C.J. Kendeg Onla*, G. Guidkaya, E.D. Kenmoe Fankem, A. Dountio Tchioffo and J.Y. Effa

Department of Physics, University of Ngaoundére, Ngaoundéré, Cameroon

Submitted on 24 August 2024; Accepted on 04 October 2024; Published on 16 October 2024

To cite this article: C.J. Kendeg Onla, G. Guidkaya, E.D. Kenmoe Fankem, A. Dountio Tchioffo and J.Y. Effa, “Reluctance Network Modeling and Optimization of Brushless Doubly-Fed Machines for Wind Power Generation,” Insight. Electr. Electron. Eng., vol. 1, no. 1, pp. 1-11, 2024.

Copyright: 

Abstract
Brushless doubly-fed reluctance machine (BDFRM) and brushless doubly-fed induction machine (BDFIM) have great potential to become third-generation electrical machines for wind turbine applications instead of doubly-fed induction machine (DFIM). However, due to their complicated operating principle, they have not yet been commercialized. In the idea of reducing the materials cost, increasing the performances of these machines, and allowing their commercial breakthrough, it is important to propose a new approach to the design and optimization of these kinds of machines. This paper contributes by combining the reluctance network (RN) model developed for those machines together with the particle swarm optimization (PSO) algorithm. Previously, a new RN model has been developed and validated by finite elements (FE) analysis for BDFRM modeling. This model simultaneously takes into account magnetic saturation, rotor motion, and stator winding distribution. It also offers an interesting trade-off between precision and computational time compared to the FE model. Therefore, the developed model is firstly extended to the BDFIM structure and then coupled to the PSO algorithm and the result is an accurate and fast BDFRM and BDFIM design optimization tool. This tool is then used to generate a 1kW machine design with optimized performances.

Keywords: brushless doubly fed induction machine; brushless doubly fed reluctance machine; finite elements; particle swarm optimization; reluctance network; wind power generation

Abbreviations: DFIM: doubly-fed induction machine; BDFRM: brushless doubly-fed reluctance machine; BDFIM: brushless doubly-fed induction machine; RN: reluctance network; PSO: particle swarm optimization; FE: finite elements; PW: power winding; CW: control winding; EEC: electric equivalent circuit; MEC: magnetic equivalent circuit; BDFMs: brushless doubly-fed machines; KVL: Kirchhoff’s voltage law; KCL: Kirchhoff’s current law; GEPP: Gauss elimination with partial pivoting; FEA: finite elements analysis; PM: proposed model; MOPSO: multi-objective particle swarm optimization

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