Recent Advances in Electrical & Electronic Engineering - Volume 12, Issue 4, 2019

Background: This paper evaluates the impact of implementing dynamic line rating on the reliability indices of an electric power system. Methods: In order to obtain this goal, the implementation process of a dynamic rating model that considers the required variables for its functioning is built. A validation of the model is performed based on the measurements of an ASCR conductor in laboratory. Results: Subsequently, a reliability analysis that includes the use of the dynamic rating is structured and the impact on the reliability indices of dynamic rating within the electrical system is verified. Conclusion: In this way, it was possible to observe that considering dynamic rating on the transmission lines represents a positive impact on the reliability indices within the electric power system and allows the optimization of the use of existing infrastructure.

Background: The symbolic nodal analysis acts as a pivotal part of the very large scale integration (VLSI) design. Methods: In this work, based on the terminal relations for the pathological elements and the voltage differencing inverting buffered amplifier (VDIBA), twelve alternative pathological models for the VDIBA are presented. Moreover, the proposed models are applied to the VDIBA-based second-order filter and oscillator so as to simplify the circuit analysis. Results: The result shows that the behavioral models for the VDIBA are systematic, effective and powerful in the symbolic nodal circuit analysis.

Background: The aim of this paper is to investigate data fusion techniques based on an Extended Kalman Filter (EKF), and more specifically, the nonlinear dynamic estimation of a wheelchair navigation system. Methods: Three data fusion techniques are presented and a comparison between them is studied. It combines the noisy measurement data coming from several sensors to obtain the best estimate of position while reducing the measurement uncertainties. Results: By using the MATLAB, the performance of these techniques is checked with simulated data and performance metrics are calculated for evaluation of the algorithms. Detailed mathematical expressions are provided which could be useful for algorithm implementation. Conclusion: The results show that the algorithm based on a measurement fusion technique gives a good estimate when compared with another one.

Background: Advances in video compression technology have been driven by everincreasing processing power available in software and hardware. Methods: The emerging High-Efficiency Video Coding (HEVC) standard aims to provide a doubling in coding efficiency with respect to the H.264/AVC high profile, delivering the same video quality at half the bit rate. Results: Thus, the results show high computational complexity. In both standards, the motion estimation block presents a significant challenge in clock latency since it consumes more than 40% of the total encoding time. For these reasons, we proposed an optimized implementation of this algorithm on a low-cost NVIDIA GPU developed with CUDA language. Conclusion: This optimized implementation can provide high-performance video encoder where the speed reaches about 85.

Background: The involvement in action video gaming alters the cognitive abilities and hence affects the neural functionality. Electroencephalogram (EEG) favorably provides the measure. Wavelet coherence, which is a wavelet transform based feature that provides useful information regarding synchronized activity between two signals. It does not depend on the stationarity of the signal and hence very much relevant for non-stationary EEG application. Methods: We aimed to examine how the task-related synchronization pattern of action video game players (AVGPs) differs from non-AVGPs. EEG data were collected from thirty-five young and healthy male participants while performing an attention inhibition task and a visuospatial short-term memory-retention task. The sub-frequency components, theta, alpha, beta and gamma bands of EEG were extracted using Discrete wavelet transform (DWT). The intra and inter-hemispheric coherence in EEG sub-frequency bands were assessed as a feature for the analysis. Results: Theta, alpha, beta and gamma coherence has shown a significant difference (p<0.05) between AVGPs and non-AVGPs in both the visuo-spatial tasks in intra and inter-hemispheric functionality. More than 90% classification accuracies are achieved with ANFIS algorithm. Results also indicate that frontoparietal connectivity is significantly improved in AVGPs in both the visual sensory tasks considered. Conclusion: These EEG based analysis reports enhanced neural communication with improved attention inhibition and short-term memory retention in AVGPs. Result also established the Wavelet coherence as an effective tool in understanding the neural communication among different brain locations.

Background: This document presents a new approach to estimate speed and rotor position of the salient-pole Permament Magnet Synchronous Motor (PMSM). Methods: To eliminate the mechanical sensors, we apply a sensorless field oriented control (FOC) by using an improved full-order sliding mode observer (FO-SMO) at different speeds (low and high speeds). The stability of the Slide Mode Observer (SMO) was demonstrated with the Lyapunov function. Results: The simulation results prove good properties of the proposed estimation method. Conclusion: The sensorless field oriented control strategy of the salient-pole PMSM has been proposed in this paper using an improved full-order SMO.

Background: A synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed in this paper via the joint classification of the target region and shadow. Methods: The elliptical Fourier descriptors (EFDs) are used to describe the target region and shadow extracted from the original SAR image. In addition, the relative positions between the target region and shadow are represented by a constructed feature vector. The three feature vectors complement each other to provide more comprehensive descriptions of the target’s physical properties, e.g., sizes and shape. In the classification stage, the three feature vectors are jointly classified based on the joint sparse representation (JSR). JSR is a multi-task learning algorithm, which can not only represent each component properly but also exploit the inner correlations of different components. Finally, the target type is determined to the class with the minimum reconstruction error. Results: Experiments have been conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method achieves a high recognition accuracy of 96.86% for 10-class recognition problem under the standard operating condition (SOC). Moreover, robustness of the proposed method is also superior over the reference methods under the extended operating conditions (EOCs) like configuration variance, depression angle variance, and noise corruption. Conclusion: Therefore, the effectiveness and robustness of the proposed method can be quantitatively demonstrated by the experimental results.

Background: Using the solar energy by photovoltaic arrays is constantly increasing and has been considered as one of the cleanest sources of energy in recent years. One of the ways to reduce the cost of photovoltaic systems is to maximize the power delivered to the load. On the other hand, changing the load leads to change the operating point of the solar conversion system and causes deviation from the maximum power point (MPP). Methods: For this reason, in various research studies, attention has been paid to MPPT methods applicable in photovoltaic systems. In this paper, a comparison is performed between conventional MPPT methods including Perturb and Observe (P), Incremental Conductance (INC), Fractional Open Circuit Voltage (FOCV), Ripple Correlation Control (RCC) and Extremum Seeking Control (ESC). Only current and voltage parameters of the PV panel are measured instantly and used to generate control signals. However, the output voltage of the PV cells is relatively low without using the DC-DC converters. Results: Therefore, high-performance DC-DC converters need to convert the low voltage PV arrays into high DC voltages to handle the 220 VAC systems. Conclusion: Accordingly, in this study, conventional DC-DC converters including Boost, Buck and Buck-Boost converters are investigated, and each of them is simulated using different MPPT controllers and the results are compared together. It is worth noting that all of the simulations are carried out using MATLAB/Simulink.

Background: Modern communication devices are very much dependent on the operation of low profile antennas. The objective of this paper is to perform the design and simulation of a rectangular microstrip patch antenna at a resonant frequency of 9.5 GHz. Methods: Design of the antenna is given with various substrates like FR4, Teflon and Ceramic substrates at the desired frequency. For each substrate, the performance of the antenna is measured in terms of its return loss and Voltage Standing Wave Ratio (VSWR). Results: Ansoft High-Frequency Structure Simulator is used to simulate the antenna characteristics. Conclusion: Performance characteristics of the antenna with three different substrates are compared to identify the substrate that provides the accurate return loss and VSWR.

Background: According to statistics, short circuit faults are the second most frequent faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their early stage. Methods: A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault severity estimator. However, to develop the second method, the model was required only in the training process of an artificial neural network (ANN). Since stator faults have a signature on symmetrical components of phase currents, the magnitudes and angles of these components were used with the mean speed value as inputs of the ANN. A simulation on MATLAB of both techniques has been performed with various stator frequencies. Results: The suggested schemes prove a unique efficiency in the estimation of incipient turn to turn fault. Besides, the ANN based scheme is less complex which reduces its implementation cost. Conclusion: To monitor the stator of an induction motor, the choice of the appropriate algorithm should be done according to the system in which the motor will be installed. If the motor is directing connected to the grid or fed via an inverter with a variable DC bus voltage, the observer would be better, otherwise, the ANN algorithm is recommended.

Background: Due to the high level of photovoltaic power plants (PVPPs) penetration into power grids, disconnections of these plants during faults are no longer possible as it may cause problems concerning stability, quality, and operation of the power system. Therefore, new grid codes have been established with low voltage ride-through (LVRT) capability standard requirements for grid-connected PVPPs that should be met. Therefore, for an efficient LVRT control, the fast and precise sag detection strategy is essential for the system to switch from normal operation to LVRT mode of operation. Methods: For this purpose, this paper presents a two automatic fault detection methods which are RMS-based (d-q) components of grid voltage and positive sequence voltage. These methods were utilized to determine the beginning and end of a voltage sag and to determine the sag depth to regulate the required reactive current that should be injected according to the LVRT standard requirements. The operating method depends on calculating present grid voltage under faults to the nominal voltage that identifies the sags’ depth and therefore inject the required amount of reactive power accordingly. Also, a comparison between the two proposed methods regarding response speed and accuracy was made. The effectiveness of these detection strategies is that it can be integrated into the voltage source inverter (VSI) without utilizing additional external hardware or software programming. Results: The simulation results demonstrated a good precision and how straightforward the proposed methods’ usage is, proving that the RMS method is faster and more accurate than positive sequence method. Conclusion: In conclusion, it was found that RMS detection algorithm is preferred for a more accurate and efficient LVRT control.