雷の衝突時の風力タービンのブレードモニタリングのための多式データセット
Tao Li1, Chenxi Li1, Yuxin Qin2
1School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou, China.
Scientific data
|August 31, 2025
まとめ
この研究は,雷の衝突時の風力タービンのブレードを監視するための珍しいマルチモダルのデータセットを導入します. このデータは,雷の検出とブレードダメージの評価のための予測モデルを開発し,風力エネルギーの信頼性を高めるのに役立ちます.
科学分野:
- エンジニアリング
- 材料科学
- 再生可能エネルギー
背景:
- 風力タービンのブレードは 雷に弱いので 構造の整合性と性能が損なわれます
- 雷の発生時の包括的な動的データを取得することは困難で,効果的な監視と損害の評価を妨げます.
研究 の 目的:
- 雷の衝突時の風力タービンのブレードモニタリングのための新しいマルチモダルデータセット (MDWTBM-LS) を導入する.
- 雷の検出と刃の損傷の評価のための高度な予測モデルの開発を容易にする.
- 風力発電システムの機械学習,状態モニタリング,故障予測の研究を支援する.
主な方法:
- 振動,負荷,ストレスの測定を含むマルチモダルのデータを収集します.
- リアルタイムでデータを取得するための光ファイバー負荷センサーと光ファイバー加速度計センサーを使用しています.
- 刃の動作状態を記録し,攻撃後の損傷の評価を行った.
主要な成果:
- 風力タービンのブレードが雷に反応する珍しいマルチモダルデータセット (MDWTBM-LS) が作成されました.
- このデータセットは,リアルタイムで運用状況と損害情報を提供します.
- このデータセットは研究開発を促進するために公開されています.
結論:
- MDWTBM-LSデータセットは,風力タービンの雷の保護における予測モデリングの進歩に不可欠です.
- このデータを活用して 強化されたモニタリングとメンテナンスの戦略を策定できます
- この資源は,風力発電施設の信頼性向上と運用最適化に貢献します.
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