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 …
Application of machine learning methods in fault detection and classification of power transmission lines: a survey
The rising development of power systems and smart grids calls for advanced fault diagnosis
techniques to prevent undesired interruptions and expenses. One of the most important part …
techniques to prevent undesired interruptions and expenses. One of the most important part …
A KNN based random subspace ensemble classifier for detection and discrimination of high impedance fault in PV integrated power network
This paper proposes an ensemble Random Subspace (RS) classifier for discrimination of
High Impedance Fault (HIF) in photovoltaic connected power network. The design and …
High Impedance Fault (HIF) in photovoltaic connected power network. The design and …
Transmission line faults in power system and the different algorithms for identification, classification and localization: a brief review of methods
Transmission lines are one of the most widely distributed engineering systems meant for
transmitting bulk amount of power from one corner of a country to the farthest most in the …
transmitting bulk amount of power from one corner of a country to the farthest most in the …
Ensemble bagged tree based classification for reducing non-technical losses in multan electric power company of Pakistan
Non-technical losses (NTLs) have been a major concern for power distribution companies
(PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal …
(PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal …
Sensor data-driven bearing fault diagnosis based on deep convolutional neural networks and S-transform
G Li, C Deng, J Wu, X Xu, X Shao, Y Wang - Sensors, 2019 - mdpi.com
Accurate and timely bearing fault diagnosis is crucial to decrease the probability of
unexpected failures of rotating machinery and improve the efficiency of its scheduled …
unexpected failures of rotating machinery and improve the efficiency of its scheduled …
Automated brain disease classification using exemplar deep features
Automated brain disease classification is one of the complex and widespread issues for
machine learning and biomedical engineering. Various models and papers have been …
machine learning and biomedical engineering. Various models and papers have been …
Transformation of smart grid using machine learning
With the advent of distributed and renewable energy sources, maintaining the stability of
power grid is becoming increasingly difficult. Traditional power grid can be transformed into …
power grid is becoming increasingly difficult. Traditional power grid can be transformed into …
Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals
This work aimed to use machine learning to diagnose four heart valve disease conditions
and normal heart sounds. This paper proposed the automated classification of normal, aortic …
and normal heart sounds. This paper proposed the automated classification of normal, aortic …
[PDF][PDF] Novel power transformer fault diagnosis using optimized machine learning methods
Power transformer is one of the more important components of electrical power systems. The
early detection of transformer faults increases the power system reliability. Dissolved gas …
early detection of transformer faults increases the power system reliability. Dissolved gas …