直流法潮流計算とExtreme Learning Machineを用いた高速な確率潮流計算法の開発

カテゴリ：部門大会

論文No：027

グループ名：【B】令和7年電気学会電力・エネルギー部門大会

発行日：2025/9/5

タイトル(英語)：Development of Fast Probabilistic Power Flow Calculation Method using DC Power Flow Method and Extreme Learning Machine

著者名：大澤拓門（苫小牧工業高等専門学校），赤塚元軌（苫小牧工業高等専門学校），鳥田宏行（苫小牧工業高等専門学校）

著者名(英語)： Takuto Ohsawa (National Institute of Technology, Tomakomai College), Motoki Akatsuka (National Institute of Technology, Tomakomai College), Hiroyuki Torita (National Institute of Technology, Tomakomai College)

キーワード：確率潮流計算,不確実性,機械学習,Extreme Learning Machine,直流法潮流計算,Probabilistic Power Flow,Uncertainty,Machine Learning,Extreme Learning Machine,DC Power Flow method

要約(日本語)：The rapid penetration of renewable energy resources has introduced significant uncertainty into modern power systems, necessitating accurate pre-assessment to ensure secure operation. Probabilistic power flow (PPF) analysis is a powerful technique for quantifying such uncertainty, but its reliance on repeated AC power-flow solutions makes it computationally prohibitive. This study proposes a fast PPF framework that couples the linear DC power flow (DC method) model with an Extreme Learning Machine (ELM) surrogate. Numerical experiments on benchmark IEEE systems confirm that the proposed approach preserves the precision of conventional Monte-Carlo-based PPF while reducing total computation time by approximately 100 times.

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