نشریه مهندسی برق
سال چهل و هشتم شماره 2 (پیاپی 84، تابستان 1397)

This paper presents a new methodology for uncertain polytopic linear parameter-varying (LPV) modeling of power systems based on parameter set mapping (PSM) with principle component analysis (PCA). At first, an LPV representation of the system dynamics is generated by linearization of its usual differential-algebraic equations about the transient operating points. Then, the PCA-based PSM algorithm is used to reduce the number of models and generate a reduced polytopic LPV model. Because of the system nonlinearity and approximations of model reduction, some uncertainties are considered for each model. A robust pole placement controller is designed to assign the poles of polytopic model in a linear matrix inequality (LMI) region such that the response of the system has a proper damping ratio. A sufficient condition is also proposed to guarantee the asymptotic stability of the closed loop model against the uncertainties. Finally, the proposed controller is synthesized as a power system stabilizer (PSS). It is considered for a single-machine power system and then it is simulated in multi-machine case and compared its performance with a tuned standard conventional PSS and other cases of the controller. The results show the robust performance of the proposed controller especially in different operation conditions and faults.

In order to increase performance of genetic algorithms, many approaches with aim of preserving diversity have been published. However, most of these approaches can be only applied to continuous optimization problems. This does not mean that genetic algorithms do not need population diversity, when they are applied to combinatorial optimization problems. In fact, defining the concept of similarity between solutions of combinatorial optimization problems, due to their apparent differences, is not straightforward. For example, for travelling salesman problem, how to measure similarity between solutions? This paper presents diversity preservative strategies which are based on similarity between solutions. These strategies not only can be applied to continuous optimization problems, but also by proposing novel semantic-oriented approaches to compute similarity between solutions of combinatorial optimization problems, it is possible to apply to combinatorial optimization problems, successfully.

Diversity of the noise fields in real environments makes difficult to design an efficient de-noising system. This paper addresses the problem of speech enhancement in real environments. Towards this goal, we present a hybrid scheme based on the adaptive beam-forming and post filtering. In order to capture the adaptive conditions of the environment, we adopt two algorithms: steepest descent and least mean square. We also consider the application of the post-filters on the output of the beam-former. As an advantage, the proposed structure not only produces some controlled nulls to reduce the directional noises, but also adaptively minimizes the power of diffuse and uncorrelated noises. The study of the convergence of the proposed algorithms and also the behavior of the diffuse noise in real conditions are other issues considered in this paper. Numerical results of our comprehensive implementation confirm the efficiency of the proposed scheme in suppression of the real environmental noises.

In this paper, a novel switching control method based on two switching logic contained Harris index and fuzzy supervisor in presence of disturbance is proposed for controlling a cart position and angle of an inverted pendulum. The Harris index is calculated using Markov parameters and output variance of the closed loop system. In order to show the performance of the switching logic, simultaneous usage of advantages of several different controllers in a control loop of a nonlinear systems subject to disturbance with variable power is proposed for the first time. The designing stages of proposed method include three steps. First, proper controllers including proportional–integral–derivative, linear quadratic regulator and sliding mode control have been designed. Then, Harris index and finally the fuzzy supervisor are also designed. The Harris index is determined in order to choose the proper controller as first switch logic. The fuzzy supervisor is designed to improve and supervision on performance of Harris index as second switching logic. Numerical Simulation results show that the proposed method at any moment due to changes disturbance, choose the best controller and it can reduce chattering, domain of control signal and sensitivity to disturbance.

In this paper, a new method is presented to feature extraction from facial images. The main purpose of this paper is probe image retrieval from a big database. By increasing the size of the database, the similarities between people increases and the separation capability decreases. The proposed method increases the distance between peoples in feature space by extracting appropriate features. This method is based on properties of the human vision system and sequentially extracts features in top-down manner. For this purpose, spatial- frequency features are used. In this method, by applying concentric windows in different size on the facial image, the content of each window are mapped to frequency space. The change of frequency components in different windows forms the feature space of image. Then frequency component with high separation capability between face images is remained by appropriate filter. In the end, the final image is retrieved from database by Euclidean distance criterion. In this paper the FERET database is used. Recognition rate compared with the best current method in similar size of database, with 2% improvement reached to 99%. By increasing database size to 990 classes, 90.4% of recognition rate is achieved.

In This study a new method for spectral estimation of signals in the presence of observation noise is proposed. Back ground noise has a non-Gaussian symmetric alpha stable distribution. Signal parameters are modeled by an AR process, using spectral methods. After the calculation of the produced bias in signal parameters, the bias is removed using the generalized Yule–Walker equations. In alpha stable distribution, there is no closed form formula for probability distribution function and cumulative distribution function. Also, the infiniteness of variance in this distribution, limits the performance of correlation coefficient based methods and other standard parameter estimation methods. In this paper, a new closed form formula based on covariation coefficient is proposed which removes the bias using an iterative algorithm. The simulation results show that, by using the proposed method, there is a 20 percent improvement in estimation accuracy of signal parameters in 10dB SNRs and higher ones. Moreover, the results show the estimations of the proposed method is more robust to displacement of poles compared to the classic Yule-Walker method and displays a significant improvement in comparison with the high-order Yule Walker method for AR models, whose poles are close to the boundaries of the unit circle.

Linearity and power dissipation of ramp signals are the main key aspects for many applications such as single-slope ADCs. This paper presents a low power ramp generator with linearity improvement and a negative feedback for compensation of the variations in process, voltage, and temperature (PVT). In addition an approach for offset cancelation of ramp generator is presented. Derived equations of the proposed ramp generator circuit show the linearity improvement and PVT compensation of the output ramp, with proper choosing of device sizes. In addition, for proving of linearity enhancement, the circuit design and simulations were done in TSMC 0.18-μm technology with Cadence software. Corners analysis and Monte Carlo Simulation results show that linearity of the circuit improved more than 1-bit and 2-bit, respectively. While power dissipation of the circuit and total layout core area are not increased so much in comparison with conventional circuit.

When a fault occurs in transmission line, first it should be detected, then the type of the fault should be recognized and in final step, location of the fault should be estimated. Fault detection as fast as possible helps to do the whole procedure faster. In this paper, a novel method for transmission line fault detection based on three-phase instantaneous active power and moving average technique is proposed. Three-phase instantaneous active power signal is the total of instantaneous active power for each phase. This signal is constant during normal operation of the network but in fault situation, some changes will happen and by utilization of moving average concept, the fault can be detected. The proposed fault detection method is evaluated in five different systems and the results denote the high accuracy and quickness of the proposed approach. For validation of the suggested method, a comparison is carried out with seven similar previous methods.

With the intense exploitation and expansion of distributed uncontrollable energy resources along with increasing demand side participation in modern distribution power systems and networks, the Transactive energy (TE) management framework has emerged as a topic of keen interest, in particular, addressing the concept of maintaining equilibrium state of system between the local power supply and load demand. In this paper, an innovative model is devised within an optimal framework of Transactive energy management based on load responsiveness in multi-functional home Microgrids in an electricity retail market, along with the smart static bidding strategy for the production and consumption resources by using the ability of relaxation algorithm based Nikaido-Isoda functional theory. In the proposed implementation, home Microgrids which consists of variable nature distributed energy resources, energy storage systems and responsive load devices, the transfer of energy with adjacent neighbouring Microgrids and electricity retail companies, with the aim of maximizing profits and sustaining internal load demand, is studied. Furthermore, the consumers would be able to cooperate with each other using the proposed algorithm in order to minimize market clearing price while maximizing their profit by reaching to the Nash equilibrium. Based on this multi-functional method that takes into account the functional objectives of uncertainty parameters related to uncontrollable energy resources, the load demand and electricity price, the amount of delivered power and optimum price of participating players in the market, the overall profit is determined. The simulation results highlights the effectiveness of the proposed algorithm as a global system of practice which can be used to progressively engage players for active participation and simultaneously improving the profits for home-type Microgrids.

Due to the rising interest of using smart grids and its unreliable nature, the reliability analysis is highly regarded. The application of communication infrastructures and information technology in smart grids has created a system consisting power and cyber networks (control, monitoring, and protection networks). Due to the fact that cyber networks are prone to failure, using methods that cover the proper operation of both cyber and power networks together are essential for assessing reliability indices of smart grids. The importance of considering direct cyber network failures on reliability assessment is also studied. Cyber network topology transition is the main solution for the improvement of reliability assessment that is discussed in this paper. The method is simulated on an actual distribution network of Hormozgan Regional Electrical Company (HREC) of Iran. By calculating the reliability indices, the results clarify that smart grid reliability assessment will not be improved without considering an optimal cyber network topology. So, optimization techniques are used for improving reliability assessment, with the consideration of the constraints.

Due to the impossibility of predicting all possible states for agents in a wide dynamic multi-agent system, machine learning methods are useful tools to control agent behavior. Simulated Robot Soccer is a well known multi agent benchmark to evaluate machine learning algorithms. In this paper, QV-Learning algorithm (a well known reinforcement learning algorithm) is used to improve the performance of the attack in 2D robots soccer team. The reinforcement signal is defined based on the players involved in the attack can reach the ball in front of goal or lose the ball; They receive positive and negative reward according to the mentioned status, respectively. We use the idea of division the reinforcement signal proportional to the amount of expertness (knowledge) of agents to improve the performance. Here, the expertise is defined as the difference between highest action value and lowest action value in the each state. The simulation results show using the idea of expertise improves the train speed and the performance.

In this paper, a novel scheme for the design of the actuator fault-tolerant controller based on the fault identification in the linear systems has been presented. Fault identification has been proposed for the fault detection, determination of the faulty actuator (fault isolation) and estimation of the fault uncertain dynamics. For fault detection, a diagnostic observer has been designed for monitoring the condition of the system at every instance and generating the residual signal. After detecting the fault, the fault isolation module composed of a bank of observers has been activated to determine the faulty actuator. After determining the faulty actuator, estimation of the fault uncertain dynamics has been achieved by solving a linear matrix inequality. Then, a fault-tolerant controller has been proposed using the output of the fault identification module. The proposed scheme ensures the boundedness of the signals of the closed-loop system in the presence of the fault and satisfies the tracking objective. Simulation and comparison results were presented for verifying the effectiveness and performance of the proposed approach.

Channel estimation is one of the most decisive factors in evaluating performance of the MIMO systems. There are different methods for estimation of MIMO systems, that, adaptive algorithm is which. Among adaptive algorithms, Least Mean Square (LMS) algorithm is the most popular because it has the features of a proper filter including simplicity in applying and no complexity in using. In the other hand, some of important detectors in MIMO systems use QR decomposition for signal extraction, which is of a higher level of complexity. In this paper, a new method for MIMO channel QR estimation is proposed and instead of estimating the channel matrix H and them decomposing QR in detector, from the beginning, Q and R estimated directly actually, channel matrix is detained through the estimation of Q and R. according to MATLAB simulation applying LMS algorithm and QR decomposition in channel estimation with iteratively and/or SNR increment will result in the error reduction.

This paper has proposed an energy management system for domestic house applications considering electric vehicle. The first step is linked to optimal and adaptive scheduling of appliances and is defined as a multi objective function model with some fuzzy coefficient and owner comfort factors. The important aspect of this tool is to help customers respond to real time electricity price. In the second step, emergency demand response program (EDRP) modeling and the possibility of energy exchange has been investigated. In the third step, the electric vehicle charging planning has been investigated as adaptive and uncertainty. In energy management part, specific scenarios is simulated which reflect 60.96% of energy consumption reduction and the optimal scheduling of electric vehicle charging too. In the emergency demand response part two specific scenarios are simulated which reflect 66.77% of energy consumption reduction and peak shaving of 1 kW per house.

In this paper, an energy storage system is proposed for saving regenerative braking energy based on supercapacitor. This storage system includes a supercapacitor and two bidirectional half-bridge DC-DC converters. Since the metro network includes a forward path and a backward path, by using this proposed storage system it is possible to exchange energy between forward and backward lines. Also, it is affordable in terms of operating costs and reduction of losses because of using one storage devices for both paths. Therefore, the main purpose of this paper is designing storage system and also determination of its control parameters using Particle Swarm Optimization (PSO) algorithm with the objective of energy saving in metro network. Simulations has been done in MATLAB / Simulink software and based on actual data of Mashhad urban railway. The simulation has been done in two modes, with and without energy storage system. Some operational parameters including bus voltage and currents of substations have been obtained in these two modes. Comparison of simulation results shows the effectiveness of the proposed method.

In today's wide power systems, there is no possibility to test all lines for compensator devices placement. Therefore, formation of a list of priority system lines is essential. The most important methods for this purpose are locational marginal price difference method, congestion cost contribution method and line outage sensitivity factor method. In this paper, these three methods have been compared and with the introduction of congestion-causing lines and de-congestion lines as new concepts, the locational marginal price method has been modified and improved. Then in the proposed method, series compensators placement process is also examined with investigation cost consideration. To comparison the methods, all of the methods are simulated on New England 39-bus standard system and results confirm the efficiency of the proposed method.

In this paper, a stochastic model is introduced to study the influence of demand response programs on economic and system security indices in uncertain conditions. The power system under study includes thermal and wind power sources and superconductive magnitude energy storage in which, the uncertainties of load and wind power and stochastic outages of generation units and transmission lines are considered. The problem is formulated through a two stage stochastic optimization model and is solved by utilizing Mixed Integer Linear Programming (MILP) technique. The results of the studies of the 6-bus system illustrates that the employment of energy storage resources and demand response along with the generation reserve not only reduce the costs of operation in power system, but also maintain its security level through managing the uncertainties. Sensitivity analyses show that using the appropriate capacity of demand-side reserve not only reduces the cost of the power system but also improve system reliability and avoids additional investment. Moreover, The result s show that increasing load forecasting error has more effects on the operation cost of the power system.

In this paper, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) is introduced. The proposed method consists of four main processes including partitioning, clipping, mapping and equalization. At first, the input image histogram is partitioned into three portions with a same number of pixels. Next, the histogram clipping process is performed by comparing the clipped threshold level with the average intensities occurred on each sub-histogram. Then, each sub-histogram is mapped to a new dynamic range using simple calculations and finally, equalization process of each histogram is independently accomplished. The proposed TDCHE technique is presented to achieve multiple objectives of maximum average information content (entropy), enhancement ratio control and maintaining the reasonable brightness. In addition, this method leads to a natural enhancement in the output image by providing sharp images, while preserving maximum details far from unnatural phenomena such as intensity saturation and noise enhancement. Evaluation of the proposed TDCHE method performance in terms of information content as well as visual quality shows perceived superiority of the proposed algorithm in comparison to the previously presented methods based on histogram equalization.

In this article, a compact planar monopole antenna for Bluetooth and ultra-wideband applications with frequency band notch is presented. The proposed antenna is comprised of a radiating patch with two internal and external sections and defected ground. Due to the low profile structure, this antenna is designed for use in handheld and wearable devices in UWB and Bluetooth bands. Since this antenna is marked with Semnan University’s logo, it can be used as a sign on staff’s clothes. Dimension of this antenna is 12×16 mm2 which is fed by a meandered micro-strip line. The return loss and radiation pattern measured results corroborate well with the simulated results.

Today, green cloud computing has been concerned due to the reduction of environmental impacts. One of the criteria that has been emphasized in green cloud computing is energy consumption of data centers. One way to reduce energy consumption, which we is emphasized in this paper, is tasks scheduling management and consolidation of virtual machines. In this paper, an algorithm is presented to manage both tasks scheduling and load balancing. This algorithm, called the Shuffled Frog-Leaping provides a significant improvement against other existing models in terms of energy consumption and migration of virtual machines using memory, collaboration and sharing information among frogs, high convergence speed and better flexibility against local optimum problem. In this paper, the dynamic resource management is based on the consolidation of virtual machines and is implemented according to service level agreement by the proposed method. The difference between this method and other existing methods is that it shows improvement of time, speed and accuracy of convergence parameters. Experimental results show that the proposed method outperforms existing ones in terms of energy consumption, number of virtual machine migrations and service level agreement violation.

This paper presents a new scheme for the start up and control of a sensorless switched reluctance motor- generator using an auxillary field in motor mode. The proposed motor-generator is 4 to 2 with rotors of different geometric shapes and has two separate layers with a stator and rotor used in each layer. The main idea is that a high-frequency low amplitude sinusoidal pulse is injected to phase and a phase current feedback is achieved. Using AM demodulation, phase current waveform is obtained and thus rotor position is discovered. A sinusoidal digital signal is generated by digital signal processor (DSP) which is then given to digital to analog converter (D / A). Then it is applied to the auxiliary motor and its effect on motor phases is sampled by the analog to digital converter (A / D) where after processing by the DSP, commands necessary to fire are sent to phase transistor gate. In addition, delays are compensated and lagging is also investigated.

This paper considers shared backup path protection in comparison with shared segment protection for elastic optical networks. The problem is formulated in an integer linear programming framework. The objective is to minimize the weighted function of the total used spare capacity and the maximal index of frequency slots used in the network. For the proposed shared segment protection scheme we assume that the network nodes are empowered with the frequency spectrum switching capability. Using this capability, we are able to decompose the working path into smaller segments and protect that segments against possible failures. Each segment of a connection behave as a self-healing and independent unit. If one link of working path is subject to any unexpected interruption, the restoration is performed only within the corresponding protection for that segment. In the case that the nodes are not empowered with the frequency spectrum switching capability, we use shared path protection. Since the physical distance of each segment and the possibility of simultaneous failure of segments for a given path is rare, the required spare capacity in the segment based scheme is decreased compared to the path protection. Simulation results show that the total spare capacity used in the shared path protection is 14/5% greater than the proposed shared segment protection scheme. Also, shared segment protection is more resilient against the simultaneous links failure events compared to the path protection.

Web services as independent software components are published on the Internet by service providers and services are then called by users’ request. However, in many cases, no service alone can be found in the service repository that could satisfy the applicant satisfaction. Service composition provides new components by using an interactive model to accelerate the programs. Prior to service composition, the most important issue in finding suitable candidate services samples is their compliance with non-functional requirements. Thus, designing an efficient way to combine a chain of connected services is important. Recently, numerous studies have been done to reduce the search time in finding a service composition. However, many of these methods to examine and investigate all Web services in a Web repository require a long time, which occupy the user's time significantly. This paper provides an approach for automatic quality-aware service composition as well as the users’ preferences in achieving the optimum composition results. For this purpose, modified graph coloring method to filter the data before compositions in large-scale data is used which decreases selected services set. The application of KPL algorithm in this study provided some proper solutions to the user so that these solutions can be used instead of the best composition if necessary. Therefore, the results derived from the analysis of the proposed method, indicates a good optimization in runtime and memory consumption.

In this paper , a topology is proposed to realize a new transimpedance amplifier (TIA). The proposed topology reduces the input and output impedances by using a common source transistor as a voltage-current feedback. In this topology instead of using a resistor to convert voltage to current, we convert transistor transconductance into transimpedance, and then by applying an electrical current to drain the required voltage appears at the gate terminal. Furthermore, a TIA circuit is designed on the proposed topology. Simulation of the designed TIA for 1.8V 0.18µm CMOS technology shows that gain of 59dBΩ with 1dBΩ gain ripple of the bandwidth of 10.6GHz can be achieved. While the whole TIA circuit consumes 18mW from 1.8V power supply the simulated average input current noise spectral density is about 21 within the TIA frequency band. Above result is calculated with 300fF parasitic capacitance of photodiode. In this topology new tradeoffs are possible which make a further degree of freedom which are not available in the previous topologies.

Due to the significant impact of fractures on the better and more effective production and harvesting, the identification of this phenomenon is a very important. Imaging logs are very powerful tools to study the fractures in the boreholes. In an imaging log, a fracture is seen in the form of a sine curve. In this paper, first, the useful features are extracted by using feature extraction methods such as the Gabor filter bank, Zernike moments, Hu's seven invariant moments and directional Walsh-Hadamard transform, and then, a SOM neural network is used to classify the imaging log. The experimental results showed that the proposed algorithm is able to detect the existing fractures successfully with high accuracy in the imaging logs. In the proposed algorithm, the feature extraction methods are used, which are suitable for extracting the feature of texture objects. The results show that the accuracy of the proposed method is very high to extract fracture pixels, and it has also low sensitivity to noise in the imaging logs. The proposed algorithm in this paper was applied to two types of FMI and RMI image datasets, and the result of the classification has better accuracy in comparison with other algorithms.

Recommender systems for websites do web personalization online and intelligently using recommendations to the users. These systems can be developed by web usage mining techniques which model user navigation patterns. In other words, data mining methods can be used to discover user access patterns from web logs. Then, recommendations to the users are provided based on these patterns. A variety of recommender systems based on web usage mining have been proposed, although improving the efficiency and complexity of them is still a challenging issue. In this paper, a recommender system called Pino has been proposed. A new approach for mining access patterns is proposed in Pino. In this approach, users' navigation patterns are modeled with a directed and weighted graph. Its vertices are webpages and the edges indicate their correlation based on co-occurrence frequencies between webpages. The weight of edges is calculated on the basis of the inverse conditional probability of viewing the webpages by considering their order. Then, webpages are clustered by partitioning this graph based on shortest paths. Recommendations will be generated based on discovered patterns with constant time complexity and with consistency to the statelessness property of HTTP protocol. Pino has been evaluated on web server logs. The effectiveness of recommendations has been evaluated by criteria applicability recommendation, correct recommendations, accuracy and coverage. Evaluation results indicate the ability of the system to improve quality of recommendations so that the harmony mean of these criteria in Pino system has reached 57% improved by 12% compared to previous systems.

In this paper, the communications between follower agents in the leader-following tracking control of Multi-agent systems with multivariable single integrator dynamics, are reduced. A fully distributed control scheme is proposed in which a local non-combinational decision variable (DV) is continuously observed. If the norm of the DV’s measurement error violates a time-dependent threshold, an event is occurred and the DV is sampled, stored in a data store unit (DSU) and sent to a neighbor-to-neighbor low bandwidth communication network (CN). Also, the last sampled neighbors’ data received from the CN, are stored in the DSU. A control law computed from the last stored data, is fed to the agents’ dynamics. It is shown that under this control law and by using the proposed event-triggering mechanism, the follower agents’ tracking error remains bounded and the resultant closed-loop system does not exhibit Zeno behavior. The numerical simulation is included to verify the effectiveness of the proposed control scheme.

The distillation process is important process in the chemical industry and has wide application in industry. Distillation tower is used by chemical engineers as a popular tool to separate materials and is the most common method for separating materials. Keeping constant the product composition in the distillation column is very important from control perspective. Control of these complicated processes need intelligent methods to adopt the appropriate decision for control based on the behavior of the system. Between intelligent methods, fuzzy technique has superior response in complex systems control and so is used in this study. At first, a type-1fuzzy controller is designed for non-linear model of distillation tower. In design of this Fuzzy controller, genetic algorithm (GA) is used for optimization of fuzzy. It has been shown that the fuzzy controller is better than conventional PI one. Then the type-1 fuzzy controller has been replaced with type-2 fuzzy controller and to optimize the performance of fuzzy-2 control, its output gains is determined by GA and has been shown that the performance of type-2 is better than type-1 in various points of view. In this study, the MATLAB/SIMULINK software has been used for modeling and implementing the proposed methods.

Data accuracy is one of the important aspects of data quality. According to high volume of data sources an automatic method is required. In this article an automatic method is proposed for cleaning of data with various data types. Initially, records that may contain incorrect attributes are detected using functional dependency, so that each record that inconsistent more than records for one functional dependency, probably is error. Thereafter ensemble learning is done for each attribute of data source. Ensemble learning contains 3 classifiar naïve bayes, decision tree and MLP, and voting is combination strategy. It is trained using correct records that identified in the initial steps. After training, each incorrect attribute is placed as target and predict values for it. Proposed method is able to clean data in data sources with different data types. Experiments show the true negative rate in detecting error part of the proposed algorithm is averagely 93.7% and in cleaning error part is averagely 90.6%. Also experiments show that evaluation parametrs are improved in proposed method compared with 2 similar methods.

In this paper a novel class C voltage controlled oscillator (VCO) is presented. The presented structure reveals proper starting time characteristics, lower phase noise, higher oscillation amplitude and lower consumption power. Simultaneous application of digitally tuned Varactor diode and 4th order tank circuit improve phase noise characteristics of the VCO. Amplitude feedback mitigates the phase noise and power consumption of the structure. The presented VCO oscillates in 3.2GHz-4.2GHz frequency range with the bais voltage of 0.5V-1V. For robustness analysis of the circuit against process, voltage, and temperature variations (PVT), phase noise comparison for FF@-40°C and SS@85°C technology corners, in addition to TT@25°C, are simulated for offset frequency of 1MHz. Additionally, for estimation of non-ideality effects of the circuit on frequency range variations, Mont Carlo simulations with 50 samples is performed. It is assumed in this simulation that capacitors and inductor have Gaussian distribution with ±5% and ±10% variation range, respectively. The VCO is implemented with 180nm TSMC CMOS technology and is simulated with the ADC commercial software.

The main goal of this paper is to structural and mathematical analysis of the network losses in different load levels. In order to achieve this aim, at the first step, the market is dispatched and the structural generation decomposition is proposed in lemma 1. This analytical decomposition reveals the sensitivity factor of marginal unit's generation to each bus load variation. At second step, in Lemma 2, the analytical relationship between the change of lines flow and load changes has been obtained. Finally the Loss Index for each bus, LIn and the network LIsys analytically is defined, using lemmas 1&2. LIn analytically indicates the variation of network losses by variation of load at bus n. It is expected that the LIn be the positive value. But, according to the market problem, load and generation disperse and congestion LIn may be the negative value, unexpectedly. In this paper, in view point of the losses, the critical load point of each bus and the network is defined as the bus load or the network load value that change the sign of the LIn and LIsys, respectively. Moreover, the critical area of the load in viewpoint of the losses is defined as the load area that leads the maximum network losses. These results can be useful to decision making on the location options of the DSM programs. Finally the three IEEE standard networks are analyzed.

In this paper, we have supposed that there is meaningful relationships between the class labels in the multi-label classification problems and based on it, we have proposed two novel methods to improve the base classifier chains (CC) method for multilabel classification. In this paper, association rules are employed to determine the order of classifiers in the CC method for the first time. In the proposed methods, association rule mining is first employed to model the relationships between the class labels and then, an association graph is built based on the extracted rules and finally, the classifier chains is built based on the obtained graph. As there is meaningful relationships between the class labels in the real multi-label problems such as classifying the images and texts and medical applications, the proposed methods will improve the classification results in such contexts. Extensive experimental evaluations conducted on the benchmark datasets in the multi-label classification context show that to use the associations between the labels in constructing the classifier chains improves the results obtained by the main evaluation measures.

Association rules are used to find hidden relationships and dependencies among different itemsets in the database that is extracted in the form of the rule, but the problem with this approach is the discovery of sensitive information and the treatment of information privacy. The sanitization process data is considered as a NPHard problem. In this article, we try to reduce support the sensitive itemsets in the transactional database using multi-objective genetic algorithms and the support-based approach. The proposed approach with the transaction deletion that includes sensitive itemsets leads to less support sensitive itemsets than the minimum support threshold and leads to the database sanitization. In each iteration of our method leads to increase the speed and reduce the performance criteria by one time of the scanning of the sensitive transaction instead of scanning the entire database of transactions. In addition, to reduce the effects of hiding the sensitive itemsets, the transactions sort based on the shortest length, the most sensitive itemsets and the least non-sensitive itemsets.

Application of power electronic converters in wind turbines results in generation of harmonic and interharmonic. Also, the switching of converters results in turbines active output power distortions. Harmonic interaction of wind farms with external grid, harmonic propagations in it and amplification of these propagations due to resonance of underground transmission cables, can leads to increase in power quality problems. This paper presents a method for study and classification of conventional current harmonic emissions in a wind farm. This method uses the concept of transfer function in order to evaluate the harmonic contributions of each wind turbines at point of connection as well as the contribution of external grid background harmonic voltage on each turbine current harmonic distortion. In this regard, extraction of voltage and current transfer functions, transfer admittance and impedance is done based on circuit theory and harmonic modeling of wind farm components from medium voltage (MV) side of turbine transformer in frequency domain. Two classification of harmonic propagation called as “primary emission” and “secondary emission” is evaluated based on these functions. With use of this method, the contribution of each turbine and also the contribution of external grid at overall wind farm emission can be decomposed. Also, the impact of transfer function of conventional LCL filter used in wind turbines, on each those transfer functions, is studied. The important conclusion is that the presence of this filter causes that the amplitude of primary harmonic transfer function from one turbine to another turbine, in special frequency range, be more than the amplitude of primary harmonic transfer function from one turbine to the grid.

In this paper we deal with the optimal coordinated voltage control issue. A common control procedure for this problem is the model predictive control. A major step toward model predictive control in coordinated voltage control issue is the prediction of voltage response trajectory. This paper presents a new technique to predict the trajectory of voltage response based on two points. These two points are as follow: 1- The moment immediately after applying control actions 2- The steady state of the system after applying these actions. The new method is based on this principle that in coordinated voltage control the direction of voltage response alterations based on control measures is monotonic. The control method utilized in this paper is adaptive and flexible. The adaptive feature refers to controllability of system with a change at the operating point. By flexibility feature, we mean solving an optimization problem with a multiobjective SPEA2 optimization algorithm and providing a set of nondominated solutions which enable system operator to determine and apply the best control actions based on economic and operational conditions aided by a multiple criteria decision-making technique based on a priority matrix.

Now days using of distributed generations (DG) are growing. Without considering appropriate controller, their operation is not useful and efficiency. DG has less impact on the network frequency than synchronous generator (SG) due to its low inertia compared to SG. DGs using power electronic converters are connected to low-voltage networks, so that converters work in voltage control mode and power control mode.In this paper, a new control method to investigate the role of participatory voltage source converters with synchronous machine to frequency control of the microgrid has been presented. In this regard, the cascaded frequency-angle loop control with adaptive nonlinear controller in order to power sharing and frequency control is used. In the following, new algorithm for evaluating the effects of VSCs with synchronous generator is presented. Finally, the accuracy and authenticity of the proposed method, using simulations used in software Simulink Matlab evaluated and approved.

In this paper, a stochastic programming model is proposed for energy management of a Virtual Power Plant (VPP) including solar power stations, energy storage facilities and demands interconnected within a microgrid. In the stochastic programming model, uncertainty in availability of microgrid components is modeled as a scenario set. Additionally, uncertainty in energy prices and solar power productions is modeled as single-point prediction. Energy management of the VPP is implemented in two stages of decision-making and power-dispatch in each time period. An energy management system (EMS) determines the power traded with main grid in decision-making stage considering probable contingency scenarios. In power-dispatch stage, the EMS makes appropriate decisions on energy consumption and production, while monitoring conditions of microgrid components and updating available information in real-time. The energy price and solar energy production are forecasted based on real-world historical data collected from the New England electricity market, US. Results obtained by a case study indicate performance of the stochastic programming model compared to robust optimization and deterministic models.

This paper proposes a generalized carrier-based pulse width modulation (GCB-PWM) technique suitable for multi-level back to back (BTB) converters. The objective of GCB-PWM technique is to produce a pulse pattern in such a way that the first non-eliminated harmonic contentof the output voltage is eliminated or reduced below its permitted value without increasing the switching frequency. This is achieved by fixing the amplitude modulation index in some special values but changing the intermediate dc link voltages instead; leading to salient advantages such as the total harmonic distortion (THD) improvement and output filter size reduction. The proposed GCB-PWM technique has been applied on a seven-level BTB cascaded H-bridge (CHB) system. Simulation results show the superiority of this technique over the conventional CB-PWM from the THD and filter size points of view.

This paper proposes a framework to demonstrate the impact of renewable energy sources' (RESs) uncertainties on the optimal sharing of energy and optimal allocation of different types of RESs in the distribution system to minimize energy losses. Because of the huge annual lossesin distribution networks, loss reduction in these networks will be desired from the distribution system operator's perspective. Loss reduction can have significant savings in energy consumption and justify investments to improve network efficiency. One of these investments is optimal location and sizing of distributed generations (DGs) such as clean and renewable generation sources in the network. Uncertainty in RES- based DGs power generatiom and energy demand, makes the planning and operation of distribution networks a challanging problem. As a result, it is assumed that RESs can share a small percentage of their nominal capacity.The proposed method, guarantees the optimal energy sharing and placement of RESs for all possible operating conditions.The inherent uncertainties of RESs are modeled via scenario base method. Penetration limit and number of DGs determined by sensitivity analysis. The proposed model is implemented on the IEEE standard 33-bus radial test systems, and solved by General Algebraic Modeling System (GAMS) optimization software. The simulation results show that appropriate dispatch of energy on the existing resources and minimization of energy loss could be obtained with respect to optimal energy sharing and placement of RES. Analysis results of probabilistic and deterministic cases, substanciate the lossreduction about 35%.

Distributed generation by PV systems in residential buildings, is a suitable approach to reduce the peak load of the grid and extending the use of renewable energies. A big challenge in persuading the consumers to use such solar systems, is to create economic feasibility. In this paper, techno-economic feasibility study of a residential PV system with two pricing methods is presented. SAM software is used to model the system. The results showed that the tiered pricing method has a better economic efficiency than flat pricing method; where the net present value and payback time have relative better values. The net present value and payback time in tiered pricing are 3709$ and 5.2 years, while in flat pricing method, these values are calculated as 2468$ and 6.5 years.