چکیده مقاله
Switched Reluctance Generators are promising for wind energy conversion systems due to their robustness and simple construction However, their nonlinear magnetic characteristics make the control design more challenging This paper proposes an emotional learning based controller to optimize the firing angles of the generators under variable wind conditions Inspired by the limbic system and amygdala model, the controller employs a fuzzy Bayesian inference mechanism to adaptively regulate the turn on and turn off angles To achieve maximum power extraction and reduce losses, these angles are optimized based on the proposed emotional learning approach The method is implemented and validated through MATLAB/Simulink simulations, demonstrating improved efficiency and dynamic performance for renewable energy applications
کلیدواژهها
نویسندگان
آرمین سرکوبی
M Sc Student, Department of Electrical Engineering, University of Birjand / department of electrical and computer engineering
Hojjat Hajiabadi
Ph D in Electrical Engineering, University of Birjand / department of electrical and computer engineering
محسن فرشاد
Ph D in Electrical Engineering, Associate Professor, University of Birjand / department of electrical and computer engineering
شیوه ارجاع
Sarkoobi, Armin and Hajiabadi, Hojjat and Farshad, Mohsen,1404,Emotional Learning-Based Firing Angle Optimization for Switched Reluctance Generator in Wind Energy Systems,1st International & 7th National conference on Mechanical-civil engineering and advanced technologies,Esfarayen
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی و هفتمین کنفرانس ملی مهندسی مکانیک، عمران و فناوری های پیشرفته19 آبان 1404 · اسفراین