Ant Colony Optimized Fuzzy Control Solution for Frequency Oscillation Suppression

• Published in: Electric power components and systems Vol. 45; no. 14; pp. 1573 - 1584
• Main Authors: Bhateshvar, Yogesh Krishan, Mathur, Hitesh Datt, Siguerdidjane, Houria, Bansal, Ramesh C.
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 27-08-2017; Taylor & Francis Ltd
• Subjects: Ant colony optimization; Automatic; Automatic control; automatic generation control; Design optimization; Electric power generation; Energy storage; Engineering Sciences; Fuzzy control; Fuzzy logic; fuzzy logic controller; genetic algorithm; Genetic algorithms; Magnetic energy storage; Optimization techniques; Proportional integral derivative; Smart grid; superconducting magnetic energy storage; Superconductivity; two-area power system; Automatic Generation Control; Ant Colony Optimization; Superconducting Magnetic Energy Storage; Fuzzy Logic Controller; Two-Area Power System; Genetic Algorithm
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2017.1362073

This paper presents a novel approach in addressing a critical power system issue, i.e., automatic generation control (AGC) in a smart grid scenario. It proposes the design and implementation of an optimized fuzzy logic controller (FLC) for AGC of interconnected power network. There are three different sources of power generation considered in the two-area interconnected model of power system network. First area is equipped with a single reheat thermal unit and a superconducting magnetic energy storage (SMES) unit, while another area has a hydro-unit with SMES. A multi-stage optimization strategy for the optimal solution of FLC for tie-line and frequency oscillation suppression is proposed in this paper using an ant colony optimization technique. The optimization of FLC is carried out in four different stages. The first stage is the optimization of range of input and output variables; the second stage is the optimization of membership function; the third and fourth stages are the optimization for rule base and rule weight optimization, respectively. The performance of the proposed controller is also compared with another control approaches to stabilize P
tie-line
and Δf oscillations; these are the Ziegler-Nichols-tuned proportional-integral-derivative (PID) controller and genetic algorithm optimized PID controller. A comprehensive analysis of the traditional techniques and proposed techniques is presented on the basis of major dynamic performance parameters, i.e., settling time and peak overshoot.