Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 213 tok/s Pro
GPT OSS 120B 458 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

An efficient open-source implementation to compute the Jacobian matrix for the Newton-Raphson power flow algorithm (1804.06742v1)

Published 18 Apr 2018 in stat.CO and cs.CE

Abstract: Power flow calculations for systems with a large number of buses, e.g. grids with multiple voltage levels, or time series based calculations result in a high computational effort. A common power flow solver for the efficient analysis of power systems is the Newton-Raphson algorithm. The main computational effort of this method results from the linearization of the nonlinear power flow problem and solving the resulting linear equation. This paper presents an algorithm for the fast linearization of the power flow problem by creating the Jacobian matrix directly in CRS format. The increase in speed is achieved by reducing the number of iterations over the nonzero elements of the sparse Jacobian matrix. This allows to efficiently create the Jacobian matrix without having to approximate the problem. A comparison of the calculation time of three power grids shows that comparable open-source implementations need 3-14x the time to create the Jacobian matrix.

Citations (8)

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.