多戦略の適応型COAと改善された重量カーネルのELMに基づいた故障診断モデル:風力タービンのブレードアイス化に関するケーススタディ
Xingtao Wu1, Yunfei Ding1, Ruizhi Zhao2
1School of Electrical Engineering, Shanghai Dianji University, Shanghai, China.
PloS one
|August 28, 2025
まとめ
マルチストラテジー・アダプティブ・コアティ・オプティマイゼーション・アルゴリズム (MACOA) とインプルーブド・ウェイトド・カーネル・エクストリーム・ラーニング・マシン (IWKELM) を用いた新しいモデルは,風力タービンのブレード・アイシングの故障検出の精度と速度を向上させます. この方法により,風力発電システムの診断の信頼性が向上します.
科学分野:
- 再生可能エネルギーシステム
- 工学における人工知能
- 欠陥診断と予測
背景:
- 風力タービンの刃の結氷は,発電効率と運用安全性に大きな影響を及ぼします.
- 効率的な風力タービンの管理には,氷の欠陥の正確で迅速な診断が不可欠です.
- 既存の診断方法は,リアルタイムのアプリケーションに必要な正確性と速度を欠いている可能性があります.
研究 の 目的:
- 風力タービンのブレード氷の故障に対する高度な診断モデルを開発する.
- 既存の機械学習モデルの最適化性能と診断精度を向上させる.
- メタヒューリスティック・最適化と 機械学習を組み合わせた新しいアプローチを導入する.
主な方法:
- 混沌としたレヴィ飛行のマッピングや改善されたオブジェクト機能などの強化された機能を持つ多戦略適応コーティ最適化アルゴリズム (MACOA) を提案しました.
- サンプル分布を考慮するために加重パラメータを最適化することによって,改良された加重カーネルエクストリームラーニングマシン (IWKELM) を開発した.
- MACOAとIWKELMを統合し,Random Forest (RF) を使って,MACOA-IWKELM診断モデルを作成しました.
主要な成果:
- MACOA-IWKELMモデルは,2つの実際のSCADAデータセットで92.22%と96.94%の高い診断精度を達成しました.
- 50回の実験で低標準偏差 (2.53%と1.92%) が示され,モデルの安定性を示した.
- 比較実験により,提案されたモデルの性能が他の診断方法よりも優れていることが確認されました.
結論:
- MACOA-IWKELMモデルは,風力タービンのブレード氷の故障を検出するための堅牢で正確なソリューションを提供します.
- 機械学習と高度な最適化アルゴリズムの統合は,診断能力を大幅に改善します.
- この研究は,故障検出の改善により,風力発電システムの信頼性と効率を高めるのに寄与しています.
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