International Journal of Smart Electrical Engineering
Volume:5 Issue: 2, Spring 2016

This research uses a comprehensive method to solve a combinatorial problem of distribution network expansion planning (DNEP) problem. The proposed multi-objective scheme aims to improve power system's accountability and system performance parameters, simultaneously, in the lowest possible costs. The dynamic programming approach is implemented in order to find the optimal sizing, siting and timing of HV/MV substations, feeders and distributed generations. Based on the input data, the results should be closer to the reality. So, the relevant uncertainties must well incorporate in DNEP modeling to achieve the best possible strategy. The most important uncertainties are the load forecasting, market price errors as well as the uncertainties related to the intermittent nature of the output power of renewable energy resources. Given that DNEP is a multi-objective optimization problem including several objective functions such as: cost based function, voltage deviation, voltage stability factor and measuring the amount of produced emission. NSGA-II as an appropriate alternative results several non-dominated solutions where finally fuzzy set theory is used to select the best compromise solution among them. The proposed scheme is applied to 54-bus system distribution network. The comparison study validates the efficiency of suggested method in the presence of distributed generations.

Palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. Texture is one of the most important features extracted from low resolution images. In this paper, a new local descriptor, Local Composition Derivative Pattern (LCDP) is proposed to extract smartly stronger and more distinguishing texture features from palmprint images by composition of both radial and directional derivative information among local neighbors using a threshold function with an adaptive threshold value which result from local directional derivative information. The distribution of the LCDP is modeled by local spatial histogram and histogram intersection function is used to measure the similarity between spatial histograms of two different palm print images. Then, nearest neighbor classifier is used to classify them. Experiments on the Hong Kong Polytechnic University (PolyU) 2D_3D_palmprint database demonstrate the effectiveness of the LCDP in palmprint recognition versus well-known local pattern descriptors.

This paper proposes the exchange market algorithm (EMA) to solve the combined economic and emission dispatch (CEED) problems in thermal power plants. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In order to show the capabilities of EMA in solving CEED problem, several experimentations are conducted on systems with 6, 10, and 40 generation units applying valve-point effects and network power losses in a multi objective function consists of system fuel cost and emission level. The obtained results are compared with other advanced techniques such as Strength Pareto evolutionary algorithm, non-dominating sorting genetic algorithm II, multi objective evolutionary algorithm, fuzzy clustering-based particle swarm optimization, multi objective differential evolution, gravitational search algorithm, modified bacterial foraging algorithm, etc. The results well demonstrate the practical advantage of the exchange market algorithm over the other approaches.

Smart home is composed of several controllers with different plants in control. If each controller works independently, without considering the mutual effect of the others in the control process, the whole system could definitely not converge to an optimum desired status and may not ever reach the demanded condition. The function of different controller system may has conflict In some condition or emergency cases so the whole system could not converge to the steady state and receive to the demanded condition .According to the mentioned problem a new approach is presented in this paper using an integrated fuzzy controller in order to control light, temperature and emergency conditions in a realistic Matlab Simulink simulation. All environmental parameters that have effect on controlling different parts of smart home consider as an input parameter of system, such as Light, Heat and Smoke. The obtained results represent the presented approach and fuzzy model to be able and easy to implement the controller for smart home applications.

Abstract In this paper, a fuzzy PID with new structure is proposed to solve the load frequency control in interconnected power systems. in this study, a new structure and effective of the fuzzy PID-type Load frequency control (LFC) is proposed to solve the load frequency control in interconnected power systems. The main objective is to eliminate the deviations in the frequency of different areas and tie-line power flow with the minimum settling time. In the structure of fuzzy PID, four gains are adjusted by a multi-objective algorithm. The genetic algorithm (GA) is used to generate the Pareto front. The best compromise solution from the obtained Pareto set is then chosen by a Fuzzy-based approach. In addition, we suggest a new control strategy based on the fuzzy PID for the LFC problem. The simulation results show that the frequency and tie-line power flow deviations are effectively damped and settling time in responses is considerably reduced.

Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for prediction of various traffic parameters. In this paper, an aggregated approach is proposed for traffic flow prediction. The approach is based on the adaptive neuro-fuzzy inference system (ANFIS) and the macroscopic traffic flow model (METANET). Macroscopic modeling tool, METANET, is used to simulate the Hemmat highway/Tehran. After simulation, validation is done using real measurements to show the reliability of the simulation results. In order to calibrate the model, genetic algorithm was followed. The outcome suggests that the proposed approach as a hybrid method obtains a more accurate forecast than the neuro-fuzzy model alone.

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temperature, maximum day temperature, minimum dew temperature, average dew point temperature, maximum dew temperature, maximum humidity, average humidity and minimum humidity are collected from weather forecasting station in Hamedan city province. By studying these parameters and daily electrical energy consumption registered in Distribution Company of Hamedan city province and using statistical analysis factors, the parameters which affect daily electricity consumption have been recognized. By applying ESN neural network modeling this load with recognized parameters has been carried out and load forecasting has been assessed. Forecasting result indicates high accuracy of ESN network system for load forecasting short term.