جستجوی مقالات مرتبط با کلیدواژه « particle swarm optimization (pso) » در نشریات گروه « برق »

The household energy management system (HEMS) can optimally schedule home appliances for transferring loads from peak to off-peak times. Consumers of smart houses have HEM, renewable energy sources and storage systems to reduce the bill. In this article, a new HEM model based on the time of usage pricing planning with renewable energy systems is proposed to use the energy more efficiently. The new meta-heuristic whale optimization algorithm (WOA) and the common meta-heuristic of particle swarm optimization (PSO) are used to achieve that. To improve the performance, a mapping chaos theory (CWOA) is proposed. Also, an independent solar energy source is used as a support of the microgrid to achieve a better performance. It is concluded that the energy saving achieved by the proposed algorithm is able to decrease the electricity bill by about 40-50% rather than the WOA and PSO methods. The proposed system is simulated in MATLAB environment.

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.

Photovoltaic (PV) panel produces electricity depending on a variety of characteristics, including the PV module model, design specifications, and ambient circumstances such as temperature and sun irradiation. To analyze and model the effect of these factors on PV performance, a PV model is significant to be studied and modeled in advance. It is desirable to be compatible with the real-physical behavior of the PV panel. This paper presents mathematical modeling, design, and simulation of the three-diode model (3DM) MPPT controller instead of using conventional single/double diode PV models. The proposed PV model is analyzed, verified, and simulated at various temperature and irradiance levels. Furthermore, Particle Swarm Optimization (PSO) as a multi-objective algorithm is used for the Maximum Power Point Tracking MPPT controller to enhance the performance of the module and PV array system. A DC/DC boost converter is combined with the proposed 3DM model and connected through a resistive load. Results show that adopting PSO-based MPPT improves the performance of the PV panel compared to the traditional MPPT and verified the theoretical background.

Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite (UPCB) in the primary dentition from the surface electromyography (sEMG) activity of masticatory muscles. In this study, 40 children (4–6y) were selected and divided into UPCB (n = 20) and normal occlusion (NOccl; n = 20) groups. The preferred chewing side was determined using a visual spot-checking method. The chewing rate was determined as the average of two chewing cycles. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. The data of the subjects were diagnosed by the dentist. In this study, the fast Fourier transform (FFT) analysis was applied to sEMG signals recorded from subjects. The number of FFT coefficients had been selected by using Logistic Regression (LR) methodology. Then the ability of a multilayer perceptron artificial neural network (MLPANN) in the diagnosis of neuromuscular disorders in investigated. To find the best neuron weights and structures for MLPANN, particle swarm optimization (PSO) was utilized. Results showed the proficiency of the suggested diagnostic system for the classification of EMG signals. The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite.

The development of electric energy consumption, the high cost of installation of large power plants, the development of transmission lines, price jumps, running out of fossil fuels, environmental pollution and competitive space power systems render the use of distributed generation sources with significant growth.Among the benefits of these resources, reducing losses, improving voltage profile and power quality, improving reliability indices and reducing subscribers' interruptions can be mentioned. The important issue regarding DGs is the placement problem and determining the size of these resources. Improper placement of these resources and their inept production, not only does not improve the network performance, but also it may have adverse consequences on the network voltage profile and losses. Therefore, placement and determining the optimal size of DGs is one of the key issues in distribution system development planning from the perspective of distribution network operator which is addressed in this paper. In this paper, placement and size determination of wind turbines in distribution networks is studied to improve reliability indices. The reliability indices considered are System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average Service Availability Index (ASAI) and ENS. Placement optimization problem and size determination of wind distributed generation sources have been conducted using hunting search (HuS) algorithm, and the results are compared to particle swarm optimization (PSO). The positive effect of wind generators on improving reliability, as well as the efficiency of the proposed method is studied through simulations on 8 bus radial network.

One of the trends in modern management is the consideration of the principle of customer orientation and customer satisfaction. Following this approach, online shopping, as one of the goods and services distribution channels, is also inclined to maintain and expand relations with customers. Nonetheless, what has become even more highlighted in the competition arena is going beyond customer satisfaction by predicting the customers’ behavior in order to properly respond to their needs and ultimately, establish loyalty. One of the methods used to know the customers is the clustering approach. Clustering is a data mining technique that takes a number of items and places them in clusters based on their attributes. One of the problems of the k-means clustering is that it has no specific method for primary determination or calculation of the cluster centers. Therefore, in order to optimize the clusters, we use the particle swarm optimization (PSO) algorithm. In the end, we analyze these clusters using the RFM model to analyze the customers’ behavior. What is achieved by analysis of each cluster is finding the cluster of the most loyal customers. In this study, we specify the number of optimized clusters using the k-means clustering algorithm. Then, we use the obtained number of clusters for the primary adjustment in the particle swarm optimization algorithm. Finally, we score the achieved clusters by the RFM method so that we can identify the most loyal customers that are placed in the cluster with the highest score.

In recent decades, the development of telecommunications infrastructure has led to the rapid exchange of data between the distribution network components and the control center in many developed countries. Considering the numerous benefits of the Distributed Generators (DGs), these changes have made more motivations for distribution companies to utilize these kinds of generators more than ever before. The Volt & Var control in distribution networks is one of the greatest control plans which can be influenced via DGs.  In this study, a new approach is presented for the Volt & Var control which the output reactive powers of the DGs, Static Var Compensators (SVCs), Load Tap Changers (LTCs), Interruptible Load (IL), and the settings of the local controllers are selected as control variables. The proposed approach is a non–linear optimization problem; hence, a novel and robust meta–heuristic algorithm based on the Harmony Search Algorithm (HSA) is presented with high-speed converge. Also, this paper presents an approach to incorporate the model of the DGs and SVCs in the load flow equations of distribution systems. The feasibility and effectiveness of the proposed approach are illustrated on a real–life distribution network, part of the Tehran province electrical distribution network.

Transformers are one of the most valuable assets of power systems. Maintenance and condition assessment of transformers has become one of the concerns of researchers due to a huge number of transformers has been approached to the end of their lifetimes. Transformer’s lifetime depends on the life of its insulation and the insulation’s life is strongly influenced by its moisture attraction as well. Thus, regarding the importance of moisture analysis, in this paper, a new method is introduced for moisture content determination in the transformer insulation system. The introduced method uses the dielectric response analysis in the frequency domain based on heuristic algorithms such as genetic algorithm and particle swarm optimization. First, the master curve of the dielectric response is modeled. Afterward, using the proposed method the master curve and the measured dielectric response curves are compared. By analyzing the comparison results, the moisture content of the paper insulation, the electrical conductivity of the insulating oil, and the dielectric model dimensions are determined. Finally, the proposed methods are applied to several practical samples and their capabilities are compared to the well-known conventional method.

Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved; Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion.

The original version of Grey Wolf Optimization (GWO) algorithm has small number of disadvantages of low solving accuracy, bad local searching ability and slow convergence rate. In order to overcome these disadvantages of Grey Wolf Optimizer, a new version of Grey Wolf Optimizer algorithm has been proposed by modifying the encircling behavior and position update equations of Grey Wolf Optimization Algorithm. The accuracy and convergence performance of modified variant is tested on several well known classical further more like sine dataset and cantilever beam design functions. For verification, the results are compared with some of the most powerful well known algorithms i.e. Particle Swarm Optimization, Grey Wolf Optimizer and Mean Grey Wolf Optimization. The experimental solutions demonstrate that the modified variant is able to provide very competitive solutions in terms of improved minimum objective function value, maximum objective function value, mean, standard deviation and convergence rate.

Interline power flow controller (IPFC) is a concept of AC flexible control of the transmission system (FACTS), with the ability to series compensation and power flow management in multi lines of a post power. Out of all FACTS devices interline power flow converter (IPFC) is considered to be most flexible, powerful and versatile. IPFC has the capability of compensating multi-transmission line. The proper placement of interline power flow controller (IPFC) can improve the transmission line congestion problem to a great extent. This paper proposed, the optimal location of the IPFC in electrical power systems, using the particle swarm optimization algorithm. Expression of sample figure and analysis of the sample system shows that IPFC is effective to minimize the power losses in the power system.

Castellated beams and composite action of beams are widely applicable methods to increase the capacity of the beams. Semi-rigid connections can also redistribute internal moments in order to attain a better distribution. Combination of these methods helps to optimize the cost of the beam. In this study, some meta-heuristic algorithms consisting of the particle swarm optimization, colliding bodies optimization, and enhanced colliding bodies optimization are used for optimization of semi-rigid jointed composite castellated beams. Profile section, cutting depth, cutting angle, holes spacing, number of filled end holes of the castellated beams and rigidity of connection are considered as the optimization variables. Constraints include the construction, moment, shear, deflection and vibration limitations. Effect of partial fixity and commercial cutting shape of a castellated beam for a practical range of beam spans and loading types are studied through three numerical examples. The efficiency of three meta-heuristic algorithms is compared.

Nowadays, using distributed generation (DG) resources, such as wind and solar, also improving the voltage profile in distribution companies has been considered. As optimal placement and sizing of shunt capacitors become more prevalent, utilities want to determine the impact of the various capacitors placement in distribution systems. Locating and determining the optimal capacity of shunt capacitors in order to reduce power losses and improving the voltage profile and using the maximum capacity of transmission lines, are one of the common problems in the design and control of power systems. Using the shunt capacitors, not only improve voltage profiles, but also reduce system losses. In this study, a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are proposed for simultaneous placement of capacitors and DG resources in order to reduce power losses and improve the voltage profile in a case study radial distribution network. Simulations applied on IEEE 70-bus and 86-bus test system, and finally solutions of the proposed algorithms are compared.