International Journal of Smart Electrical Engineering
Volume:8 Issue: 1, Winter 2019

A host of technologies has been developed to achieve these aims of the smart grid. Some of these technologies include plug-in electric vehicle, demand response program, energy storage system and renewable distributed generation. However, the integration of the smart grid technologies in the power system operation studies such as economic emission unit commitment problem causes two major challenges. Pumped storage unit with the capability of storing energy can provide spinning reserve and consequently decrease total cost and environmental emission. The goal of this study is to develop and examine a hybrid GA-heuristic and deterministic optimization algorithm for solving the UC problem for a smart grid with considerations for pumped storage as an energy storage system. Simulation results show improvements in total cost and environmental emission by 1.27 and 4.09%, respectively.

A power system is a nonlinear one. When turbulence occurs in the power system, the stability of the system depends on the initial operating conditions and the nature of the turbulence. Nowadays renewable energy sources including photovoltaic have a key role to meet high demand of modern societies and to maintain voltage of the buses, while they also provide clean electrical energy. However, increasing the penetration level of photovoltaic systems will affect the power grid behavior. Hence, it is necessary to analyze the impact of their penetration level on the voltage stability, reliability and design of the grid, as well as the economic aspects. Investigating the voltage stability of the buses is significant in order to determine whether the amount of photovoltaic systems penetrations are enough to maintain the voltage of the bus and also to find  the perfect and most beneficial location for these system in the power grid, which is discussed in this paper. In addition, a standard 30-bus test system stimulated by ETAP software, in order to evaluate the impact of Photovoltaic system penetration level on improving voltage stability.

Heart diseases are among the most important causes of mortality in the world, especially in industrial countries. Using heart sounds and the features extracted from them are among the non-aggressive diagnosis and prognosis methods for heart diseases. In this study, the time-scale, Cepstral, frequency, temporal and turbulence features are saved and extracted from the heart sounds, and then they are given to the multi layer perceptron neural network in order to be classified. Two methods, namely the UTA feature selection method and the genetic algorithm are separately performed for feature selection, and by introducing the effective features, it will be shown that in the best classification accuracies of 96 and 83 are achieved for the i-stethoscope and the digital stethoscope recorded heart sounds respectively. Totally when selecting the features using the UTA algorithm, a 4.25% increase has occurred on average in the classification accuracy for the i-stethoscope. Also, in the genetic algorithm, approximately 0.75% increase has occurred on average in the classification accuracy by selecting only 7 features.

The effect of temperature change on dynamic performances of an industrial robot with six axes of freedom is studied in this paper. In general, the strain and stress are produced not only by the external exciting force, but also by temperature change. The strain energy that is caused by temperatureThis paper describes how the temperature variation effects the dynamic performance of an industrial robot with six axes of freedom. For the purposeedesign and construction of multi axes data acquisition and logging system, which could be used to collect a number of parameters from several sources by means of different types of transducers. The design of the system concentrated on the use of both inductive position and platinum film temperature sensors. The series of tests for investigating the robot performance regarding positioning repeatability and accuracy and their associated results did reveal that the multi axes data acquisition system operated satisfactorily.This work has also demonstrated that thermal expansion plays a significant role in variation of repeatability during warm-up period of the Rediffusion robot.

One of the important issues in designing a brain-computer interface system is to select the type of mental activity to be imagined. In some of these systems, mental activity varies with user intent and action that must be controlled by the brain-computer system, and in a number of other signals, the received signals contain the same activity-related mental activity that should be performed by the brain-computer system. Take up The purpose of this paper is to identify and distinguish between multiple movements of the hand, including lifting and lowering the whole hand, from the electromagnetic signal (EEG) signal and the control of a robot by these signals. Since the purpose of using motor signals is selected from the various channels, channels 3c and 4c are selected as the preferred channel. This set of signals in total was about six healthy people. In this paper, support vector machines (SVM), multilayer perceptron (MLP) and probabilistic neural network (PNN) were designed to extract data properties.

Frequency response analysis (FRA) is a sensitive ‎method established for testing the mechanical integrity of ‎transformers. However, interpretation of FRA signature still ‎needs expert opinions and there is no FRA interpretation code ‎generally accepted. Various mechanical faults with different ‎extents on power transformers are required to aid FRA ‎interpretation. To address this challenge, in this paper a high ‎definition 3D FEM model of a three-phase, three-winding, 125-‎MVA power auto-transformer was constructed using finite ‎element method and verified by experimental FRA ‎measurements. Various radial deformations and axial ‎displacements were formed using FEM model and the extracted ‎parameters of the winding detailed model were used to obtain ‎power transformer frequency responses. The commonly used ‎appropriate statistical indices in FRA diagnosis were utilized for ‎interpreting FRA results. The study showed that the frequency ‎range between 1-100 kHz is mainly affected by the mechanical ‎faults and the 1st resonance of the double-peak feature of the ‎auto-transformer FRA remains unchanged during winding ‎radial deformation and axial displacement.‎