A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads
Abstract
Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a fault occurs. Induction motors are usually equipped with several types of protection with different operation mechanisms, making it challenging to develop adequate yet not overly complex protection models and determine their parameters for aggregate induction motor models. This paper proposes an optimization-based nonlinear regression framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Using a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework. Sensitivity studies are presented to demonstrate the impact of lack of available motor load information on the accuracy of the protection models.
Keywords
Cite
@article{arxiv.1912.07582,
title = {A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads},
author = {Soumya Kundu and Zhigang Chu and Yuan Liu and Yingying Tang and Qiuhua Huang and Daniel James and Yu Zhang and Pavel Etingov and David P. Chassin},
journal= {arXiv preprint arXiv:1912.07582},
year = {2019}
}
Comments
accepted for presentation at The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020)