Amirkabir International Journal of Electrical & Electronics Engineering
Volume:49 Issue: 2, Summer - Autumn 2017

Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Par 1 of the two parts paper focuses on Fourier-based techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fetection in IMs are analyzed in details. Then, their competency and their drawbacks for extracting indices in transient and steady state modes are criticized from different aspects. The considerable experimental results are used tocertificate demonstrated discussion. Different kinds of faults, including eccentricity, broken bar and bearing faults as major internal faults, in IMs are investigated. The use of efficient signal processing tools (SPTs) to extract proper indices for faultdetection in induction motors (IMs) is the essential part of any fault recognitionprocedure. In the first part of the present paper, we focus on Fourier-based techniques, including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault detection in IMs are analyzed in detail. Then, their competency and their drawbacks to extract indices in transient and steady state modes are criticized from different aspects. Different kinds of faults, namely, eccentricity, broken bar, and bearing faults as the major internal faults in IMs, are investigated.

An optimized method for data hiding into a digital color image in spatial domain is provided. The graph coloring theory with different color numbers is applied. To enhance the security of this method, block correlations method in an image is used. Experimental results show that with the same PSNR, the capacity is improved by %8, and also security has increased in the method compared with other methods. In the correlation block-based image method, data hiding capacity of the host image varies according to image type and defined threshold level. In the proposed algorithm, during graph explanation, independent pixels placed side by side were colored. Then, based on “pixel block correlation data hiding” process is done. This method grows the security and capacity of hiding process. Besides, this increases the effects of image format and correlation threshold on security and capacity.

Instantaneous Frequency Measurement (IFM) devices are the essential parts of any ESM, ELINT, and RWR receiver. Analog IFMs have been used for several decades. However, these devices are bulky, complex and expensive. Nowadays, there is a great interest in developing a wide band, high dynamic range, and accurate Digital IFMs. One Digital IFM that has suitably reached all these requirements is mono-bit zero-crossing IFM, made by some different producers at present. In this paper, the performance of mono-bit digital Instantaneous Frequency Measurement (IFM) device is analyzed. This analysis includes quantization error, thermal noise, clock jitter, comparator bias and also “Pulse-on-Pulse” occurrence. The error limits due to all these factors are computed and analyzed, and a unified approach to the system design is presented In this paper, the performance of mono-bit digital Instantaneous frequency measurement (IFM) device is analyzed. This analysis includes quantization error, additive (thermal) noise, clock jitter, comparator bias and also “Pulse-on-Pulse” occurrence. The error limits due to all these factors are computed and analyzed, and a unified approach to the system design is presented.

A multilevel inverter is capable of generating high-quality stepwise pseudo-sinusoidal voltage with low THD , applicable to high-power and high-voltage systems. These types of topologies may require a large number of switches and power supplies. This leads to much cost, large size, and complicated control algorithms. Thus, newer topologies are being proposed to decrease the number of power electronic devices for a large number of levels in output voltage. Recently, a new multilevel inverter has been reported in the literature to reduce component count. Its structure requires a lower number of active switches as compared to the existing ones. The available literature presents a generalization of the topology with an especial asymmetrical sources ratio, but no investigations are made for other symmetrical or asymmetrical sources ratio with cascaded configurations. This study presents a comprehensive analysis of cascaded topologies with the proposed basic units. The topology is analysed for both symmetric and asymmetric DC source configurations. Also, two algorithms for asymmetric source configuration suitable for cascaded structures are proposed. Moreover, the design and simulation of a 147-level inverter are presented under an optimal number of DC sources and power switches. Furthermore, experimental validation is performed by implementing a laboratory prototype.

This paper presents analysis and evaluation of classical direct torque control (DTC), for controlling a symmetrical six phase induction motor (SPIM) under open phase fault conditions. The machine has two three-phase windings spatially shifted by 60 electrical degrees. The strategy of the proposed method consists of choosing the switching modes according to the configuration of living phases in such a way that it generates vectors that have higher amplitude in α-β plane while their projections on z axis give zero or near zero amplitude vectors. The goal is reducing parasitic currents and torque ripples of SPIM under faulty mode. Based on the theoretical analysis, it will be shown that in the open phase fault conditions, the only non-pulsating operation is obtained by opening the fault three-phase winding. Experimental test results are provided o support theoretical analysis in open phase fault conditions for SPIM.

Electricity demand is forecasted to double in 2035, and it is vital to address the economics of electrical energy generation for planning purposes. This study aims to examine the applicability of Gravitational Search Algorithm (GSA) and the newly improved GSA (IGSA) for optimization of the mixed-integer non-linear electricity generation expansion planning (GEP) problem. The performance index of GEP problem is defined as the total cost (TC) based on the sum of costs for investment and maintenance, unserved load, and salvage. In IGSA, the search space is sub-divided for escaping from local minima and decreasing the computation time. Four different GEP case studies are considered to evaluate the performances of GSA and IGSA, and the results are compared with those from implementing particle swarm optimization algorithm. It is found that IGSA results in lower TC by 7.01%, 4.08%, 11.00%, and 6.40%, in comparison with GSA, for the four case studies. Moreover, as compared with GSA, the simulation results show that IGSA requires less computation time, in all cases.

In a photovoltaic system, sun light energy is converted to electricity. The generated electricity has a low DC voltage. In order to increase voltage generated by photovoltaic cells (PV), an additive DC-DC converter is required to raise the low voltage to a good level which provides the conditions for connection to DC-DC converters. Low wastes, low costs, and high efficiency are some other specifications of such converters. This paper presents a new structure for an additive DC-DC converter with inductive and capacitor switching for increasing high voltage gain to be used in PV system. It is based on the inductive and non-insulated switching which increases voltage in a duty cycle up to 10 times of input voltage. In addition, using a switch, low elements, and also low voltage stress on the switch is the advantage of this new setup. The easy increasing of levels to reach the higher voltages is another benefit of this structure. The paper continues with the analysis of circuit function and PWM (Pulse Width Modulation) adjustments. PSCAD/EMTDC software is used for confirming the authenticity of the performance of the suggested model. The results are presented.

Evaluating the power densities emitted by GSM1800 and GSM900 BTS antennas is conducted via two methods. Measurements are carried out in half a square meter grids around two antennas. CST Microwave STUDIO software is employed to estimate the power densities in order for detailed antenna and tower modeling and simulation of power density. Finally, measurements obtained from computational and experimental methods were compared through the contour lines using the statistical Surfer software. After measuring and simulating all values, it turns out that power density is generally lower than the permissible exposure limits although exceeds the limits in some sample points . According to the measurements, simulation error in stations GSM900 and GSM1800 are 10% and 8%, respectively. Findings from contour-line-maps illustrates that direct measurement method follows the same emission pattern as the computational method does. It validates the computational approach and the models attained for BTS power density estimation.

In order to provide an efficient conversion and utilization of solar power, solar radiation data should be measured continuously and accurately over the long-term period. However, the measurement of solar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence, several studies were proposed in the literature to find mathematical and physical models to estimate and forecast the amount of solar radiation such as stochastic prediction models based on time series methods. This paper proposes a hybridization framework, considering clustering, pre-processing, and training steps for shortterm solar radiation forecasting. The proposed method is a combination of a novel data clustering method, time-series analysis, and multilayer perceptron neural network (MLPNN). The proposed TransformedMeans clustering method is based on inverse data transformation and K-means algorithm that presents more accurate clustering results when compared to the K-Means algorithm; its improved version and also other popular clustering algorithms. The performance of the proposed Transformed-Means is evaluated using several types of datasets and compared with different variants of K-means algorithm. The proposed method clusters the input solar radiation time-series data into an appropriate number of sub-datasets which are then preprocessed by the time-series analysis. The preprocessed time-series data provide the input for the training stage where MLPNN is used to forecast the solar radiation. Solar time-series data with different solar radiation characteristics are also used to determine the accuracy and the processing speed of the developed forecasting method with the proposed Transformed-Means and other clustering techniques.

In this paper, a homogenous multi-sensor fusion method is used to estimate the true angular rate and acceleration with a combination of four low cost (

In this paper, a novel method is presented for detection and classification of the faulty phase/region in the stator winding of synchronous generators on the basis of the resulting harmonic components that appear in the terminal voltage waveforms. Analytical results obtained through Decision Tree (DT) show that the internal faults are not only detectable but also they can be classified and the related region can be estimated. Therefore, this scheme can be used to protect the synchronous generators against the various internal faults. Fuji technical documents and data sheets for an actual salient pole synchronous generator (one unit of an Iran’s hydroelectric power plants) are used for the modeling. Simulations in Maxwell software environment are presented. All the related parameters, such as B-H curve, unsymmetrical air gap and pole saliency, slot-teeth effect, and other actual parameters, are considered to obtain a comprehensive model to generate acceptable terminal voltage waveforms without any simplification.

K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies in neurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this pattern are of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysis of this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocation approaches. This method is able to decompose signals into their time-varying oscillatory ingredients. In addition, it provides a time-frequency representation with less blurring compared to wavelet transform. In this paper, firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper p-values. This paper proposes SST for K-complex detection. The proposed method is based on a so-called “detection of K-complexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimization and decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual. Applying the Teager-Kaiser energy operator and setting a threshold on the low-frequency component result in K-complex detection. We modify DETOKS using SST. The proposed method is applied to DREAMS dataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels were extremely different, the automatic detection is considered as the third expert’s scoring and data is re-labeled by a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposed method, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.

In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario, which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re- GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P) and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessing the data set based on curve fitting, reducing the data dimension and identifying the most effective feature sets according to data correlation, training classification algorithms, and finally predicting new data based on classification algorithms. The results derived from five compound schemes are investigated and compared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and Storage Capacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable, Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. On the other hand, GA-ANN is a Wrapper method and despite Relief can adapt itself to the machine learning algorithm. Meanwhile, Ro-P scheme is useful for analyzing vague and imprecise information and, unlike GA-ANN, has less calculative costs. Among these five schemes, Ro-GAPSO is a more precise one, which has less calculative cost and does not become stuck in local minima. Experimental results show that Re-P outperforms other proposed and existing methods in terms of computational time complexity.