Open AccessReal‐time parameter estimation based intelligent controllers for AGC operation under varying power system dynamic conditions
Author(s) -
Pathak Nikhil,
Verma Ashu,
Bhatti Terlochan Singh,
Nasiruddin Ibraheem
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5331
Subject(s) - automatic generation control , control theory (sociology) , controller (irrigation) , electric power system , power (physics) , estimation theory , computer science , control engineering , engineering , control (management) , physics , algorithm , quantum mechanics , artificial intelligence , agronomy , biology
In this paper, real‐time parameter estimation based intelligent controller has been proposed for the optimum automatic generation control (AGC) operation of multi‐area interconnected power system. In earlier AGC studies, supplementary controllers were designed for presumed power system dynamic conditions, such as constant system loadings, fixed values of power system dynamic model parameters. However in practical AGC system, these power system model parameters, namely frequency bias parameter B , steam chest and reheater time constants T SC & T RH , power system time and gain constants T p & K p , load sensitivity factor D , etc., continuously varies depending upon the consumer's load demand and number of power generating units participating in AGC. The controller's operation no longer remains optimum as these system parameters changes from their initial values. In view of the above, parameter estimation based real‐time intelligent controller has been designed which automatically adjusts its controller gain settings to its optimum conditions in real‐time after sensing changes in power system model parameters. At first, different methodologies are presented to estimate dominant power system model parameters. Then, presented methodologies are utilised in the design of intelligent controller for AGC system. The proposed controller is successfully tested upon IEEE 39 bus system with various case studies.