光伏インバーターの堅牢な故障診断のための二重グラフ注意ネットワーク
Ananta Bijoy Bhadra1, Most Humayra Khanom Rime2, Yeahia Sarker3
1Department of Electrical and Computer Engineering, Georgia Southern University, Statesboro, GA, 8151, USA.
Scientific reports
|August 26, 2025
まとめ
この研究は,正確な光伏 (PV) インバーター故障診断のためのデュアルグラフ注意ネットワーク (DualGAT) を導入します. この新しいディープラーニングモデルは 97.35%の精度で 騒音環境下でも信頼性の高いエネルギー変換を実現しています
科学分野:
- 電気工学
- 人工知能
- 再生可能エネルギーシステム
背景:
- 光伏 (PV) インバーターは安定したエネルギー変換に不可欠です.
- PVインバーターのオープン回路の故障を早期に検出することは,性能の低下や機器の故障を防ぐために不可欠です.
研究 の 目的:
- PVインバーターの正確な故障診断のための堅牢なディープラーニングの枠組みを開発する.
- インテリジェント・コンディション・モニタリングを通じて PVシステムの信頼性を高める.
主な方法:
- 空間的 (DisGAT) と時間的な (TempGAT) 注意メカニズムを統合したダブルグラフ注意ネットワーク (DualGAT) フレームワークが提案されました.
- MATLAB/Simulinkでグリッドに接続されたPVインバーターの詳細なシミュレーションモデルを作成しました.
- インバーター電流信号から特性を抽出するために,ディスクリート波形変換 (DWT) が使用されました.
主要な成果:
- デュアルGATモデルは,故障診断において97.35%のテスト精度を達成しました.
- このフレームワークは,従来の機械学習やその他のディープラーニング方法と比較して,大幅に優れていることが示されました.
- 高い診断性能は,騒音や信号の歪み下で維持された.
結論:
- DualGATフレームワークは, PVインバーターにおける複雑な時空の故障特性を効果的に捉えます.
- このアプローチは,PVベースの電力システムにおけるインテリジェント状態モニタリングのための有望な解決策を提供します.
- モデルの頑丈さは,PVインフラストラクチャの信頼性の高い運用と保守を保証します.
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