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

被引:5
作者
Sabo, Aliyu [1 ]
Odoh, Theophilus Ebuka [1 ]
Shahinzadeh, Hossien [2 ,3 ]
Azimi, Zahra [2 ,4 ]
Moazzami, Majid [2 ,5 ]
机构
[1] Univ Putra Malaysia UPM, Fac Engn, Dept Elect Elect Engn, Adv Lightning & Power Energy Syst ALPER, Serdang 43400, Selangor, Malaysia
[2] Islamic Azad Univ, Smart Microgrid Res Ctr, Najafabad Branch, Najafabad 8514143131, Iran
[3] Amirkabir Univ Technol, Dept Elect Engn, Tehran 1591634311, Iran
[4] Islamic Azad Univ, Sci & Res Branch, Dept Elect & Comp Engn, Tehran 1477893855, Iran
[5] Islamic Azad Univ, Najafabad Branch, Dept Elect Engn, Najafabad 8514143131, Iran
关键词
meta-heuristic algorithms; low-frequency oscillation; electromechanical modes; smart damping controller; power system stabilizer; STABILITY ENHANCEMENT; ALGORITHM;
D O I
10.3390/en16052465
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:25
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