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This paper investigates an optimal methodology for mitigating low-frequency oscillation concerns in power systems. The study explores the synergistic integration of a power system stabilizer (PSS) and a flexible alternating current transmission system (FACTS) to formulate an intelligent controller. A comprehensive analysis encompasses various PSS design strategies, including lead-lag (LL), proportional-derivative-integral (PID), and fractional-order proportional-integral-derivative (FOPID) controllers. The FACTS device selected for this investigation is a static VAR compensator (SVC), highlighting the exceptional efficacy of FOPID-based PSS over alternative strategies with a power oscillation damper. The study extends its scope to encompass a comparative assessment of two distinct optimization algorithms: the moth flame optimization (MFO) and the antlion optimization (ALO). The research is conducted using a single-machine infinite bus power system (SMIB) as the case study platform. A total of four diverse test scenarios are executed under varying operating conditions. The evaluation of the developed method employs six distinct performance indices to investigate the developed controller thoroughly. The outcomes reveal that the MFO-optimized FOPID-PSS and SVC controller outperforms other control schemes. This optimized configuration demonstrates substantial improvements across all performance indices. These findings underscore the superior capabilities of the proposed approach in enhancing power system stability and performance. [ABSTRACT FROM AUTHOR]

This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention, and contingency analysis plays a vital role in assessing the adverse effects of losing a single element or more on the integrity of the power system network. However, the number of possible scenarios that should be investigated would be enormous, even for a small network. On the other hand, artificial intelligence (AI) techniques are well known for their remarkable ability to deal with massive data. Rapid developments in AI have led to a renewed interest in its applications in many power system studies over the last decades. Hence, this paper addresses the application of the hierarchical clustering algorithm supported by principal component analysis (PCA) for power system contingency screening and ranking. The study investigates the hierarchy clustering under different clustering numbers and similarity measures. The performance of the developed framework has been evaluated using the IEEE 24-bus test system. The simulation results show the effectiveness of the proposed framework for contingency analysis. [ABSTRACT FROM AUTHOR]

Inter-area oscillations not only limit the transferred bulk power but can extend to isolate the areas and may cause the blackout in some parts of the system or all the system. This paper depicts the improvement process of power system stability by using the modified particle swarm optimization (PSO) technique to optimize the lead-lag power system stabilizer (PSS) parameters offline to improve its performance. PSO modified by adjusting the damping boundary condition to prevent the particles from an outing of the searching space which improves the optimization process. Optimized PSS structure is a conventional lead-lag PSS (IEEE type- PSS1A) with speed deviation input signal. Proposed PSS performance compared with bacterial foraging based lead-lag PSS, and a simplified multi-band PSS: IEEE® type PSS4B. A comparison process applied to the system divided into two areas 11-bus 4-generators. Furthermore, performance indices as Eigenvalue, damping ratio, participation factor, maximum overshoots, settling time, and steady-state error used to utilize the analysis. The simulation results clarify the strength of the proposed PSS over the other compared PSSs. Simulation results in mathematical analysis prove that the proposed PSS improves the overall system stability better than the BG based lead-lag PSS by (23.02835%) and the MB-PSS by (94.14835%). [ABSTRACT FROM AUTHOR]