Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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A Design and Analysis of Pressure Predictive Model for Oscillating Water Column Wave Energy Converters Based on Machine Learning

진동수주 파력발전장치를 위한 머신러닝 기반 압력 예측모델 설계 및 분석

  • Seo, Dong-Woo (Division of Modeling and Simulation, KISTI) ;
  • Huh, Taesang (Division of Modeling and Simulation, KISTI) ;
  • Kim, Myungil (Division of Modeling and Simulation, KISTI) ;
  • Oh, Jae-Won (Technology Center for Offshore Plant Industries, KRISO) ;
  • Cho, Su-Gil (Technology Center for Offshore Plant Industries, KRISO)
  • 서동우 (한국과학기술정보연구원 가상설계센터) ;
  • 허태상 (한국과학기술정보연구원 가상설계센터) ;
  • 김명일 (한국과학기술정보연구원 가상설계센터) ;
  • 오재원 (선박해양플랜트연구소) ;
  • 조수길 (선박해양플랜트연구소)
  • Received : 2020.09.07
  • Accepted : 2020.11.06
  • Published : 2020.11.30
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Abstract

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

최근 다양한 산업/제조 현장에서 운영 효율화를 위한 디지털 트윈(digital twin) 기술 연구가 활발하게 수행 중이고, 화석 연료의 점진적 고갈과 환경오염 문제는 파력발전소와 같은 신재생/친환경 발전방식을 요구한다. 하지만, 파도의 에너지에 의해서 전기를 생산하는 파력발전에서 변동성이 높은 파도에너지에 의해서 발전량과 고장 등의 운영효율화 요소가 밀접하게 관련되어 있어 이들 사이의 관계를 이해하고 예측하는 것이 매우 중요하다. 따라서 첫 번째로 파고 데이터, 진동수주(OWC: Oscillating Water Column, 이하 OWC) 챔버의 센서 데이터 등과 같은 변동성이 높은 데이터 간에 의미 있는 상관관계 도출이 필요하다. 두 번째로 도출된 상관관계를 기반으로 추출된 데이터로 예측 상황을 학습함으로써 원하는 정보를 예측할 수 있는 방법론 연구가 이루어져야 한다. 본 연구에서는 파력발전 시스템의 디지털 트윈으로 스마트 운용 및 유지보수가 가능하도록 실제 파력발전소의 IoT 센서 데이터를 이용하여 OWC의 압력 예측을 위해 머신러닝 프레임워크를 활용한 워크플로우 기반의 학습모델을 설계하고, 검증 및 평가 데이터셋을 통한 압력 예측분석의 유효성을 확인한다.

Keywords

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