Majlesi Journal of Electrical Engineering
Volume:5 Issue: 3, Sep 2011

In this paper, a new method is proposed to allocate transmission loss in pool-based electricity markets. This method is based on using the impedance matrix of the network and the admittance equivalent circuit seen from the network buses. After performing load flow equations, the losses of each bus are calculated using the impedance matrix of the network and the reduced admittance matrix and the injected currents from each bus. These losses are properly and fairly shared between network buses for fair loss allocation in proportion to the percent of penetration the currents of each bus. Furthermore, using partial derivatives of the active power losses with respect to the bus currents coefficients, a sensitivity analysis has been done for proving the fairness of the proposed method. In addition to its simplicity, the suggested method assigns the losses properly and fairly between the buses. Finally, this method has been tested on a benchmark IEEE 14-bus network and the results are compared with other existing methods.

In this paper, we present a modified sum-of-sinusoids (SOS) based simulator for a two-dimensional (2-D) non-isotropic scattering channel. With a new parameter computation method called equal probability area (MEPA), the proposed model can be applied on arbitrary 2-D scattering environments and also can be generalized to multi-path channels with respect to the principle of set partitioning. Simulation results verify that the first and second order statistics of the output channels approximate the reference model with a high precision and when the theoretical results are unknown, it can be used as a reference for unusual distributions.

Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching over-voltages. The most effective method for the limitation of the switching over-voltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.‎ We introduce a harmonic index that it’s minimum value is corresponding to the best case switching time.‎ Also, this paper ‎presents an Artificial Neural Network (ANN)-based approach to ‎estimate the optimum switching instants for real time applications. In the proposed ANN, Levenberg–Marquardt ‎second order method is used to train the multilayer perceptron. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.

The increased use of nonlinear devices in the industry has resulted in the direct increase of harmonic distortion in power systems during these last years. Active filter systems are proposed to mitigate current harmonics generated by nonlinear loads. The conventional scheme based on two-level voltage source inverter controlled by a hysteresis controller has several disadvantages and cannot be used for medium or high power applications. To overcome these drawbacks and improve the APF performance there’s a great tendency to use multilevel inverters controlled by intelligent controllers. Three level (NPC) inverter is one of the most widely used topologies in various industrial applications such as machine drives and power factor compensators. On the other hand, artificial neural networks are under study and investigation in other power electronics applications. In order to gain the advantages of the three-level inverter and artificial neural networks and to reduce the complexity of classical control schemes, a new active power filter configuration controlled by two MLPNN (Multi-Layer Perceptron Neural Network) is proposed in this paper. The first ANN is used to replace the PWM current controller, and the second one to maintain a constant dc link voltage across the capacitors and compensate the inverter power losses. The performance of the global system including power and control circuits is evaluated by Matlab-Simulink and SimPowerSystem Toolbox simulation. The obtained results confirm the effectiveness of the proposed control scheme.

One of the most important problems in heart signal processing is the extraction of fetal electrocardiogram (FECG). One of the reasons that we are interested in FECG extraction is that this signal consists of important characteristics about healthy conditions of fetus. Based on available conditions, Blind Source Separation is a suitable method for this problem. Existence of noise in observed signals from electrodes on the mother's body, can affect the separation performance. Therefore signal de-noising is an important stage in this problem. In this study, using wavelet transform and optimum selection of its parameters in FECG extraction has been investigated. The first reason for using wavelet transform is to remove noise from the observed signals and the second reason is to apply it into BBS algorithms. Depending to the noise level in signals, wavelet transform can be used before or after signal separation, also it can be used both before and after signal separation. Simulation results show the performance of each method in different conditions for obtaining the desired signal at the presence of noise.

One of the fundamental structures of inverters with soft switching is the use of quasi-resonant DC link (QRDCL) circuits, in which element switching occurs in the zero voltage (ZVS) and / or zero current (ZCS) conditions and also the use of pulse width modulation (PWM). One of the indices of distinction and superiority of these converters is losses in the QRDCL circuit, less losses, and the increase of converter’s frequency. This paper aims to calculate losses of the QRDCL and the design control circuits of improved QRDCL voltage inverter. In this regard, at first, the benefits of soft switching and general characteristics of various types of topologies used in inverters with soft switching is studied and then, the desired improved inverter with the capability of EDPWM (Enhanced Double PWM) which uses the single-phase soft switching technique (SPSS) is introduced. For proper functioning of the circuit, a particular type of the sinusoidal pulse width modulation (SPWM) is used, which its characteristics is studied and then the desired control circuit inverter is proposed. Finally, the full simulation of the inverter is conducted and the obtained results of analyzing the functionality of the circuit and mathematical equations governing the circuit are compared with the results come from computer simulation.

A brain computer interface (BCI) records the activities of the brain and classifies it into different classes. BCIs can be used by both severely motor disabled as well as healthy people to control devices. In this work we have concentrated on the development and application of a novel medical technology to measure the patient’s brain activity, translated it with intelligent software, and used the translated signals to drive patient-specific effectors. In this work, we deal with the EEG pattern recognition approach based on brain computer interfaces. Electroencephalographic (EEG) signals produced by the brain are used as input to our BCI system. Both offline and online BCI approaches are introduced where the offline approach was done using Dataset IA motor imagery EEG recordings and the online approach was done using our own BCI system. We have described our BCI system and its efficiency for moving the hands to right or left online. First, the measurement of the EEG and the components of a BCI system are explained. Second, the data acquisition system we developed is described in detail. Lastly, our BCI system, including all different techniques used for artifact removal, feature extraction, and classification is presented. Our results give an ideal solution for people with severe neuromuscular disorders, such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury, people who are totally paralyzed, or “locked-in”, helping them to have a communication channel with others.

In this paper, voltage sag is compensated by the DVR (Dynamic Voltage Restorer) in distribution systems. This device is applied between the sensitive load and the supply in order to inject voltage in series to correct the voltage sag. Subsequently, all the other various DVR compensation techniques in the distribution system are explained. Due to the restriction of the energy storage in DVR’s capacitors, it is essential to minimize the active power injected by the DVR. Thus, a minimum active power injection method is proposed to compensate the voltage sag. Performance of this method is evaluated under balanced and unbalance voltage sag in a distribution system.

In this paper, we propose a wavelet based watermarking system. The system use wavelet transform for red, green and blues channel independently. We use space-time coding for encoding the watermark message before data embedding. The bit-error-rate of recovered message is calculated. The embedding factor is selected in such a way that the host video maintains the same peak signal to noise ration with/without using space-time coding. The developed system is further examined, when host video faces compression and noise addition. Result shows the effectiveness of proposed watermarking system, especially when space-time coding is used.