Machine learning applications in power system fault diagnosis: Research advancements and perspectives
Newer generation sources and loads are posing new challenges to the conventional power
system protection schemes. Adaptive and intelligent protection methodology, based on …
system protection schemes. Adaptive and intelligent protection methodology, based on …
Recent developments in machine learning for energy systems reliability management
This article reviews recent works applying machine learning (ML) techniques in the context
of energy systems' reliability assessment and control. We showcase both the progress …
of energy systems' reliability assessment and control. We showcase both the progress …
[HTML][HTML] A survey of fault prediction and location methods in electrical energy distribution networks
One of the main factors that disrupt reliability and stop energy provision is the fault
occurrence in distribution networks. Thus, accurate and fast fault prediction and location in …
occurrence in distribution networks. Thus, accurate and fast fault prediction and location in …
Fault location in power distribution systems via deep graph convolutional networks
This paper develops a novel graph convolutional network (GCN) framework for fault location
in power distribution networks. The proposed approach integrates multiple measurements at …
in power distribution networks. The proposed approach integrates multiple measurements at …
AC microgrid protection–A review: Current and future prospective
Microgrid is an important component of the evolving smart-grid. It has the ability to increase
reliability, decrease costs, and enlarge penetration rates for distribution generation systems …
reliability, decrease costs, and enlarge penetration rates for distribution generation systems …
A deep learning based intelligent approach in detection and classification of transmission line faults
Detection and classification of transmission line (TL) faults are key factors for the fault root
cause analysis and rapid restoration of the power network. Deep learning can extract …
cause analysis and rapid restoration of the power network. Deep learning can extract …
Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification
This paper introduces a novel self-attention convolutional neural network (SAT-CNN) model
for detection and classification (FDC) of transmission line faults. The transmission lines …
for detection and classification (FDC) of transmission line faults. The transmission lines …
Fault classification in power system distribution network integrated with distributed generators using CNN
Fault detection is the critical stage of the relaying system and their successful completion in
minimum time is expected for fault clearance. With the increasing usage of distributed …
minimum time is expected for fault clearance. With the increasing usage of distributed …
Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults
Timely and accurate detection of transmission line faults is one of the most important issues
in the reliability of the power systems. In this paper, in order to assess the effects of …
in the reliability of the power systems. In this paper, in order to assess the effects of …
Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems
Quantum computing (QC) and deep learning have shown promise of supporting
transformative advances and have recently gained popularity in a wide range of areas. This …
transformative advances and have recently gained popularity in a wide range of areas. This …