Tunicate Swarm Algorithm And Bat Algorithm For Power System Stability Enhancement In A Smib-Upfc Network

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Department of Electrical and Electronic Engineering


A major challenge in highly interconnected power system is ensuring the stability of their networks. Even though low-frequency oscillations may not appear to be too harmful at first glance but failure to dampen out the oscillatory signals can result in the system losing synchronization. Flexible AC Transmission Systems (FACTS) devices are effective in suppressing oscillations as well as enhancing power transfer rates. Among many devices within FACTS, the Unified Power Flow Controller (UPFC) is one of the most sophisticated and effective power flow controllers available. UPFC parameter optimization concerns real time power systems problem with multiple objectives. In this thesis, the optimization of UPFC parameters is carried out using two novel optimization approach named Bat Algorithm (BA) and Tunicate Swarm Algorithm (TSA) to dampen out the small signal oscillations, and thereby enhancing the stability of the system. The goal function is based on the minimization of the damping ratio, and a widely used lead-lag compensator-type PSS structure is used to improve the damping of the system. The algorithm's resilience is demonstrated by its ability to lead the PSS model independent of the initial assumption. Under three-phase faults, the approach's performance is studied, and simulation results confirm the usefulness of the offered solutions. The time domain simulation results of the proposed BA-tuned UPFC and TSA-tuned UPFC are compared with the results of a conventional fixed gain UPFC to measure the efficacy of the proposed algorithms. In addition, the eigenvalues of both the optimized and traditional UPFCs are compared. The recommended controller outperforms the old controller in every way, as evidenced by both the eigenvalue analysis and the time domain representation of system parameters.


submitted by Md. Saber Hossen and Mir Fahim Ul Haque bearing Matric ID. ET171046 and ET171083 of session Spring 2017