Downtime reduction via predictive maintenance

Insurances have been a very successful mechanism created to buy peace of mind and are present in many areas, with the renewable wind energy sector being no different. There are various flavours (service-only, uptime guarantee, energy-based availability) that provide owners with different levels of comfort to sleep at night. 

So are wind portfolio owners fully covered under current wind industry maintenance contracts? That’s absolutely not the case. 

One of the areas in which wind portfolio owners can create more value is by increasing the availability of their assets. In fact, the energy production losses due to downtime can easily scale to millions of EUR/year. Figure 1 shows the average yearly revenue loss due to turbine downtime. We present a range of wind fleet sizes and yearly downtimes. For example, a 1GW wind fleet with 4% yearly downtime has a 5 MEUR/year revenue loss. This 4% is the equivalent of the typical 3% uncovered downtime and an extra 80-hour service package which is also a service contract standard. 

As expected, the revenue losses increase rapidly, in absolute terms, for larger fleets. A 3GW wind portfolio has approximately 11 MEUR/year revenue impact with 97% uptime. And a considerably large 5 GW fleet faces a 12.6 MEUR/year even if a respectable 98% uptime is achieved, a value that is only guaranteed by service providers under some of the most expensive agreements.

Figure 1: Yearly revenue loss for different yearly downtimes and fleet sizes. Assuming a 24% capacity factor and energy price of 60Eur/MWh (source).

It is important to note that in this analysis we are disregarding the fact that penalties applied to the maintenance provider, in case the contracted uptime is not achieved, do not fully cover the losses incurred by the owner. Reducing downtime is of paramount importance to address its energy loss impact. It is always beneficial to have the wind turbines delivering energy to the grid rather than stopped.

But if the maintenance service is done by the OEM or an external provider, can wind fleet operators/owners still do something to reduce downtime? Yes, and Jungle has the right solutions to enable it.

The solution is a two-step approach: knowing and then acting. First, we need a predictive analytics solution in place that identifies ongoing issues. The step after is to deliver these insights to the maintenance providers that can act upon them. 

Wind asset owners can regain control over their fleet with our predictive maintenance solutions. 

One application of having a predictive analytics solution is to ensure that your assets are receiving the love they deserve (and that you paid for!). Another is to empower and support the maintenance provider, which perhaps has the potential to even be more fruitful than the first one. Both parties have much to benefit from a good relationship with open and transparent communication. Help you, help me!


Reducing downtime with Jungle’s predictive maintenance solution 

We now present, in more detail, six ways to reduce downtime via the insights provided by our predictive maintenance solution. With our solutions maintenance visits can be:

1. Relevant

Maintenance services can be the cure but also the problem. If a turbine is unnecessarily stopped, it can lead to energy production loss with no real benefits. Knowing which deviations are happening and being able to cipher through them to understand which are quickly developing or represent a real risk is key to decide when to intervene. For example, it would not be a good call to schedule a maintenance if a deviation is rather small with a low risk of bringing the wind turbine offline. With a predictive maintenance solution, the user would be able to closely track the ongoing anomaly. In case it does not worsen significantly, perhaps it can be tackled during a maintenance visit scheduled for another reason. This leads us to the next point.

2. Multi-purpose

It is very important to maximize the usefulness of a maintenance crew visit to a wind turbine. A piece of key information to pass onto the maintenance crews is if abnormal behaviour is detected in several turbine components since they may inspect and tackle multiple issues in the same visit. Figure 2 shows the capabilities of our predictive intelligence solution in detecting and alerting for simultaneous components presenting abnormal behaviour. This insight may contribute to a reduction in turbine visits, leading to less downtime and also alleviate overstretched maintenance teams to focus on the most important ongoing issues.

Figure 2: Our normality models are able to identify the development of abnormal behaviour in different turbine components at the same. These insights are displayed in our dashboard.

3. Better informed

Even better than simply knowing that several components may require attention, is if this information is further expanded with more context about the anomalous behaviour. In Figure 3 we show one of the insight plots of our product. The user can easily explore the data and associated abnormal behaviour. With such data visualisation tools, users can filter, compare, and ask the data for more contextual information. We empower users to answer questions such as:

  • Which sensors are deviating from the expected? 
  • Are they above or below expected? 
  • Under which operating conditions does this happen? 
  • How large are the deviations?

The number of inspection visits to the turbine may be reduced if such knowledge is known before any inspection has even taken place! Furthermore, the crew may already take the necessary tools and replacement parts and immediately act upon anomaly confirmation.

Figure 3: Our product offers a greater level of insights to the user. It is easy to dissect and dig deeper into the alarms and the asset’s state when they occur.

4. Early

Being aware of deviations since the very beginning is one of the most important aspects of having access to the insights of our normality models (see here a more detailed introduction to them). This unlocks multiple things. First, acting early on a deviation may help greatly reduce the maintenance difficulty, decrease substantially costs of spare parts and the service duration. Acting quickly may also stop the failure of further developing and affecting other components of the turbine. 

In Figure 4, we show the alarms for a turbine gearbox for a 6-month period. We can see that our system has detected an issue since the beginning of July (for more detail about this detection and our alarm system please check our dedicated post to this topic here). Since no intervention was made to the gearbox the failure kept worsening. Eventually, it reached a point in which the oil leakage was so severe that the system was not able to keep the temperatures within the expected ranges any longer (period D).

Figure 4: Failure detected by our normality models. The failure started with an oil leakage and eventually impacted different parts of a gearbox because it was not addressed early.

Another direct benefit that’s easy to understand, but difficult to quantify, is the extension of the remaining useful life of the components. High temperatures will have a greater impact on the wear and tear of the mechanical components. Avoiding them being operated under abnormally high temperatures will help them to operate properly for longer periods of time. 

If an abnormal behaviour continues to develop, it may reach stages in which the turbine itself will trigger its self-defence mechanisms. Turbines automatically self-curtail their power production to reduce the load on the system. This means that whilst the turbine continues to operate it’s not delivering as much energy as it would have under normal conditions. We refer the curious reader to our post on the topic.


5. Timely

Knowing about ongoing abnormal behaviours creates an opportunity to schedule maintenance during more suitable periods of the year. Scheduling maintenance in the summertime is generally better since the wind resource tends to be lower during such periods. Hence, there is a higher probability of finding good maintenance windows that will have lower energy production impact (see our dedicated post on finding good maintenance windows here). Figure 4 shows an example of this. Even though our alarms started to alarm at the beginning of July (period B), the maintenance was only performed at the end of November, a month with higher wind mean speeds. In fact, the maintenance intervention occurred during a 24-hour period during which the wind turbine could have been producing.

Turbines located in areas of difficult access are a perfect use-case for timely maintenance interventions. It’s much harder to serve offshore wind turbines during the winter months due to weather conditions. Not only is it a very harsh environment and dangerous to the maintenance crews, but it can also simply not be feasible to travel to the wind farm. 

Not being able to reach wind turbines is not specific to the offshore environment though. For example, turbines located in mountainous regions can also be hardly reachable during the winter months due to closed roads, or in regions that have monsoon seasons. Lastly, turbines installed in areas that freeze during winter also present a challenge to serve throughout the entire year. In fact, there is a growing trend to install wind turbines in such environments due to the cheaper ground rental price and the fact that most suitable places are already taken.


6. Successful

A very useful feature of having a digital version of an asset is to see if an ongoing issue was properly fixed and the asset returned to its expected behaviour. This means that wind owners and maintenance teams may understand the impact and usefulness of each maintenance intervention. Was the issue actually tackled? Is it fully solved?

In the process of solving an issue, unfortunately, it may also happen that we create another one in the process. Disconnected cooling fans or loosen bolts are eventualities that may occur. It is important to immediately detect such situations to avoid serious problems further down the road.

In Figure 5 we show the impact of an unfortunate maintenance visit. The slip ring of the generator was operating as expected until the maintenance but was left operating at temperatures 20°C higher than it should. This situation was left unchanged until almost two months after when another stop was performed to fix this issue. The wind turbine owner was not compensated by this extra downtime since it fits within the maintenance contract budget. 

Figure 5: An abnormal behaviour was detected in the generator slip ring immediately after a maintenance intervention. The maintenance crew had to eventually return to the turbine to fix the anomaly.

Value creation through downtime reduction

Wind portfolio owners can reduce the yearly downtime of their fleet by leveraging all the advantages described above. Figure 6 shows the revenue increase obtained when the year downtime (shown on the y-axis) is reduced by 20%. For example, a 1.5-GW fleet with an average of 4% of yearly downtime may recoup 1.5 MEUR if it reduces its downtime by 20%. A 4-GW portfolio running under a very respectable 97% uptime may still increase its revenue by 3 MEUR/year if the downtime is further reduced by 20%.  This shows that even wind fleets with good maintenance contract coverage can increase their earnings if a more hands-on approach is applied. 

Figure 6: Yearly extra revenue obtained by reducing downtime by 20%. The cost reduction is shown for different yearly downtimes and fleet sizes.

Wrapping it up

If you feel that more could be extracted from your maintenance operations and you identified yourself with our mission and approach to predictive maintenance, feel free to visit our product page. Send us a message to hello@jungle.ai, and we will be in touch shortly after. Let us help you reduce uncertainty from your operations! 


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