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High-temperature power reduction state identification for wind turbines using feature correlation analysis and deep learning methods

Identifizierung des Zustands der Hochtemperatur-Leistungsreduzierung bei Windkraftanlagen mit Hilfe von Merkmalskorrelationsanalyse und Deep-Learning-Methoden

Abstract

The excessively high temperature of gearbox oil in summer leads to power reduction of wind turbines (called high-temperature power reduction state), which does harm to the performance of the power generation performance of the wind turbine. The timely and accurate identification of high-temperature power reduction state will contribute to improving the operation efficiency of the unit. This study proposes a new method based on the vine copula model and algorithm of convolutional neural network cascading to the bidirectional long short term memory network with attention mechanism (CNN-BiLSTM-attention). Firstly, the vine copula model is used to analyze the correlation of the features in supervisory control and data acquisition (SCADA) system, and the features that can reflect the high-temperature power reduction state are extracted comprehensively. Secondly, CNN is used to mine the coupling relationship between features and extract deep spatial features. Finally, BiLSTM is used to extract the time-series information in the depth spatial feature further for high-temperature power reduction state identification, and the attention mechanism is introduced to sense and identify the relevant network weights adaptively to enhance the influence of important information. The experimental results show that the method has high identification accuracy in the identification of high-temperature power reduction state. Accurate and reliable identification results can provide reference for formulating the operation and maintenance scheme of wind turbine reasonably.

Zusammenfassung

Die übermäßig hohe Temperatur des Getriebeöls im Sommer führt zu einer Leistungsreduzierung von Windkraftanlagen (sog. Hochtemperatur-Leistungsreduzierungszustand), was der Leistung der Stromerzeugungsleistung der Windkraftanlage schadet. Die rechtzeitige und genaue Identifizierung des Hochtemperatur-Leistungsreduktionszustands wird dazu beitragen, die Betriebseffizienz der Anlage zu verbessern. In dieser Studie wird eine neue Methode vorgeschlagen, die auf dem Vine-Copula-Modell und dem Algorithmus des Faltungsneuronalen Netzes basiert, das mit dem bidirektionalen Langzeitgedächtnisnetz mit Aufmerksamkeitsmechanismus (CNN-BiLSTM-Attention) kaskadiert ist. Erstens wird das Vine-Copula-Modell verwendet, um die Korrelation der Merkmale im Überwachungs- und Datenerfassungssystem (SCADA) zu analysieren, und die Merkmale, die den Zustand der Hochtemperaturleistungsreduzierung widerspiegeln können, werden umfassend extrahiert. Zweitens wird CNN verwendet, um die Kopplungsbeziehung zwischen den Merkmalen zu ermitteln und tiefe räumliche Merkmale zu extrahieren. Schließlich wird BiLSTM verwendet, um die Zeitreiheninformationen in den räumlichen Tiefenmerkmalen zu extrahieren, um den Zustand der Hochtemperatur-Leistungsreduzierung zu identifizieren, und der Aufmerksamkeitsmechanismus wird eingeführt, um die relevanten Netzwerkgewichte adaptiv zu erkennen und zu identifizieren, um den Einfluss wichtiger Informationen zu verbessern. Die experimentellen Ergebnisse zeigen, dass die Methode eine hohe Erkennungsgenauigkeit bei der Identifizierung des Hochtemperatur-Leistungsreduktionszustands aufweist. Genaue und zuverlässige Identifizierungsergebnisse können als Referenz für die Formulierung des Betriebs- und Wartungsplans von Windkraftanlagen dienen.

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Correspondence to Xinxin Huang.

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Conflict of interest

X. Yang, X. Huang, X. Gao and Z. Tao declare that they have no competing interests. I hereby certify that to the best of my knowledge, the authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Appendix

Appendix

Appendix A

Table 9 Calculation result by AIC

Appendix B

Table 10 The best copula functions for pair copulas

Appendix C

Table 11 τk, ρs and τi,sum corresponding to pair copula

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Yang, X., Huang, X., Gao, X. et al. High-temperature power reduction state identification for wind turbines using feature correlation analysis and deep learning methods. Forsch Ingenieurwes 86, 225–239 (2022). https://doi.org/10.1007/s10010-022-00586-y

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  • DOI: https://doi.org/10.1007/s10010-022-00586-y