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Title: Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

Abstract

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [3]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. PacifiCorp, Portland, OR (United States)
  3. Idaho Power Company, Boise, ID (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1509840
Report Number(s):
PNNL-SA-139624
Journal ID: ISSN 0885-8950
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Name: IEEE Transactions on Power Systems; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; classification and regression trees; feature selection; machine learning; multinomial logistic regression; series compensation

Citation Formats

Li, Xinya, Fan, Xiaoyuan, Ren, Huiying, Hou, Zhangshuan, Huang, Qiuhua, Wang, Song, and Ciniglio, Orlando. Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems. United States: N. p., 2019. Web. doi:10.1109/TPWRS.2019.2912711.
Li, Xinya, Fan, Xiaoyuan, Ren, Huiying, Hou, Zhangshuan, Huang, Qiuhua, Wang, Song, & Ciniglio, Orlando. Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems. United States. doi:10.1109/TPWRS.2019.2912711.
Li, Xinya, Fan, Xiaoyuan, Ren, Huiying, Hou, Zhangshuan, Huang, Qiuhua, Wang, Song, and Ciniglio, Orlando. Mon . "Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems". United States. doi:10.1109/TPWRS.2019.2912711.
@article{osti_1509840,
title = {Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems},
author = {Li, Xinya and Fan, Xiaoyuan and Ren, Huiying and Hou, Zhangshuan and Huang, Qiuhua and Wang, Song and Ciniglio, Orlando},
abstractNote = {One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.},
doi = {10.1109/TPWRS.2019.2912711},
journal = {IEEE Transactions on Power Systems},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
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This content will become publicly available on April 22, 2020
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