Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and 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 150 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 34 tok/s Pro
GPT-4o 113 tok/s Pro
Kimi K2 211 tok/s Pro
GPT OSS 120B 444 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Differential Geometric Foundations for Power Flow Computations (2005.05038v1)

Published 27 Apr 2020 in eess.SY, cs.SY, and math.OC

Abstract: This paper aims to systematically and comprehensively initiate a foundation for using concepts from computational differential geometry as instruments for power flow computing and research. At this point we focus our discussion on the static case, with power flow equations given by quadratic functions defined on voltage space with values in power space; both spaces have real Euclidean coordinates. The central issue is a differential geometric analysis of the power flow solution space boundary (SSB, also in a simplifying way, called saddle node bifurcation set, SNB) both in voltage and in power space. We present different methods for computing tangent vectors, tangent planes and normals of the SSB and the normals' derivatives. Using the latter we compute normal and principal curvatures. All this is needed for tracing the orthogonal projection of points on curves in voltage or power space onto the SSB on points closest to the given points on the curve, thus obtaining estimates for their distance to the SSB. As another example how these concepts can be useful, we present a new high precision continuation method for power flow solutions close to and on the SSB called local inversion of the power flow map from voltage to power space, assuming the dimension of power flow's Jacobean zero space, called KERNEL, is one. For inversion, we present two different geometry-based splitting techniques with one of them using the aforementioned orthogonal tracing method.

Summary

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

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions 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.