International Journal of Smart Electrical Engineering
Volume:3 Issue: 1, Winter 2014

This paper proposes an efficient Tracking method for velocity control of an electro-hydraulic servo system (EHSS) in the presence of flow nonlinearities and internal friction. The tracking method controller is a kind of feedback error learning structure. In the proposed method, the Feedback Error Learning (FEL) algorithm is used to control the velocity. There is no need to compute the system jacobian in FEL method which in turn makes its using more suitable for practical scenarios. This procedure illustrates that EHSS control can be successfully. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.

Nowadays using of new energies in the form of dispersed resources in the worlds is wide spreading. In this article we will design a dispersed production source in the form of a solar/wind hybrid power plant in order to supply the energy of a residential unit according to a sample load pattern. The aim of aforementioned design is to reduce its costs in a period of 20 years. In order to optimize system costs we will use a new algorithm which is based on collective intelligence namely gravitational search algorithm and also we will use particle swarm optimization algorithm. At the end of the article we will present anemometry and radiation data which is collected from an area in Ardebil and Mashhad, note that these data is optimized based on our suggested algorithm. Finally we can conclude that with an appropriate design of dispersed production resources we will be able to effectively reduce costs and make renewable energy usage more economically.

Designing of a PID controller is a very common method for industrial process control and due to its very simple and efficient function; it is used in a wide variety of industrial applications. PID controller to reduce the steady state error and dynamic response of the system is used. PID controller design is an inevitable problem in setting the coefficients need to try a lot of trial and error, therefore the optimization of parameters in this controller is attention of many researcher and there are many methods to find optimal parameters of PID controller. Fast and exactly adjustment of the parameters optimized controller is to create high quality answers. In this paper, an optimized tuning method for PID controller is presented. In this method the PSO algorithm is used to design the parameters of an AVR (Automatic Voltage Regulation) system using various fitness functions. Easy implementation, stable convergence characteristic and high computational efficiency are among advantages of presented method.

Typographical data entry errors and incomplete documents, produce imperfect records in real world databases. These errors generate distinct records which belong to the same entity. The aim of Approximate Record Matching is to find multiple records which belong to an entity. In this paper, an algorithm for Approximate Record Matching is proposed that can be adapted automatically with input error patterns. In field matching phase, edit distance method is used. Naturally, it had been customized for Persian language problems such as similarity of Persian characters, usual typographical errors in Persian, etc. In record matching phase, the importance of each field can be determined by specifying a coefficient related to each field. Coefficient of each field must be dynamically changed, because of changes of typographical error patterns. For this reason, Genetic Algorithm (GA) is used for supervised learning of coefficient values. The simulation results show the high abilities of this algorithm compared with other methods (such as Decision Trees).

In this paper presents a Maximum Power Point Tracking (MPPT) technique based on the Hill Climbing Search (HCS) method and fuzzy logic system for Wind Turbines (WTs) including of Permanent Magnet Synchronous Generator (PMSG) as generator. In the conventional HCS method the step size is constant, therefor both steady-state response and dynamic response of method cannot provide at the same time and in the fixed step size of HCS method. The propose method of this paper is improvement the performance HCS method, in order to reach this goal; the fuzzy logic system has been used. The fuzzy logic system based on operation condition determined the step size instantaneously, such as both steady-state response and dynamic response of method be proper at the same time, therefore, efficiency of the new method that used variable step size strategy, will be guaranteed, the results of simulation in environment MATLAB/Simulink software have been shown to be effectiveness of the proposed method.

Protection systems have vital role in network reliability in short circuit mode and proper operating for relays. Current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. Therefore, proper and quick identification of Current transformer saturation is so important. In this paper, an Artificial Neural Network (ANN) which is trained by two different swarm based algorithms; Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) have been used to discriminate between Current transformer saturation and fault currents in power transformers. In fact, GSA operates based on gravity law and in opposite of other swarm based algorithms, particles have identity and PSO is based on behaviors of bird flocking. Proposed approach has two general stages. In first step, obtained data from simulation have been processed and applied to an ANN, and then in second step, using training data considered ANN has been trained by GSA & PSO. Finally, a proposed technique has been compared with one of the common training approach which is called Genetic algorithm (GA).

With the rapid development of the internet, the amount of information and data which are produced, are extremely massive. Hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. Data mining can overcome this problem. While data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. As the speed of data mining is very important, this paper proposes four faster classification algorithms in comparison with each other. In this paper, A Multi-Layer perceptron (MLP) Network is trained with Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Invasive Weed Optimization (IWO) separately. The classifications are done on Wisconsin Breast Cancer (WBC) data base. At the end, to illustrate the speed and accuracy of these classifiers, they are compared with each other and two other types of Genetic algorithm classifiers (GA).

In vehicular ad hoc networks there is the ability to communicate between vehicles. This Communication which is wireless should be rapid and reliable. These networks have unique characteristic. This communication among vehicles has many advantages in safety and comfort applications. Since, the roads are always encountered to accidents and risks, using optimized tools could be useful in these situations. In this study, AODV and ADV routing algorithms has been compared through their performance metrics in a city scenario, include delay, network loss, collision and throughput were investigated through NCTUns 6.0.