Magdy B. Eteiba

Heat Conduction Problems in

In this paper, the finite-element method is used to perform heat transfer analysis to obtain the steady state and the transient temperature distribution of gas cooled-insulated power transformers. All significant parameters that influence transformer operation have been included. Also, the analysis results, which are obtained from the thermal analysis, could be reviewed at any specified location within the transformer as a function of time. The predicted temperature values are compared with experimental results reported in the literature.

{\rm SF}_{6}

This paper presents an accurate algorithm for locating faults in a medium voltage underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses five ANFIS networks and consists of 2 stages, including fault type classification and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents. Other four ANFIS networks are utilized to pinpoint the faults (one for each fault type). Four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents, are used to train the Neuro-fuzzy inference systems in order to accurately locate the faults on the cable. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances.

Gas Cooled-Insulated Power Transformers Solved by the Finite-Element Method

Abstract The aim of this paper is to present a feasibility study of a grid connected photovoltaic (PV) and biomass Integrated renewable energy (IRE) system providing electricity to rural areas in the Beni Suef governorate, Egypt. The system load of the village is analyzed through the environmental and economic aspects. The model has been designed to provide an optimal system configuration based on daily data for energy availability and demands. A case study area, Monshaet Taher village (29° 1′ 17.0718”N, 30° 52′ 17.04”E) is identified for economic feasibility in this paper. HOMER optimization model plan imputed from total daily load demand, 2,340 kWh/day for current energy consuming of 223 households with Annual Average Insolation Incident on a Horizontal Surface of 5.79 (kWh/m2/day) and average biomass supplying 25 tons / day. It is found that a grid connected PV-biomass IRE system is an effective way of emissions reduction and it does not increase the investment of the energy system.

Fault Location in Underground Cables using ANFIS Nets and Discrete Wavelet Transform

Motivated by the new feed-in tariffs for electrical power projects generated from renewable energy resources, the present study is investigating the potential of on-grid hybrid solar/wind/Biomass power generation in Egypt. The arrangement is modeled and examined through the environmental and economic prospects. The optimization, sizing and economic assessment of the introduced systems were accomplished using computer package software called HOMER. A case study area, Monshaet Taher village at Beni-Suef Governorate, Egypt with (29° 1′ 17.0718″N, 30° 52′ 17.04″E) is placed for economic feasibility in this study. HOMER optimization model plan was designed with annual average solar radiation scaled of 5.93 (kWh/m2/day), annual average wind velocity for the location is 4.92 m/Sec and average biomass supplying 105 tons per day. The proposed grid connected solar/wind/Biomass hybrid system is an efficient method to cut emissions and the investment of the energy system is not increased.

Feasibility Study of Grid Connected PV-Biomass Integrated Energy System in Egypt

The effects of cooling methods, gas pressure, gas velocity, electrical loading, and environmental parameters on the steady-state temperature distribution of SF6 gas-cooled-insulated power transformers are investigated by using the finite-element method. The contributions of convection and radiation to the total heat-transfer rate inside and outside the transformer are included. The results provide useful sensitivity information which can be accessed and manipulated by the transformer designers or operators. This sensitivity information significantly reduces the analysis and computations via identifying the important parameters that most affect the temperature distribution in this type of transformer.

Viability study of grid connected PV/Wind/Biomass hybrid energy system for a small village in Egypt

Abstract The commensurate and the incommensurate fractional order Permanent Magnet Synchronous Motor (PMSM) models have been recently introduced to emulate the motor performance in the chaotic behavior region accurately. These models provide more flexibility, more degrees of freedom for the motor design, and a deeper vision on its physical behavior than the integer order one. In this chapter, the parameters of the fractional order models are extracted by using novel meta-heuristic optimization algorithms. The original algorithms are developed via the integration with chaos maps to adjust some of their parameters. The original algorithms are the Grey Wolf and Grasshopper Optimizers while the developed versions are the Chaotic Grey Wolf and Chaotic Grasshopper Optimizers. A comparison of the results of these algorithms is carried out. The main outcome proves that the chaotic algorithms have the ability to extract the models parameters with less error and less execution time compared with the original ones.

Sensitivity of Steady-State Temperatures of SF

This paper studies the optimization of operation of islanded microgrids. The objective is to minimize the fuel consumption and power losses of an islanded microgrid with distributed energy resources (DERs). The optimization algorithm identifies available output of non-dispatch able DERs, then active/reactive dispatch is carried out to minimize the objectives according to fitness functions and constraints. The optimization algorithm also considers slack bus selection as it affects both line losses and running costs. Non dominant Sorting Genetic Algorithm II (NSGA-II) is used to solve the multi-objective optimization problem. The algorithm is tested on two load profiles to simulate all possible conditions.