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Go to Editorial ManagerWith the aim of enhancing the small signal stability of electric power systems, the present paper evaluated and compared some power system stabilizers (PSSs). The dilemma of small signal instability is avoided by equipping the generator’s automatic voltage regulator (AVR) with a backup controller known as a PSS. Conventional PSS operates with acceptable efficiency when designed to suit specific operating conditions, but there are limitations and drawbacks that arise when disturbances lead to fluctuation in system parameters. Strengthening the design methodology for PSS in the face of these limitations is achieved by adopting artificial intelligence. This research presents a fuzzy, neural system-based approach to the development of PSS. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to design the Fuzzy Neural Power Systems stabilizer (FNPSS) . ANFIS eliminates the disadvantages of using fuzzy logic and neural networks independently in PSS design. The single machine infinite bus (SMIB) power system was used as a case study to evaluate the effectiveness of the proposed methodology. Additionally, the study includes root locus scheme for loop of voltage regulation by utilizing proportional Integral controller, P-I controller, a widely used traditional linear design technique, for comparison. The simulation results confirm the effectiveness of the method, demonstrating the superiority of the ANFIS design method over other PSS designs. MATLAB, along with Control System Toolbox and SIMULINK, is used for simulation and design.
Increasing the penetration of Renewable Energy Sources (RES) into power systems created challenges and difficulties in the management of power flow since RES have variable power production based on their sources, such as Wind Turbines (WT), which depend on the wind speed. This article used Optimal Power Flow (OPF) to reduce these difficulties and to explain how the OPF can manage the power flow over the system, taking different cases of WT power production based on the different wind speeds. It also used Fixable AC Transmission (FACT) devices such as Thyristor-Controlled Series Compensators (TCSC) to add features to the controllability of the power system. The OPF is a non-linear optimization problem. To solve this problem, the artificial intelligence optimization technique is used. Particle Swarm Optimization (PSO) has been used in the OPF problem in this article. The Objective Functions O.F. discussed here are losses (MW), Voltage Deviation VD (p.u.), and thermal generation fuel Cost ($/h). This article used the wind turbine bus magnitude voltage and the reactance of TCSC as a control variable in OPF. To test this approach, the IEEE 30 bus system is used.