Detection and Classification of Single Phase to Ground Faults Under High Resistance Ground Paths in Power Systems using Machine Learning

2021 ◽  
Author(s):  
A. Nakho ◽  
Y. Hamam
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
High Resistance ◽  
Single Phase ◽  
Ground Faults

scholarly journals Computer simulation of overvoltages in 6–10 kV distribution networks for single-phase ground faults

2020 ◽  
pp. 34-41
Author(s):  
K. S. Kurochka ◽  
V. I. Tokochakov ◽  
E. A. Karabchikova ◽  
K. A. Panaryn
Keyword(s):  
Power Systems ◽  
Single Phase ◽  
Software Tool ◽  
Simulation Models ◽  
Distribution Networks ◽  
Electric Power Systems ◽  
Automated Generation ◽  
Emergency Situations ◽  
Additional Qualification ◽  
Ground Faults

Nowadays, when designing and operating electric power systems, special attention is paid to predicting emergency situations and minimizing losses in case they occur. The most common method of overvoltage investigations is the use of simulation models in specialized application packages, which imposes additional qualification requirements on the electrical engineer.The authors propose modeling technologies and a software tool for modeling and research of overvoltages that occur in 6–10 kV distribution networks at single-phase ground faults, including the method of visual design of the distribution network through the use of information technology and automated generation of mathematical model for its further study to analyze overvoltages.

Structure prediction of multi-principal element alloys using ensemble learning

2019 ◽  
Vol 37 (3) ◽  
pp. 1003-1022 ◽  
Author(s):  
Amitava Choudhury ◽  
Tanmay Konnur ◽  
P.P. Chattopadhyay ◽  
Snehanshu Pal
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Solid Solution ◽  
Structure Prediction ◽  
Single Phase ◽  
Structural Features ◽  
Valence Electron Concentration ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases. Design/methodology/approach In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm. Findings The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set. Originality/value In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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