Optical fiber has the great advantages of capacity and reliability. That is why network providers started to deploy FTTx (Fiber-To-The-x) optical access using various PON (Passive Optical Network) architectures. The leading technology right now is Gigabit PON (GPON). However, with increasing amount of multimedia we need to further develop existing technologies to go on with these high demands. Fiber-To-The-Home (FTTH) using 10G-PON technology for broadband access application is effective solution for high speed networks with high capacity. In this paper, we look at the passive optical network in the city of Košice and based on the real network we created simulation model of downlink of 10G-PON based FTTH with triple-play service.
Our project describes a system for the automation and monitoring of a smart house. The system consists of several sensors such as: temperature sensor, humidity sensor, light sensor and presence sensor. The data from sensors is processed and transmitted to the central module via Xbee-ZigBee or to a smartphone through the Bluetooth module. The central module connects at the Internet via WiFi and through an application we can remotely monitor the state of the house or control various devices within the enclosure.
Neural networks are used as triggers at highenergy physics detectors. These triggers can separate the event that must be saved for later analysis from the other events or noises. Using the raw data of the detector, the signal and the background can be separated offline. After separation, sets of signals and backgrounds can be used to train the neural network. A gas-filled detector (multiwire proportional chamber) was used to study the trigger at different noise levels to find the most stable neural network that tolerates the random hits. The ratio of the recognized and the unrecognized signal and background events is used for the measurement. Its stability is part of the systematical uncertainty.
Interleaved boost converters (IBCs) are cascaded in parallel in most of the applications. This novel approach connects IBC in series cascade. The IBC has an optimal operating duty cycle of 0.5. Normally, photovoltaic source voltage is low because of space constraints. In order to boost the source voltage, a conventional boost converter is replaced with series-cascaded IBC in this paper. The single-stage IBC also boosts the voltage to twice the input voltage. In the proposed converter, output voltage is about four times the input voltage with the same 0.5 duty cycle. A mathematical model is developed and simulated for the proposed work in MATLAB/Simulink platform. The output of the proposed circuit is analysed through fast Fourier transform to know the harmonic content due to the switching. The system is tested for stability with signal-flow graph modelling. The proposed work is realised using hardware and tested to validate the model.
In the perspective of current trends in engineering education, aiming at meeting industry requirements, especially in the field of power electronics and motion control, the article presents a way of teaching electric drive control in undergraduate engineering programmes using experimental setups with AC motors equipped with industrial frequency converters. The setups consist of two motors: induction and PMSM (each one can act as a motor or a load machine) and a number of other elements necessary in contemporary drive systems: speed sensors, temperature sensors and braking resistors. While using such setups students can learn about various issues related to AC motor control, both in terms of scalar and field-oriented control methods in all three drive operating modes: torque, velocity and position control. The laboratory setups allow students to familiarize themselves with such detailed issues as: vector control without a speed sensor, various ways of voltage control in a DC input circuit of the voltage inverter during motor braking or the influence of the type and value of load torque on drive system operation. Classes can have a classical form or they can be taught in the open-laboratory system.
The possibility of using neural networks to detect eccentricity of induction motors has been presented. A field-circuit model, which was used to generate a diagnostic pattern has been discussed. The formulas describing characteristic fault frequencies for static, dynamic and mixed eccentricity, occurring in the stator current spectrum, have been presented. Teaching and testing data for neural networks based on a preliminary analysis of diagnostic signals (phase currents) have been prepared. Two types of neural networks were discussed: general regression neural network (GRNN) and multilayer perceptron (MLP) neural network. This paper presents the results obtained for each type of the neural network. Developed neural detectors are characterized by high detection effectiveness of induction motor eccentricity.
Properties and control of a doubly fed induction machine operating under unbalanced grid voltage conditions have been presented. The proposed method does not include symmetrical sequences decomposition and is realized in a rotating frame not synchronized either with the grid voltage vector or with the stator flux vector. The method uses a reference torque and the reference q component of instantaneous power for calculation of the reference stator current. Next, calculation of magnetizing current for a given unbalanced grid voltage is used to assign the reference rotor current. Due to the fact that the reference current contains both a positive and a negative sequence, a proportional-integral-resonant controller is used. The main control target is the non-oscillatory waveform of torque, whereas other separate strategies like symmetrical stator current or sinusoidal rotor current can be easily obtained by adequate filtration of the reference control signals of the stator or rotor currents, respectively. The simulation results of the 2 MW model have been presented for a doubly fed induction generator as well as the results of laboratory tests with the use of a small scale 7.5 kW machine.
The article presents the results of research on the use of an axial flux in the diagnostics of induction motor stator winding fed by a frequency converter. Voltage signal waveforms proportional to the axial flux were recorded during motor operation under various conditions and were analyzed with regard to the detection of stator winding short circuits. The taps of the selected coil turns of stator phases were introduced into the tested motor, which allowed to physical modelling inter-turn short circuits. The structure and operation of the computer system used to monitor the state of induction motor windings were discussed. The developed diagnostic system was made in the National Instruments LabVIEW environment. The analysis of faults of the axial flux was made in the detection of induction motor stator winding. The results of experimental research conducted using the developed diagnostic system have also been presented.
Electromagnetic compatibility requirements for immunity tests have been presented and the emission levels of electromagnetic disturbances in adjustable speed power drive systems have been determined. The disturbances are divided into two groups: low-frequency (up to 50 harmonics of the power supply system) and high-frequency (above 150 kHz). The lack of legal regulations on permissible disturbance level in the frequency range from 2.5 kHz to 150 kHz has also been mentioned. This range is considered high frequency by the authors. Standarized compatibility levels and sample measured parameters of the electromagnetic environment have also been shown. An exemplary reaction of the drive to the most common disturbances in the power supply system has been presented.