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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]

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]

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]

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]