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Title: Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation

Abstract

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

Authors:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [2]
  1. The University of Texas at Austin
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity
OSTI Identifier:
1605534
Report Number(s):
NREL/CP-5D00-75247
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the 2020 IEEE Power and Energy Society General Meeting (IEEE PES GM), 2-6 August 2020, Montreal, Canada
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distributed generation; distribution system; ensemble regressor; machine learning; sensor allocation; voltage prediction

Citation Formats

Furlani Bastos, Alvaro, Santoso, Surya, Krishnan, Venkat K, and Zhang, Yingchen. Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation. United States: N. p., 2020. Web.
Furlani Bastos, Alvaro, Santoso, Surya, Krishnan, Venkat K, & Zhang, Yingchen. Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation. United States.
Furlani Bastos, Alvaro, Santoso, Surya, Krishnan, Venkat K, and Zhang, Yingchen. Fri . "Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation". United States. https://www.osti.gov/servlets/purl/1605534.
@article{osti_1605534,
title = {Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation},
author = {Furlani Bastos, Alvaro and Santoso, Surya and Krishnan, Venkat K and Zhang, Yingchen},
abstractNote = {Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}

Conference:
Other availability
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