Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer
3. MEDICAL AND HEALTH SCIENCES
Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. The modified optimizer's validation is first investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS.
El-Dabah, M.A., Hassan, M.H. & Kamal, S. (2022). Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer. IEEE Access, vol. 10. doi:10.1109/ACCESS.2022.3195892
This work was supported by the National Research and Development Agency of Chile (ANID) under Grant ANID/Fondap/15110019. The work of Hossam M. Zawbaa was supported by the European Union's Horizon 2020 Research and Enterprise Ireland under Marie Sklodowska-Curie Grant 847402.
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