{"id":10717,"date":"2022-03-18T14:25:44","date_gmt":"2022-03-18T14:25:44","guid":{"rendered":"https:\/\/annea.ai\/?p=10717"},"modified":"2024-05-06T15:54:06","modified_gmt":"2024-05-06T15:54:06","slug":"modelling-wind-turbine-failures-based-on-weather-conditions","status":"publish","type":"post","link":"https:\/\/annea.ai\/de\/modelling-wind-turbine-failures-based-on-weather-conditions\/","title":{"rendered":"Which Weather Conditions Are More Likely To Cause Turbine Failures?"},"content":{"rendered":"
\n

Ausfallmodelle f\u00fcr Windkraftanlagen k\u00f6nnen dazu beitragen, die Abbauprozesse der Komponenten zu verstehen und die Betreiber in die Lage versetzen, bevorstehende Ausf\u00e4lle vorherzusehen. Normalerweise basieren diese Modelle auf dem Alter der Systeme oder Komponenten. Unsere Forschung zeigt jedoch, dass auch die Wetterbedingungen vor Ort das Ausfallverhalten der Turbine erheblich beeinflussen. Bei\u00a0ANNEA<\/a> we follow a novel approach for modelling wind turbine failures based on the environmental conditions to which they are exposed to. Taking into account the environmental conditions that are more likely to provoke component failures. This could enhance predictive maintenance models significantly.<\/p>\n

\n

How have previous studies contributed towards answering this question?<\/strong><\/h3>\n

It has been proven that not only the turbine age, but also certain combinations of weather conditions can affect their life-time negatively. During the useful life-time, the assumption of constant failure rates does not always hold true, especially when considering shorter time-intervals such as the failure occurrences on a monthly basis. There are significant variations in failure rates throughout the year that need to be taken into account by the wind farm operators. With this they can react properly to upcoming failure by initiating preventive or opportunistic maintenance actions.<\/p>\n

The industrial research has proved that the failure behaviour of wind turbines and their components is strongly influenced by the meteorological conditions the systems are exposed to. Nonetheless, no models have been developed yet to directly describe the wind turbine\u2019s failure behaviour based on combinations of external covariates.<\/p>\n

In this article we describe a novel approach to model the failures of wind turbines during the useful life including the effect of environmental conditions. The model was applied to a 2 MW case study wind farm and the failures of the whole turbine system, as well as four main components. Failures are defined as events that can be associated to a component breakdown, which causes a wind turbine\u2019s stop and needs intervention such as replacement or repair.<\/p>\n<\/section>\n

Data collection<\/h3>\n
The model uses a case study including 30 turbines located in a wind farm in Spain. The wind turbines are geared, three bladed and pitch-regulated machines, with a rated capacity of 2MW each. The data set consists of failure logbooks, SCADA and meteorological tower (met-mast) data collected during three years of operation. The model covariates include the monthly average wind speed (WS) and turbulence intensity (TI), and the monthly maximum wind speed (MaxWs), measured at a height of 45 meters at the wind farm met-mast. Also included are the monthly mean relative humidity (RH), precipitation (Rain) and ambient temperature (Temp) taken from closely located weather stations. Additionally, as an indicator for the time operating at full capacity, the monthly mean power production (PWR) taken from the turbines SCADA systems divided by the rated capacity was chosen.<\/p>\n

Zu Beginn der Datenerhebung waren die Turbinen f\u00fcnf Jahre alt. Es wird ein durchschnittliches Windparkjahr modelliert, wobei der Beobachtungszeitraum mittels einer Expositionsvariablen, dem sogenannten Modell-Offset, in das Modell eingef\u00fchrt wird. Somit kann das Modellergebnis als die Ausfallrate in einem durchschnittlichen Betriebsjahr betrachtet werden.<\/p>\n

Model used<\/h3>\n

A regression model based on a generalised linear model (GLM) is applied to the data. The model is set up with a Poisson response distribution and a logarithmic link function. Subsequently, a ridge regression is employed to estimate the model parameters.<\/p>\n

In a first step the model is applied to the whole data base, without further distinguishing between the failed components. Subsequently, the failure data of four main components: the gearbox, generator, pitch and yaw system are extracted from the same set and the model is applied again. To analyse the importance of each input variable, the standardised model coefficients are compared. This is commonly done to interpret which of the covariates contributes the most to modelling the output and helps to see which weather conditions are important for modelling failures.<\/p>\n<\/section>\n

Performance metrics and performance<\/h3>\n
Eine Zusammenfassung der Modellleistungskennzahlen f\u00fcr die f\u00fcnf verschiedenen F\u00e4lle ist in der folgenden Tabelle dargestellt:<\/p>\n
\n
\"\"
Table reference:\u00a0\u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012).\u00a0View the\u00a0article online\u00a0for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n
\u00a0<\/section>\n

The R2 value<\/h4>\n

The R2 value ranges from 0 to 1 and shows how well the model fits to the data, where 1 indicates the best fit. It can be seen that in general the model performs well for all five failure classes. The models for the generator and pitch system, however, showed lower R2 and higher error values than the other ones. This indicates that for these two components additional covariates, which were not included in the model, could be of importance. This will be assessed in further studies.<\/p>\n

\n

Die nachstehende Abbildung zeigt die urspr\u00fcnglichen Ausf\u00e4lle (schwarz) und die modellierten Daten (gr\u00fcn) f\u00fcr Ausf\u00e4lle von Windenergieanlagen ohne Unterscheidung zwischen ihren Komponenten.<\/p>\n

\n
\"Source:
Figure reference:\u00a0\u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012).\u00a0View the\u00a0article online\u00a0for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n
\u00a0<\/section>\n

Die Werte sind aus Gr\u00fcnden der Vertraulichkeit auf die maximale Anzahl von Ausf\u00e4llen normiert. Als Beispiel f\u00fcr eine separat modellierte Windturbinenkomponente zeigt Abbildung 2 die urspr\u00fcnglichen und modellierten Daten f\u00fcr Ausf\u00e4lle des Giersystems:<\/p>\n

\n
\"Source:
Figure reference:\u00a0\u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012).\u00a0View the\u00a0article online\u00a0for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n
\u00a0<\/section>\n

Es zeigt sich, dass in beiden F\u00e4llen die meisten Ausf\u00e4lle im zweiten bis vierten Monat des Jahres zu verzeichnen sind. Dies steht im Einklang mit fr\u00fcheren Untersuchungen, wonach Ausf\u00e4lle haupts\u00e4chlich in den Wintermonaten und\/oder in den \u00dcbergangszeiten zwischen den Jahreszeiten auftreten.<\/p>\n<\/section>\n

Which variables had the biggest effect?<\/h3>\n
Figures 3 to 7 show the standardised coefficient magnitudes. Positive coefficients (blue) indicate that with increasing covariates the output increases as well, while negative coefficients (orange) respectively describe the opposite behaviour. As the input data was centred and scaled, these coefficients can be interpreted as the influence of every model covariate on the dependent variable. For an easier interpretation the plots show the magnitudes as fraction of the most important regressor, which is scaled to 1.<\/p>\n
\n
\"Source:
Figure reference:\u00a0\u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012).\u00a0View the\u00a0article online\u00a0for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n
\u00a0<\/section>\n

Wie in Abbildung 3 zu sehen ist, haben die Eingangsparameter TI, Temp und WS die gr\u00f6\u00dfte Bedeutung f\u00fcr die Modellierung der Daten einschlie\u00dflich der Ausf\u00e4lle von Komponenten der Windkraftanlagen. Eine hohe mittlere monatliche Turbulenzintensit\u00e4t, eine hohe mittlere Windgeschwindigkeit und niedrige Temperaturen spielen eine wichtige Rolle. Dies stimmt mit fr\u00fcheren Studien \u00fcberein, in denen festgestellt wurde, dass h\u00f6here mittlere Windgeschwindigkeiten und niedrige Temperaturen mit einer h\u00f6heren Anzahl von Ausf\u00e4llen von Windenergieanlagen korreliert werden k\u00f6nnen. Niedrige DWR-Werte scheinen das Ausfallverhalten ebenfalls zu beeinflussen, allerdings nicht sehr dominant. Da die Wirkleistung in der Regel positiv mit der Windgeschwindigkeit korreliert ist und positive Koeffizientenwerte f\u00fcr die Windgeschwindigkeit und negative f\u00fcr die DWR-Variable aufweist, k\u00f6nnte dies widerspr\u00fcchlich erscheinen. Unter fehlerhaften Bedingungen liegt die Leistung der Windenergieanlage jedoch h\u00e4ufig unter der erwarteten Kapazit\u00e4t, und eine unzureichende Leistung kann als Indikator f\u00fcr Komponentenausf\u00e4lle angesehen werden.<\/p>\n

\n
\"\"
Figure reference: \u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012). View the article online for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n
\u00a0<\/section>\n

<\/h3>\n

The specific effects weather conditions had on components<\/h3>\n

Die verschiedenen Komponenten reagieren unterschiedlich auf bestimmte Kombinationen von Umweltbedingungen. Daher sollten diese getrennt analysiert werden, um aussagekr\u00e4ftigere Ergebnisse zu erhalten.<\/p>\n

Pitch system<\/h4>\n

For the pitch system model (Figure 4) low temperatures and high monthly maximum wind speeds are significant. In addition to that, high relative humidity and turbulence intensity play a role when modelling the pitch system failures. These are the conditions where the pitch system is mostly active in order to regulate the rotor speed. Thus it is subject to higher stresses and possible damages.<\/p>\n

Yaw system<\/h4>\n

Abbildung 5 zeigt die standardisierten Koeffizientengr\u00f6\u00dfen f\u00fcr Giersystemausf\u00e4lle. Die Unterschiede zwischen den Koeffizientengr\u00f6\u00dfen sind nicht so gro\u00df wie bei den anderen Komponenten der Windkraftanlage. In diesem Modell scheinen viele meteorologische Faktoren eine Rolle zu spielen. Es ist eine deutliche Unterperformance der Anlage zu erkennen, da steigende Windgeschwindigkeiten und sinkende DWR-Werte zu einer erh\u00f6hten Anzahl von Ausf\u00e4llen des Giersystems f\u00fchren. Dies legt die Vermutung nahe, dass trotz der h\u00f6heren mittleren Windgeschwindigkeiten die Windrichtung h\u00e4ufig wechselte und das Giersystem st\u00e4ndig auf der Suche nach der besten Windrichtung sein musste. Somit f\u00fchrte eine m\u00f6gliche Gierfehlstellung zu h\u00f6herem Verschlei\u00df und Minderleistung. Dies sollte jedoch durch Einbeziehung der Windrichtung in weiteren Studien genauer untersucht werden.<\/p>\n

\n
\"\"
Figure reference: \u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012). View the article online for updates and enhancements.<\/figcaption><\/figure>\n<\/section>\n<\/section>\n
<\/section>\n
\n

Gearbox<\/h4>\n<\/section>\n
\n

Modelling the number of gearbox failures is influenced mostly by high wind speeds, as displayed in Figure 6. As the mean wind speeds increase, the load on the gearbox also increases and the component is more likely to fail.<\/p>\n

Generator<\/h4>\n

Das Ausfallmodell des Generators (Abbildung 7) wird haupts\u00e4chlich durch die zunehmende Turbulenzintensit\u00e4t und die Leistungsabgabe beeinflusst. Erh\u00f6hte TI f\u00fchrt zu einer h\u00f6heren Belastung des Generators, der sich an diese variierenden Eingangsgeschwindigkeiten anpassen muss. Positive Koeffizienten f\u00fcr die Variablen DWR und WS zeigen an, dass vor den Ausf\u00e4llen keine Minderleistung zu verzeichnen war. Au\u00dferdem besagt er, dass bei h\u00f6herer Stromerzeugung die Wahrscheinlichkeit eines Ausfalls der Generatoren steigt. Generatorenausf\u00e4lle treten in der Regel aufgrund pl\u00f6tzlicher \u00c4nderungen der Turbulenzintensit\u00e4t und stark schwankender Windbedingungen abrupt auf. Au\u00dferdem spielt die Niederschlagsmenge eine wichtige Rolle, da das Eindringen von Wasser die elektronischen Ger\u00e4te stark beeintr\u00e4chtigt.<\/p>\n

Die hier vorgestellten Modelle dienen also dazu, zu ermitteln, welche Umweltparameter das Ausfallverhalten bestimmter Komponenten von Windkraftanlagen beeinflussen. Diese Informationen k\u00f6nnen dazu beitragen, Ausf\u00e4lle zu antizipieren und die Modelle zur vorausschauenden Wartung deutlich zu verbessern.<\/p>\n

This is a summary of the\u00a0original article: \u201cModelling Wind Turbine Failures based on Weather Conditions\u201d by Maik Reder and Julio J. Melero (2017 J. Phys.: Conf. Ser. 926 012012).<\/em><\/p>\n

Sehen Sie sich den\u00a0Artikel online.<\/a>\u00a0f\u00fcr Updates und Erweiterungen.<\/em><\/p>\n<\/section>\n<\/section>\n

\u00a0<\/section>","protected":false},"excerpt":{"rendered":"

Wind turbine failure models can help to understand the components\u2019 degradation processes and enable the operators to anticipate upcoming failures. Usually, these models are based on the age of the systems or components.<\/p>","protected":false},"author":1,"featured_media":6385,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[66],"tags":[],"yoast_head":"\nModelling wind turbine failures using weather conditions | ANNEA<\/title>\n<meta name=\"description\" content=\"Modelling wind turbine failure using the weather can help explain components\u2019 degradation and enable the anticipation of upcoming failures.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/annea.ai\/de\/modelling-wind-turbine-failures-based-on-weather-conditions\/\" \/>\n<meta property=\"og:locale\" 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