Work

Development of Fast Probabilistic Power Flow Calculation Method Using DC Power Flow Method and Extreme Learning Machine

Year: 2026

Type: article

Abstract: ABSTRACT 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. By augmenting ELM inputs with DC‐PF results and employing Latin Hypercube Sampling, the method achieves both high speed and high accuracy. 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 140 times

Source: Electronics and Communications in Japan

DOI: 10.1002/ecj.70027

Authors: Takuto Ohsawa, Motoki Akatsuka, Hiroyuki Torita

Institution: National Institute of Technology, Tomakomai College

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: bronze

Funder: Japan Society for the Promotion of Science