Affiliations: [a] Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran | [b] Electrical Engineering and Information Technology Department, University of Federico II, Naples, Italy
Correspondence:
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Corresponding author. Alireza Zohoori, Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran. Tel.: +98 21 77240431; Fax: +98 21 77240490; E-mail: alirezazohouri@iust.ac.ir.
Abstract: Nowadays, the quickest developing rate among all kinds of renewable energy generation belongs to wind power industry. Hence, proposing an optimization framework to achieve the best design of wind power conversion system, especially wind electrical generator is required more than ever. Recently, flux switching permanent magnet (FSPM) generators that are well known for rigid structure, high torque density and lower weight have attracted high attention where these features bring more ability respect to permanent magnet synchronous (PMS) generator. In this paper a thorough analysis is carried out to find an optimal design of FSPM generator by interrogating the possibility to employ different structures for small scale wind conversion systems. To achieve this purpose, a precise challenge is laid down in investigation of various design characteristics. Hence, a multi-objective function depending on mass, cost, volume and electromagnetic performance of the generator is taken into account. In the sequel, considering multi attribute decision making (MADM) techniques, a framework based on an improved analytical hierarchy process (AHP) composed by a hybrid scheme formed by artificial neural network (ANN) and classical AHP is proposed to achieve trade-off design of the FSPM. A numerical study has been implemented on different test cases to assess the performance of the proposed framework. The obtained results prove the goodness of the proposed technique and suggest interesting solutions for the considered applications.
Keywords: Flux switching permanent magnet (FSPM) generator, analytical hierarchy process (AHP), design optimization, direct drive turbine, artificial neural network (ANN)
