Amended GWO approach based multi-machine power system stability enhancement

Area of Science:

• Electrical Engineering
• Control Systems
• Computational Intelligence

Background:

• Electromechanical oscillations arise in power networks due to generator installations and system interconnections.
• Low-frequency oscillations (LFOs) are a significant concern in consolidated power networks, impacting stability.
• Effective damping of LFOs is crucial for reliable power system operation.

Purpose of the Study:

• To propose and evaluate variants of the grey wolf optimization (GWO) algorithm for tuning power system stabilizer (PSS) parameters.
• To enhance the damping of low-frequency oscillations in a multi-machine power system.
• To improve the settling characteristics of system oscillations by optimizing eigenvalue placement.

Main Methods:

• Developed hybrid versions of GWO, including modified GWO (MGWO), MGWO-PSO, MGWO-SCA, and MGWO-CSA.
• Framed an objective function to improve damping ratios and shift system eigenvalues.
• Validated proposed methods using 23 benchmark functions and nonparametric statistical tests (Feldt, Anova, Quade).

Main Results:

• The proposed hybrid GWO variants demonstrated competent stabilizer performance in damping LFOs.
• Statistical analysis confirmed the effectiveness of the developed algorithms on benchmark functions and the test system.
• Comparative analysis under self-clearing faults showed the suitability of the proposed techniques for enhancing stability.

Conclusions:

• The hybrid GWO variants offer a robust approach to PSS parameter tuning for improved multi-machine power system stability.
• The proposed methods effectively damp low-frequency oscillations and enhance system response under various conditions.
• Eigenvalue analysis confirmed the improved damping characteristics and settling times achieved by the optimized PSS parameters.