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Title: A Deep End-to-End Model for Transient Stability Assessment With PMU Data

Abstract

Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of themore » proposed model over the prior methods.« less

Authors:
 [1]; ORCiD logo [1];  [2];  [1];  [3];  [1];  [2]
  1. Huazhong Univ. of Science and Technology, Wuhan (China). State Key Lab. of Advanced Electromagnetic Engineering and Technology, Electric Power Security and High Efficiency Key Lab., School of Electrical and Electronic Engineering
  2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science
  3. Huazhong Univ. of Science and Technology, Wuhan (China). School of Electronic Information and Communications
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1564163
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Access
Additional Journal Information:
Journal Volume: 6; Journal Issue: n/a; Journal ID: ISSN 2169-3536
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Feature extraction; Power system stability; Stability analysis; Machine learning; Phasor measurement units; Voltage measurement

Citation Formats

Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Shi, Dongyuan, Bai, Xiang, Duan, Xianzhong, and Liu, Yilu. A Deep End-to-End Model for Transient Stability Assessment With PMU Data. United States: N. p., 2018. Web. doi:10.1109/ACCESS.2018.2872796.
Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Shi, Dongyuan, Bai, Xiang, Duan, Xianzhong, & Liu, Yilu. A Deep End-to-End Model for Transient Stability Assessment With PMU Data. United States. doi:10.1109/ACCESS.2018.2872796.
Zhu, Qiaomu, Chen, Jinfu, Zhu, Lin, Shi, Dongyuan, Bai, Xiang, Duan, Xianzhong, and Liu, Yilu. Mon . "A Deep End-to-End Model for Transient Stability Assessment With PMU Data". United States. doi:10.1109/ACCESS.2018.2872796. https://www.osti.gov/servlets/purl/1564163.
@article{osti_1564163,
title = {A Deep End-to-End Model for Transient Stability Assessment With PMU Data},
author = {Zhu, Qiaomu and Chen, Jinfu and Zhu, Lin and Shi, Dongyuan and Bai, Xiang and Duan, Xianzhong and Liu, Yilu},
abstractNote = {Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.},
doi = {10.1109/ACCESS.2018.2872796},
journal = {IEEE Access},
number = n/a,
volume = 6,
place = {United States},
year = {2018},
month = {10}
}

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