The impact of Static Var Compensator based-on Genetic Algorithm on a power system

Constant changes to power networks, such as the incorporation of renewable energy sources and smart grid technology, heighten the importance of reliable voltage stability control techniques. This work explores the potential game-changing effects of applying Genetic Algorithm (GA) optimization to Static Var Compensators (SVCs) in the context of the multi-machine system. Due to the nonlinear and complex nature of power networks, conventional methods for improving SVC parameters frequently fall short. This research takes advantage of Genetic Algorithms, which mimic natural selection and evolution, to find the best possible settings for SVCs through a series of recurrent searches. The testing environment is the widely recognized multi-machine system, which is a simulation of actual power grids. New insights into improving power system performance are provided by this research, which examines the complementary link between optimization methods and voltage stability control. Results show that optimizing with GA can increase voltage stability margins and system robustness. Through dynamically supplying or absorbing reactive power, reducing voltage oscillations, assisting with load balancing, decreasing line losses, and improving transient and dynamic stability, the results showed that SVC can greatly enhance the voltage profile.

Keywords:

Static Var Compensator , Genetic Algorithm , voltage stability , Power system , optimization