نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال یازدهم شماره 1 (پیاپی 32، بهار 1392)

Solar panels exhibit non-linear current–voltage characteristics producing maximum power at only one particular operating point. The maximum power point changes with temperature and light intensity variations. Different methods have been introduced for tracking the maximum power point based on offline and online methods. In this paper a new method is presented to improve the performance of maximum power point tracking in off-grid solar panels. The proposed algorithm is a combination of two loops, set point calculation and fine tuning loops. First the set point loop approximates the maximum power using offline calculation of the open circuit voltage. The exact amount of the maximum power will, then, be tracked by the fine tuning loop which is based on perturbation and observation (P&O) method. The proposed method is simulated in Matlab/Simulink environment and experimentally verified using a laboratory prototype. In maximum power point tracking, the effects of frequency variation and disturbance amplitude on dynamic response and steady state performance are examined. Simulation and experimental results are compared with other methods and the effectiveness of the proposed method is evaluated.

The purpose of this paper is optimal location of distributed generation in electric distribution networks. Load uncertainty and desired voltage range has been modeled using fuzzy data theory. The objective function includes loss reduction, improvement of profile index and voltage stability index with their relevant constraints, voltage constraints and transmittable power from the line. Load variation has been shown for three different time durations (peak, off peak and average).PSO technique has been used to optimize the objective function while Max-Min method has been applied to select the answer. Results produced from the proposed model have been provided in 5 different scenarios on a 33 bus system of IEEE.

In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" or "non-islands". Needed features for classification are extracted by DWT of DG transient voltage signal. DIgSILENT, MATLAB and WEKA softwares are used for simulation.Proposed method is tested on a CIGRE medium voltage distribution system with two different types of DGs. The final method is chosen from among 162 relay projects with respect to different criteria, including accuracy, speed, simplicity and cost efficiency is the best.With The analysis done in the best relay selection for DGs, the voltage signal, the mother wavelet db4 and seventh level wavelet transform are used. Simulation results show that this method in compared with existing methods, can detect the electrical islands, with a shorter time and higher accuracy.

appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In this paper an intelligent- method have been used to optimizing the design of SynRel motors based on magnetic saliency ratio. To achieve this aim, all of the motor parameters including stator geometry, axial length of machine, winding type, and number of flux barriers in rotor are assumed constant and just position of the rotor flux barriers are optimized. These positions have been defined by six parameters. Changing these parameters, the magnetic saliency of machine is calculated by finite element analysis (FEA). Using these values to train a neural network (NN), a modeling function is obtained for magnetic saliency of SynRel machine. Considering this NN as the target function in genetic algorithm (GA), the parameters of SynRel machine have been optimized and the best rotor structure with highest magnetic saliency has been obtained. Finally the abilities of NN in correct estimation of magnetic saliency and motor synchronization were approved by FEA and dynamic simulation.

In this paper, we introduce an efficient and previously unreported approach to enhance the quality of corrupted narrowband speech signal using joint Vector Taylor Series (VTS) and Bandwidth Extension (BWE) algorithms. First, feature vectors extracted from the noisy narrowband signal have modified applying VTS technique. Then, the estimation of corresponding wideband features have derived from the compensated parameters using two different artificial BWE methods (Envelope prediction with GMM and Neural Network). Finally, the distance between the wideband feature vectors and their estimated values evaluated using Log Spectral Distortion (LSD) measurement criteria. The results of implementation clearly show the advantage of proposed idea to improve the quality of the contaminated speech. In addition, we show that artificial BWE of speech signal, based on the neural network envelope extension outperforms better results in comparison with the GMM algorithm.

In this paper, we propose a new feature extraction algorithm which is robust against noise. In the proposed algorithm, a non-linear filter with temporal masking are used for speech feature extraction and by applying delta spectral characteristics instead of delta cepstral, the accuracy of speech recognition is improved. Almost, all present Automatic Speech Recognition (ASR) systems use cepstral-delta and delta-delta characteristics for speech feature extraction. The aim of this paper is to reach the robust speech features which provide more accurate speech recognition under different noisy conditions. This is achieved by focusing on speech key features (especially non-stationary speech features) which highly differ from the noise signals. The obtaining experimental results show that the accuracy of speech recognition improves in comparison with traditional methods such as PLP and MFCC.