Over the past decades, the wind energy sector has grown significantly, leading to a worldwide effort to minimise the overall cost of a wind farm. One of the main cost drivers is directly related to operation and maintenance (O&M) actions. Current tendencies in O&M practice are shifting from rather costly corrective strategies to preventive and predictive approaches. Crucial for setting up these cost effective strategies is understanding profoundly when and how wind turbine (WT) components fail. The failure severity, in terms of caused downtime and repair cost, as well as the frequency of failure occurrences are also important. These are available from analysing historical failure databases and maintenance logbooks from manufacturers and operators. The classification of the components and their sub-assemblies regarding their physical location and functionality is essential, using a so called taxonomy or component breakdown. One proposed way to optimise this is by using SCADA alarms.
Then, the failure base allows for the calculation of the frequencies of component failures and the resulting WT downtimes. Using the outcome of the analysis to build reliability models and failure prediction tools allows us to estimate the WT component degradation over time and to anticipate failures. However, there are still serious problems regarding recent practices and one of the main issues is lack of available failure data. Due to the lack of available data, many reliability models and maintenance decision tools are based on assumed failure rates, which do not sufficiently represent reality. Supervisory Control and Data Acquisition (SCADA) alarms can complement available failure logs with additional information. Most modern WTs are equipped with SCADA systems, generating a huge amount of information for use, mostly free of additional cost.
How SCADA can help predict failures: A study
In order to investigate the correlation between the alarms extracted from the SCADA system and the actual failure occurrences, an analysis of the data from the most widely installed modern technologies was necessary. As older turbines are not necessarily equipped with SCADA systems or only operate relatively limited ones, they were excluded. The different technologies are indicated by their rated power and drive train setup – being either direct drive or geared WTs.
As for confidentially reasons no manufacturer names can be published, the letters A to G indicate the WT makes. The numbers 1 to 5 refer to the respective SCADA system used within these turbines. Turbine types A, D, E, F, G have a DFIG and types B and C have a synchronous generator. In the study analysed a total of 440 WTs over a period of three years, resulting in 1320 operational years. It registered and processed an overall number of 653 failures and 1345036 alarms. The failures and alarms per turbine are displayed as rounded values.
Figures 1 & 2
Figure 1 displays the possible alarms for each SCADA system. Figure 2 shows the composition of the alarms actually recorded for each system within the given observation period. They consist of alarms related to a specific WT component, alarms due to extreme environmental conditions, and others that could not be assigned to any component, e.g. grid restrictions. Comparing the two figures, shows that for WT types A, B, C and D many weather related alarms were recorded, indicating extreme conditions, which could be responsible for certain component failures. Turbines B, C and D showed a quite similar share of the three alarm categories recorded. Also the share of possible alarms of these two SCADA systems are alike.
Figures 3 & 4
Figures 3 and 4 represent the contribution of the component related alarms to the total number of recorded alarms. This is compared to the failures per year and turbine, normalised to the total number of failure occurrences. It is taken for granted that the CMS is connected to the SCADA system and functioning well in monitoring the turbine. This means that high numbers of alarms indicate a problem. Showing many alarms but few failures, indicates that the component is well monitored and failures are prevented by shutting down the turbine before they occur.
It is remarkable that many alarms due to environmental conditions but hardly any blades and controller alarms were recorded. At the same time, however, a large number of blade and controller failures appeared in the data set. The generator also showed relatively high failure rates as well as the second highest number of alarms. The alarms assigned to the generator had the highest share of all component related alarms. It is assumed that the generator is equipped with an extensive CMS to prevent failures. Being direct drive turbines, types B and C showed the lowest total number of failures. Many alarms were assigned to the controller and yaw system. The frequency converter also showed a large number of alarms, however, did not have any failures.
The SCADA system actually indicated generator problems fairly well by reporting many alarms while very few generator failures occurred. Similar to type A, a high number of alarms due to heavy weather conditions can be related to controller and blade failures. Hence, it is assumed that for direct drive technologies the controller, yaw system and blades are suffering more likely from unfavourable weather conditions than other components.
Type D Turbines
Type D turbines, represented the oldest technology with the lowest rated capacity per turbine. They showed the highest number of failures per WT, and a fairly high number of alarms due to environmental conditions. Many blade failures occurred, however, no alarm could be associated to the blades. The number of alarms related to the gearbox, the communication system and the bearings were quite high, indicating that the latter are well monitored by the SCADA system. The pitch system, the controller and the generator, however, did not provoke many alarms, although showing relatively high failure rates. This could be due to the fact, that the SCADA system in the older technology is not as advanced as it is in newer ones.
Type E turbines
The highest number of alarms was registered for WTs of type E. Similar to types A and D, the generator caused many failures whilst very few alarms were registered. Especially the hydraulic system showed an extremely large number of alarms. This leads to the theory, that for type E turbines the hydraulic system alarms can indicate failures of other components. Very few weather related alarms were observed. Like others, type E also showed very few alarms for the pitch and yaw system whilst suffering from many failures of these components.
For WT types F, G no blade alarms but many yaw system and weather related ones were recorded. Vibrations in the foundation were indicated by the SCADA system as well as several failures of this part. Showing many alarms and very few failures, the pitch system, the generator and the hydraulic group seemed to be well monitored. The gearbox showed the most critical behaviour, with very few alarms but very high failure rates, and should be monitored better.
Conclusion
The comparison of recorded alarms and historical failure data for five different SCADA systems and WT types showed that for certain components there are significantly more alarms than actual failures – and contrariwise. In general, high numbers of component alarms and low failure rates indicate that the SCADA system is helping to avoid failures from occurring. Blade and controller failures showed to occur frequently in the presence of alarms indicating harsh environmental conditions. Nonetheless, it is very hard to obtain a global conclusion on how much the SCADA system is adding value to (missing) failure data, as the information provided by the different systems vary strongly. Thus, for each SCADA type the relation between component failures and the respective alarms was demonstrated.
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