Over the past decades, the wind energy sector has been growing significantly and efforts are being made 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 to understand profoundly when and how wind turbine (WT) components fail. Moreover, the failure severity, in terms of caused downtime and repair cost, as well as the frequency of failure occurrences need to be known. These can be obtained from analysing historical failure databases and maintenance logbooks provided by manufacturers and operators. The components and their sub-assemblies have to be classified regarding their physical location and functionality, using a so called taxonomy or component breakdown. Then, the frequencies of component failures and the resulting WT downtimes are derived from the failure database. The outcome of the analysis can then be used to build reliability models and failure prediction tools in order 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 – not representing sufficiently well the reality. Supervisory Control and Data Acquisition (SCADA) alarms are proposed to complement available failure logs with additional information. Most modern WTs are equipped with SCADA systems, generating a huge amount of information that can be obtained mostly free of additional cost.
In order to investigate the correlation between the alarms extracted from the SCADA system and the actual failure occurrences, the data from the most widely installed modern technologies had to be analysed. 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 WT makes are indicated by the letters A to G. The respective SCADA system used within these turbines is referred to with the numbers 1 to 5. Turbine types A, D, E, F, G are equipped with a DFIG and types B and C with a synchronous generator. In total 440 WTs were analysed over a period of three years, resulting in 1320 operational years. An overall number of 653 failures and 1345036 alarms were registered and processed. The failures and alarms per turbine are displayed as rounded values.
Abbildung 1 zeigt die möglichen Alarme für jedes SCADA-System. Abbildung 2 zeigt die Zusammensetzung der tatsächlich aufgezeichneten Alarme für jedes System innerhalb des gegebenen Beobachtungszeitraums. Sie bestehen aus Alarmen, die sich auf eine bestimmte WEA-Komponente beziehen, aus Alarmen, die auf extreme Umweltbedingungen zurückzuführen sind, und aus anderen, die keiner Komponente zugeordnet werden konnten, z. B. Netzeinschränkungen. Ein Vergleich der beiden Abbildungen zeigt, dass für die WEA-Typen A, B, C und D viele wetterbedingte Alarme aufgezeichnet wurden, die auf extreme Bedingungen hinweisen, die für bestimmte Komponentenausfälle verantwortlich sein könnten. Bei den Anlagen B, C und D war der Anteil der drei aufgezeichneten Alarmkategorien recht ähnlich. Auch der Anteil der möglichen Alarme dieser beiden SCADA-Systeme ist ähnlich.
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, 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.
Die höchste Anzahl von Alarmen wurde bei WEA des Typs E registriert. Ähnlich wie bei den Typen A und D verursachte der Generator viele Ausfälle, während nur sehr wenige Alarme registriert wurden. Insbesondere das Hydrauliksystem wies eine extrem hohe Anzahl von Alarmen auf. Dies lässt die Vermutung zu, dass bei Anlagen des Typs E die Alarme des Hydrauliksystems auf Ausfälle anderer Komponenten hinweisen können. Es wurden nur sehr wenige wetterbedingte Alarme beobachtet. Auch beim Typ E wurden nur sehr wenige Alarme für das Pitch- und Giersystem angezeigt, während diese Komponenten häufig ausfallen. Bei den WT-Typen F und G wurden keine Blattalarme, aber viele Alarme für das Giersystem und wetterbedingte Alarme registriert. Vibrationen im Fundament wurden vom SCADA-System angezeigt, ebenso wie mehrere Ausfälle dieses Teils. Mit vielen Alarmen und sehr wenigen Ausfällen schienen das Pitchsystem, der Generator und die Hydraulikgruppe gut überwacht zu sein. Das Getriebe zeigte das kritischste Verhalten, mit sehr wenigen Alarmen, aber sehr hohen Ausfallraten, und sollte besser überwacht werden.
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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