Journal of Wind Energy (풍력에너지저널)

Korea Wind Energy Association (한국풍력에너지학회)
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Development of a Deep Learning-based Long-term PredictionGenerative Model of Wind and Sea Conditions for Offshore Wind Farm Maintenance Optimization

해상풍력단지 유지보수 최적화 활용을 위한 풍황 및 해황 장기예측 딥러닝 생성모델 개발

  • Sang-Hoon Lee ;
  • Dae-Ho Kim ;
  • Hyuk-Jin Choi ;
  • Young-Jin Oh ;
  • Seong-Bin Mun
  • 이상훈 (한국전력기술(주) 전력기술원 스마트융합실) ;
  • 김대호 (한국전력기술(주) 전력기술원 스마트융합실) ;
  • 최혁진 ((주)해안해양기술) ;
  • 오영진 (한국전력기술(주) 전력기술원 스마트융합실) ;
  • 문성빈 (한국전력기술(주) 전력기술원 스마트융합실)
  • Received : 2022.03.31
  • Accepted : 2022.04.30
  • Published : 2022.06.30
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Abstract

In this paper, we propose a time-series generation methodology using a generative adversarial network (GAN) for long-term prediction of wind and sea conditions, which are information necessary for operations and maintenance (O&M) planning and optimal plans for offshore wind farms. It is a "Conditional TimeGAN" that is able to control time-series data with monthly conditions while maintaining a time dependency between time-series. For the generated time-series data, the similarity of the statistical distribution by direction was confirmed through wave and wind rose diagram visualization. It was also found that the statistical distribution and feature correlation between the real data and the generated time-series data was similar through PCA, t-SNE, and heat map visualization algorithms. The proposed time-series generation methodology can be applied to monthly or annual marine weather prediction including probabilistic correlations between various features (wind speed, wind direction, wave height, wave direction, wave period and their time-series characteristics). It is expected that it will be able to provide an optimal plan for the maintenance and optimization of offshore wind farms based on more accurate long-term predictions of sea and wind conditions by using the proposed model.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 재원으로 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구입니다(과제번호: 20203010020050).

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