Data-driven Static Equivalence with Physics-informed Koopman Operators

doi:10.17775/CSEEJPES.2022.08750

Data-driven Static Equivalence with Physics-informed Koopman Operators

2024-01-01

1

OA

AI

DOI：10.17775/CSEEJPES.2022.08750

原文链接

原文求助

摘要

En 中文

With deployment of measurement units, fitting static equivalent models of distribution networks (DNs) by linear regression has been recognized as an effective method in power flow analysis of a transmission network. Increasing volatility of measurements caused by variable distributed renewable energy sources makes it more difficult to accurately fit such equivalent models. To tackle this challenge, this letter proposes a novel data-driven method to improve equivalency accuracy of DNs with distributed energy resources. This letter provides a new perspective that an equivalent model can be regarded as a mapping from internal conditions and border voltages to border power injections. Such mapping can be established through 1) Koopman operator theory, and 2) physical features of power flow equations at the root node of a DN. Performance of the proposed method is demonstrated on the IEEE 33-bus and IEEE 136-bus test systems connected to a 661-bus utility system.

Keyword:

Mathematical models

Load flow

Generators

Fitting

Power measurement

Network topology

Load modeling

Data-driven

distribution network

Koopman operator theory

static equivalent model

Data-driven Static Equivalence with Physics-informed Koopman Operators

摘要

期刊

机构

引用论文