Determination of the Condition of Solid Insulation in High-Power Transformers Based on 2-Furfuraldehyde and Methanol Markers Using Neural Networks

Author(s):  
Ancuta-Mihaela Aciu ◽  
Maria Cristina Nitu ◽  
Marcel Nicola ◽  
Claudiu-Ionel Nicola
2017 ◽  
Vol 80 (7) ◽  
pp. 1129-1135 ◽  
Author(s):  
Hoda Molavi ◽  
Abbas Yousefpour ◽  
Akbar Mirmostafa ◽  
Ali Sabzi ◽  
Sepideh Hamedi ◽  
...  

2014 ◽  
Vol 911 ◽  
pp. 260-265
Author(s):  
Doina Elena Gavrilă ◽  
Ilies Ciprian ◽  
Horia Catalin Gavrilă

Good operation of power transformers is very important because they are among the most expensive equipment in the network generation, transmission and electricity distribution. It became necessary regular monitoring of such systems and determinate the life of a transformer, it preventing breakdown and corresponding loss occurrence.Ageing in the insulation system of power transformers is determinate by influence of air, moisture, temperature, mechanical and electrical stresses and insulation contamination. One of the most important ageing indicators in transformers is the water content in the solid part of the insulation. The water reduces drastically the dielectric strength of solid insulation, accelerate ageing in paper and can cause water vapor bubbles.In RVM method a voltmeter determines the recovery voltage (RV) after charging the insulation with a DC voltage. By subsequent relaxation and repeated charging for varied times the so called polarization spectrum can be created. RV range provides an indication on the condition in which there is the insulation of transformer. The paper analyzes measurements on two high power transformers in operation, determining the moisture content of solid insulation.


1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.


1998 ◽  
Vol 103 (C6) ◽  
pp. 12853-12868 ◽  
Author(s):  
Carlos Mejia ◽  
Sylvie Thiria ◽  
Ngan Tran ◽  
Michel Crépon ◽  
Fouad Badran

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.


Sign in / Sign up

Export Citation Format

Share Document