[1]
|
Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications
Green Energy and Technology,
2024
DOI:10.1007/978-3-031-37909-3_11
|
|
|
[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]
|
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
|
|
|
[4]
|
Fault detection through discrete wavelet transform in overhead power transmission lines
Energy Science & Engineering,
2023
DOI:10.1002/ese3.1573
|
|
|
[5]
|
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
|
|
|
[6]
|
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
|
|
|
[7]
|
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
|
|
|
[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]
|
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
|
|
|
[10]
|
Fault Classification with Convolutional Neural Networks for Microgrid Systems
International Transactions on Electrical Energy Systems,
2022
DOI:10.1155/2022/8431450
|
|
|
[11]
|
Learning approach based DC arc fault location classification in DC microgrids
Electric Power Systems Research,
2022
DOI:10.1016/j.epsr.2022.107874
|
|
|
[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]
|
Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features
Electric Power Components and Systems,
2022
DOI:10.1080/15325008.2022.2135647
|
|
|
[14]
|
Transmission Line Fault Localization With Mesh and Surface Analysis Using PCA Features
Electric Power Components and Systems,
2022
DOI:10.1080/15325008.2022.2135647
|
|
|
[15]
|
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
|
|
|
[16]
|
Learning approach based DC arc fault location classification in DC microgrids
Electric Power Systems Research,
2022
DOI:10.1016/j.epsr.2022.107874
|
|
|
[17]
|
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
|
|
|
[18]
|
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
|
|
|
[19]
|
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
|
|
|
[20]
|
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
|
|
|
[21]
|
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
|
|
|
[22]
|
Robot Intelligence Technology and Applications 5
Advances in Intelligent Systems and Computing,
2019
DOI:10.1007/978-3-319-78452-6_13
|
|
|
[23]
|
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
|
|
|
[24]
|
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
|
|
|
[25]
|
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
|
|
|
[26]
|
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
|
|
|
[27]
|
Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning
IEEE Transactions on Industrial Electronics,
2018
DOI:10.1109/TIE.2017.2726961
|
|
|
[28]
|
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
|
|
|
[29]
|
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
|
|
|
[30]
|
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
|
|
|
[31]
|
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
|
|
|