A tractable ellipsoidal approximation for voltage regulation problems
Systems and Control
2019-03-12 v1 Machine Learning
Optimization and Control
Machine Learning
Abstract
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of our approach resides in approximating the feasible region of uncertainty with an ellipsoid. We formulate this problem using a learning model similar to Support Vector Machines (SVM) and propose a sampling algorithm that efficiently trains the model. We demonstrate our approach on a voltage regulation problem using standard IEEE distribution test feeders.
Cite
@article{arxiv.1903.03763,
title = {A tractable ellipsoidal approximation for voltage regulation problems},
author = {Pan Li and Baihong Jin and Ruoxuan Xiong and Dai Wang and Alberto Sangiovanni-Vincentelli and Baosen Zhang},
journal= {arXiv preprint arXiv:1903.03763},
year = {2019}
}
Comments
accepted by ACC2019 http://acc2019.a2c2.org/