Stability improvement of the large-scaled JAMALI power electricity system of Indonesia by using power system stabilizer based on electric Eel foraging optimization

As one of the large-scaled power electricity systems in Indonesia, the stability of the Jawa-Madura-Bali (JAMALI) power electricity system is crucial to ensure secure operation, especially during dynamic disturbances. In the modern era, the Power System Stabilizer (PSS) optimization by using Artificial Intelligence (AI) becomes a viable option to mitigate this problem. In this paper, an innovative AI optimization for PSS is proposed, ensuring simplicity, scalability, and compliance with the grid codes. For the first time, Electric Eel Foraging Optimization (EEFO) is introduced to calculate the global PSS parameters based on various performance indicators as objective functions, featuring IAE, ITAE, ISE, and ITSE. Based on the convergence curve analyses within 30 runs and 100 iterations for each objective function, EEFO is 9.38 %, 17.14 %, 21.62 %, and 30.95 % faster than Puma Optimizer, Evolutionary Mating Algorithm, Particle Swarm Optimization, and Genetic Algorithm, respectively. EEFO has also produced 5.64 % to 13.2 % and 3.94 % to 8.09 % better accuracy and consistency than the compared algorithms. Additionally, phase and eigenvalue analyses have been conducted to validate the EEFO-based PSS compliance with the damping ratio in the Indonesian grid codes. The best damping ratio of EEFO-based PSS is achieved by using ITAE as the objective function of 0.41, followed by ISE, IAE, and ITSE of 0.39, 0.38, and 0.37, respectively. For responses (frequency, power angle, and voltage) in different dynamic disturbances, the best improvement regarding the oscillations (undershoot, overshoot, and peak-to-peak) is achieved by EEFO-based PSS with ISE, while the best settling time improvement is achieved by EEFO-based PSS with ITAE. Thus, wider preferences are provided by the use of various performance indicators as objective functions in PSS optimization cases.