分散光伏センサーネットワークにおける信頼性の高い通信:大規模な言語モデル駆動のアプローチ
1Power Dispatch Control Center, Guizhou Power Grid Co., Ltd., Guiyang 550000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
この研究は,分散型太陽光発電 (DPV) システムの監視のための新しい枠組みを導入し,最適化されたデータ伝送と大型言語モデル (LLM) 診断を通じて信頼性を高めます. このアプローチは,遅延時間を大幅に削減し,産業用エネルギーソリューションの故障分析を改善します.
科学分野:
- エネルギーシステムエンジニアリング
- 産業用IoTにおける人工知能
- ネットワークの最適化
背景:
- 分散型太陽光発電 (DPV) システムは,持続可能な産業用エネルギーを提供しますが,監視の課題に直面しています.
- 現在のモニタリングシステムは,データ伝送の制約と,タイムリーな故障検出に苦労しています.
研究 の 目的:
- 信頼性の高いDPVシステムモニタリングのための階層的な最適化フレームワークを開発する.
- インテリジェントなデータ管理と診断を通じて,DPVシステムにおけるレイテンシーとダウンタイムを最小限に抑える.
主な方法:
- ネットワーク層でヒステリーシスベースのトラフィックシェーピングの統合.
- 物理情報に基づくプロンプトエンジニアリングによる大型言語モデル (LLM) 駆動型診断の適用,ゼロショット根本原因分析.
- 重要な故障データの送信のための優先順位に基づくスケジューリング.
主要な成果:
- 10Mbpsのゲートウェイの帯域幅下でP50の遅延を46.08%から49.87%削減しました.
- 広範なトレーニングデータなしで有効なゼロショット根本原因分析が実証されています.
- 詳細なLLM対応の評価を通じて,正確な故障診断を可能にしました.
結論:
- 提案された枠組みは,DPVシステムの監視の信頼性と効率性を高めます.
- LLM駆動型診断は,産業用IoTの故障分析に強力でデータ効率の高いアプローチを提供します.
- この統合ソリューションは,DPVシステム管理における主要な技術的制約に対処しています.
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