A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem

No Thumbnail Available

Date

2020

Authors

Ceylan, Oğuzhan
Taşkın, Gülsen
Paudyal, Sumit

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Journal Issue

Abstract

Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.

Description

Keywords

Gaussian process regression, Machine learning, Power systems, Support vector regression

Turkish CoHE Thesis Center URL

Fields of Science

Citation

1

WoS Q

Scopus Q

Source

Volume

Issue

Start Page

End Page