scholarly journals Mathematical Models for the Design of Electrical Machines

2020 ◽  
Vol 25 (4) ◽  
pp. 77
Author(s):  
Frédéric Dubas ◽  
Kamel Boughrara
Keyword(s):  
Mathematical Models ◽  
Electrical Engineering ◽  
Electrical Machines ◽  
Engineering Applications

Electrical machines are used in many electrical engineering applications [...]

Virtual Instrumentation in Electrical Engineering Applications

2020 ◽  
Vol 101 (2) ◽  
pp. 193-199
Author(s):  
Gunabalan Ramachandiran ◽  
Jagadeesh Kandhasamy ◽  
Allirani Saminathan
Keyword(s):  
Electrical Engineering ◽  
Virtual Instrumentation ◽  
Engineering Applications

Operation, diagnosis and repair of electrical

2020 ◽  
Author(s):  
Vladimir Polischuk
Keyword(s):  
Undergraduate Students ◽  
Transmission Lines ◽  
Electrical Engineering ◽  
Electrical Equipment ◽  
Electrical Machines ◽  
Technical Maintenance ◽  
Maintenance Service

In the textbook fundamentals of the theory of diagnostics of electrical equipment, organization of technical maintenance, service and repair. The methods of organization of maintenance of electrical machines, transformers, transmission lines and cables. Designed for undergraduate students enrolled in the specialty "power and electrical engineering".

Trends in Electrical Engineering Education — A 25-Year Retrospective

1988 ◽  
Vol 25 (3) ◽  
pp. 209-217 ◽  
Keyword(s):  
Engineering Education ◽  
Computer Architecture ◽  
Computer Aided Design ◽  
Electrical Engineering ◽  
Electrical Machines ◽  
Project Work ◽  
Digital Electronics ◽  
Electrical Engineering Education ◽  
Aided Design

The paper considers the changes which have occurred in Electrical Engineering Education over the last twenty-five years, with reference to specific articles published during the lifetime of IJEEE. 25 years ago courses in electrical engineering included substantial coverage of mechanical engineering topics and detailed treatment of electrical machines. Today the emphasis has shifted very considerably towards digital electronics, computer architecture, programming and computer aided design (CAD). Greater attention is paid to project work and the role of the engineer in society.

Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

2021 ◽  
Vol 143 (11) ◽  
Author(s):  
Zeeshan Tariq ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Mathematical Models ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Pvt Properties ◽  
Empirical Correlations ◽  
Oil Viscosity ◽  
Engineering Applications ◽  
Wide Range ◽  
Black Oil

Abstract Pressure–volume–temperature (PVT) properties of crude oil are considered the most important properties in petroleum engineering applications as they are virtually used in every reservoir and production engineering calculation. Determination of these properties in the laboratory is the most accurate way to obtain a representative value, at the same time, it is very expensive. However, in the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the PVT properties. This study demonstrates the combined use of two machine learning (ML) technique, viz., functional network (FN) coupled with particle swarm optimization (PSO) in predicting the black oil PVT properties such as bubble point pressure (Pb), oil formation volume factor at Pb, and oil viscosity at Pb. This study also proposes new mathematical models derived from the coupled FN-PSO model to estimate these properties. The use of proposed mathematical models does not need any ML engine for the execution. A total of 760 data points collected from the different sources were preprocessed and utilized to build and train the machine learning models. The data utilized covered a wide range of values that are quite reasonable in petroleum engineering applications. The performances of the developed models were tested against the most used empirical correlations. The results showed that the proposed PVT models outperformed previous models by demonstrating an error of up to 2%. The proposed FN-PSO models were also compared with other ML techniques such as an artificial neural network, support vector regression, and adaptive neuro-fuzzy inference system, and the results showed that proposed FN-PSO models outperformed other ML techniques.

Ill-Posed Inverse Problems in Electrical Engineering Applications

2021 ◽  
pp. 235-258
Author(s):  
Andrei Ceclan ◽  
Dan D. Micu ◽  
Levente Czumbil ◽  
Horia Andrei ◽  
Marian Gaiceanu ◽  
...  
Keyword(s):  
Inverse Problems ◽  
Electrical Engineering ◽  
Engineering Applications ◽  
Ill Posed
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