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風速計タワーにおける複数の風速センサーのための階層的データ融合アルゴリズム

Junhong Duan1, Hailong Zhang1, Chao Tu2

  • 1Gansu Electric Power Company of State Grid, Lanzhou 730046, China.

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|January 28, 2026
PubMed
まとめ

本研究では、複数の風速計を使用した正確な風速測定のための階層的データ融合戦略を紹介します。この手法は、風力発電および気象アプリケーションにおけるデータ品質と処理効率を大幅に向上させます。

キーワード:
Q学習アクィラ最適化データ融合エクストリームラーニングマシン（ELM）アンセンテッドカルマンフィルター（UKF）

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科学分野:

  • 工学
  • データサイエンス
  • 環境科学

背景:

  • 正確な風速測定は、風力発電および気象監視にとって非常に重要です。
  • 風力タワーの風速計からのマルチセンサーデータ融合は、高精度の風速データを得るための主要な方法です。
  • 既存の融合方法には、品質と効率に関する課題があります。

研究 の 目的:

  • マルチセンサー風速データの融合の質と効率を向上させるための階層的データ融合戦略を提案すること。
  • 風速測定システムにおけるデータ処理の精度と速度を向上させること。

主な方法:

  • 2段階の融合アプローチ：ノイズ除去と融合のためのファジィ論理とロバスト性因子強化アンセンテッドカルマンフィルター（FLR-UKF）を使用したローカル融合。
  • 強化された探索能力を持つQ学習改良アクィラ最適化（QLIAO-ELM）によって最適化されたエクストリームラーニングマシン（ELM）を使用したグローバル融合。

主要な成果:

  • 従来のアンセンテッドカルマンフィルター（UKF）と比較して、FLR-UKFは二乗平均平方根誤差（RMSE）を26.46％〜28.6％削減しました。
  • QLIAO-ELMは、標準ELMおよびISSA-ELMと比較して、それぞれ27.1％および14.0％のRMSE削減を達成しました。
  • 提案手法は、風速データ融合における精度と効率の向上を示しました。

結論:

  • 階層的データ融合戦略は、マルチセンサー風速測定の精度と効率の両方を効果的に向上させます。
  • 新しいFLR-UKFおよびQLIAO-ELM手法は、風データ処理における既存の手法よりも大幅な改善を提供します。
  • このアプローチは、再生可能エネルギーおよび気象学にとって不可欠な高精度の風速情報に対する信頼性の高いソリューションを提供します。