Implementing Optimization Techniques in PSS Design for Multi-Machine Smart Power Systems: A Comparative Study

Aliyu Sabo, Theophilus Ebuka Odoh, Hossien Shahinzadeh, Zahra Azimi and Majid Moazzami ()
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Aliyu Sabo: Advanced Lightning and Power Energy System (ALPER), Department of Electrical/Electronic Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Theophilus Ebuka Odoh: Advanced Lightning and Power Energy System (ALPER), Department of Electrical/Electronic Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Hossien Shahinzadeh: Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran
Zahra Azimi: Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran
Majid Moazzami: Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran

Energies, 2023, vol. 16, issue 5, 1-25

Abstract: This study performed a comparative analysis of five new meta-heuristic algorithms specifically adopted based on two general classifications; namely, nature-inspired, which includes artificial eco-system optimization (AEO), African vulture optimization algorithm (AVOA), gorilla troop optimization (GTO), and non-nature-inspired or based on mathematical and physics concepts, which includes gradient-based optimization (GBO) and Runge Kutta optimization (RUN) for optimal tuning of multi-machine power system stabilizers (PSSs). To achieve this aim, the algorithms were applied in the PSS design for a multi-machine smart power system. The PSS design was formulated as an optimization problem, and the eigenvalue-based objective function was adopted to improve the damping of electromechanical modes. The expressed objective function helped to determine the stabilizer parameters and enhanced the dynamic performance of the multi-machine power system. The performance of the algorithms in the PSS’s design was evaluated using the Western System Coordinating Council (WSCC) multi-machine power test system. The results obtained were compared with each other. When compared to nature-inspired algorithms (AEO, AVOA, and GTO), non-nature-inspired algorithms (GBO and RUN) reduced low-frequency oscillations faster by improving the damping of electromechanical modes and providing a better convergence ratio and statistical performance.

Keywords: meta-heuristic algorithms; low-frequency oscillation; electromechanical modes; smart damping controller; power system stabilizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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