Dereje, Shiferaw (Phd0Meskerem, Fanta2022-07-162023-11-282022-07-162023-11-282021-11http://etd.aau.edu.et/handle/12345678/32252In this study, MATLAB is used to develop wind farm layout optimization and pitch angle control methods for the wind energy system. The upstream wind turbine reduces the wind speed that passes through the downwind turbine when numerous wind turbines are arranged in close proximity and at random in a wind farm. The wake effect is the name for this phenomena. This effect has an impact on the wind farm's energy production. As a result, while planning and constructing effective wind farms, an optimum layout that takes into account the wake effect is critical. This research proposes a genetic algorithm-based wind farm layout optimization methodology for optimal power output while minimizing wake loss. As a result, power output has increased from 94.25 percent to 96 percent. SIMULINK findings from prior studies for 26, 30, and 32 turbines in a 2000m2 farm area are used to validate this. The change in wind speed is the other most significant difficulty in wind energy systems. The power level rises above the authorized safe level when the wind speed exceeds the rated value of the wind turbine. As a result, the wind turbine rotor is subjected to a highly nonlinear aerodynamic load. This load causes blade fatigue and vibration, resulting in rotor blade damage. To overcome the load and manage the quantity of aerodynamic collected power, an Adaptive Fuzzy PID pitch angle controller is developed in this article. Furthermore, it improved the transient stability of the wind energy system. When compared to the PID controller, simulations show that the suggested controller is superior in terms of feasibility, overshoot, and settling time.en-USGenetic Algorithmwake effectPitch Angle PID controllerAdaptive Fuzzy-PIDThesis