A quantitative comparison of wavelet based feature vectors for classification of power quality disturbances.

被引：0
|
作者：
Dash, PK [1 ]
Lee, IWC [1 ]
Basu, KP [1 ]
Morris, S [1 ]
Sharaf, AM [1 ]
机构：
[1] Silicon Inst Technol, Bhubaneswar, Orissa, India
来源：
IECON'03: THE 29TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1 - 3, PROCEEDINGS | 2003年
关键词：
wavelet transform; S-Transform; power quality;
D O I：
10.1109/IECON.2003.1280023
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a comparison between different wavelet feature vectors for power quality disturbance classification problems. Three different wavelet algorithms are simulated and applied on nine classes of power quality disturbances. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results.
引用
收藏
页码：454 / 459
页数：6
相关论文
共 50 条
  • [41] An effective wavelet-based feature extraction method for classification of power quality disturbance signals
    Uyar, Murat
    Yildirim, Selcuk
    Gencoglu, Muhsin Tunay
    ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (10) : 1747 - 1755
  • [42] Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform
    Garousi, Maryam Rahmati
    Shakarami, Mahmoud Reza
    Namdari, Farhad
    JOURNAL OF ELECTRICAL SYSTEMS, 2016, 12 (01) : 158 - 173
  • [43] A Generalized Classification Framework for Power Quality Disturbances Based on Synchrosqueezed Wavelet Transform and Convolutional Neural Networks
    Vishwanath, Y. S. Upendra
    Esakkirajan, S.
    Keerthiveena, B.
    Pachori, Ram Bilas
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [44] Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network
    Xi, Yanhui
    Li, Xule
    Zhou, Feng
    Tang, Xin
    Li, Zewen
    Zeng, Xiangjun
    ENERGY SCIENCE & ENGINEERING, 2023, 11 (09) : 3232 - 3249
  • [45] Classification method of Power Quality Disturbances based on RVM
    Shen, Yue
    Liu, Guohai
    Liu, Hui
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 6130 - 6135
  • [46] Classification of Power Signal Disturbances Using Wavelet Based Neural Network
    Sushama, M.
    Das, G. Tulasi Ram
    Lakshmi, A. Jaya
    Chandana, K.
    2008 JOINT INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON) AND IEEE POWER INDIA CONFERENCE, VOLS 1 AND 2, 2008, : 1012 - 1016
  • [47] A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
    Khokhar, Suhail
    Zin, Abdullah Asuhaimi Mohd
    Memon, Aslam Pervez
    Mokhtar, Ahmad Safawi
    MEASUREMENT, 2017, 95 : 246 - 259
  • [48] Dual-tree complex wavelet packet transform and regularized extreme learning machine-based feature extraction and classification of power quality disturbances
    Samanta, Indu Sekhar
    Rout, Pravat Kumar
    Swain, Kunjabihari
    Cherukuri, Murthy
    Mishra, Satyasis
    ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS, 2023, 16 (1): : 369 - 394
  • [49] Feature Extraction and Classification Techniques for Power Quality Disturbances in Distributed Generation: A Review
    Singh, Nivedita
    Ansari, M. A.
    Tripathy, Manoj
    Singh, Vivek Pratap
    IETE JOURNAL OF RESEARCH, 2023, 69 (06) : 3836 - 3851
  • [50] Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
    Borges, Fabbio A. S.
    Fernandes, Ricardo A. S.
    Silva, Ivan N.
    Silva, Cintia B. S.
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2016, 12 (02) : 824 - 833
12345