Search

In today's transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as the imbalance between generation and demand-side (consumption) leading to increasing the cost of operating the power system. On the other hand, distributed generators (DGs) in distribution networks (DNs) can be a relatively good alternative to-the-disconnected GUs with their ability to react quickly. Therefore, robust cooperation-for integrated TN&DN is essential. However, it is impossible to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators' independence, and the information privacy. Thus, in this paper, a linearized ACOPF model for the robust cooperation of TN&DN problem is presented along with an efficient hierarchical decentralized solution method to solve the robust cooperation of integrated TN&DN in which the information-privacy-is-maintained-for both network operators. In this paper, the numerical simulations are analyzed for standard IEEE 30/118-bus transmission and IEEE 33/69-bus distribution networks. Finally, the paper validates through simulations the accuracy and computational-efficiency-of the proposed robust optimization problem and hierarchical decentralized method. [ABSTRACT FROM AUTHOR]

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator's voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency. [ABSTRACT FROM AUTHOR]

The choice of a mathematical model of eye movement has had a significant impact on scientific research. This research resulted in a controlling robotic arm that could follow the movement of the human eye and thoroughly examine the structure of the human eye. Exploring the previous models, a model equivalent to a robot with two degrees of freedom is presented. Then, by designing and simulating an optimal linear square controller on the desired system, we could model an eye robot. The purpose of tracking is to target the system, which is shown by simulating this controller. This paper describes how to design a fuzzy controller for the system by presenting the simulation results. Finally, the performance of the two control methods in non-zero initial conditions, rapid eye movements, and uncertainty in the model system constructions are compared. The simulation results show that despite the system control provided by both control methods, the fuzzy control method has a better performance for tracking fast eye movements. This method is resistant to modeling uncertainties by using the qualitative behavior of the system instead of its exact mathematical model. Also, the smaller volume of simulation of control inputs in the fuzzy controller will significantly reduce costs by using this controller. [ABSTRACT FROM AUTHOR]

In this paper, to solve the multi-objective problem of distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the flexible manufacturing system lot-sizing with the co-production problem is modeled. It is also simplified using the relationships between variables. One of the common methods for solving such problems is the fix and optimize algorithm, which is generally used in a one-dimensional approach. Also, a two-dimensional fix and optimize algorithm is applied to solve the problem. This algorithm is compared with two common algorithms using simulated data series. Design/methodology/approach: In this paper, Flexible Manufacturing System Lot-sizing with Coproduction problem is modeled using mixed-integer programming. The production of products in this flexible system varies with the change of production mode, and a different mixture of products is produced for each production mode. Also, the planning interval includes T periods, and the demand for each product in each given period is constant. In each period, a fixed setup cost is added to the production and maintenance variable costs, if production occurs. The objective function of the model minimizes the sum of fixed setup costs and production and maintenance variable costs of inventory in each period and each production mode. Problem constraints include setup forcing constraints, inventory balance constraints, initial inventory constraints, co-production constraints, production mode constraints, non-negative variables constraints, and binary variable constraints. Among the methods proposed to solve this group of problems, the fix and optimize method is one of the most effective and general methods. The basic idea of this approach is that due to the difficulty of solving the main problem with a longtime interval, a problem with a shorter time interval called the time window is solved instead. Except for the variables in the time window, other integer variables are considered continuous variables, so the resulting problem is easier to solve. In the following steps, the time window variables in the current step are assumed constant, and this repetition will continue until the end of the desired periods. Time windows can be considered with or without overlap. In this paper, two innovative one-dimensional fix and optimize algorithms based on time and production mode variables and a new two-dimensional algorithm based on time and production mode variables are applied to solve the model using simulated data at three levels of small, medium, and large scales. MATLAB 2016 software is used to code the algorithms of this study, and numerical calculations are performed by a personal computer with Intel®Core™i3-7100@3.90GHz processor and 8 GB RAM. Findings: The research results indicated the significant superiority of the proposed two-dimensional algorithm in terms of response time over the two one-dimensional algorithms. It is important to note that in terms of the quality of the answer in the studied problems, no significant difference was observed. Research limitations/implications: In many real cases, due to the fact that the cost parameters in different production situations (e.g., the oil, gas, and petrochemical downstream industries) are close to each other and in practice, determining production conditions in accordance with other parameters such as demand is independent of production costs, the efficiency of the proposed algorithm will be more visible in this article. The most important limitation in this study was the lack of real data for a flexible production system with correlated products, which is why simulated data were used to validate the model and test the proposed algorithms. Practical implications: In the future, researchers can use real-time case studies based on the proposed model and algorithms in this paper. They can also add other features to the model, such as limited production capacity and allowable shortages. Manufacturing plants that have features similar to this study can benefit from the findings to optimize production costs. Social implications - Applying the results of this research can increase the productivity of production units and the use of non-renewable energy resources. Originality/value: In this paper, a mathematical model (MILP) was proposed for the Flexible Manufacturing System Lot-sizing with Co-production problem. In addition, an innovative twodimensional fix and optimize algorithm was developed. [ABSTRACT FROM AUTHOR]

Purpose: Project deviations are so significant that various methods have been proposed to measure them over the years. Earned Duration Management (EDM) is a method that measures project evaluation indexes only by considering the past trend of project execution compared to the baseline program. It ignores all the other factors, which cause much information to be lost to guide managers. Therefore, in this study, a process is proposed. During this process, it is possible to develop EDM indicators using key performance indicators of project quality in a fuzzy manner and based on the identified substantial risks. Design/methodology/approach: A list of risks was initially identified based on the PMBOK standard's category, and project managers selected the most important ones. In the next step, based on the most critical risks, two key indicators of project equipment and execution performance were defined. Such indicators were presented to managers again to determine their future impact on the project. By defining a triangular fuzzy number, the definite evaluations became fuzzy, since experts expressed the effect in linguistic variables. Finally, the indicators entered into the EDM, and the method was developed. Findings: In this study, Key Performance Indicators were used to increase EDM’s accuracy. First, the three risks of delay in receiving project resources, lack of timely allocation of financial resources, and disruption in materials preparation were identified as the main risks. Then, to study the status of the risks, two indicators were defined (PSI, PEI). These two indicators were estimated, up to 50% of the simulated project. Concerning the first indicator, the more the value, the more acceptable project performance was. Considering another indicator, the situation was contrariwise. Then, the future status of the indicators was examined by consulting managers and making the indicators in a fuzzy manner. The effect of the indicators was determined through the EDTC variable in the EDM. It should be noted that the final estimated time was used to measure the accuracy of the proposed method. The studied project was simulated using the prevailing situation in Matlab software. The end time of this simulated project was estimated at 1300 days. Then, the project was evaluated day by day till its 50% completion using EDM, and the end time of 1225 days was estimated. The same evaluation was performed for the proposed method, and the final time (1270, 1247, 1240) was determined. Because this time was closer to the simulated time, it was evident that the proposed method had the highest accuracy. Research limitations/implications: In this study, to prevent information disclosure, project simulation was used instead of the actual project to evaluate the indicators and advance the project. Therefore, although the accuracy was estimated in this paper, its more exact value might be requested in future research. Consequently, the proposed method can be applied in an actual project, and the accuracy of the model can be measured, consequently. Practical implications: The proposed process and method in this research can be used in the implementation and planning stages of any other project. The process should be localized because the risks vary from project to project. Besides, the critical risks of each project cannot be compared with the other projects. Therefore, after examining each project's risks and defining indicators related to the selected risks, the proposed method can be applied in all project-based organizations and on any project type. Social implications: Today, the construction and operation of projects are among the factors that affect the countries' growth and development. Deviations from the budgets allocated to the project are an integral part of project implementation and are not dependent upon a specific area or even a particular management and implementation method. Therefore, proper planning and control of projects are essential. This paper provides an effective model for senior project managers to complete projects on time. Successful implementation of projects leads to economic prosperity. Also, improving the quality and performance provides a calmer social environment for people and creates many environmental benefits. Originality/value: In this study, the indicators of the EDM method were developed using the key performance indexes. Such indexes were associated with the quality of project performance execution and were estimated under uncertainty conditions; thereby, more information can be transferred to the project management. The defined performance indicators are measurable during the process, and their impact on the ongoing project is monitored. The innovations of this study are twofold: Applying the performance index developed the EDM method; and defining the quality performance index in fuzzy terms. [ABSTRACT FROM AUTHOR]

Conventional droop-based techniques have widely been used in controlling Voltage Source Inverter (VSI) based on distributed generation units within a Micro-Grid (MG). Despite the simplicity and high reliability of the controller, the low-inertia nature of VSIs threatens the system stability against probable disturbances. Thus, from the stability viewpoint, it is essential to carefully monitor the system operating point to determine the stable region of the parameters affecting the micro-grid stability. To this end, a complete dynamic model for an inverter-based micro-grid has been derived. According to this model, states affecting the dominant eigenvalues of the system are identified using eigenvalues analysis and modal analysis. Then, bifurcation theory is used to predict the parameter stability margin and identify the type of bifurcation. In this paper, by introducing a bifurcation index, the appropriate virtual impedance X/R ratio is selected from the viewpoint of both small-signal analysis and bifurcation analysis. Finally, a multi-objective optimization framework is presented to prevent such a problem in parallel to other operational goals. [ABSTRACT FROM AUTHOR]

Active superconducting fault current limiter (ASFCL) is a new type of FCLs which can limit the fault current at different levels. It also has a particular ability to compensate for voltage series in the electrical power systems. In this paper, an optimal coordination method is proposed to calculate both optimal setting parameters of overcurrent relays (OCRs) and limiting the impedance of ASFCL. The optimal parameters of OCRs are obtained using the maximum pickup current and minimum trip time method. In the next step, by applying a new DG unit to the microgrid, the optimal limiting impedance of ASFCL is obtained so that all OCRs could be coordinated without changing the relay setting parameters. Using MATLAB software, a typical distribution system connected to a microgrid with ASFCL is simulated and the simulation results are evaluated in terms of the fault current limitation and OCRs coordination. Simulation results confirm the appropriate performance of the proposed ASFCL for the above-mentioned purposes. [ABSTRACT FROM AUTHOR]

The implemented method for data recovery in some practical code division multiple access (CDMA) systems is the matched filter detector which has zero decision threshold in the symmetric modulations. Because in the multiuser communication systems, the cochannel interference changes the observation noise model to Non- Gaussian one, using zero thresholds is not optimal anymore. In this paper, we obtain the optimal threshold of the matched filter detector in the presence of the block faded interference and fast frequency flat Rayleigh faded desired user signal. We show that the probability of error for this system can be decreased using the obtained optimal threshold. To this end, we derive the analytic probability of error of the communication system with interference which leads to the condition of having an optimal threshold for this system and its value. We have also compared the obtained analytic results with the Monte Carlo simulation curves to validate the derived analytic expressions. [ABSTRACT FROM AUTHOR]

In this paper, a new control method based on the linear matrix inequality is used to control the speed of a hybrid stepper motor. The proposed method is proved based on the Lyapunov criterion. The proposed method has a higher degree of freedom than conventional robust controllers, which can be used to better control the system. The proposed method also exhibits high robustness against the uncertainty of the hybrid stepper motor parameters as well as the applied disturbances. Also in this method, the effect of rapid changes in the reference speed of the hybrid stepper motor are considered so that the controller can have more effective tracking. The proposed control method is compared with the Self-tuning PI controller and PI (Ziegler-Nichols) controller in multiple scenarios considering the uncertainty of the parameters and different disturbances. The obtained results indicate that the proposed controller has a better performance against the disturbances and uncertainty of the parameters in comparison with the abovementioned controllers. It should be noted that the simulations have been performed in Matlab. [ABSTRACT FROM AUTHOR]

One of the most important challenges that threaten the reliability of network-based chips is soft errors. Such problems occur mainly in buffers on the chip networks. Given that these buffers make up between 50 and 90 percent of the area of routers, the likelihood of soft forms is high. In addition, the increase in chip technology size (from 130 nm to 22 nm) has caused the rate of occurrence of single-event upsets to be 6 to 7 times higher. The shrinking size of the technology has also increased the likelihood of converting single-event upset errors set to multi-event upsets, so it is predicted that by 2021 most soft errors of the multi-event reversal type will occur. Many different methods of avoiding and tolerating soft errors do not have the ability to deal with multi-event events due to different patterns. On the other hand, the rate of crosstalk fault is increasing. The purpose of this paper is to provide a method that, while improving the tolerance of single-event reversal events in buffers, is also able to tolerate multi-event upsets. The proposed method, called Priority Flit, is based on the different importance of tailer and data flits. In other words, the fault tolerance method against soft errors in tailor and data is different. The second one is leafing and the second one is based on crosstalk avoidance and Hamming code. Results show improvements in terms of hardware and performance with respect to the stateof- the-art mechanism. [ABSTRACT FROM AUTHOR]

Most of the proposed methods for investigating the transient and short-term voltage (STV) stability have assumed that only one of the two phenomena exists, i.e. either transient instability or STV instability. While these two phenomena exist simultaneously with one of them being dominant, according to the operating point of the system and disturbance condition. A unified online prediction method is presented in this paper to distinguish between the stable and unstable conditions. In the proposed method, by employing the intelligent wide area measurement system (IWAMS), the frequency deviation and the active power imbalance are calculated by the generation buses, which are considered as the intelligent generation buses (IGB). By tracking the trajectories of the frequency deviation and the active power imbalance and checking some simple defined rules, early prediction of both transient and STV instability can be achieved. In order to evaluate the performance of the proposed algorithm, the method has been tested on single machine infinite bus (SMIB) system, single machine load bus (SMLB) system, IEEE 39-bus system and IEEE 118-bus system. According to the obtained results, the proposed algorithm has the overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a non-homogeneous NDEA model for measuring the efficiency of a mixed network with an open structure at each stage, interlayer connections, and uncertainties in inputs, outputs, and intermediate processes. Design/methodology/approach: To model the problem, a new model has been proposed first to determine the efficiency of each layer, based on the average weight of the layers. Then, the model has been developed to calculate the total efficiency. The Bertismas and Sim approach has been used to consider the uncertainty of inputs, outputs, and intermediate processes. Pistachio orchards in Yazd province have been selected as a case study, and relevant data of 10 cities in the Yazd province has been collected from the Agricultural Jihad Organization of Yazd province. Findings: The results indicated that with increasing the deviation in uncertain network data from 0.01 to 0.1, the average total efficiency decreased from 0.933 to 0.915. Such a reduction was also observed in the efficiency score of each of the DMUs. Reducing the average levels of efficiency and tightening the constraints on the proposed models reduced the number of units on the Performance boundary and eliminated the need to use super-efficient models to re-evaluate the efficiency. In other words, the computation of the total efficiency of decision-making units and sub-layers was enhanced. Research limitations/implications : The proposed approach was developed based on the model of Charans et al. (1978). As a limitation, the weighted sum of the outputs was less than or equal to the weighted sum of the inputs. The proposed model can be developed further to include other factors, such as undesirable outputs. Practical implications: Findings can provide a more accurate understanding of the performance of the components of decision-making units to managers and decision-makers. This study highlights the usefulness of the proposed models as a decision tool in agricultural units. Social implications: The developed models can be used in various social contexts. According to the case study, it is overemphasized that water resources management is significantly important for Yazd as a province located in an arid and desert region. Therefore, increasing the efficiency of orchards by identifying and improving inefficient processes, can lead to helpful-agricultural consequences in this province. Originality/value: While many studies have been conducted in the field of NDEA, this is the first study in the field of open non-homogeneous mixed networks considering uncertainty in all data, simultaneously. [ABSTRACT FROM AUTHOR]