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Shiraz, Iran

Shiraz University of Technology is the second public university in the Fars Province in higher technological education, basic and applied research. In 2004, the Government offered technical assistance for establishing an institute of higher education in engineering in Shiraz.Currently the University has about 1100 students, with 3 Bachelor's degree programs and 31 Master's degree & Ph.D. degree programs. Wikipedia.

In recent years, Distributed Generators (DGs) connected to the distribution network have received increasing attention. The connection of enormous DGs into existing distribution network changes the operation of distribution systems. Because of the small X/. R ratio and radial structure of distribution systems, DGs affect the daily Volt/Var control. This paper presents a new algorithm for multiobjective daily Volt/Var control in distribution systems including Distributed Generators (DGs). The objectives are costs of energy generation by DGs and distribution companies, electrical energy losses and the voltage deviations for the next day. A new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. Since objectives are not the same, a fuzzy system is used to calculate the best solution. The plausibility of the proposed algorithm is demonstrated and its performance is compared with other methods on a 69-bus distribution feeder. Simulation results illustrate that the proposed algorithm has better outperforms the other algorithms. © 2010 Elsevier Ltd. Source

Niknam T.,Shiraz University of Technology
Expert Systems with Applications | Year: 2011

This paper presents an efficient multi-objective honey bee mating optimization (MHBMO) evolutionary algorithm to solve the multi-objective distribution feeder reconfiguration (DFR). The purposes of the DFR problem are to decrease the real power loss, the number of the switching operations and the deviation of the voltage at each node. Conventional algorithms for solving the multi-objective optimization problems convert the multiple objectives into a single objective using a vector of the user-predefined weights. This transformation has several drawbacks. For instance, the final solution of the algorithms extensively depends on the values of the weights. This paper presents a new MHBMO algorithm for the DFR problem. The proposed algorithm utilizes several queens and considers the queens as an external repository to save non-dominated solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. The proposed algorithm is tested on two distribution test feeders. © 2010 Elsevier Ltd. All rights reserved. Source

Alizadeh M.,Shiraz University of Technology
Journal of Alloys and Compounds | Year: 2011

In this study, aluminum metal matrix composites reinforced with 5 and 10 vol.% B4C particulates were fabricated by repeated roll bonding process. The microstructure of the composites, evaluated by optical microscopy, showed the B4C particles are properly distributed in the aluminum matrix. A combined microstructure strengthening analysis suggested by Sekine and Chen was used to predict the yield strength of the Al/B4C composites. In addition, the yield strength of the composites was determined by tensile tests and compared with the calculated yield strength. The results indicated that there is a good agreement between the calculated yield strength and experimental value. © 2010 Elsevier B.V. All rights reserved. Source

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder-Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects. © 2009 Elsevier Ltd. All rights reserved. Source

Zarei J.,Shiraz University of Technology
Expert Systems with Applications | Year: 2012

This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden. © 2011 Elsevier Ltd. All rights reserved. Source

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