電気配電システムの電圧安定性評価のための機械学習アルゴリズム
Molla Addisu Mossie1, Tefera Terefe Yetayew2, Girmaw Teshager Bitew3
1Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia. mollaaddisu2@gmail.com.
Scientific reports
|August 30, 2025
まとめ
機械学習モデルはエチオピアの 電力網の電圧の安定性を 正確に予測します グラデント・ブースティングとランダム・フォレストは ネットワークの回復力を高めるために 重要なバスを迅速に評価し 特定します
科学分野:
- 電気工学
- コンピューター・インテリジェンス
背景:
- 電圧の不安定性は,電力システムにおける重要な問題であり,運用能力と伝送を制限します.
- 従来の電圧安全性評価方法は計算が密集しており,リアルタイムでの応用が困難です.
- 機械学習は 効率的で正確な電圧安定性分析のための有望な代替手段です
研究 の 目的:
- エチオピアの配電ネットワークにおける高速電圧安定性指数 (FVSI) の予測のための機械学習モデルを開発し評価する.
- 電圧安定性評価のための線形回帰,ランダムフォレスト,グラデントブースト,およびサポートベクトルマシンの性能を比較する.
- 研究された電力システムにおける不安定性の高いリスクを持つ重要なバスを特定する.
主な方法:
- 応用線形回帰,ランダムフォレスト,グラデーションブースト,サポートベクトルマシンモデル.
- 35台のバスと53台のエチオピアの配送ネットワークで,名目および異なる負荷条件 (10-150%) でFVSIが予測されています.
- R2とルーツ・ミーン・スクエア・エラー (RMSE) メトリクスを用いてモデルの精度を分析し,FVSIの値分析を行った.
主要な成果:
- グラデーション・ブースト (GB) とランダム・フォレスト (RF) のモデルは,優れた精度を示した (R2はそれぞれ0.9998と0.999).
- GBモデルは,低RMSE値 (例えば,53バスシステムでは0.0002) で最高精度を達成した.
- 両システムの特定のバスは,緊急の監視を必要とする重要な不安定なリスクポイントとして特定されました.
結論:
- 集成的な機械学習方法,特にGBとRFは,迅速な電圧安定性の評価に非常に有効です.
- この研究は,ネットワークの回復力を向上させるための標的型介入の重要な分野を成功裏に特定しました.
- 電力配電ネットワークの電圧崩壊を防ぐには,リアルタイムで電圧安定性を正確に予測することが重要です.
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