Fault classification in power systems using EMD and SVM

被引：81

作者：

Babu, N. Ramesh ^{[1
]}

Mohan, B. Jagan ^{[1
]}

机构：

[1] VIT Univ, Sch Elect Engn, Vellore, Tamil Nadu, India

来源：

AIN SHAMS ENGINEERING JOURNAL | 2017年 / 8卷 / 02期

关键词：

Fault classification; Empirical Mode Decomposition (EMD); Support Vector Machines (SVMs); EMPIRICAL-MODE DECOMPOSITION;

D O I：

10.1016/j.asej.2015.08.005

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In recent years, power quality has become the main concern in power system engineering. Classification of power system faults is the first stage for improving power quality and ensuring the system protection. For this purpose a robust classifier is necessary. In this paper, classification of power system faults using Empirical Mode Decomposition (EMD) and Support Vector Machines (SVMs) is proposed. EMD is used for decomposing voltages of transmission line into Intrinsic Mode Functions (IMFs). Hilbert Huang Transform (HHT) is used for extracting characteristic features from IMFs. A multiple SVM model is introduced for classifying the fault condition among ten power system faults. Algorithm is validated using MATLAB/SIMULINK environment. Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy. (C) 2015 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

引用

页码：103 / 111

页数：9

共 50 条

[11] Fault diagnosis of power transformer using SVM and FCM
Lee, Dae-Jong
Lee, Jong-Pil
Ji, Pyeong-Shik
Park, Jae-Woon
Lim, Jae-Yoon
CONFERENCE RECORD OF THE 2008 IEEE INTERNATIONAL SYMPOSIUM ON ELECTRICAL INSULATION, VOLS 1 AND 2, 2008, : 112 - +
[12] AN EFFECTIVE AND NOVEL FAULT DIAGNOSIS TECHNIQUE BASED ON EMD AND SVM
Xian, Guang-ming
Zeng, Bi-qing
NEURAL NETWORK WORLD, 2010, 20 (04) : 427 - 439
[13] Fault diagnosis of airborne fuel pump based on EMD and SVM
Chen J.
Liu Y.
Chen Y.
Nie K.
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (08): : 1687 - 1696
[14] A Study on SVM with Feature Selection for Fault Diagnosis of Power Systems
Wang, Yufei
Wu, Chunguo
Wan, Liming
Liang, Yanchun
2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 2, 2010, : 173 - 176
[15] Application Study of EMD-AR and SVM in the Fault Diagnosis
Yang Wei-xin
Wang Ping
PROCEEDINGS OF 2014 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-2014 HUNAN), 2014, : 93 - 96
[16] Fault Types Classification Using Support Vector Machine (SVM)
Awalin, Lilik J.
Naidu, Kanendra
Suyono, Hadi
5TH INTERNATIONAL CONFERENCE ON GREEN DESIGN AND MANUFACTURE 2019 (ICONGDM 2019), 2019, 2129
[17] Extract Fault Signal via DWT and Penetration of SVM for Fault Classification at Power System Transmission
Zakri, Azriyenni Azhari
Darmawan, Syukri
Usman, Jafaru
Rosma, Iswadi Hasyim
Ihsan, Boy
2018 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (ICON EEI): TOWARD THE MOST EFFICIENT WAY OF MAKING AND DEALING WITH FUTURE ELECTRICAL POWER SYSTEM AND BIG DATA ANALYSIS, 2018, : 191 - 196
[18] Binary SVM Approach for Security Assessment and Classification in Power Systems
Kalyani, S.
Swarup, K. Shanti
2009 ANNUAL IEEE INDIA CONFERENCE (INDICON 2009), 2009, : 434 - 437
[19] REALTIME FAULT-DETECTION AND CLASSIFICATION IN POWER-SYSTEMS USING MICROPROCESSORS
BARROS, J
DRAKE, JM
IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1994, 141 (04) : 315 - 322
[20] Active Power Oscillation Property Classification of Electric Power Systems Based on SVM
Liu, Ju
Yao, Wei
Wen, Jinyu
He, Haibo
Zheng, Xueyang
JOURNAL OF APPLIED MATHEMATICS, 2014,

← 1 2 3 4 5 →