Bacteria foraging optimisation algorithm based optimal control for doubly-fed induction generator wind energy system

Bakir H., Merabet A., Dhar R. K., Kulaksiz A. A.

IET RENEWABLE POWER GENERATION, cilt.14, sa.11, ss.1850-1859, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 11
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1049/iet-rpg.2020.0172
  • Dergi Adı: IET RENEWABLE POWER GENERATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Computer & Applied Sciences, Greenfile, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1850-1859
  • Anahtar Kelimeler: genetic algorithms, stators, PI control, optimal control, power generation control, wind power plants, wind turbines, power convertors, rotors, asynchronous generators, conventional tuning method, genetic algorithm optimisation method, optimised control parameters, DFIG wind energy experimental setup, bacteria foraging optimisation algorithm, optimal control, doubly-fed induction generator wind energy system, proportional-integral controllers, control system, PI controller, rotor currents, optimised offline, modelled DFIG wind energy system, POWER-FLOW SOLUTION, TURBINE
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

In this study, an optimisation method, based on bacteria foraging, is investigated to tune the parameters of the proportional-integral (PI) controllers in a doubly-fed induction generator (DFIG) wind energy system connected to the grid. The generator is connected to the grid directly at the stator and through the back-to-back converter at the rotor. The control system includes PI controllers, at the rotor side, to regulate the rotor currents and PI controller to regulate the dc-link voltage for efficient power transfer. The control parameters, of three PI controllers, are optimised offline using the bacteria foraging optimisation algorithm and a modelled DFIG wind energy system. Various performance criteria, based on the tracking errors, are used to assess the efficiency of the optimisation method. Furthermore, the conventional tuning method and genetic algorithm optimisation method are conducted and compared to the bacteria foraging optimisation method to demonstrate its advantages. The optimised control parameters are evaluated on a DFIG wind energy experimental setup. Experimental and simulation results are provided to validate the effectiveness of each optimisation method.