Mutunda, Patient Sony <br/><br/>
REAL TIME STABILITY CONTROL OF HYBRID POWER MICRO-GRID NETWORKS BASED ON ANN CONTROLLER / 
PATIENT SONY MUTUNDA; SUPERVISOR: ASST. PROF. DR. ZIYA DEREBOYLU, CO-SUPERVISOR: ASST. PROF. DR. MOEIN JAZAYERI 
 - xiii, 122 sheets:  charts, tables;  30 cm.  1 CD ROM 
<br/><br/>Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical-Electronic Engineering Department 
<br/><br/>Includes References (sheets 109-115) 
<br/><br/> ABSTRACT<br/>In recent years, the use of interconnection of multiple power source generators that <br/>are usually located upstream and included in transmission network are getting more <br/>complex and difficult to manage. According to the upstream electricity grid <br/>conditions, micro-grids can operate in grid-connected and islanded modes. Also, to <br/>maintain the frequency and voltage stability as fast as possible whenever the <br/>possibility of irregularity and disturbance are defined is the critical role which is <br/>played by the energy storage system. The microgrid system used in this research <br/>serves as the basis for both an overview of the microgrid's local functioning and an <br/>effective control system that uses an artificial neural network controller to optimize <br/>stability and improve the analysis of the microgrid's power irregularity. The proposed <br/>microgrid is composed of wind sources, solar power sources, which will supply <br/>power as efficiently as possible as input of the system and monitor the DC voltage <br/>regulation and keep it stable at the DC bus of the microgrid. Due to the weakness of <br/>the conventional PID controller itself to maintain the stability in no linear system and <br/>adapt to the load and generators in the hybrid power system; the proposed system <br/>comes to the existence and will be simulated on MATLAB Simulink and as a result, <br/>the performance of the proposed ANN control system will be evaluated regarding the <br/>conventional PID. The simulation results further demonstrated that the ANN <br/>controller is more accurate and efficient than the PID and the adaptation capacity of <br/>the ANN in the system is better than the PID as result the ANN outperform the PID <br/>in regulation of the DC voltage. <br/>Keywords: Artificial Neural Network, Battery Energy Storage System, Microgrid, <br/>Proportional Integral Derivative, Power System, Photovoltaic System, Wind Energy <br/>Conversion System. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Electrical-Electronic Engineering--Dissertations, Academic