A hybrid gravitational search and pattern search algorithm for tuning damping controller parameters for a unified power flow controller—A comparative approach

Abstract

AbstractThis paper mainly focuses on the development of a hybrid gravitational search algorithm and the pattern search (hGSA‐PS) algorithm for the optimal tuning of a unified power flow controller (UPFC) ‐based damping controller. The optimal parameter assessment for a damping controller craves a decisive tuning method, which can reduce the system error, thus improves the system stability. The design problem associated with the proposed controllers is formulated as an optimization problem and the hGSA‐PS is used to find the UPFC‐based damping controller parameters. Thus, by achieving the optimal settings of the variables of the damping controller, the damping of the system is reduced, and concurrently stability of the system is maintained. A detailed investigation is carried out considering the 4 alternatives UPFC‐based damping controller. As evidence, the numerous system response curves confirm that the planned hybrid GSA‐PS algorithm achieves improved performance compared with the conventional algorithms and the recent hybrid GA‐GSA algorithm. The proposed work basically compares the hGSA‐PS algorithm with the recent developed hybrid GA‐GSA algorithm and the conventional GSA algorithm by considering the convergence rate. Finally, the stability performance of a Simulink‐based 2 area 4‐machine power system is analyzed by applying the hGSA‐PS algorithm. From the simulation results, it can be presumed that the proposed hybrid approach is the best method for outlining the UPFC‐based damping controller.