Stability Enhancement of Sulselrabar Electricity System Using Mayfly Algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Djalal, Muhammad Ruswandi, Robandi, Imam, Prakasa, Mohammad Almas
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Algorithms; Artificial intelligence; Compensators; Controllers; Coordination; Damping; Damping ratio; Eigenvalue; Eigenvalues; Electric power systems; Electricity distribution; Generators; Heuristic methods; Mayfly Optimization Algorithm; Multi-Band PSS2B; Optimization; Oscillators; Parameters; Particle swarm optimization; Power system stability; Power systems; Rotors; Stability; Static VAr compensators; SVC; Swarm intelligence; Systems stability; Time domain analysis; Voltage generators
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3283598

An additional controller in an electric power system is currently required to increase system stability, especially when a disturbance occurs. The stability of the multimachine system can be increased by installing a Static Var Compensator (SVC) and Power System Stabilizer (PSS). However, the SVC and PSS equipment require precise coordination to determine the optimal location and parameters. This study presents an optimal analysis of SVC coordination with Single-Band PSS1A and Multi-Band PSS2B (MB-PSS2B) in the South, Southeast and West Sulawesi (Sulselrabar) electrical systems. An artificial intelligence method based on the Mayfly Optimization Algorithm (MOA) is proposed to optimize the location and parameters of the SVC and PSS. A comparative investigation related to controller parameter optimization from previous work was used to measure the effectiveness of the MOA based on the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). Performance analysis using the time domain simulation method to review the speed deviation response, field voltage response, and rotor angle response for each generator, as well as eigenvalue analysis for each control scheme when there is a change in load disturbance on generators 1 and 13. The results show an increase in bus voltage from critical to marginal conditions and a decrease in network losses after installing SVC on bus 31 of 40 MW capacity. The application of MB-PSS2B based on MOA provided an increased damping ratio, optimal speed response, rotor angle, field voltage generator, and eigenvalue system after installing 14 PSS.