Suleyman Sungur Tezcan

Boltzmann analysis of electron swarm parameters in binary CF 4 + Ar mixtures

The electron swarm parameters, namely electron mean energy, drift velocity, effective ionization coefficient and limiting values of number density reduced electric fields, E/N, are calculated in CF4+Ar mixtures for various CF4 concentrations that vary from 2 to 100% over a range of E/N from 50Td to 600Td by solving Boltzmanns equation. In this study Boltzmanns equation was solved using finite difference method under steady-state Townsend condition. One of the most important results of this study indicated that at higher E/N values inelastic processes due to argon began to control the swarm energy thus leading this binary mixture act essentially like pure argon.

Prediction of breakdown voltages in N2 + SF6 gas mixtures

This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N2 + SF6 gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N2 + SF6 have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.

Back diffusion of electrons in argon subjected to uniform time invariant orthogonal electric and magnetic fields

In this study, the processes of back diffusion in Ar subjected to crossed fields are analyzed by using the Monte Carlo simulation method in the E/N range of 50 to 500 Td (1 Td = 1 × 10–17 V cm2) for 0 < B/N < 25 × 10−19 T cm3. At a given constant E/N, escape factors decrease with an increasing crossed, reduced magnetic field B/N. This reduction in the escape factor is more pronounced in the lower E/N range. Furthermore, the mean number of collisions of back scattered electrons is quite large, and at a given E/N, the mean number of collisions decreases as the crossed B/N increases.

Assessment of electron swarm parameters and limiting electric fields in SF6+CF4+Ar gas mixtures

The electron swarm parameters, namely electron drift velocity, mean energy, effective ionization coefficient and limiting values of number-density-reduced electric fields, E/N, are calculated and assessed in SF6+CF4+Ar ternary mixtures for SF6 concentrations that vary from 1% to 20% over a range of E/N from 50Td to 600Td by solving Boltzmanns equation. The limiting electric field values for the ternary mixtures are deduced from the graphs of the calculated effective ionization coefficients as a function of E/N. Furthermore, breakdown voltage measurements are conducted for the ternary mixtures. The limiting electric field values obtained from the measured breakdown voltages are compared with those of the Boltzmann analysis and found to be in good agreement. The insulation strength of the ternary mixtures with constant Ar concentrations indicated an increase with increasing contents of SF6 while the share of CF4 in the mixture reduced in the investigated E/N range up to 600 Td with SF6 concentrations varying from 1% to 20%. Moreover, a significant synergy is observed due to the non-linear nature of the mixture, and the maximum synergy appears to be around 10% SF6 in the mixtures of this study with fixed Ar concentration. Furthermore, theoretical calculations of breakdown voltages from Boltzmann analysis are found to be in good correlation with the experimental values.

Magnetic insulation in nitrogen subjected to crossed fields

In the E/N range from 150 to 400 Td (1 Td = 10-17 Vcm2), the combination of crossed magnetic fields resulting in avalanche growth inhibition in nitrogen are evaluated by means of a Monte Carlo simulation. The simulation technique employed analysis swarm development without any a priori assumptions on electron energy distribution functions and electron collisional frequencies. For the combined crossed fields evaluated, variation of number of free electrons, average positions of the swarm and pulsed Townsend energies with respect to sampling times are reported. The pulsed Townsend energies indicate considerable reduction in the mean energies when the electron avalanche at a given E/N is inhibited upon the application of a specific magnetic field value. Effectively reduced electric fields are calculated from the magnetic deflection angles obtained from the simulation. It is observed that the calculated effectively reduced fields with the related pulsed Townsend mean energies favor the effectively reduced field concept in the combined fields.In the E/N range from 150 to 400 Td (1 Td = 10-17 Vcm2), the combination of crossed magnetic fields resulting in avalanche growth inhibition in nitrogen are evaluated by means of a Monte Carlo simulation. The simulation technique employed analysis swarm development without any a priori assumptions on electron energy distribution functions and electron collisional frequencies. For the combined crossed fields evaluated, variation of number of free electrons, average positions of the swarm and pulsed Townsend energies with respect to sampling times are reported. The pulsed Townsend energies indicate considerable reduction in the mean energies when the electron avalanche at a given E/N is inhibited upon the application of a specific magnetic field value. Effectively reduced electric fields are calculated from the magnetic deflection angles obtained from the simulation. It is observed that the calculated effectively reduced fields with the related pulsed Townsend mean energies favor the effectively reduced fie...

Economic load dispatch using vortex search algorithm

Economic load dispatch (ELD) is one of most fundamental issue for energy generation and distribution in power systems. For this purpose, different optimization techniques are developed and applied to ELD problem. In this paper, vortex search algorithm (VSA) is proposed and used for solving ELD problem. VSA method was developed from nature by observing the state of stirring liquids. Transmission losses, valve point loading effect, ramp rate limits and prohibited operating zone constraints are considered to solve ELD problem with VSA method. The feasibility and effectivity of this method is demonstrated for different cases. Obtained results are compared with different developed algorithms and these results clearly point out that proposed VSA method gives successfully outputs.

Effective ionization coefficients, limiting electric fields, and electron energy distributions in CF3I + CF4 + Ar ternary gas mixtures

This paper reports on the effective ionization coefficients, limiting electric fields, electron energy distribution functions, and mean energies in ternary mixtures of (Trifluoroiodomethane) CF3I + CF4 + Ar in the E/N range of 100–700 Td employing a two-term solution of the Boltzmann equation. In the ternary mixture, CF3I component is increased while the CF4 component is reduced accordingly and the 40% Ar component is kept constant. It is seen that the electronegativity of the mixture increases with increased CF3I content and effective ionization coefficients decrease while the limiting electric field values increase. Synergism in the mixture is also evaluated in percentage using the limiting electric field values obtained. Furthermore, it is possible to control the mean electron energy in the ternary mixture by changing the content of CF3I component.

Calculation of breakdown voltages in Ar+SF/sub 6/ using an artificial neural network

An artificial neural network is proposed to predict the breakdown voltages in Ar+SF/sub 6/ gas mixtures. The proposed neural network is designed with one hidden layer that includes twenty-five neurons. The output layer of the ANN consists of one neuron, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available for Ar+SF/sub 6/ have been used. The results of this ANN are compared with the experimental data as well as calculated data using the streamer criterion. With the proposed ANN, the average relative errors on breakdown voltages are found to be 3.85% for training and 4.32% for testing. Since the average errors are less than 5%, it is recommended to use ANN to predict the breakdown voltages.