Enhancement of Voltage Stability by Optimal Integration of Wind Source with GRU Based SVC Compensator

Renewable energy sources are becoming more and more important as a sustainable energy source due to rising energy demand. Wind power is a major renewable energy source, but its integration into the traditional system presents control and stability challenges. To overcome these issues, an efficient scheme for optimal integration of wind energy and neural network based Static Var Compensators (SVC) was designed to regulate power flows in a stable manner. At first, using optimization to detect the exact location of the wind source in a standard IEEE-30 bus system. The cost and voltage stability of the bus system are taken into consideration as the objective function. After integration of the wind source, the generation may vary by varying the wind speed thus causing system instability which was reduced by injecting the reactive power. In the suggested work, SVC was included into a weak bus system to provide reactive power as needed to sustain steady power flows. Using a neural network system, which analyzes bus voltage to produce a pulse signal, the SVC control approach was implemented. For every atmospheric condition and fault duration, the suggested model suited the data quite well. The proposed model maintains stability between 0-1, and the performance of the neural network controller is 0.99 accuracy, 0.99 precision, and 0.99 F1_score. The results of the simulation show that SVC with an advanced controller tends to lower voltage fluctuation and improve system stability as compared with the current one.