A Fast Firing Angle Optimization Approach for Current-Controlled Switched Reluctance Generators in Wind Power Applications
The adequate choice of excitation parameters is detrimental to the high performance operation of switched reluctance generators. Such task, however, is a complex problem which lacks solutions with simple analytical formulations. In this context, this paper presents a performance optimization procedure for switched reluctance generators operating in the current controlled region, below base speed. The proposal allows optimal firing angles to be determined based on the particle swarm optimization algorithm. A cost function is designed as a means to ensure performance with a compromise between reduced torque ripple and increased energy efficiency. A comparison with a traditional exhaustive search algorithm is provided, highlighting the reduced computational complexity of the proposal. Moreover, an original statistical analysis is presented as a means to demonstrate the low dispersion of the PSO-based procedure. Experimental results are provided in order to demonstrate the performance of the wind energy conversion system operating with optimal parameters.
Authors
- Gaoliang Fang (ORCID: https://orcid.org/0000-0002-3746-0698)
- César José Volpato Filho (ORCID: https://orcid.org/0000-0002-1766-2305)
- H.A. Gründling (ORCID: https://orcid.org/0000-0002-7739-2159)
- Filipe Pinarello Scalcon (ORCID: https://orcid.org/0000-0002-8976-2188)
- Rodrigo Padilha Vieira (ORCID: https://orcid.org/0000-0002-0558-133X)
- Andrew M. Knight (ORCID: https://orcid.org/0000-0002-7934-4150)
- Gustavo Xavier Prestes (ORCID: https://orcid.org/0000-0002-5134-8010)
Institutions
- University of Calgary (CA)
- University of Prince Edward Island (CA)
- Universidade Federal de Santa Maria (BR)
- Gamesa (Spain) (ES)
- Siemens Gamesa Renewable Energy (Spain)
Publication Details
- Journal
- Eletrônica de Potência
- Published
- 2025-08-27
- DOI
- https://doi.org/10.18618/rep.e202550
- Citations
- 2
- Primary Topic
- Electric Motor Design and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.46