Uddin et al., 2022 - Google Patents
On the protection of power system: Transmission line fault analysis based on an optimal machine learning approachUddin et al., 2022
View HTML- Document ID
- 6492429721373841433
- Author
- Uddin M
- Hossain M
- Fahim S
- Sarker S
- Bhuiyan E
- Muyeen S
- Das S
- Publication year
- Publication venue
- Energy Reports
External Links
Snippet
Transmission lines (TLs) of power networks are often encountered with a number of faults. To continue normal operation and reduce the damage due to the TL faults, it is a must to identify and classify faults as early as possible. In this paper, the design and development of …
- 230000005540 biological transmission 0 title abstract description 46
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shakiba et al. | Application of machine learning methods in fault detection and classification of power transmission lines: a survey | |
Ray et al. | Support vector machine based fault classification and location of a long transmission line | |
Li et al. | Real-time faulted line localization and PMU placement in power systems through convolutional neural networks | |
Ozcanli et al. | Islanding detection in microgrid using deep learning based on 1D CNN and CNN-LSTM networks | |
James et al. | Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks | |
Uddin et al. | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach | |
Zhang et al. | Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network | |
Zhang et al. | Novel fault location method for power systems based on attention mechanism and double structure GRU neural network | |
Gao et al. | Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model | |
Topaloglu | Deep learning based a new approach for power quality disturbances classification in power transmission system | |
Rizeakos et al. | Deep learning-based application for fault location identification and type classification in active distribution grids | |
Haq et al. | Improved performance of detection and classification of 3-phase transmission line faults based on discrete wavelet transform and double-channel extreme learning machine | |
Dash et al. | Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm | |
Liu et al. | Dual-channel convolutional network-based fault cause identification for active distribution system using realistic waveform measurements | |
Gilanifar et al. | Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning | |
MansourLakouraj et al. | A multi-rate sampling PMU-based event classification in active distribution grids with spectral graph neural network | |
Sung et al. | TL–LED arc net: Transfer learning method for low-energy series dc arc-fault detection in photovoltaic systems | |
Han et al. | Faulted-Phase classification for transmission lines using gradient similarity visualization and cross-domain adaption-based convolutional neural network | |
Kurup et al. | Ensemble models for circuit topology estimation, fault detection and classification in distribution systems | |
Zhang et al. | Detection of single-phase-to-ground faults in distribution networks based on Gramian Angular Field and Improved Convolutional Neural Networks | |
Ilius et al. | A Machine Learning–Based Approach for Fault Detection in Power Systems | |
Sahoo et al. | Online fault detection and classification of 3-phase long transmission line using machine learning model | |
Wang et al. | An automatic identification framework for complex power quality disturbances based on ensemble CNN | |
de Alencar et al. | A fault recognition method for transmission systems based on independent component analysis and convolutional neural networks | |
Xi et al. | Fault detection and classification on insulated overhead conductors based on MCNN‐LSTM |