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Dr. Hesham Said Abd Elmonsif Ali :: Theses :

Title Applications of Artificial Neural Network to Analyze Transient Overvoltages on Electrical Power Systems
Type MSc
Supervisors Sayed A. Ward, Reda E. Morsi, Mahmoud N. Ali
Year 2015
Abstract The insertion of the power transformer or the capacitor bank to the electrical power system causes transient overvoltages on the system buses. These overvoltages pose a threat on the system equipment insulations. The protective devices trip the equipment having overvoltages more than the predetermined setting of these protective devices. So, the reliability and the continuity of the electrical transmission and distribution systems are adversely affected. In order to mitigate the transformer restoration overvoltages, switching control of the switched circuit breaker poles is hired. Harmonic index method is an effective method for circuit breaker poles switching control. The software package (MATLAB/SIMULINK) is used in this thesis as a simulation tool to estimate and mitigate the transformer restoration overvoltages. The simulated results obtained from the simulation tool are used to train two artificial neural networks. One of them is used to predict the transformer restoration overvoltages and the other is used to slate the best switching angle of the circuit breaker to mitigate these overvoltages. These artificial neural networks are trained and tested to apply them on the New Cairo substation transformer (220/66/22kV-125MVA). The obtained results from the software package and the trained artificial neural networks are presented and compared with the field measured data obtained from the ministry of electricity. On the other hand, the capacitor bank energization overvoltages are simulated using MATLAB/SIMULINK. The usage of circuit breaker with preinserted resistor or the zero voltage crossing switching control of the switched circuit breaker is used to reduce the transient overvoltages due to the capacitor bank insertion process. Artificial neural network is trained by the obtained results from the software package to predict the capacitor bank energization overvoltages. This artificial neural network is trained, tested and applied on the industrial Obour substation capacitor bank (22kV-5.4MVAR). The obtained results from the simulation tool and the trained artificial neural network are presented and compared with the field measured data obtained from the ministry of electricity.
Keywords Artificial Neural Network, Transient Overvoltages
University Benha University
Country Egypt
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Title Enhancement of Thermal and Dielectric Characteristics for Transformer Oil Using Nanoparticles
Type PhD
Supervisors Sayed A. Ward, Adel Z. El Dein, Diaa-Eldin A. Mansour, Essam A. Shaalan
Year 2019
Abstract In order to expand and upgrade the electrical power grid, the performance improvement of power transformers is indispensable. Oil-filled transformers are one of the popular types of power transformers. Transformer oil (Base insulating oil) provides both thermal cooling and electrical insulation functions. So, improving its thermal and dielectric properties affects positively the whole performance of power transformer. Recently, nanotechnology was used as an effective science in the field of transformer oil development to enhance its thermal and dielectric properties under the name of nanofluids. In this thesis, barium titanate nanoparticles were inserted into the base insulating oil by a concentration of 0.005, 0.01, and 0.02 gram per liter as individual nanofluid samples. Some measurements were done for these samples. These measurements are heat transfer coefficient, breakdown voltage, relative permittivity, conductivity, and dielectric dissipation factor. The dispersion of barium titanate nanoparticles into the base insulating oil enhances the thermal properties by more than 20% for all individual nanofluid samples but some dielectric properties were negatively affected by this dispersion. To overcome this problem of dielectric properties degradation, other three hybrid nanofluid samples were prepared using three different types of metal oxide nanoparticles; titania, alumina and silica. These samples were prepared by adding a concentration 0.01 gram per liter of each metal oxide nanoparticles together with 0.005 gram per liter of barium titanate nanoparticles into the base insulating oil. Same measurements were done for these hybrid samples as same as individual samples. The thermal and dielectric properties of hybrid nanofluid samples were examined to study the behavior of nanoparticles hybridization on transformer oil properties. Due to the different surface charging of barium titanate and titania nanoparticles, the best enhancement of breakdown voltage and heat transfer coefficient was achieved. Dynamic light scattering technique was used to evaluate the particle size distribution of nanoparticles into the hybrid samples and to clarify the corresponding physical interpretation behind the obtained enhancement. On the other hand, thermal model of 1 MVA, 50 Hz, 22/0.4 kV stepping down immersed oil distribution transformer was introduced using COMSOL Multiphysics Software. The COMSOL Multiphysics Software is commercial software that resolves the heat transfer in fluids based on the finite element method. The liquid materials that used for the proposed model were the base insulating oil and the best prepared nanofluids sample. Internal heat energy, temperature gradient, and temperature distribution were evaluated using COMSOL Multiphysics Software. The obtained results introduced that; the thermal stress on the base insulating oil inside transformer can be reduced by about 10 oC due to the presence of the hybridization of barium titanate and titania nanoparticles. Based on these results, the filling of distribution transformer by HNFS/TNP oil instead of base insulating oil doubles the lifetime of the oil in the service and improves the performance of the transformer.
Keywords Transformer Oil, Nanoparticles, Dielectric Properties, Thermal Properties
University Benha University
Country Egypt
Full Paper -

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