Smarter management of turbine assets: monitoring system

Smarter management of turbine assets:
How to get the most from your condition
monitoring system
Pressure is ratcheting up to asset managers to find to lower wind farm costs and increase
efficiencies. Right now, the lowest hanging fruit in controlling costs is tackling operations and
maintenance budgets. Even though acquisition, installation and commissioning of wind farms are the
greatest contributors to the levelized cost of wind energy, the contribution from operations and
maintenance (O&M) makes up between 15%-25% [1,2]. This is the only portion of the total cost that
can be controlled once the wind farm is in operation. With a potential dearth of new installed capacity,
this is the industry’s sweet spot.
This focus on O&M is a positive sign of a maturing industry. It has shined a spotlight on condition
monitoring, the process of detecting faults in equipment before it fails. As with any new or cross-over
technology, the connection between using that technology and ultimate cost savings is not always
clear. Simply installing a condition monitoring system on a wind turbine will not save any money. It is
a necessary technology, but not sufficient on its own. The only way to reduce costs is to use the
condition monitoring information to make more efficient and effective maintenance
decisions.
What drives the O&M costs of a wind farm?
The total maintenance costs (unscheduled and scheduled) for a typical wind farm are between
30% and 40% of the total operating expense. Successfully controlling these costs can make the
difference between a profitable year and an unprofitable one. O&M activities matter because they
affect not just costs, but also revenue. When a wind turbine is off-line for repair, it is not generating
power. While the direct cost of an unscheduled maintenance call may be proportionally low, the
maintenance event may have a significant impact on revenue if the turbine remains inoperable for two
weeks or more.
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Operating cost data from a major US asset owner.
Many wind farms are geographically isolated and present difficult working conditions for
technicians. When one must ascend a tall tower with tools and work in a space-constrained nacelle,
performing even basic maintenance isn’t trivial. For example, a routine oil change is much more
difficult when consumables and waste must be moved up- and down-tower. The unique characteristics
of wind farm O&M makes the optimization of these activities critical to profitability.
How can wind turbine O&M costs be reduced?
The conversion of mechanical energy into electrical energy requires rotating machinery
comprised of shafts, bearings, and gears—the latter two requiring constant lubrication to avoid
damage. The three basic maintenance strategies—reactive, preventive, and predictive—are best
understood when compared to an everyday maintenance event familiar to most readers: changing the
oil in their cars.
In a reactive maintenance paradigm, equipment is run until it fails. This is similar to driving a
vehicle off the sales lot and never changing the oil. Eventually, the car’s engine will seize and render
the vehicle useless. The downside of this maintenance strategy is obvious, but it doesn’t mean many
turbines have been, and continue to be, operated in just this manner.
In a preventive maintenance (PM) paradigm, components are changed out on a time-based
schedule, just as most drivers change the oil in their cars every 3000 miles. While this may seem like a
good way to perform maintenance, it is inherently inefficient. PM reduces the amount of unplanned
maintenance, but does this by increasing the amount of planned maintenance. PM can also ‘waste’ a
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component that still has significant remaining useful life. Additionally, it can’t detect early-life failures
that are inevitable with some small fraction of components.
The alternative to these two maintenance paradigms is predictive maintenance (PdM). The
concept behind PdM is that maintenance should only be performed when a component is degrading,
but before failure. Returning to the car example, there are many vehicles today that monitor the
remaining useful life of the oil and tell the owner when to change it based on the measured condition
of the oil. The PdM strategy maximizes the service life of each component, reducing the cost of
premature replacement, while at the same time eliminating the collateral damage that can occur if a
component is allowed to run until it fails. It reduces the amount of scheduled maintenance needed to
ensure safe operation while also mitigating the risk of component failure. The PdM strategy has all the
strengths of the reactive and preventive paradigms while eliminating many of their weaknesses.
Where should I focus my condition monitoring efforts?
Many in the O&M world are also familiar with Reliability- Centered Maintenance (RCM).
RCM is not a maintenance methodology itself, but a framework for choosing which of the maintenance
strategies (reactive, preventive and predictive) to apply to each component in the system. RCM seeks
to balance the cost and effort needed to maintain each component with the environmental, health and
safety (EH&S) concerns, and
economic consequences of that
component’s failure.
The use of predictive
maintenance should focus on
the subsystems that create
significant downtime and add
significant cost when they fail.
The downtime caused by a
subsystem failure on a wind
turbine is not simply dictated by
the amount of time it takes to
perform the needed repair; it
can be affected by parts
availability and lead time, or the
need for special equipment. For
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Aggregated downtime caused by per turbine subsystem failures
from National Renewable Energy Laboratory [3].
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example, it may take several days to replace a failed gearbox, but it may take a week to have one
delivered and another week before a crane can be scheduled to install it.
The figure shows aggregated downtime caused by turbine subsystem failures. The three
subsystems that comprise the turbine drive train – the gearbox, generator, and the main bearing –
account for more than half of the downtime experienced during the six year period. This makes them
prime candidates for condition monitoring.
In addition to the downtime, the cost of repairing these subsystems is substantial [4]. For a 1.5
MW turbine, the replacement/repair cost of the gearbox is approximately $152,000 and the generator
is $53,000. For a 2.0 MW machine, those costs escalate to $216,000 for a gearbox and $77,000 for the
generator. Additional labor and crane rental costs can add $50,000 to $250,000 to the total cost
depending upon the site [5].
What types of condition monitoring technologies are available?
It is important to note that no condition monitoring system (CMS) can avoid component failure
once it has started. The rate at which a component fails is determined by the equipment installed,
maintenance performed, and wind conditions to which the turbine is exposed. A condition monitoring
system is valuable because it can be used to reduce the risk of collateral damage due to component
failure and allow for optimization of O&M activities through early detection of faults. The value of early
detection cannot be overstated. Many condition monitoring systems can detect faults before
catastrophic failure, but the value of doing it several months before catastrophic failure is much greater
than several days before failure.
In line with the old adage “an ounce of prevention beats a pound of cure,” oil monitoring
systems are used to measure whether the conditions are right for a fault to emerge. In a recent wind
turbine service journal, the service provider estimated that over 70% of all turbine bearing faults are
due to lubricant problems [6]. Unfortunately, oil monitoring systems are not a silver bullet—it cannot
detect other mechanical faults, such as gear, bearing, and shaft cracks. Hence, additional monitoring
systems are needed to implement a predictive maintenance strategy.
Vibration monitoring can be used to sense faults in gears, shafts and bearings once they have
emerged. These monitoring systems are usually very sensitive to early stage faults and have been
proven in other industries. The drawback of vibration monitoring systems is that modern wind turbines
operate in widely varying conditions (e.g. speeds and torque) which makes the analysis of the vibration
very difficult. Analysis requires a great deal of expertise and processing of the data to create
actionable information for a site manager.
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Oil debris monitoring (not to be confused with oil monitoring) monitors debris particles in the
lubrication system. This can be done either in-line, where the oil passes through the sensor on the
way to the filter, or at the system sump, where debris particles tend to gather naturally. These
systems typically use an inductive sensor to monitor ferrous (e.g. steel) particles in the oil. They are
well-suited to tracking surface fatigue faults (e.g. bearing spalls) where significant material is lost
during the failure, but they cannot isolate that failure to a single component. Nor can they sense faults
that do not cause material loss, such as cracks. Oil debris monitoring systems are less expensive than
a typical vibration monitoring system.
No discuss of condition monitoring would be complete without mentioning the use of SCADA
data to detect system faults. There are many cases where using SCADA is possible and even advisable
for fault detection, such as with faults in the electrical system, or in a turbine control sensor, but
SCADA does not work well for detecting and diagnosing gear, bearing or shaft faults because it allows
just a few days’ time to react.
The following figure depicts the importance of early fault detection and the relative value of each
system previously discussed on a system where water has entered the oil supply.
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Conclusion: Evaluating the performance of a condition-monitoring
system
Unfortunately there is no yardstick to evaluate the performance of condition-monitoring systems.
Germanischer Lloyd maintains a certification process for vibration-based and oil debris condition
monitoring systems based on a specification, but does not address performance. In the end, “success”
is measured by what you are trying to do with the system. The solution for someone trying to
eliminate collateral damage will be very different than the solution for someone trying to optimize O&M
costs over the long-term. For others, a combination of technologies provides the best solution. It all
depends on what you aim to do with the information coming from your system.
When evaluating a system, a buyer should consider three things: the timeliness, accuracy, and
actionability of the information being produced:
Timeliness: Understanding how early the system detects potential faults is key. Knowing
about a failure months ahead of time allows you to plan your maintenance schedule in
advance, while knowing just days in advance only allows you to react to an impending failure.
• Accuracy of the information is also essential. A false alarm or missed detection can erode
user confidence and the overall value of the system. That is why it is imperative to
understand how the CMS vendor manages uncertainty surrounding condition monitoring and
how they set their alarm thresholds.
• Actionability: Are the services of a qualified engineer required to analyze the data and
provide an interpretation to the user? Or does the system analyze the data itself and deliver
indicators that the user can take action on with little or no training? CMS systems vary widely
in terms of how they deliver data to the user.
Condition monitoring systems can provide tremendous value for O&M providers by helping to
•
reduce maintenance costs and optimize availability. To maximize the return on investment, it’s
important to focus resources on those components that pose the greatest risks to cost and profitability
if and when they fail.
References
1. http://www.wind-energy-the-facts.org/en/part-3-economics-of-wind-power/chapter-1-cost-of-onland-wind-power/operation-and-maintenance-costs-of-wind-generated-power.html
2. E-ON Climate and Renewables, March 2011; “An overview of our business activities.” Retrieved
from: http://www.eon.com/de/downloads/ECR_Company_Profile_March_2011.pdf
3. Sheng, S. & Veers, P., “Wind Turbine Drivetrain Condition Monitoring – An Overview.” NREL/CP5000-50698. Retrieved from: www.nrel.gov/docs/fy12osti/50698.pdf
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4. Poore, R. & Walford, C., ”Development of an Operations and Maintenance Cost Model to Identify
Cost of Energy Savings for Low Wind Speed Turbines.” NREL/SR-500-40581. Retrieved from:
www.nrel.gov/docs/fy08osti/40581.pdf
5. Vachon, W. “Crane Considerations Related to Maintaining Wind Turbines.” Presented at 2006 Wind
Turbine Reliability Workshop. Retrieved from:
http://windpower.sandia.gov/2006reliability/wednesday/10-billvachon.pdf
6. ON Service Journal – September 2011, Retrieved from:
http://www.availon.com/downloads/onservice-issues/onservice-september-2011.pdf
Infigen Energy provided valuable input in this paper.
About the author:
Brogan Morton is the Product Manager for TurbinePhD, a vibration-based predictive health
monitoring system, at Renewable NRG Systems. Brogan started his career performing engineering
research on the diagnostics and prognostics of mechanical components in aerospace systems. Morton
holds a Master’s degree in mechanical engineering from the University of New Hampshire and an MBA
from Idaho State University.
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www.renewableNRGsystems.com
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