Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network
- سال انتشار: 1403
- محل انتشار: ماهنامه بین المللی مهندسی، دوره: 37، شماره: 7
- کد COI اختصاصی: JR_IJE-37-7_010
- زبان مقاله: انگلیسی
- تعداد مشاهده: 27
نویسندگان
Laboratory of Applied and Automation and Industrial Diagnostic (LAADI), University of Djelfa, Djelfa, Algeria
Laboratory of Electrical and Automatic Systems Engineering (LGSEA), University of Bouira, Bouira, Algeria
Laboratory of Applied and Automation and Industrial Diagnostic (LAADI), University of Djelfa, Djelfa, Algeria
Laboratory of Identification, Commande, Control and Communication (LI۳CUB), University of Biskra, Algeria
چکیده
In the realm of wind power generation, cascaded doubly fed induction generators (CDFIG) play a pivotal role. However, the classical proportional integral derivative (PID) controllers used within such systems often struggle with instability and inaccuracies arising from wind variability. This study proposes an enhancement to overcome these limitations by incorporating a single hidden layer neural network (SHLNN) into the wind power conversion systems (WPCS). The SHLNN aims to complement the PID controller by addressing its shortcomings in handling nonlinearities and uncertainties. This integration exploits the adaptive nature and low computational demand of SHLNNs, utilizing historical wind speed and power data to form a more resilient control strategy. Through Matlab/Simulink simulations, this approach is rigorously compared against traditional PID control methods. The results demonstrate a marked improvement in performance, highlighting the SHLNN's capacity to contend with the intrinsic variabilities of wind patterns. This contribution is significant as it offers a sophisticated yet computationally efficient solution to enhance CDFIG-based WPCS, ensuring more stable and accurate energy production.کلیدواژه ها
Wind power generation system, Cascaded doubly fed induction generator, Proportional Integral Derivative, Single Hidden Layer Neural Networkاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.