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This research delves into enhancing the efficiency and stability of the Sulselrabar system by employing Proportional Integral Derivative (PID)-Power System Stabilizer (PSS) and Flexible AC Transmission System (FACTS) controls. The proposed control approach integrates PID-PSS1A for PSS and Static Var Compensator (SVC) for the FACTS device. Achieving optimal performance necessitates a coordinated strategy, thus this study incorporates optimization techniques for PID-PSS and SVC. It utilizes the artificial intelligence method Craziness Particle Swarm Optimization (CRPSO) for optimization, with conventional Particle Swarm Optimization (PSO) employed for comparison purposes. Analysis of SVC indicates that the recommended placement and capacity for bus 31 are determined as 40 MVar, while the CRPSO method suggests an optimal rating of 80 MVar. The implementation of CRPSO-based PID-PSS-SVC presents minimum power losses, namely active power is reduced to 8.7% and reactive power is reduced to 3.8%, while stability increases through damping system analysis by 25.13%. [ABSTRACT FROM AUTHOR]

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The introduction of additional controllers is essential in modern electric power systems to enhance their stability, particularly during disturbances. One effective method is the implementation of power system stabilizers (PSS). However, precise coordination of PSS equipment is necessary to determine the optimal locations and parameters. This study focuses on the optimal analysis of Multi-Band PSS3C (MB-PSS3C) coordination in integrated Wind Power Plant (WPP) systems in South, Southeast, and West Sulawesi (Sulselrabar). An artificial intelligence approach, utilizing the Mayfly Optimization Algorithm (MOA), is suggested for optimizing both the location and parameters of the PSS. Comparative investigations were conducted to assess the efficacy of MB-PSS3C in comparison with SB-PSS1A and MB-PSS2B, based on previous research. The performance analysis employed the time domain simulation method, reviewing the speed deviation response, field voltage response, PSS output voltage response, and rotor angle response for each generator. Eigenvalue analysis was performed for each control scheme. Load changes were applied to generators 1 (BAKARU) and 11 (WPP SIDRAP) to evaluate the performance of the system. The application of the MOA-based MB-PSS3C results in an increased damping ratio, improved speed response, and a more optimal rotor angle. MB-PSS3C provides a larger additional damping signal to the generator exciter, as indicated by the increase in the field voltage on the generator.

This study proposes the application of a single-band power system stabilizer type 1A (SB-PSS1A) to enhance the damping ratio in multimachine systems. The mayfly optimization algorithm (MOA) method is employed to optimize the performance of the SB-PSS1A. The South Sulawesi, Southeast Sulawesi, and West Sulawesi regions of the Sulselrabar system are covered by the proposed control plan. To evaluate the effectiveness of the proposed controller, frequency domain analysis and time domain simulation analysis with varying load changes are presented. The accuracy of the MOA method is compared to the swarm intelligence method based on particle swarm optimization (PSO) and firefly algorithm (FA). The analysis results indicate an improvement in system stability, as demonstrated by an increased damping ratio, reduced oscillation overshoot in generator speed and minimum rotor angle, and faster settling time. By implementing SB-PSS1A on 14 generators using the MOA optimization method, a damping ratio of 0.5868 was achieved. [ABSTRACT FROM AUTHOR]