Robust Power System Stabilizer For Multi-Machine Power Networks Using Tunicate Swarm Algorithm And Equilibrium Optimizer

Abstract

Low-frequency oscillations (LFO) in power systems can cause a lot of interruptions, which can cause the system to become unstable. Low-frequency oscillation has frequently been reduced and long-term stability improved through the use of stabilizers in power systems. This thesis represents two new methods of modeling robust power system stabilizers (PSS) for multi machine networks using the Equilibrium optimizer (EO) and tunicate swarm algorithm (TSA). To improve system damping, a damping ratio-based goal function is considered, and a commonly utilized traditional lead-lag type PSS structure is employed. LFO recurring interruptions are the fundamental cause of system instability and dependability. Data from the past shows that the employment of stabilizers in power systems has resulted in a reduction in low-frequency oscillations, and that long-term stability has steadily improved with the passage of time. In power system stabilizer we optimize the parameter of lead-lag type PSS for a robust power system using TSA and EO. Specifically, we investigate the performance of two separate systems, one with four machines on a 11-bus system and the other with ten machines on a 39- bus system. By optimizing the parameter of the TSA-based PSS and the EO-based PSS demonstrate the robustness and effectiveness of the two power network systems. The comparison of the simulation findings with other established optimization technique like particle swarm optimization (PSO) shows that TSA and EO are more efficient and robust. According to the simulation findings, the TSA and EO technique decreases the settling time and maximum overshoot more significantly than the other choices