Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

Abstract

The primary focus of this paper is to assess an
interconnected power system using different optimization
techniques. The main purpose is to employ different optimization
techniques, including genetic algorithms (GA) and particle
swarm optimization (PSO), to systematically enhance the
performance of a multi-area or two-area automatic generation
control (AGC) system, aiming to optimize the three PID
controllers gain values and improve system performance under
diverse loading conditions. The modeling of the two areas
includes components such as governors, turbines, and loads, with
the tie line representing the interconnection between the two
areas. Two case studies are conducted exploring different loading
conditions in the megawatt (MW) range, including increasing
load demand and decreasing load demand. The analysis involves
four scenarios, covering without any kind of controller, another
with solely a proportional integral derivative (PID) controller, a
PID controller enhanced through a genetic algorithm (GA), and
lastly, a PID controller improved through particle swarm
optimization (PSO). The optimization process utilizes the integral
time absolute error (ITAE) as the objective function to evaluate
the system's performance. The simulation outcomes for ITAE,
settling time, overshoot, and undershoot for frequency deviation
of area one, area two, and power deviation in the tie-line are
compared with previous similar studies to assess the novelty of
this work. The article highlights the importance of the multi-area
AGC system and the significance of different optimization
techniques in enhancing its performance.