 Copyright © 2016 by ASME
18 Data → Information → Insight → Actionable Intelligence
It is clear that the integration of critical power plant assets,
data, processes, and people will provide understanding not
previously possible, and will advance informed and improved
decision making. However, at this time the path to information
and insight (hindsight) is much clearer than the path to
actionable intelligence (foresight) that involves predicting the
probability of future outcomes and taking action based upon
predicted outcomes [10].
REFERENCES [1] EPRI, “Developing an Equipment Reliability Program
Model”, EPRI 3002001348, February 2015.
[2] G. Bade, “The Top 10 Trends Transforming the Electric
Power Sector”, Utility Dive, September 17, 2015.
[3] M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J.
Justus, P. Engel, M. Harnisch, “Industry 4.0 The Future of
Productivity and Growth in Manufacturing Industries”,
The Boston Consulting Group, April 2015.
[4] M. Hermann, T. Pentek, B. Otto, “Design Principles for
Industrie 4.0 Scenarios: A Literature Review”, Working
Paper No. 01 / 2015.
[5] M Krueger, R. Drath, H. Koziolek, Z. Ouertani, “A New
Era”, ABB Review 4|14, pp. 70-75, 2015.
[6] K Schwab, "The 4th Industrial Revolution", January 2016.
[7] M. Porter, J. Heppelmann, “How Smart, Connected
Products Are Transforming Competition”, Harvard
Business Review, November 2014.
[8] M. Cook, M. Muiter, “Estimating Failure Avoidance
Costs”, uptime oct/nov 11, 2011.
[9] EPRI, "Predictive Maintenance Program Development and
Implementation", EPRI TR-108936, January 1998.
[10] J. Cutts, “Ghost in the Machine: The Predictive Power of
Big Data Analytics”, Technology Trends to Watch 2015,
pp. 5-10, Consumer Electronics Association, 2015.
ACKNOWLEDGEMENTS
The authors greatly appreciate the dedication and technical
expertise of the members of Duke Energy's Maintenance &
Diagnostics Team. Also, the sponsorship of Charlie Gates,
senior vice-president and chief fossil-hydo officer. Collectively,
this has enabled the SmartGen vision (Smart, Connected Power
Plant Assets) become reality.
Duke Energy would like to acknowledge the contributions
of the Electric Power Research Institute (EPRI), as well as
several other key industry partners in the development and
application of processes and technology necessary to enable
these smart, connected assets in the Duke fleet of bulk power
generation assets. EPRI has led an industry consortium in
defining and developing the equipment reliability process
outlined in this paper, and has been instrumental in supporting
the application of this equipment reliability process across the
existing Duke Energy FHO fleet. Additionally, EPRI has
provided guidance and oversight around the development of the
strategic enterprise platform for online, real-time condition
monitoring now in place at Duke Energy. This includes things
such as identification of new sensor opportunities, guidelines
on signal processing and signal assessment, monitoring and
alarming strategies, cybersecurity implications, data
management, and other related activities – all of which falls
under EPRI’s Integrating Information for Insight and
Intelligence for Generation (I4GEN) initiative aimed at shaping
and realizing the future of power generation. Finally, special thanks to National Instruments for being the
ideal technology partner for the entire SmartGen journey. Early
in the project Duke recognized a critical gap in the market
offerings for enterprise condition monitoring software that was
based on accepted industrial standards while being both open
and scalable. National Instruments stepped up to the challenge
and quickly brought InsightCM™ to market to address Duke's
challenges. Innovations such as this are not new to NI - for
almost 40 years, NI has helped scientists, engineers, and
companies like Duke Energy understand their world and make
better business decisions though quantitative discovery. The NI
technology platform referenced for smart, connected power
plant assets is a prime example of how the Industrial Internet of
Things (IIoT) is improving operational efficiency through
sensor data, edge processing, analytics, and enterprise software.
Solutions such as this, built on open, off the shelf platforms,
help technology keep up with evolving challenges and
empower domain experts to innovate. Open platforms are
essential for keeping the costs appropriate in brownfield
installations and are becoming increasingly more important as
IIoT solutions roll out to a variety of industries.
8 Copyright © 2016 by ASME
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