Abstract: Wind Turbine Generator systems (WTGs) have become one of the most popular renewable energy sources in many nations because of their low costs, low maintenance, and great operational reliability. With the Grid-connected WTG, power, frequency, and voltage are synchronized. In wind farms, reactive power compensation devices like STATCOM are employed. Using a STATCOM, the source and load voltages can be kept stable, while improving the power quality, while a series voltage is injected between them. We build a dynamic model of SEIG–STATCOM that feeds nonlinear loads through the use of WTG and STATCOM operating in concert with vector control based on phase-locked loops (PLL), in order to anticipate how the system will behave in transient scenarios. The dynamic responsiveness of the WTG and harmonic elimination are provided by a three-phase 48 pulse inverter working as a STATCOM to provide the reactive power required by SEIG in order to maintain a consistent terminal voltage with changing wind speed input and loads. Voltage and power quality were improved by using an artificial neural network (ANN) on a self-excited induction generator with a constant speed prime mover connected to the grid (Artificial Neural Network). An ANN-based control method was designed for WTG and STATCOM in the event of a malfunction. The MatLab software is used to mimic the PI Fuzzy and ANN controllers' performance. Maintaining synchronicity is done by balancing the active powers at both ends. When the voltage profile is out of whack, the ANN controller maintains a stable voltage profile by sending reactive power to the WTG through STATCOM.

Artificial Neural Network(ANN),Self-Excited Induction Generator, STATCOM (Static Compensator),Phase-Locked Loops (PLL).