Development of classification model of power system fault by using PMU big-data

被引：0

作者：

Kang S.-B. ^{[1
]}

Ko B.-K. ^{[1
]}

Nam S.-C. ^{[1
]}

Choi Y.-D. ^{[1
]}

Kim Y.-H. ^{[1
]}

Jeon D.-H. ^{[1
]}

机构：

[1] Next Generation Transmission and Substation Laboratory, KEPCO Research Institute

来源：

Transactions of the Korean Institute of Electrical Engineers | 2019年 / 68卷 / 09期

关键词：

Big-Data; CNN; Fault Classification; PMU; WAMS;

D O I：

10.5370/KIEE.2019.68.9.1079

中图分类号：

学科分类号：

摘要：

Recently, innovative techniques in artificial intelligence such as machine learning have emerged to efficiently process huge amounts of big data delivered from PMUs to WAMS. Through processing raw data and analyzing big data, It delivers highly useful and valuable system status information to system operators. The types of machine learning vary depending on the usage, but the CNN (Convolution Neural Network) model is mainly used for the post analysis and fault detection(classification) in the power system. In this paper, based on PMU big data, we study the power system fault classification model by using CNN Model. Using Convolution neural network model based on KERAS, the database for each fault type was built and supervised learning was conducted for the model. The constructed model was verified with test data and the validity of the model was verified by inputting the actual power system fault data for the trained model. As a result, developed model classified correctly for the actual fault. Copyright © The Korean Institute of Electrical Engineers

引用

下载

页码：1079 / 1084

页数：5

共 50 条

[1] Model oriented system design on big-data
Kushiro, Noriyuki
Matsuda, Shodai
Takahara, Kunio
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014, 2014, 35 : 961 - 968
[2] Classification techniques for Disease detection using Big-data
Shah, Jaimin
Patel, Raj
2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER TECHNOLOGIES AND OPTIMIZATION TECHNIQUES (ICEECCOT), 2019, : 140 - 145
[3] Signal Features for Classification of Power System Disturbances using PMU Data
Biswal, Milan
Hao, Yifan
Chen, Phillip
Brahma, Sukumar
Cao, Huiping
De Leon, Phillip
2016 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), 2016,
[4] Challenge and Prospects for Fault Diagnosis of Power Battery System for Electrical Vehicles Based on Big-data
Wang Z.
Li X.
Yuan C.
Li X.
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2021, 57 (14): : 52 - 63
[5] A Minimax Approach for Classification with Big-data
Krishnan, R.
Jagannathan, S.
Samaranayake, V. A.
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 1437 - 1444
[6] Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data
Lan, Tu
Lin, You
Wang, Jianhui
Leao, Bruno
Fradkin, Dmitriy
2021 IEEE PES INNOVATIVE SMART GRID TECHNOLOGY EUROPE (ISGT EUROPE 2021), 2021, : 468 - 472
[7] Development of Power System Situation Recognition Technology Using PMU Data
Nam S.
Ko B.
Koo B.
Kang S.
Shin J.
Transactions of the Korean Institute of Electrical Engineers, 2020, 69 (11): : 1640 - 1648
[8] Big-Data Analytics for Arrhythmia Classification using Data Compression and Kernel Methods
Lillo-Castellano, J. M.
Mora-Jimenez, I.
Moreno-Gonzalez, R.
Montserrat-Garcia-de-Pablo, M.
Garcia-Alberola, A.
Rojo-Alvarez, J. L.
2015 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), 2015, 42 : 661 - 664
[9] Automatic Power System Event Classification Using Quadratic Discriminant Analysis on PMU Data
Rafferty, Mark
Liu, Xueqin
2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2020,
[10] Automated Detection and Classification of Critical Power System Events using ML on PMU Data
Mohamed, T.
Kezunovic, M.
Paudel, Y.
Vittal, V.
Pal, A.
Joshi, D.
Menuka, K. C.
Venkatasubramanian, M. V.
Torresan, G.
2024 INTERNATIONAL CONFERENCE ON SMART GRID SYNCHRONIZED MEASUREMENTS AND ANALYTICS, SGSMA 2024, 2024,

← 1 2 3 4 5 →