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風力タービンの基礎のモニタリングのための繰り返し回転差-Zスコアと機械学習の割り算
Renjie Li1, Xiangxing Lu1, Jizhang Zhao2,3,4
1Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan, China.
PloS one
|September 5, 2025
まとめ
この研究は,風力発電所のモニタリングにおける異常を検出し,欠けているデータを補う新しい方法を導入します. 再生可能エネルギーインフラの 構造的健全性に関する正確な評価を保証します
科学分野:
- エンジニアリング
- データサイエンス
- 再生可能エネルギーシステム
背景:
- 構造体健康モニタリング (SHM) は,特に再生可能エネルギーにおいて,エンジニアリングの構造に不可欠です.
- 現場でのデータ収集は,機器の不安定性や環境の複雑性などの課題に直面し,データ異常やギャップにつながります.
- 風力発電所のような構造物の正確な性能評価は 精密で完全な監視データに依存しています
研究 の 目的:
- 風力発電所のモニタリングデータにおけるデータ異常やギャップに対処するため (溶接ナイルの張力,アンカーケーブルの軸の力,コンクリートの張力).
- 困難な現場環境での異常検出とデータ割り算のための堅固な方法を開発し,検証する.
- 長期的な安全性評価のための構造的モニタリングデータの信頼性と完全性を高める.
主な方法:
- 効率的な異常検出のための反復的なロール差-Zスコア方法を提案しました.
- データの再構築のために線形インターポレーションとLightGBMを組み合わせた機械学習インプテーションフレームワークを開発した.
- 山東省の風力発電所の強化プロジェクトで実用データを使った実験を行いました.
主要な成果:
- イテラティブ・ローリング・ディファレンスのZスコア・メソッドは,最大80%のデータ損失でも,堅固な異常検出を証明した.
- 推定フレームワークは,連続した欠陥データに対して0.0214-0.0227の平均二乗誤差 (MSE) と0.14-0.15の根の平均二乗誤差 (RMSE) を達成した.
- 継続的なデータ不足のシナリオでは,最大50%のデータ損失で信頼性の高いデータ再構築が達成されました.
結論:
- 開発された方法は,風力発電所のモニタリングデータの質を改善するための信頼できる解決策を提供します.
- データの整合性を向上させることで,構造状態の評価と安全性の評価がより正確になります.
- この研究は,再生可能エネルギーのインフラストラクチャの長期的な構造的信頼性に貢献します.
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