000			03059nam a22002777a 4500
003			KOHA
005			20240219160951.0
008			240219d2023 cy d|||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3201 _bM88 2023
100	1		_aMutunda, Patient Sony
245	1	0	_aREAL TIME STABILITY CONTROL OF HYBRID POWER MICRO-GRID NETWORKS BASED ON ANN CONTROLLER / _cPATIENT SONY MUTUNDA; SUPERVISOR: ASST. PROF. DR. ZIYA DEREBOYLU, CO-SUPERVISOR: ASST. PROF. DR. MOEIN JAZAYERI
264			_c2023
300			_axiii, 122 sheets: _bcharts, tables; _c30 cm. _e1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical-Electronic Engineering Department
504			_aIncludes References (sheets 109-115)
520			_aABSTRACT In recent years, the use of interconnection of multiple power source generators that are usually located upstream and included in transmission network are getting more complex and difficult to manage. According to the upstream electricity grid conditions, micro-grids can operate in grid-connected and islanded modes. Also, to maintain the frequency and voltage stability as fast as possible whenever the possibility of irregularity and disturbance are defined is the critical role which is played by the energy storage system. The microgrid system used in this research serves as the basis for both an overview of the microgrid's local functioning and an effective control system that uses an artificial neural network controller to optimize stability and improve the analysis of the microgrid's power irregularity. The proposed microgrid is composed of wind sources, solar power sources, which will supply power as efficiently as possible as input of the system and monitor the DC voltage regulation and keep it stable at the DC bus of the microgrid. Due to the weakness of the conventional PID controller itself to maintain the stability in no linear system and adapt to the load and generators in the hybrid power system; the proposed system comes to the existence and will be simulated on MATLAB Simulink and as a result, the performance of the proposed ANN control system will be evaluated regarding the conventional PID. The simulation results further demonstrated that the ANN controller is more accurate and efficient than the PID and the adaptation capacity of the ANN in the system is better than the PID as result the ANN outperform the PID in regulation of the DC voltage. Keywords: Artificial Neural Network, Battery Energy Storage System, Microgrid, Proportional Integral Derivative, Power System, Photovoltaic System, Wind Energy Conversion System.
650		0	_aElectrical-Electronic Engineering _vDissertations, Academic
700	1		_aDereboylu, Ziya _esupervisor
700	1		_aJazayeri, Moein _eco-supervisor
942			_2ddc _cTS
999			_c292206 _d292206