COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2015 | Volume:6 | Issue:3|Papers Count:8

A new cascaded multilevel transformerless inverter topology is introduced in this paper for connecting DG sources to power utility. Usual transformerless circuits are designed based on current injection capability, current harmonic minimization, and some physical constraints like isolation between DG and the network. The proposed circuit of this paper can compensate the current harmonic of the nonlinear load (as in APFs) and inject maximum possible active power as a DG source interface, simultaneously. A fuzzy controller is proposed to adjust the optimal set point of inverter between compensating mode and maximum active power injection mode. Proposed scheme is simulated in MATLAB/SIMULINK to evaluate the circuit performance both in the maximum active power injection mode, and harmonic compensation mode. Then a 1 kw single phase prototype of the circuit is used for experimental evaluation of the paper. Both simulative and experimental results prove that such a circuit can inject a well-controlled current with desired harmonics and THD, while having a smaller switching frequency and better efficiency, related to previous 3-phase inverter schemes in the literature.

One of the most effective techniques for energy management in wireless sensor networks is clustering. How to establish clusters and the method used to choose cluster heads are the most important factors in creating optimal clusters. In fact, appropriate cluster head selection results in longer network lifetime and more data delivery to the sink. In this paper, after exploring clustering algorithms based on computational intelligence techniques and evaluating the strengths and weaknesses of each, we provide two novel clustering schemes termed as Energy Aware Genetic Clustering Algorithm (EAGCA) and EAGCA*. In these centralized algorithms, we try to achieve an optimal clustering by using global information such as number of neighbor nodes, the energy of neighbor nodes, local distance and local traffic load on each node. Compared to some well-known clustering algorithms such as LEACH and EAERP, simulation results demonstrate that EAGCA and EAGCA* are able to create more appropriate clusters in terms of energy consumption, life time and total number of received data to the sink.

In this paper, a security-constraint demand response and unit commitment problem (SCDRUC) is proposed in which demand response programs (DRPs) are modeled as commercial resources beside the generation units. Indeed, the main aim of this paper is to introduce the security-constraint unit commitment problem in which the presence of DRPs is modeled as the available and necessary resources. Therefore, by integrating the generation units with demand side resources, the SCDRUC problem is proposed. The optimization results of the proposed problem will determine the generation of units as well as the demand response participation plan in order to minimize the operational cost while security constraints are satisfied. The IEEE 24-bus test system is utilized to investigate the simulation results. All the numerical analysis approves the performance and efficiency of the proposed model.

Brushless direct current (BLDC) motors have found wide application in the industrial in the past few decades, thanks to such advantages compared with the other motors. So, design of this motor has been the subject of many research projects. In this paper, the design of a BLDC motor using the Pareto method based on the seeker optimization algorithm (SOA) is provided. Increasing the motor efficiency and the ratio of torque delivered to the size of the motor are the base of designing. Flexibility of solutions earned by the algorithm is one of the proposed method advantages. The simulation studies were performed on a 220 V, 3 kW motor. The simulation results indicate the proposed algorithm is effective in BLDC motor optimization.

Because of photography limitations, it is sometimes impossible to achieve an image of high quality and sufficient clarity by taking just one picture from a scene. Therefore, various methods of image fusion have been proposed. On the other hand, random population-based algorithms have been used extensively for optimization. These algorithms are often inspired by the physical processes or the behavior of the living beings. Gravitation search algorithm (GSA) is an optimization algorithm that is based on the gravitation and mass concept and the search agents in this algorithm are masses. In this study, GSA is used to optimize the image fusion process when using images with different focuses. In this way, spatial frequency measure is used. The performance of proposed method is compared with two other methods: a peer approach when the particle swarm optimization (PSO) algorithm is used instead of GSA, and a pixel-based fuzzy approach. Experimental results show that the proposed method has a superior performance and this method can be applied to colored images, as well.

In this paper, direct torque control of induction motor with space vector modulation (DTC-SVM) is presented, which is comprised of two PI controllers, the first one is for torque controlling and another one is for flux controlling. Due to the characteristics of nonlinear control systems, classic controllers do not work well in different conditions. In this paper the flux controller is classic PI controller (CPI). A fuzzy controller is used for regulation of PI coefficients. It is used of a particle swarm optimization (PSO) algorithm to get optimized parameters for classic controllers of torque and flux. In this paper, the performance of the proposed drive is tested in various operating conditions And has been shown that the use of fuzzy self-tuning PI controller (STFPI) provide better condition than the classic form and Improves the dynamic response of drive. Obtained Simulation results in Matlab/Simulink confirms the correction of proposed algorithm.

Nowadays, due to the growth of energy consumption, power systems planning is one of the most important affair which simultaneously effects on the operation and maintenances of power systems in the future. Hence, in this paper a new multi-objective framework is proposed for dynamic Generation and Transmission Expansion Planning (GTEP) in deregulated power systems. The investment cost and operation cost are considered as long term and short term economical criteria. Also, congestion cost and average loss of load cost are considered as market and reliability criteria, respectively. Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to cope with mentioned optimization problem. Moreover, fuzzy satisfying method has been applied to obtain the final optimal plan according to different satisfaction levels of objectives. To show the effectiveness and applicability of the proposed algorithm in electricity market environment, the planning methodology has been tested on the IEEE-RTS and a part of Iranian power system in Khorasan province (a real life case study). Obtained results also are separately compared with Generation Expansion Planning (GEP) and Transmission Expansion Planning (TEP).

In this paper, a new method for islanding detection of distributed generation is introduced. The method uses voltage jump vector and phase voltage variations for islanding detection. Phase voltage variations are obtained from PMUs which is placed over the grid on some buses. To prove the efficiency of the proposed algorithm, different islanding conditions and non-islanding conditions are simulated and compared with a traditional relay. The obtained results show that the new method is reliable and has a very small non detection zone. Proposed method is applied on a distributed network included wind turbine equipped with permanent magnet synchronous generator. To perform the simulations, the software PSCAD/EMTDC is used.