• Title, Summary, Keyword: Offshore plants maintenance

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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;Huh, Taesang;Kim, Myungil;Oh, Jae-Won;Cho, Su-Gil
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.11
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    • pp.672-682
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    • 2020
  • 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.