نشریه مهندسی برق
سال چهل و نهم شماره 4 (پیاپی 90، زمستان 1398)

In this paper, an algorithm is proposed for fault detection in HVDC lines using transient characteristic of the fault component of voltage and current measured on one side of the transmission line. In the proposed algorithm, when the starting unit of the protection system confirms the occurrence of fault, the fault voltage, and current correlation level is used to determine the fault direction and then by using high-frequency harmonic component of the fault current the fault is detected in the protective zone. Finally, by removing the coupling between the currents of positive and negative poles the faulted line is distinguished from a healthy line in the DC bipolar system. The simulation results for a variety of faults verify the validity of the proposed method.

Mobile crowd sensing is a new paradigm that uses the data sensed by mobile users to extract population data and then provides people-centered services. However, most users do not tend to participate voluntarily in this process. Therefore, availability of incentive mechanisms is mandatory to encourage users’ participation. In this paper, based on users-submitted data, a score-based incentive mechanism is proposed. This mechanism measures data quality via obtained contextual information from user data, and accordingly, gives score to the user. The fuzzy inference system is used in the center of the incentive mechanism to calculate the score. The proposed mechanism is evaluated via simulation and the effect of each parameter on the score acquired by user is shown. Simulation results show that this mechanism could be helpful in solving the problem of price imbalance in different areas of measurement and also is able to collect high-quality data at each measurement point with few number of participants.

In recent years, optical devices based on photonic crystals have received much attention due to valuable features of photonic crystals such as low losses, very low group velocity, flexibility in shape and dimensions as well as being suitable for integrated circuits in Nano-scale. In this paper, using two-dimensional photonic crystals, the design of optical de-multiplexer as one of the most essential elements of integrated optical circuits, is investigated. For this purpose, an ultra-compact structure of resonator-based de-multiplexers is demonstrated and then by creating two high performance 90° bends along with engineering in the structure of bending wall rods at the output channels, transmission efficiency average 94.28%  in one of the output channels has been obtained.

In this paper, impact of spatio-temporal correlation of wind production on imbalance cost in real-time market is statistically analyzed. To achieve this goal, the market clearing problem of the real-time market is formulated. Wind power production is modeled as negative electric load, which is assumed to be inelastic. Inter-temporal constraints of generating units and transmission limitations are incorporated into the market clearing model. Wind power uncertainty is modeled through a set of scenarios based on spatio-temporal correlation among wind farms captured from real-world historical data. Simulation results are provided through IEEE 24-bus Reliability Test System. The impact of spatio-temporal correlation of wind power production on real-time market price and associated imbalance cost is statistically studied. Additionally, its impact on imbalance cost incurred by wind producers under single- and dual-price settlement mechanisms is investigated. Results obtained in different cases show that expectation of revenue (cost) resulting from wind power generation excess (deficit) in single-price settlement mechanism are more (less) than the other mechanism. However, difference between standard deviations of wind producers’ revenue (cost) in these two settlement mechanisms can be positive or negative depending on the wind power forecast error.

Induction motors play an indispensable role in setting up the industry’s wheel and production cycle. Therefore, much research is being done to improve their construction and production and to monitor their condition. This paper aims to study and investigate a common fault related to these motors, i.e. the oil whirl fault within their sleeve bearing, and try to provide a useful and effective solution in order to diagnose this fault. For this purpose, firstly, an induction motor that has one of its bearings with oil whirl fault, is modeled and simulated by appropriate definition of the air gap function and using the modified winding function approach. Then, the stator current is obtained under both the healthy and faulty conditions to detect an effective and non-invasive method for the fault diagnosis. The stator current signal is processed using a combination of the empirical mode decomposition and Hilbert transform, called Hilbert-Huang transform and a suitable index for detecting oil whirl fault is proposed. Then, the efficiency of the proposed index is evaluated by applying it to the stator current signal obtained from the practical induction motor and its efficiency is proved in practice.

The lightning strikes create fast and high amplitude transient over-voltages in power systems which are called lightning impulse voltages. To ensure the quality of the insulation system of the transformer to deal with such over-voltages, at quality control high voltage test fields of manufacturers, similar voltages are produced by an Impulse Generator and applied to the transformer. Since frequency response of the transformer has a decisive role in the generated voltage waveform and given that the frequency responses of transformers with different designs are different, a main challenge in this field is to produce an impulse waveform with specifications specified in IEC60060-1. In high voltage test fields using an empirical method, the parameters of the impulse generator such as series and parallel resistors are set to achieve a standard impulse wave shape. This try and error empirical method is time consuming and besides, the standard wave shape cannot be achieved in some special design transformers. To overcome these problems, a precise simulation method for transformer impulse test circuit is provided in this paper and the genetic algorithm is adopted to determine the optimum values of impulse generator resistors. Finally, the simulation results are validated using the experimental works.

In low switching frequency strategies, regulated DC sources along with the switching angles increase the system’s degrees of freedom to effectively mitigate or eliminate harmonics. Utilizing the variable DC sources increase the cost, losses and complexity of the system. This paper presents a new method for cascaded H-bridge multilevel inverters based on discrete variable DC source to reduce number of the regulated DC sources. In this paper, an optimization procedure for output voltage regulation is devised based on the discrete variable DC sources, which results in  effective reduction of harmonics and improvement of the output voltage quality. In the optimization method, the near optimum values of DC sources and switching angles are obtained using multi objective genetic algorithm. To verify the feasibility of the new solution, a laboratory prototype is implemented based on seven level cascaded H-bridge inverter with three cells in each phase. Simulation and experimental results validate the effectiveness of the proposed method based on the discrete variable DC sources in reduction of  harmonics and voltage regulation.

This paper models and investigates the incorporation of optimal transmission switching (TS) into integrated generation and transmission maintenance scheduling (IMS). The IMS with TS (IMSwTS) problem is formulated as a mixed-integer linear programming model. This problem is hard to solve by off-the-shelf commercial optimization solvers since on the one hand maintenance scheduling variables couple several time intervals together and on the other hand TS variables. A solution approach is proposed in this paper to address the problem’s high computational burden which decomposes the IMSwTS  problem into two subproblems, an IMS subproblem and an optimal TS subproblems, and solves them iteratively. The proposed model and decomposition approach are implemented on IEEE reliability test system. The results demonstrate that adding TS to IMS alters the maintenance schedule and brings cost saving.

In this research, we investigate the ambipolar current in germanene nanoribbon tunneling field effect transistor (GeNR-TFET) using combination of density functional theory (DFT) and non-equilibrium Green’s function method (NEGF). We propose two different methods to reduce the ambipolar current in the GeNR-TFET: using overlapped gate metal to cover part of the drain side and the other idea is to decrease the doping density in the drain side. The results show that by extension of the metal gate on the drain region, the hole current from the drain to channel reduces and it is possible to reduce this current more by using longer overlapping length. Also, results prove that by decreasing the doping density in the drain side compared with the source region, the ambipolar current declines. We obtain that by mixing two proposed ways, the ambipolar current can significantly be reduced. Suppression of this ambipolar current is an important challenge in digital circuit design.

The rapid growth of distributed generations (DG) in distribution grids brings new challenges in the protection coordination of overcurrent relays. So far, various adaptive techniques have been addressed to solve DG units’ mis-coordination problem, however, the un-predictable penetration of installed DG units highlights the importance of a penetration-free methodology. This study presents a novel offline, quick, applicable and cheap solution which guarantees protection coordination for any penetration and location of DG units. To do so, first, under different DG penetration levels and locations, the conventional protection performance is studied to discover the worst mis-coordination cases. Next, according to relays standards, the characteristic curve of the back-up relay is modified such that it can maintain coordination in all worst cases. The proposed methodology is applied to a power distribution network equipped with numerous DG units. The results successfully prove the effectiveness of the proposed method. This fact makes this method an attractive option for distribution systems with DG units.

The two-Dimensional Discrete Wavelet Transform (2D-DWT) is widely used in various applications for multimedia data processing, including image and video compression standards. However, this transform is computational intensive than conventional conversions, such as the discrete cosine transform. In this paper, in order to improve the performance of 2D-DWT,  we use Single Instruction, Multiple Data (SIMD) set instructions including Advanced Vector Extensions (AVX), Fused Multiply-Add (FMA), and AVX2 supported by most General-Purpose Processors (GPP). These technologies capable to process 256-bit data located in SIMD registers. The AVX technology can process eight 32-bit floating point numbers, while AVX2 processes sixteen 16-bit fixed-point numbers. In other words, it is possible to exploit 8- and 16-way data-level parallelism. In addition, two different way of parallelism, Row Column Wavelet Transform (RCWT) which processes rows and columns separately and Line-Based Wavelet Transform (LBWT) that processes both rows and columns in a single loop are used. Experimental results of different wavelet transform with different image sizes on a GPP show that the speedups of up to 28.8x yield. Furthermore, LBWT approach improves performance more than RCWT. This is because it uses memory hierarchy structure more efficiently than RCWT approach.

The output power of solar arrays is dependent on temperature and light intensity. Solar arrays have low efficiency. To increase the efficiency of photovoltaic systems, the solar array maximum power point tracking is required. The exotic phenomena like clouds, on the solar arrays will provide partial shade in these conditions and voltage curve can be several local maximum points. Conventional methods because they tend to track the maximum power point of the local convergence are not appropriate in partial shade condition. So in this paper, a new method of the maximum power point tracking with the ability to track the global maximum power point is provided. The proposed method is implemented by the microcontroller STM32F407VGT6 and DC/DC buck converter. The circuits are presented to measure temperature and irradiation. Also a comparison between the proposed method and the method of P&O to verify the performance of the proposed method is done. The experimental and simulation results confirm the accuracy of the proposed method.

In this paper, graphene has been designed for tunable scattering manipulation of a dielectric cylinder. The goal is changing scattering properties of a dielectric cylinder to ones of another cylinder with desired radius. For this purpose, scattering coefficients of the covered and target cylinders have been achieved and equated. Required surface impedance of the graphene in order to obtain the considered goal in the frequencies of 3 and 4 THz has been derived. By properly tuning the chemical potencial of the graphene, caused by induced voltage, this goal has controllably been achieved. Total scattering widths of the bare, target and covered cylinders are obtained from numerical simulations and analytical calculations.

Single phase grid-tied inverters are remarkably increasing in low-power applications such as residential and industrial power supplies. In the single phase system, the inherent ripple power at twice the line frequency results in undesirable low-frequency ripple in the dc-link voltage and output ac current. This issue can be eased through the installation of bulky electrolytic capacitors in the dc link. However, such passive filtering approach may inevitably lead to limited system lifetime and reliability. To overcome these problems, auxiliary power decoupling methods are used to reduce the size of the electrolytic capacitor and replace it with film capacitor. In this paper, By introducing a shunt switching compensator (SSC) and exploiting instantaneous power theory (pq theory), the size of the required capacitance remarkably decreased.The proposed controlling system, based on the modified pq theory and single-phase modeling, was similar to a three-phase unbalanced system regarding the structure. The system simulation was according to the 3Kw photovoltaic system used at the Birjand University. The simulation results verify the proposed power decoupling technique. At the end, a cost comparison between the proposed structure and prevalent structure is also done.

In this paper, a method is proposed for the control of a quadrotor based on sliding mode control by using Chebyshev neural networks. The proposed approach is a combination of the sliding mode controller and the Chebyshev neural network approximator that the neural network weights are tuned in real-time by using robust adaptive techniques. In this research, the dynamic model of the quadrotor is divided into two subsystems for the purpose of the position and orientation tracking control: a fully-actuated subsystem and an underactuated subsystem. For the former, the sliding surfaces are designed by using one state variable, and for the latter, the sliding manifolds are defined by a linear combination of two state variables. In this paper, the system stability is analyzed by Lyapunov theory-based techniques and the accuracy of the controller performance will be illustrated by the simulation results.

Based on the determinant of the Jacobian matrix in the power flow (PF) problem, power systems are categorized to well-conditioned, ill-conditioned and unsolvable systems. In this paper, a novel and simple approach based on Newton technique is presented to solve the PF problems in the unsolvable power flow cases in the power systems. The presented method is based on combination of the inverse of Jacobian matrix to a nonsingular diagonal matrix. Application of the proposed method causes to change the zero eigenvalues to new values in their neighborhoods. The application of the presented algorithm in various scale power systems (2-bus, 11-bus, 14-bus and 118-bus) indicates that the proposed formulation decreases the computation time and number of iterations in comparison with benchmark methods.

Acquiring appropriate tools adaptable to various tasks is the most fundamental feature of the robots dealing with environment. Therefore, it would be more beneficial to plan a mechanism amenable to control the robotic fingers. Since robotic fingers have completely nonlinear behavior and their modeling is associated with the difficulties arising from the factors such as friction, physical features of transmission mechanisms, and changes in hand’s orientation, adopting a model-independent method of control will be useful. In this paper, a Takagi-Sugeno-Kang (TSK) fuzzy controller, which adaptively updates its consequence parameters, is employed for position/force control of the fingertips grasping a light and soft ball. Designing in Cartesian space and being model-independent are some of the most important advantages of this method. In the first step, force and position reference values are calculated using a predetermined stationary grasping strategy. Afterward, the performance of the adaptive fuzzy TSK controller in maintaining the ball with and without the existence of force measurement noise and joint friction are evaluated. Furthermore, the process of catching a falling ball is divided into approaching, locking and holding phases. Finally, in the simulation section, it is shown that the adaptive fuzzy TSK controller is an efficient way for performing the aforementioned three phases.

In this paper, modeling, analyzing and controlling nonlinear systems using Polytopic linear models is considered. First, the output tracking problem is investigated for the state of the system as compared to the affine input, and then the problem is solved for the non-affine state. In the state of determining the parameters of each region to increase the problem solving speed we determine the weighted function in a specific manner that prevents interference between the regions and by solving an linear inequality matrix of controller design , in contrast to the past, it is not necessary to solve a bilinear matrix inequality and only by solving a linear one, the controller will be designed. To stability and design of the controller, a method is used to ensure both the stability of the approximate model (polytopic) and the stability of the main model (nonlinear). Finally, the results are taken and the methods proposed are used to  design of an elastic missile  system.

In this paper, a novel framework is presented to solve generation expansion planning problem in the presence of investment incentives. In the proposed model, investment incentive is a guaranteed purchase contract. Generation expansion planning is presented as a bilevel model, where the goal of upper level is maximizing profit of wind unit investor and the low level includes the market clearing optimization problem with maximizing of social welfare powered by independent system operator (ISO). This bilevel problem is transformed into an mathematical programming with equilibrium constraints problem (MPEC) using the KKT conditions. In this paper, the guaranteed contract price and the market price are obtained in the market clearing equations, and by using them and the strategic bidding by wind unit, it is decided on investment.

Self-healing as one of the most important features of smart distribution networks that increases network reliability and resiliency. Considering the necessity of studying the challenges facing outage management in smart distribution networks and developing the proposed solutions in this field, this paper investigate the impact of self-healing and automatic outage management on smart distribution networks on network reliability. In this paper, a new algorithm is proposed to evaluate the reliability of smart distribution networks by describing the problem scope and providing a control and operation model in both normal and self-healing modes. In the proposed algorithm, the Monte Carlo method is used to simulate equipment outage scenarios. In each outage scenario, in the optimization problem framework, the status of the controllable switch  to create the structure of the multi microgrid, direct load control program and the scheduling of thermal sources and energy storage in the fault period are determined. The capabilities of the proposed model are to consider the wind resources uncertainties and the distribution network operational constraint such as voltage constraints and line thermal constraints. The results of the implementation of the proposed algorithm on the modified IEEE RBTS network will improve the reliability of the network by adopting outage management strategies in smart distribution networks.

Modeling of transmission lines for the analysis of electromagnetic transient behavior requires the calculation of a modal transformation matrix in a wide range of frequencies. In this paper, a comprehensive framework for smooth calculating the transformed matrix is ​​presented. Accordingly, at each frequency, a general set for eigen vectors corresponding to each eigen value is computed. Then, on the basis of a smoothing criterion and an optimization algorithm, at each frequency the set of eigen vectors are chosen arbitrarily, avoiding sudden mutations and smoothing the fitted function for different elements The conversion matrix is​​guaranteed. The proposed algorithm is implemented on an underground cable 3 phases system and overhead line in order to obtain a transformation matrix. The simulation results indicate the effectiveness of the algorithm used to calculate the transformation matrix in a very smooth and precise manner. Also, this algorithm will continue to show significant accuracy in a situation where the frequency sampling rate is reduced, so that it exhibits acceptable tracking capability at low sampling rates.

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). In fact,the existence of conditional distribution difference across the source and target domains degrades the performance of model. Domain adaptation and transfer learning are promising solutions that aim to generalize a learning model across training and test data with different distributions. In this paper, we address the problem of unsupervised cross domain image processing in which no labels are available in test images. In fact, the proposed method transfers the source and target domains into a shared low dimensional FLDA-based subspace in an unsupervised manner. Our proposed method minimizes the conditional probability distribution difference of the source and target data via Bregman divergence. We provide a projection matrix to map the source and target data into a common subspace on which the between class scatter matrix is maximized and within class scatter matrix and cross domain distributions are minimized. Extensive experiments on 58 cross-domain image classification tasks over six public datasets reveal that our proposed method outperforms the state-of-the-art cross domain image processing approaches.

Among different active front end (AFE) rectifier control methods, model predictive direct power control (MPDPC) is one of the appropriate and effective one. This method chooses best switching state based on simple calculation of converter behavior due to consideration of all possible switching states. MPDPC present good performance in balanced network condition and can control AFE rectifier with sinusoidal input currents and unity power factor. However, in the network unbalanced condition, the MPDPC shows harmonics and disturbance in the current waveform. It's almost impossible to achieve unity power factor and sinusoidal input current waveform of AFE rectifier in the unbalanced and harmonized supply condition. A new method which is the combination of the virtual flux and MPDPC with active power ripple elimination is proposed in this paper. This method uses new approach to eliminate the active power pulsation based on calculated virtual flux. Furthermore, a new current prediction method based on the virtual flux technique is proposed. This method has the advantages of VF and MPDPC techniques at the same time. Control of AFE rectifier with sinusoidal input currents, unity power factor, high robustness in high percentage of unbalance, simple calculation under unbalanced and harmonized condition of the network are some advantages of the proposed method. Unlike the conventional methods, there is no need for the positive and negative sequences extraction which is a complex calculation. Simulation and experimental results are presented to confirm the effectiveness of the proposed method.

This paper presents a current reference circuit by employing the combination of the saturated and subthreshold transistors structures. At first, using these two structures PTAT and CTAT currents were generated and combined with together. Then to achieve low temperature variation coefficient, proper coefficients of the output currents of these two structures were combined leading to 100µA current reference. This current reference was post layout simulated in the 0.18μm CMOS TSMC technology with Cadence software and its layout size was 177.4μm×180.5μm. Post layout simulation results show that this current reference has a 3.68 ppm/°C variation in the temperature range of -40 to 120 Celsius degrees for TT transistors. In addition, its average temperature variation coefficient is 16.384 ppm/°C for 1000 iterations of Monte Carlo simulation. This circuit has a 2.9% sensitivity per one volt change of the supply voltage. This reference require to 396 millivolts headroom voltage to reach the 98% of the nominal current value. The power consumption of this circuit at the supply voltage of 1.8V was 39.67µW.

The usage of binary numerical-based Crosstalk Avoidance Codes (CACs) in communication channels of NoCs is one of the efficient crosstalk tackling mechanisms. In CACs, specific transition patterns are avoided in generated code words. However, the problem of the binary numerical-based CACs is that their overheads increase with growing the number of wires. To reduce the growth of channel width, more code words can be represented using tri-value numerical systems. In this paper, in the first step, a mechanism is proposed that calculates the number of code words that do not have 020 and 202 forbidden patterns. These transition patterns impose the worst crosstalk effects to a victim wire in tri-value systems. Then, in the second step, an algorithm is proposed that generates numerical systems without 020 and 202 forbidden patterns. Using this algorithm, a tri-value CAC called 3D Transition Opposite Direction (3D-TOD) is proposed that is able to increase the number of represented code words in specific channel width. Evaluations show that 3D-TOD can reduce the overheads of additional wires and codec, efficiently.

In this paper, an approach for robust sensor fault reconstruction of wind turbine systems in the presence of simultaneous uncertainty and disturbance is proposed. For this purpose, an adaptive sliding mode observer is designed such that the fault is reconstructed through an online adaptive law. The significance of the proposed approach in addition to its robustness against the bounded disturbances and uncertainties is that it does not require the fault and uncertainty bounds to be known a priori. An efficient algorithm is presented to adjust the design parameters based on the Linear Matrix Inequality (LMI) concept. The proposed approach is applied to a 5MWs wind turbine system and simulation results demonstrate the accuracy and desirable performance of the approach.

Human body part detection has been an important research topic in the last decade. It is widely applicable in areas such as human activity recognition, pose detection and other applications related to human movements. The objective of a human body part detection system is to associate a body part to each human pixel. Recent studies show that applying depth maps significantly improves the results of body part detection. In this study, two new features based on pixel depth difference is proposed. First feature is based on pixel-wise depth difference between the input pixel and neighbor pixels selected using a weighted circular distribution. The second feature is the difference between coefficients of polynomials fitted to neighbors of the input pixel at difference scales, making the feature invariant scaling. Random decision forest was used for pixel classification. Comparison of results with the state of the art methods reveal that the proposed method is able to distinguish and differentiate the various components of the body more accurately.

According to IMPERVA report, application layer DoS attacks have involved about 60 percent of total DoS attacks. Today, attacks have been transferred to the business layer. Web application vulnerability scanners cannot detect business logic vulnerabilities (vulnerabilities related to logic). This paper presents BLDAST, A dynamic and black-box vulnerability analysis approach that identify business logic vulnerabilities of a web application against flooding DoS attacks. BLDAST assesses web application resiliency against flooding DoS attacks in the business layer. BLDAST first extracts business logic processes of a web application. Business logic processes with high overload are selected and finally, based on selected processes, BLDAST runs business layer DoS test scenarios. The evaluation conducted on four well-known open source web applications shows that BLDAST is able to detect business logic vulnerabilities. In addition, we show that an attacker in business logic attacks can exhaust target by consuming only one percent of his resources in comparison to other layers attacks. Therefore, business logic attacks are very dangerous and BLDAST is able to identify vulnerable web applications against these attacks.

This paper presents adaptive observer design for one-sided Lipschitz systems. One-sided Lipschitz systems are a wide branch of nonlinear systems that include Lipschitz systems. Designed observer simultaneously estimate states and unknown parameters of the system and it is robust against input perturbation and limited observer gain disturbance. As using  observer will results in having desirable performance besides observer stability, a non-fragile  observer is presented and its stability is investigated based on Lyapunov theorem. Using linear matrix inequality causes minimizing the effect of disturbance on the estimation error addition to calculating the observer’s gain systematically. Two examples are presented to show the efficiently and performance of the proposed observer and comparison with a new research in this field. Finally the conclusion of the paper and the useful suggestions for future researches in this field is presented.

This paper investigates stabilization for a class of nonlinear impulsive switched systems with norm-bounded input constraint. Due to this constraint, it is only enough that the stabilization criteria and assumptions related to the nonlinearities be met on a subspace containing the origin. Certainly, these assumptions are such that they covers most of real-world systems. The purpose of this paper is to design a norm-bounded control that guarantees the exponential convergence of trajectories to a sufficient small ultimate bound in presence of uncertainties. Therefore, firstly, we present the stability criteria for a general model that ensures the convergence of all trajectories starting from a region of attraction to an ultimate bound. These conditions are in terms of a common Lyapunov function candidate and the minimum dwell-time, and it is enough to be valid on the region of attraction. Secondly, using the common quadratic Lyapunov function candidate and using the state-feedback approach, the established conditions are reformulated as a set of linear or bilinear matrix inequalities. Besides, to achieve the control parameters along with the largest convergence area and smallest ultimate bound, we propose an optimization problem. Finally, some illustrative examples are presented to demonstrate the proposed approach.

In this paper, an optical bandpass filters based on photonic crystal using asymmetric Mach-Zehnder interferometer is conceptually analyzed and designed. For this purpose, the wavelength parameters such as central wavelength, bandwidth, amplitude sharpening, and passband flattening for a single stage conceptual structure have been selected and realized based on two-dimensional hexagonal photonic crystal lattice. The proposed filter has a high degree of design freedom and by adjusting the structure design parameters, it can be used in the design of the wide and narrow bands optical filters. The 1-dB and 3-dB bandwidths for the initial structure and optical demultiplexer are obtained 12 nm, 25 nm and 4 nm, 8 nm, respectively which represents a 300 percent reduction in bandwidth and sharpening of the filter amplitude.

In this paper, a novel method has been proposed that, determines the necessary actions to restore the power system as quickly as possible in the event of an overall outage of the power system, when there is more than one black start unit in the power system, it will be divided into several islands in order to faster restoration. The proposed method uses a bi-level optimization algorithm that, determines needed actions for restoration process in an integrated approach. These actions are partitioning of the power system, units' start-up sequence, determination of optimal transmission path between buses and sequence of energizing transmission lines and buses. The proposed algorithm has been implemented on IEEE 39-bus test system to show the advantage of the method.

Increasing the number of cores to enhance computing power of processors leads to an increase in temperature for multicore systems. Thermal management is significant challenge in these processors. A proactive dynamic thermal management uses a thermal model to predict the temperature before processor temperature reaches the threshold. In this paper, some appropriate features for thermal model are read by using system measurement tools. Other features as historical and control features are created using the proposed processes. An online thermal model based on several thermal phase is proposed. For each phase, a neural network is used to forecast temperature. Different thermal phases are identified according to the parameters affecting the processor temperature using the adaptive resonance theory network. For each of the neural networks, the minimum number of proper features is selected based on the mutual information between the features. The proposed thermal model is able to detect new thermal phase at run time. Then, appropriate neural network is created for new phase. The proposed model has been evaluated to predict temperature for different time distances. The results shows the mean absolute error is less than 1 °C.

Today, many power distribution companies are confronted with meter tampering and energy theft phenomena that increase the non-technical losses of the low voltage network. The first step in eliminating these types of losses is to identify areas that are infected. The loss estimation method already provided, are not capable of covering the meter tampering and the electricity theft. Hence, in this paper, a method is proposed to estimate the loss of low voltage network using available information. The main idea of the proposed method is based on the definition of load factor index and it has been proven that in the case of meter tampering and electricity theft, this coefficient will decrease compared to the actual load factor of the feeder. The information used in this method is available in the GIS system and there is no need to install another measurement device. The proposed method has been implemented in 12 low voltage networks in Moosian  and its results have been presented.

Today, there is a great tendency to store long-term recordings of electromyogram (EMG) signals; thus, showing the importance and necessity of effective compression of this type of biomedical signals. In this paper, regarding to the relatively rapid variation of EMG instantaneous amplitudes and thus having rather high frequency components, we have proposed a compression approach in which a smooth and reversible version of the input EMG signal is generated to be compressed instead of the original one; thus, improving the compression efficiency. We have used the pre/de-emphasis technique in the Fourier domain to produce a smooth signal from the input EMG signal. The smoothed signal is then simply converted to the corresponding 2D image and finally, compressed by the Wavelet transform and Set Partitioning In Hierarchical Trees (SPIHT) codec. The proposed method is evaluated by two sets of criteria, measuring the compression efficiency (including the PRD and CF measures) and capability of preserving the clinical information (including four spectral parameters). The results show the superiority of the proposed method compared to most of existing approaches.

In this paper, a low-pass filter with usage microstrip lines and suitable performance for operation in telecommunication systems is designed, simulated and fabricated. The design of the filter is based on the fifth order chebyshev filter design rules. In order to improve the performance of the low-pass filter parameters, radial suppressor cells and open stubs are used which increase the stopband and narrow transition band (sharp roll-off-rate). The proposed low-pass filter after designed, using simulated ADS software and finally fabricated on the Rogers-RO4003c substrate with a thickness of 0.508 mm (20 mil) and a dielectric constant of 3.38. The -3 dB cutoff frequency for the proposed filter is 1.1 GHz and bandwidth of the stopband is close to 28.5 GHz. The change rate for the filter is appropriate and equal to 79 dB/GHz. The size of filter is relatively small and figure of merit is 26663. The simulation results and the results of the measurement of the fabricated sample are consistent and fairly similar. The proposed filter can be used in telecommunication systems due to good performance.

Lung cancer is one of the hardest and most dangerous types of known cancer in the world which can be detected in its beginning stages as a small mass of tissue, less than 3 cm in diameter, called a nodule. These nodules are classified to two classes of benign or malignant. In this paper, a detection system for detection and classification of lung nodules is proposed which in the first phase, lungs are separated from the CT scan images according to the active contour segmentation method. Next, based on the SIFT features the proposed Bagging classifier, classifies the lung images into two classes of patient and healthy. In the second phase, according to a fully automatic Graph-Cut segmentation method the nodules are extracted from patient images and their diameters are measured. Finally, nodules are classified to two classes of benign and malignant based on their size and texture Haralick features. To evaluate the proposed method, images of the LIDC database are used and its performance in detection of nodules compared to other methods has an accuracy of 97% and in classification of nodules to benign and malignant an accuracy of 96% is reached.

The increase in complexity and the rate of technological changes in modern software development have led to a demand for systematic methods that raise the abstraction level for system maintenance and regression testing. Model Driven Engineering (MDE) has promised to reduce extra coding efforts in software maintenance activities using traceable change management, especially in rapidly changing application. The paper presents a Z-notation based framework, called Changed-based Regression Testing (ChbRT), for formal modeling of regression testing in the context of MDE. The framework proposes to automatically propagate the changes from a software specification to testing artifacts in order to preserve consistency after system evolution. The framework is enriched by providing a new category of coverage metrics for change-based regression testing. The proposed framework is expected to be beneficial in both platform independent and specific levels of ChbRT by identifying the suitable coverage according to available testing resources. The accuracy and efficiency of the proposed framework have been evaluated and analyzed on three case studies.

In this paper, a new method for extracting the global maximum power point (GMPP) of a PV array under partial shading conditions (PSCs) is presented. In the proposed method, it is shown that, independent of the pattern and intensity of the radiation, the GMPP position is always in the neighboring of the global peaks of one of the photovoltaic (PV) strings in the PV array. Therefore, in contrast to the existing methods in which the number of local peaks, tested as a GMPP candidate, is dependent on the number of different radiation levels on the array surface, in the proposed technique this number is reduced to the number of parallel strings in the photovoltaic array and is independent of the irradiation pattern on the PV array. As a result, to track the GMPP in PSC, first the GMPP of each PV string is estimated, and then the voltage of each of the estimated points is given to the power electronic converter as a reference voltage to regulate the PV array voltage at the corresponding candidate GMPP voltage. Next, in each case, the produced power is recorded and the voltage that produces the maximum power is known as the final GMPP of the PV array. Numerical results and comparative study with similar methods confirm the desirable performance of the proposed algorithm in finding the GMPP with an acceptable speed and accuracy.

Cyber-attacks have become a serious threat to the power grid by expanding the use of communication networks and cyber-physical systems in power systems. due to the connection between communication networks (cyber layer) and Power Grids (physical layer), state estimation of power systems is vulnerable to cyber- attacks. In this paper, state estimation in the power system without any cyber-attacks has occurred, then the detection of false data injection attack power grid when the measurements made by phasor measurement unit (PMU), and the dynamic estimation of the system state variables are estimated by Estimator Kalman. The attack is applied to the communication channels between the PMU and the state estimator. The proposed method which is based on the Kalman filter and the Euclidean distance detector has a good performance in detection of complex attacks such as a false data injection attack. The effectiveness of the proposed method is shown by simulating false data injection attacks on the IEEE 14-bus system. The impact of the FDI attack on the state estimation system and the effectiveness of the proposed method is shown detection of attacks in an IEEE 14-bus system is shown.