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This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and hai'monic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP. [ABSTRACT FROM AUTHOR]

In this paper, a method for evaluating the impact of load response on microgrids is presented. Several different modes have been stimulated to better understand the problem. A hybrid optimization algorithm including gray wolf optimization algorithm and shark olfactory optimization algorithm to optimize the multi-purpose objective function under limited conditions and constraints. In addition, to determine the uncertainty in the production of renewable energy sources, the Monte Carlo method has been used to produce the scenario. The parameters considered in this method include technical parameters such as network losses, generation cost, and reliability index and voltage deviation. The proposed method is implemented using a hybrid optimization algorithm using MATLAB software on a modified 69-bus system, including wind turbines, solar power plants and energy storage systems. The results show that the proposed method will increase network productivity. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model for increasing the precision of the support vector machine classifier is proposed to detect stator windings short circuit fault detection in induction motors. The proposed method consists of three different phases, wherein the first phase the statistical features of a healthy and defective data set are extracted. The principal component analysis is used to reduce the dimensions of the obtained features. Then, different SVMs are constructed based on training data sets. To achieve a better result, the parameters of the SVM are determined by the fractionalorder chaotic particle swarm optimization algorithm. Finally, a hybrid model for combining SVMs with type-2 Fuzzy logic is implemented. The proposed approach is then applied on measured stator current data for stator winding short circuit fault detection in a three-phase induction motor with 2.2kW, 50Hz, 6 Pole. The average accuracy of 98.4% of the detection of stator winding error on laboratory data under different load conditions indicates the performance and validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the major issues in the planning stage for distribution networks is the optimal design of microgrids (MGs). One of the important goals in the design of MGs is supplying the load with the highest reliability and the lowest cost. Construction of MGs in the distribution systems may have many benefits such as local control strategy in order to minimize the interaction between different MGs, prevent the fault propagation and finally, increase the reliability of the system for consumers and distribution companies. Therefore, in this paper, with the aim of improving reliability and cost, three different approaches to constructing multi-microgrid (MMG) systems in the active distribution network (ADN) are proposed and compared. In this way, by determining the location and capacity of energy storage systems (ESSs) as well as clustering of ADN based on the mentioned objectives and proposed optimization algorithms, in different scenarios, using the Monte Carlo (MC) algorithm and the Forward-backward load flow, the objective function is calculated. Then, with the repetition and various suggestions made by the optimization algorithm, the optimal value of the objective function is obtained and the results of the designed approaches are compared. To simulate and demonstrate the effectiveness of the proposed method, the 119-bus test distribution system is used. [ABSTRACT FROM AUTHOR]

The optimal pricing of goods and services is one of the most challenging issues in economics. Scarcity of resources which causes a good or a service priced more expensive, on the one hand, and its costs of production on the other, motivate researchers to find optimum price. This paper investigates an optimal pricing scheme for Iran’s IXPs services including 1Ge ports, 10GE, and 100GE ports through a Fuzzy Geometric Programming model. “Internet exchange points” (IXPs) are infrastructures through which Autonomous Systems (AS) such as Internet Service Providers (ISPs), Content Delivery Networks (CDNs), Content Providers (CPs), and other internet networks, peer with each other to exchange internet traffic more efficiently. These points provide their members with a wide variety of services. How should these services be priced so that the maximum number of potential members can be connected to IXP and also the profit of IXP is maximized? The FGP model answers these questions. According to the results of this paper, the demand for the services of IXPs is a function of the price of ports and the number of services provided in each IXP. This demand function is normal; i.e., the quantity demanded has a negative relationship with the port price and a positive relationship with the number of services. The demand function for 1GE ports is price inelastic, while the elasticities of the price for 10GE and 100GE is greater than 1. Also, it has been shown that the optimal price of the Iranian IXP services based on the FGP model proposes a higher price of 10.3% compared to its current price, and a lower price for 10GE and 100GE ports of 1.75% and 14.5% respectively compared to the current price. The sensitivity analysis shows that the changes of price elasticity up to 90%, have no impact on the profit of IXP, but it has a small effect on the optimum price.

Conceptual design optimization of spacecraft systems is a complex and multidisciplinary process. In this case evaluation of the objective functions relies heavily on running iterative simulation models and analysis codes between various subsystems (such as structures, payload, electrical power supply, attitude determination and control, communication, command and data handling). The conventional sequential optimization approaches to such a complex design problem is time consuming and does not guarantee to achieve the best compromise among the various competing coupled subsystems, and may even lead to non-optimal design. In addition, the design search space can be multi-modal, non-convex with multiple local minima and hence it is time consuming or difficult to rapidly evaluate trade-offs between various subsystems (disciplines). To address these issues, in this paper an efficient surrogate (response surface) model-based multidisciplinary spacecraft systems design optimization technique with discrete and continuous design variables is presented. The methodology is based on the utilization of genetic algorithms (GA) for both system level and discipline level as an optimizer. Surrogate-modeling as an efficient tool is also used to decrease computational cost in discipline (subsystem) level within a collaborative optimization (CO) framework. Results obtained in this study show that the method introduced in this paper provides an effective way of improving computational efficiency of a complex space system design such as conceptual design optimization of a spacecraft. [ABSTRACT FROM AUTHOR]

Optimization is a process that has long been considered by researchers in various fields and its methods have been utilized to increase productivity while reducing costs. Moreover, it is essential to achieve simultaneously several contradictory goals. In control engineering, optimizing controller parameters to attain several different goals could be considered as a significant challenge. In this paper, a fractional-order PID controller is designed for a training helicopter with two DC motors, called Twin-Rotor Multi Input Multi Output System (TRMS). The design of this controller is based on the optimization of different objective functions with metaheuristic multiobjective optimization algorithms. Finally, their Pareto front and different evaluation criteria such as Spacing Metric and Non- uniformity of Pareto Front are employed to compare the performance of these algorithms for optimization of controller parameters on the decoupled TRMS system. The results show the superiority of the proposed multi-objective fractional-order PID controller for the TRMS system. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

Production of double-stepped metal sheet parts is considered a complex and difficult task in industries. The crankcase is such a complex double-stepped part for the automobile industry and its production with traditional methods is associated with many problems. In this paper, the formability of this stepped part has been studied experimentally and by simulation using hydrodynamic deep-drawing with radial pressure. It is shown that the crankcase can be formed successfully in one step by the hydroforming process. Moreover, the effect of fluid pressure on the thickness distribution and die filling and the effect of geometric parameters such as punch corner radius and the height of the steps on the thinning, and also optimal shape design of the original blank were investigated. The study showed that choosing the correct forming pressure can improve formability and increase the amount of die filling. It is also illustrated that by increasing the punch corner radius, the maximum thinning is reduced and the thickness distribution is improved and, by increasing the height of the steps, thinning in the wall of the first step and the punch corner radius increase; by decreasing the height of the steps, thinning position will be shifted toward the second step. Also, by optimizing the original blank, it has been concluded that the optimization of the shape of original blank has a major impact on the material flow and will delay the sheet rupture. [ABSTRACT FROM AUTHOR]

In this paper, thermo-hydraulic behavior of a solar heater is numerically investigated and optimum geometry for the angled-rib turbulator which produces the maximum thermal enhancement factor is determined by Taguchi method. A general innovative geometry is introduced for the rib, by which changes in its geometrical parameters will lead to three convenient types of turbulators, namely triangular, rectangular and trapezoidal ribs, simultaneously. Applying Taguchi method leads to the optimum geometry parameters. A L16 [44] orthogonal Taguchi array is used. The aim of the Taguchi analysis is to maximize the thermal enhancement factor. The factors employed for the analysis are rib relative pitch, height, tip width and percentage of upstream-side perpendicularity. The analysis has been carried out for a flow of Re=10000 and a constant rib width. Due to the twofold effect of turbulators on thermo-hydraulic behavior, thermal enhancement factor concept is employed, which stands for both heat transfer rate and friction factor. Results show that rib pitch and height have the most significant effect on thermal enhancement factor, respectively. The optimum rib geometry is found to be a triangular rib which has P/H=2, e/H=0.2 and 100% perpendicularity. [ABSTRACT FROM AUTHOR]

Compression stiffened panels are reinforced skins that are mainly subjected to axial compressive load and are widely used in aerospace structures. Iterative design loops are the common methods for these types of structure. Design methodology based on structural index concept is a coupled design and analysis method. In this method detailed design of the compression stiffened panel is fully accomplished based on the key parameters of structural index and material properties of the panel. The complete design is obtained in single stage in an analytical and explicit manner. In this paper the design methodology of stiffened panel using structural index concept that could be applied on selective configuration of compression stiffened panel (including selection of stiffener type and the type of panel: integral or skin-stringer) is analyzed. The results are extracted and modified from two different approaches to manage the results of common iteration methods that are currently used in the preliminary sizing of stiffened panel. This procedure could be regarded as a near optimum design and therefore would be more conservative with respect to common methods. Final results of the derived methodology are compared with reported and FEM results. The results could be considered an acceptable design; furthermore, they can be used as an appropriate starting point in numerical optimization methods. [ABSTRACT FROM AUTHOR]

One of the main research areas is providing adaptive learning systems with new style for content delivery to users. Content delivery subsystem manages and selects the appropriate contents based on the user model. In order to realize the adaption and personalization, selecting approach should consider the learning capabilities and background knowledge of the learners. Therefore an optimization problem is defined and the system must optimize the difference between the learning capabilities and the learning style of the learner with the difficulty level and the presentation style of the delivered contents. This paper has focused on two aspects. In the problem modeling section, the previous defined model has been extended. The presented model has also considered the adaption between the learning style of the learner and the presentation style of the delivered contents. In problem solving section, this paper has applied two newer optimization algorithms, NBPSO and GSA, thus resulting in producing better answers (more appropriate and more adaptive selection of content). Also, according to the previous approaches, the problem has implemented and solved using PSO. The provided results of three algorithms have been evaluated using two well-known criteria: accuracy (aiming to measure optimality of the delivered answer) and stability (to ensure of getting answer in different performances). The results showed considerable progress (more personalized and adaptive contents). [ABSTRACT FROM AUTHOR]

In this paper, the modified bee optimization algorithm is proposed to appropriate switches placement regarding distributed generation to maximize the feeders' reliability of a typical distribution network in Mazandaran. In this regard, actual network information including number of consumers, average consumption, feeders failures, feeders access time, repair time, etc. are used to determine network reliability indices. The optimal location of switches is determined by different scenarios due to the presence of distributed generation and the importance of feeders in the studied network. Moreover, the effect of cut-out fuses and ring points on feeders is considered in determining the appropriate switches placement, which is usually not considered in the reliability analysis of the distribution network and switches placement. Different indices are considered to determine the importance of feeders and a questionnaire has been prepared to utilize the personnel experiences of the distribution company to weigh these indices. Comparing and analyzing the results shows the ability of the proposed method in determining the appropriate switches locations in a real network. [ABSTRACT FROM AUTHOR]

Integrating is one of the steps that creates cooperation among supply chain members and improves the performance of the chain of custody to reduce the impact of whipping. Knowledge sharing among the members of a chain level is one of the strategic level partnerships that strengthen the integration in the chain. For this purpose, the most important step is to find the best knowledge sharing network among the members of the chain, so that the minimum cost can be incurred for the collaboration of members and the exchange of knowledge among them will be maximized. This paper presents a new mixed integer programming model on the basis of which a certain problem was formulated and implemented for the level of producers in a dairy supply chain. By sensitivity analysis, the parameters affecting the knowledge sharing network were identified. One of these important parameters is the distance between companies. Another one is the ability to exchange knowledge of companies that affects the duration of the sharing process. The findings of the study provide managers with the necessary insight regarding the resource constraints, market conditions, and the objectives of the companies. This helps them make the best decisions regarding the integration of the supply chain through knowledge sharing.

In this study, performance of gas liquid cylindrical cyclone separators and the effect of changing geometrical parameters of cyclone separators entrance are investigated. The cyclone is simulated with computational fluid dynamic methods. After choosing a suitable mesh grid for the cyclone and checking grid independency, the effect of changing entrance geometry on gas carry under and liquid carry over is investigated. Geometrical parameters, especially inlet geometrical parameters, have considerable effect on optimizing cyclone separators performance. RSM model is used for turbulence simulation of the flow and two phase flow is simulated using Eulerian-Eulerian approach. In this simulation, inlet cross section, inlet angle and inlet height relative to the cyclone bottom part are optimized. Results show that GCU decreases with decreasing nozzle's inlet angle. An optimum point for GCU was given with changing inlet altitude relative to the bottom of the cyclone and inlet nozzle's width. An optimum point for LCO was obtained with changing inlet altitude and inlet nozzle width. Increasing inlet angle causes a decrease in LCO. In optimum model, gas carry under decreases significantly and liquid carry over is eliminated. [ABSTRACT FROM AUTHOR]