Journal of Aerospace System Engineering (항공우주시스템공학회지)

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Anomaly detection in blade pitch systems of floating wind turbines using LSTM-Autoencoder

LSTM-Autoencoder를 이용한 부유식 풍력터빈 블레이드 피치 시스템의 이상징후 감지

  • Seongpil Cho (Department of Aeronautical and Astronautical Engineering, Korea Aerospace University)
  • 조성필 (한국항공대학교 항공우주공학과)
  • Received : 2024.06.03
  • Accepted : 2024.07.25
  • Published : 2024.08.31
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Abstract

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

본 논문은 부유식 풍력터빈의 블레이드 피치 시스템에서 발생하는 이상을 조기에 감지하기 위한 LSTM-Autoencoder 모델 기반의 이상징후 감지 시스템을 설명한다. 발전소 모니터링 시스템에 활용되는 센서 데이터는 주로 시계열 데이터로 구성되며, LSTM 네트워크는 이러한 시계열 데이터를 분석하기 위해 두 개의 단방향 LSTM 네트워크로 구성된다. 이를 통해 순차 데이터에 숨겨진 장기 의존성을 효과적으로 발견할 수 있다. 한편, 오토인코더 메커니즘은 정상상태 데이터로부터만 학습하여 이상상태를 분류될 수 있기 때문에 이 두 가지 네트워크를 결합하여 시스템에 발생하는 이상징후를 효과적으로 감지할 수 있다. 제안된 프레임워크의 효과를 입증하기 위해 풍력 터빈 모델에서 수집한 실제 다변량 시계열 데이터셋을 적용하였다. LSTM-AE 모델은 높은 이상징후 감지 정확도를 달성하여 우수한 성능을 보였다.

Keywords

Acknowledgement

본 논문의 기초연구 단계에서 소중한 의견을 주신 NTNU의 Torgeir Moan 및 Zhen Gao 교수님께 감사드립니다. 이 논문은 Equinor의 재원으로 MIT-NTNU-Statoil Wind Turbine Program의 지원을 받아 수행된 기초연구사업임. 이 논문은 2024년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(과제번호: 2022R1A6A1A03056784).

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