Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Design, Prototyping, and Analysis of a Low Cost Axial-Flux Coreless Permanent-Magnet Generator

Abstract: This paper presents the design, prototyping, and analysis of a relatively small and cheap axial-flux three-phase coreless permanent-magnet generator. The excitation of the machine is done by rectangular flat shaped neodymium-iron-boron magnets. A two-dimensional model of the machine is analyzed with finite-element software to obtain the machine parameters. One special feature of the constructed generator is in the design and prototyping of nonferromagnetic holders to counteract centrifugal forces acting on magnets, especially at high speeds. By implementing the nonferromagnetic holders, one can expect to construct high-speed axial-flux permanent-magnet generators at low cost.

Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch

Abstract: The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder-Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

A note on the learning automata based algorithms for adaptive parameter selection in PSO

Abstract: PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that proposed learning automata based algorithms compared to other schemes such as SPSO, PSOIW, PSO-TVAC, PSOLP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better solutions. In addition, proposed algorithms converge to stopping criteria for some of the highly multi modal functions significantly faster.