Open AccessStability Enhancement of TLBO Tuned SMIB System
Author(s) -
Kapil Parkh,
Vinesh Agarwal
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7744.038620
Subject(s) - control theory (sociology) , particle swarm optimization , settling time , electric power system , stability (learning theory) , controller (irrigation) , genetic algorithm , oscillation (cell signaling) , computer science , torque , power (physics) , engineering , control engineering , physics , algorithm , step response , artificial intelligence , chemistry , agronomy , control (management) , quantum mechanics , machine learning , biology , thermodynamics , biochemistry
In this paper investigation of the application of teaching learning based optimization (TLBO) technique for the design of a modified Phillips haffron model of SMIB installed with SSSC based controller is made. The design objectives are to reduce low frequency oscillation and improve power system stability. Simulation result are demonstrated with Eigen value analysis, where various types of disturbance is applied as mechanical torque input and reference voltage settling, variation in parameter & various loading condition. The results obtained are compared with some well-known optimization techniques, such as the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). A comparative study of results demonstrates that the results of the proposed controller were more precise and robust