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Title: Cross-Categorical Transfer Learning based Composite Load Protection Modeling

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 a novel cross-categorical transfer learning based framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Introducing a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a combination of feature extraction using transferable layers, followed by affine operations through non-transferable layers applied on the previous extracted features. Numerical results are provided to illustrate the application of the framework.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532522
Report Number(s):
PNNL-SA-141727
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ
Country of Publication:
United States
Language:
English
Subject:
transfer learning, Stacked Autoencoder, Composite load model

Citation Formats

Chakraborty, Indrasis, Kundu, Soumya, Liu, Yuan, and Etingov, Pavel V. Cross-Categorical Transfer Learning based Composite Load Protection Modeling. United States: N. p., 2019. Web. doi:10.1145/3307772.3331026.
Chakraborty, Indrasis, Kundu, Soumya, Liu, Yuan, & Etingov, Pavel V. Cross-Categorical Transfer Learning based Composite Load Protection Modeling. United States. doi:10.1145/3307772.3331026.
Chakraborty, Indrasis, Kundu, Soumya, Liu, Yuan, and Etingov, Pavel V. Fri . "Cross-Categorical Transfer Learning based Composite Load Protection Modeling". United States. doi:10.1145/3307772.3331026.
@article{osti_1532522,
title = {Cross-Categorical Transfer Learning based Composite Load Protection Modeling},
author = {Chakraborty, Indrasis and Kundu, Soumya and Liu, Yuan and Etingov, Pavel V.},
abstractNote = {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 a novel cross-categorical transfer learning based framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Introducing a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a combination of feature extraction using transferable layers, followed by affine operations through non-transferable layers applied on the previous extracted features. Numerical results are provided to illustrate the application of the framework.},
doi = {10.1145/3307772.3331026},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
Other availability
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