This paper describes the process of power system stabilizer (PSS) optimization by using bacterial foraging (BG) to improve the power system stability and damping out the oscillation during large and small disturbances in a multi-machine power system. The proposed PSS type is P. Kundur (Lead-Lag) with speed deviation as the input signal. BG used to optimize the PSS gains. The proposed BG based delta w lead-lag PSS (P. Kundur structure) (BG-PSS) evaluated in the well-known benchmark simulation problem P. Kundur 4-machines 11-buses 2-areas power system. The BG-PSS compared with MB-PSS with simplified settings: IEEE® type PSS4B according to IEEE Std. 421.5, Conventional Delta w PSS (as the proposed PSS without optimization) from P. Kundur, and Conventional Acceleration Power (Delta Pa) PSS to demonstrate its robustness and superiority versus the three PSSs types to damp out the inter-area oscillations in a multi-machine power system. The damping ratio and the real part of the eigenvalues used as the fitness function in the optimization process. The nonlinear simulation results obtained in the MATLAB / SIMULINK environment prove that the proposed PSS is highly effective, robust, & superior to the other used controllers in restrictive the inter-area oscillation in a large power system & to maintain the wide-area stability of the system. Also, the performance indices eigenvalue analysis, peak overshoot, settling time, and steady-state error used to validate the superior oscillation damping and fast recovered transient dynamic behavior over the three considered controllers.

Power system contingency studies play a pivotal role in maintaining the security and integrity of modern power system operation. However, the number of possible contingencies is enormous and mostly vague. Therefore, in this paper, two well-known clustering techniques namely K-Means (KM) and Fuzzy C-Means (FCM) are used for contingency screening and ranking. The performance of both algorithms is comparatively investigated using IEEE 118-bus test system. Considering various loading conditions and multiple outages, the IEEE 118-bus contingencies have been generated using fast-decoupled power flow (FDPF). Silhouette analysis and fuzzy partition coefficient techniques have been profitably exploited to offer an insight view of the number of centroids. Moreover, the principal component analysis (PCA) has been used to extract the dominant features and ensure the consistency of passed data with artificial intelligence algorithms’ requirements. Although analysis of comparison results showed excellent compatibility between the two clustering algorithms, the FCM model was found more suitable for power system static security investigation.