Mitigation of Low-Frequency Oscillation in Power Systems through Optimal Design of Power System Stabilizer Employing ALO

• Published in: Energies (Basel) Vol. 15; no. 10; p. 3809
• Main Authors: Bayu, Endeshaw Solomon, Khan, Baseem, Ali, Zaid M., Alaas, Zuhair Muhammed, Mahela, Om Prakash
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2022
• Subjects: Algorithms; ant lion optimization; Controllers; Damping ratio; Eigenvalues; Electric power; low-frequency oscillations; Mitigation; Nonlinear equations; Optimization; Optimization techniques; Oscillation dampers; Oscillations; power oscillation damper; power system stabilizer; Settling; Systems stability; Ethiopia
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15103809

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.