I.J. Intelligent Systems and Applications, 2019, 7, 43-53 Published Online July 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2019.07.05 Copyright © 2019 MECS I.J. Intelligent Systems and Applications, 2019, 7, 43-53 Performance Assessment of Bacterial Foraging based Power System Stabilizer in Multi-Machine Power System Nader M.A. Ibrahim Faculty of Industrial Education-Suez University/Electrical Department, Suez, Egypt E-mail: Nader.Ibrahem@suezuniv.edu.eg. Basem E. Elnaghi Faculty of Engineering-Suez Canal University / Electrical Engineering Department, Ismailia, Egypt E-mail: basem_elhady@yahoo.com Hamed A. Ibrahim Faculty of Industrial Education-Suez University/Electrical Department, Suez, Egypt E-mail: hamed_4a@yahoo.com Hossam E.A. Talaat Electrical Engineering Department, Faculty of Engineering& Technology-Future University in Egypt, Cairo, Egypt E-mail: hossam.eldeen@fue.edu.eg Received: 16 September 2018; Accepted: 31 October 2018; Published: 08 July 2019 Abstract—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. Index Terms—Multi-machine power system model, artificial intelligent, power system stabilizer optimization, bacterial foraging technique, performance indices. NOMENCLATURE   : The differentiation of rotor angle deviation.   : Rotor speed.   : The rated rotor speed in elec.   ⁄      : The mechanical power.   : Electrical power  : Damping coefficient.   : Damping constant.  : Inertia coefficient.       : Differentiation of (q & d)-axis transient voltage respectively.     : (d & q)-axis synchronous reactance respectively.       : (d & q)-axis transient reactance respectively.       : (d & q)- axis open circuit transient time constant respectively.       : (d & q)- axis open circuit sub-transient time constant respectively.     : The stator phase currents of dq transformation.     : Constants of the linearized model.  : The vector of inputs to the system  : Linearized incremental quantity   : Mechanical torque.