Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

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Energy and Power Engineering

ISSN Print: 1949-243X
ISSN Online: 1947-3818
www.scirp.org/journal/epe
E-mail: epe@scirp.org

Google-based Impact Factor: 0.66
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"Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis"
written by Qais H. Alsafasfeh, Ikhlas Abdel-Qader, Ahmad M. Harb,
published by Energy and Power Engineering, Vol.4 No.6, 2012

has been cited by the following article(s):

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CrossRef

[1]	1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
	arXiv preprint arXiv …, 2022

[2]	Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features
	Electric Power Components and …, 2022

[3]	Fault Classification with Convolutional Neural Networks for Microgrid Systems
	… Transactions on Electrical …, 2022

[4]	Learning approach based DC arc fault location classification in DC microgrids
	Electric Power Systems Research, 2022

[5]	Classification of faults in an IEEE 30 bus transmission system using fully convolutional network
	… Transactions on Electrical …, 2021

[6]	Оценка коррелированности графиков электрической нагрузки по главным компонентам ортогонального разложения
	Пром-Инжиниринг, 2021

[7]	Transmission line faults in power system and the different algorithms for identification, classification and localization: a brief review of methods
	2021

[8]	A Real-time Fault Localization in Power Distribution Grid for Wildfire Detection Through Deep Convolutional Neural Networks
	2021

[9]	Machine learning applications in power system fault diagnosis: Research advancements and perspectives
	Engineering Applications of …, 2021

[10]	Classification of Faults in Microgrids Using Deep Learning
	2020

[11]	Power system fault identification and localization using multiple linear regression of principal component distance indices
	2020

[12]	Power System Fault Detection, Classification And Clearance By Artificial Neural Network Controller
	2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019

[13]	A Novel Ensemble Approach to Multi-label Classification for Electric Power Fault Diagnosis
	2019

[14]	Fault location in HTB Lines using Digital Relays Localisation des Défauts dans les Lignes HTB en Utilisant les Relais Numériques
	2019

[15]	High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers
	Neural Computing and Applications, 2019

[16]	Advanced techniques for fault detection and classification in electrical power transmission systems: An overview
	2019

[17]	Voltage & Current Magnitude Pattern Recognization by Using Fuzzy Logic Toolbox for Fault Types Classification
	2018

[18]	Fault Types Classification Accuracy Considering Four Different Methods
	2018

[19]	Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning
	2018

[20]	Detecting and classifying transmission line faults by using artificial neural network
	2018

[21]	Determination of Source Fault Using Fast Acting Automatic Transfer Switch
	2018

[22]	SVM-Based Fault Type Classification Method for Navigation of Formation Control Systems
	Robot Intelligence Technology and Applications 5, 2017

[23]	An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices
	International Transactions on Electrical Energy Systems, 2017

[24]	Fault Detection and Classification based on Co-Training of Semi-Supervised Machine Learning
	2017

[25]	An Algorithm for Power System Fault Analysis based on Convolutional Deep Learning Neural Networks
	International Journal of All Research Education and Scientific Methods (IJARESM), 2017

[26]	Maximal electrical load modeling and forecasting for the tajikistan power system based on principal component analysis
	2017

[27]	Анализ взаимосвязей главных компонент ортогонального разложения с формой суточного графика электрической нагрузки
	2016

[28]	Анализ режимов работы и повышение устойчивости энергосистемы Республики Таджикистан с учетом увеличения нагрузок в её Северной части …
	2016

[29]	Анализ режимов работы и повышение устойчивости энергосистемы Республики Таджикистан с учётом увеличения нагрузок в её северной части
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[30]	TECHNICAL SCIENCES SERIES
	2015

[31]	Real-Time Implementation and Evaluation of a Support Vector Machine Based Fault Detector and Classifier for Distribution Grids
	2015

[32]	Tagungsband zum power and energy student summit 2015
	2015

[33]	Identification and Classification of Power System Faults using Ratio Analysis of Principal Component Distances
	TELKOMNIKA Indonesian Journal of Electrical Engineering, 2014

[34]	Condition monitoring of transmission lines in real time situation
	Electrical, Electronics and System Engineering (ICEESE), 2014 International Conference on, 2014

[1]	Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults Frontiers in Energy Research, 2023 DOI:10.3389/fenrg.2023.1101049

[2]	A new robust approach for fault location in transmission lines using single channel independent component analysis Electric Power Systems Research, 2023 DOI:10.1016/j.epsr.2023.109281

[3]	Intelligent Short-Circuit Protection with Solid-State Circuit Breakers for Low-Voltage DC Microgrids IETE Journal of Research, 2023 DOI:10.1080/03772063.2023.2181228

[4]	A new robust approach for fault location in transmission lines using single channel independent component analysis Electric Power Systems Research, 2023 DOI:10.1016/j.epsr.2023.109281

[5]	Learning approach based DC arc fault location classification in DC microgrids Electric Power Systems Research, 2022 DOI:10.1016/j.epsr.2022.107874

[6]	1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems 2022 IEEE 1st Industrial Electronics Society Annual On-Line Conference (ONCON), 2022 DOI:10.1109/ONCON56984.2022.10126859

[7]	Fault Classification with Convolutional Neural Networks for Microgrid Systems International Transactions on Electrical Energy Systems, 2022 DOI:10.1155/2022/8431450

[8]	Learning approach based DC arc fault location classification in DC microgrids Electric Power Systems Research, 2022 DOI:10.1016/j.epsr.2022.107874

[9]	A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network Measurement, 2022 DOI:10.1016/j.measurement.2021.110333

[10]	Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features Electric Power Components and Systems, 2022 DOI:10.1080/15325008.2022.2135647

[11]	Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features Electric Power Components and Systems, 2022 DOI:10.1080/15325008.2022.2135647

[12]	A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network Measurement, 2022 DOI:10.1016/j.measurement.2021.110333

[13]	Learning approach based DC arc fault location classification in DC microgrids Electric Power Systems Research, 2022 DOI:10.1016/j.epsr.2022.107874

[14]	Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods Journal of The Institution of Engineers (India): Series B, 2021 DOI:10.1007/s40031-020-00530-0

[15]	Classification of faults in an IEEE 30 bus transmission system using fully convolutional network International Transactions on Electrical Energy Systems, 2021 DOI:10.1002/2050-7038.13134

[16]	Machine learning applications in power system fault diagnosis: Research advancements and perspectives Engineering Applications of Artificial Intelligence, 2021 DOI:10.1016/j.engappai.2021.104504

[17]	A Real-Time Fault Localization in Power Distribution Grid for Wildfire Detection Through Deep Convolutional Neural Networks IEEE Transactions on Industry Applications, 2021 DOI:10.1109/TIA.2021.3083645

[18]	Machine learning applications in power system fault diagnosis: Research advancements and perspectives Engineering Applications of Artificial Intelligence, 2021 DOI:10.1016/j.engappai.2021.104504

[19]	Robot Intelligence Technology and Applications 5 Advances in Intelligent Systems and Computing, 2019 DOI:10.1007/978-3-319-78452-6_13

[20]	Advanced techniques for fault detection and classification in electrical power transmission systems: An overview 2019 8th International Conference on Modern Power Systems (MPS), 2019 DOI:10.1109/MPS.2019.8759695

[21]	High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers Neural Computing and Applications, 2019 DOI:10.1007/s00521-019-04445-w

[22]	A Novel Ensemble Approach to Multi-label Classification for Electric Power Fault Diagnosis 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 2019 DOI:10.1109/ICCSNT47585.2019.8962410

[23]	Power System Fault Detection, Classification And Clearance By Artificial Neural Network Controller 2019 Global Conference for Advancement in Technology (GCAT), 2019 DOI:10.1109/GCAT47503.2019.8978400

[24]	Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning IEEE Transactions on Industrial Electronics, 2018 DOI:10.1109/TIE.2017.2726961

[25]	Determination of Source Fault Using Fast Acting Automatic Transfer Switch 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics), 2018 DOI:10.1109/Dynamics.2018.8601484

[26]	Maximal electrical load modeling and forecasting for the tajikistan power system based on principal component analysis 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 2017 DOI:10.1109/ICIEAM.2017.8076259

[27]	An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices International Transactions on Electrical Energy Systems, 2017 DOI:10.1002/etep.2344

[28]	Condition monitoring of transmission lines in real time situation 2014 2nd International Conference on Electrical, Electronics and System Engineering (ICEESE), 2014 DOI:10.1109/ICEESE.2014.7154570

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