Save €250,000
per Wind Farm
every year
As the Bachmann Group announces its launch of Condition Monitoring Consultancy (CMC), an independent advisory service dedicated to strategic condition monitoring, CMC team lead David Futter delivers a concrete answer to the question:
“Condition monitoring is all well and good, but can it actually save any money?”
David Futter is the Head of Condition Monitoring Consultancy, part of the Bachmann Group, a BINDT Vibration Analysis Cat IV practitioner and Approved Training Coordinator.
He is a committee member of the BSI GME21/5 and GME21/7 as well as a member of the BINDT Vibration Analysis Expert Group.
This question was put to me during a conference on wind industry innovation. It comes down to the financials behind vibration analysis, the fundamental technique for condition monitoring (CM), compared with SCADA monitoring. As an engineer, I am used to explaining the technical advantages of vibration technologies. But when asked to provide a cold, hard figure, I hesitated.
Not because I didn’t have an answer. But because it is actually a much broader question, and one that relates to a wide range of additional monitoring techniques. CM must be expected to bring a return on investment in the short term. Otherwise, why use it? But what sort of ROI can wind farm owners actually expect?
I was reminded of a study I conducted for a major European energy provider about ten years’ ago. I can’t go too deeply into the details, but I can discuss the methodology, and use published reliability datasets and my own industry knowledge to illustrate it. This method was the basis for the flow chart in ISO 16079-21 related to the drivetrain monitoring of wind turbines; but the idea was based on the more general ISO 173592.
According to most CM equipment suppliers, there are always specific, quotable examples where enormous savings were delivered thanks to the successful early identification of a fault. But this does not paint a full picture. Not every installation will suffer from such costly failure mechanisms, and it is always possible that a particular fault will not be detected in time.
My method, therefore, surveyed a range of failures, and for each individual failure identified the likelihood of that failure occurring on a given turbine, coupled with the probability of its detection by a particular CM technology.
The most challenging aspect was collecting a set of representative failure modes, and then assigning probabilities to these failures. Costs were then applied to these failure modes, as well as revised costs assuming that CM recommendations are implemented. Cost estimates included not only repair and replacement of parts, but also the cost of logistics and lost power generation.
The process did not in itself differentiate between different monitoring types, so monitoring technology was additionally noted. Where multiple monitoring technologies were deployed, the probability of failure detection increased for some failure modes, but the initial and annual cost of monitoring also increased. Some external studies3,4 have gone as far as to conduct a detailed analysis about which technique makes the most difference.
But in this study, we calculated savings for two offshore wind farms (based on 2012 pricing), and two sets of published reliability data from the Reliawind5 consortium. Unfortunately, as this was an internal study I cannot share specific values, although the published data is available from the WMEP database. I can also highlight the typical patterns of identified savings.
The results: We identified savings of over €250,000 Euros per windfarm per annum in every case where a variety of monitoring techniques were applied.
It should be noted that the two specific wind farm cases predicted lower savings than the amalgamated cases from the external datasets, probably due to the newer turbines deployed on these farms, or possibly due to incomplete failure reporting in the datasets. Interestingly, the two offshore wind farms with turbines from different manufacturers foresaw savings in very different areas, even though the resulting figures were quite similar.
As part of the study, we found that several monitoring packages overlapped when it came to the type of failures they claimed to detect, with the exception of SCADA data analysis, which operates in different areas.
Monitoring techniques applied to the drivetrain; principally vibration analysis, acoustic emissions and oil analysis, all detect similar gearbox and drivetrain failures. Any increased savings from the application of two of these techniques is marginal compared to only one. There is a slight increase in detection probability and lead time, but no additional failure mechanisms are revealed.
Of these techniques, vibration analysis delivers the widest range of failure detection, and can also be implemented for structural health monitoring.
SCADA analysis is more likely to detect failures such as blocked coolers, electrical and control problems, as well as small efficiency losses, complementing the other techniques. Typically, the individual costs of failures detected by SCADA data analysis are low, but they occur relatively often, whereas the more significant failures detected by vibration analysis are very expensive, but less likely to occur.
The analysis of SCADA data accounted for around 20% of the overall savings and improved detection rates in other areas by around 5-10%. Vibration analysis accounted for up to 70% of the overall savings, with the remainder made up of improvements in detection probability through other methods such as oil analysis.
So, when it comes down to it, vibration analysis really does generate the lion’s share of savings in a comparison between CM and SCADA analysis, although SCADA data is definitely beneficial in its own right because it detects different faults and should therefore be used in addition to CM to access the full potential savings.
Since the study, wind turbine designs have improved significantly, but the cost of CM installations are also much lower. Taken together, I estimate little impact on the overall annual savings provided by a CMS system over the past decade, although recent market pressures are likely to have significantly increased potential savings from vibration analysis and CM.
But back to the original question about the actual savings. How did I respond?
“It depends”, was on the tip of my tongue, but based on this study and my confidence in the findings and capabilities of a modern CMS system, I could confidently reply:
“Condition monitoring saves at least a quarter of a million Euros per wind farm per year.” And that’s good news for everyone.
References
- ISO16079-2 Condition monitoring and diagnostics of wind turbines — Part 2: Monitoring the drivetrain
- ISO 17359 Condition monitoring and diagnostics of machines — General guidelines
- Coronado and Fischer: Fraunhofer IWES Condition Monitoring of Wind Turbines: State of the Art, User Experience and Recommendations, Jan 2015
- May, Allan; McMillan, David; Thöns, Sebastian Economic analysis of condition monitoring systems for offshore wind turbine subsystems, Proceedings of EWEA 2014
- Reliawind project Final Report: Reliability-focused research on optimizing Wind Energy system design, operation and maintenance: Tools, proof of concepts, guidelines & methodologies for a new generation