000			02237cam a2200349 a 4500
003			EG-GiCUC
005			20250223032216.0
008			190225s2018 ua f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.07.M.Sc.2018.Om.M
100	0		_aOmar Hamdi Hussein Mohamed
245	1	0	_aMaximization of wind energy conversion system using artificial intelligent techniques / _cOmar Hamdi Hussein Mohamed ; Supervised Ahmed Mohamed Ahmed Ibrahim , Mahmoud Mohamed Sayed , Tarek Abdelbadea Boghdady
246	1	5	_aتعظيم نظام تحويل طاقة الرياح باستخدام أساليب ذكاء اصطناعية
260			_aCairo : _bOmar Hamdi Hussein Mohamed , _c2018
300			_a106 P. ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electrical Power and Machines
520			_aWind has been utilized as a source of power in different applications throughout the years. Recently, due to the depletion of fossil fuel, environmental impact, and the gross in energy demand all over the world, as a result, the desire to find an alternative renewable energy sources is increased. A literature review is introduced about wind energy production and new installed capacity in Egypt and worldwide. Many optimization methods like GA, BBO, LBBO, and CSA have been applied to tune the PI parameters to obtain the optimum performance for the wind farm. In addition, an adaptive PI Neural Network (PINN) controller is introduced to tune PI parameters online and comparing its performance with the conventional PI controller with different operating conditions. The system is tested under different operating condition such as wind speed variation and faults
530			_aIssued also as CD
653		4	_aAdaptive controller
653		4	_aFuzzy controller
653		4	_aNeural network
700	0		_aAhmed Mohamed Ahmed Ibrahim , _eSupervisor
700	0		_aMahmoud Mohamed Sayed , _eSupervisor
700	0		_aTarek Abdelbadea Boghdady , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c70483 _d70483