A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network-Reference-Cited by-同舟云学术

A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network

Published:2017-01 Issue: Volume:95 Page:246-259
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Khokhar Suhail,Mohd Zin Abdullah Asuhaimi,Memon Aslam Pervez,Mokhtar Ahmad Safawi

Funder

Ministry of Higher Education (MOHE) Malaysia

Universiti Teknologi Malaysia (UTM) Malaysia

Higher Education Commission (HEC) of Pakistan

Quaid-e-Awam University of Engineering Science & Technology

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference44 articles.

1. Power system harmonics research: a survey;Singh;Eur. Trans. Electr. Power,2009

2. An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances;Ahila;Appl. Soft Comput.,2015

3. A hybrid time–frequency approach based fuzzy logic system for power island detection in grid connected distributed generation;Dash;Int. J. Electr. Power Energy Syst.,2012

4. MATLAB/Simulink based modeling and simulation of power quality disturbances;Khokhar,2014

5. Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines;Erişti;Int. J. Electr. Power Energy Syst.,2013

Cited by 205 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A framework for complex power quality disturbances classification and parameter estimation using convolutional neural network and square-root cubature Kalman filter in compressed sensing domain;Electric Power Systems Research;2024-11

2. Smart Internet of Things Power Meter for Industrial and Domestic Applications;Applied Sciences;2024-08-28

3. Noisy and non-stationary power quality disturbance classification based on adaptive segmentation empirical wavelet transform and support vector machine;Computers and Electrical Engineering;2024-08

4. A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid;Computers and Electrical Engineering;2024-07

5. Integrating Entropy-based Data Reduction and Machine Learning in Multidisciplinary Engineering Systems for Enhanced Response Prediction;International Journal of Precision Engineering and Manufacturing-Smart Technology;2024-07-01