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Register a new userControl of a Variable Speed wind Energy System for Maximum Power Using Genetic Algorithm
التحكم بنظام تحويل طاقة رياح متغير السرعة للحصول على الاستطاعة العظمى باستخدام الخوارزمية الجينية
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Tishreen University ورقة بحثية
Publication date
2020
fields
Electrical Power Engineering
and research's language is
العربية
Created by
Shamra Editor
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يقدّم هذا البحث استراتيجة تحكم بالعنفات الريحية متغيرة السرعة والموصولة مع مولد تزامني ذي مغناطيس دائم، بهدف الحصول على أعظم استطاعة ممكنة من العنفات الريحية. استخدمنا نموذج نظام تحويل طاقة الرياح متغير السرعةVWST(Variable speed Wind turbine conversion system) ، والمكون من عنفة ريحية، مولد تزامني ذي مغناطيس دائم PMSG (Permanent magnetic synchronous generator)، مقوم، مقطع رافع-خافض، قالبة، حمل، ومتحكم تقليدي (Proportional Integral Controller) PI لتثبيت الجهد الذي حصلنا عليه من العنفة الريحية والمولد التزامني عند سرعة رياح متغيرة، ومن ثم استخدمنا إحدى تقنيات الذكاء الصنعي والمتمثلة بالخوارزمية الجينيةGA (genetic algorithm) للحصول على مميزات أعظم استطاعة ممكنة من العنفة الريحية. تمت نمذجة المتحكم التقليدي PI والخوارزمية الجينية باستخدام برنامج الماتلابMATLAB R2014a ومنه حصلنا على مميزات الاستطاعة الميكانيكية للعنفة الريحية وحددنا نقاط الاستطاعة الأعظمية عند كل سرعة للرياح.
This paper presents a strategy of variable speed wind turbine connected to a permanent magnet synchronous generator; the goal is to get the most possible wind turbines. We used a wind energy conversion system model consisting of a wind turbine, permanent magnet synchronous generator, rectifier, buck-boost chopper, inverter, load, and traditional controller PI to stabilize the voltage obtained from the wind turbine and synchronous generator at a variable wind speed. Then we used one of the artificial intelligence techniques represented by the genetic algorithm to get the maximum possible wind turbine. The traditional controller PI and the genetic algorithm we modeled using the Matlab R2014a program and from it we obtained the advantages of mechanical power for wind turbine and determined maximum power points at each wind speed.
References used
DAVIS, L. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991
GOLDBERG, D. E. Genetic algorithms in search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, U.S.A, 1989
Xu, Diangyu.; Chen,YangQuan.; Atherton,Derek. "PIDControllerDesign", Society for Industrial and Applied Mathematics,2007
Daoud, Mohsen (2013). “Automated Control, Tishreen University, Directorate of Books and Publications
Rogres, Everett(2002). Understanding Buck-Boost Power Stages in Switch Mode Power Supplies.USA: Texas Instruments
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