A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads

TL;DR

This paper introduces an optimization-based nonlinear regression framework to accurately model protection mechanisms of induction motor loads in power systems, aiding in system stability analysis after faults.

Contribution

It presents a novel nonlinear regression approach for parameter estimation of induction motor protection models in composite load systems.

Findings

The framework effectively estimates protection model parameters.

Sensitivity analysis shows the impact of load information availability.

Numerical examples validate the proposed method.

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.