02-11-2012, 05:38 PM
Application of Artificial Intelligence for Tuning the Parameters of an AGC
[attachment=37977]
Abstract
—This paper deals with the tuning of parameters for
Automatic Generation Control (AGC). A two area interconnected
hydrothermal system with PI controller is considered. Genetic
Algorithm (GA) and Particle Swarm optimization (PSO) algorithms
have been applied to optimize the controller parameters. Two
objective functions namely Integral Square Error (ISE) and Integral
of Time-multiplied Absolute value of the Error (ITAE) are
considered for optimization. The effectiveness of an objective
function is considered based on the variation in tie line power and
change in frequency in both the areas. MATLAB/SIMULINK was
used as a simulation tool. Simulation results reveal that ITAE is a
better objective function than ISE. Performances of optimization
algorithms are also compared and it was found that genetic algorithm
gives better results than particle swarm optimization algorithm for
the problems of AGC.
INTRODUCTION
a large volume of work has already been reported in the
field of automatic generation control [1]-[11]. In most of
the previous works on interconnected systems, tie-line
bias control strategy has been widely accepted by utilities. In
this method, Area Control Error (ACE) is calculated through
feedback for each area and control action is taken to regulate
ACE to zero. Thus, the frequency and the interchanged power
are kept at their desired values. A bias constant is used for
each area to give relative importance to the frequency error
with respect to the tie-line power error. ACE for ith (i = 1, 2)
area is defined by utilities as [6]:
APPLICATION OF PARTICLE SWARM OPTIMIZATION
ALGORITHM
Particle swarm optimization (PSO) [7], [8] is a population
based stochastic optimization technique inspired by social
behavior of bird flocking or fish schooling. The system is
initialized with a population of random solutions and searches
for optima by updating generations. In PSO, the potential
solutions, called particles, fly through the problem space by
following the current optimum particles. In PSO system, each
individual adjusts its flying according to its own flying
experience and its companion’s flying experience. Each
particle keeps track of its coordinates in the problem space
which are associated with the best solution (fitness) it has
achieved so far. This value is called ‘pbest’. Another "best"
value that is tracked by the particle swarm optimizer is the
best value, obtained so far by any particle in the population.
This best value is a global best and called ‘gbest’.
V.ILLUSTRATIVE SYSTEM EXAMPLE
In the literature a lot of works concerning AGC have
already been reported considering conventional controllers.
Although, many studies pertaining to thermal plants are
available, only few works deal with the area of hydrothermal
systems provided with reheater and electric governor.
In the present work, investigations have been carried out on
an interconnected hydrothermal system provided with reheat
type of turbine and electric governor as shown in Fig. 3. The
system parameters are given in Appendix. MATLAB
(/Simulink) [13] is used as a simulation tool to obtain dynamic
responses for ΔF1, ΔF2 and Ptie for 1% step load perturbation
in thermal area.
[attachment=37977]
Abstract
—This paper deals with the tuning of parameters for
Automatic Generation Control (AGC). A two area interconnected
hydrothermal system with PI controller is considered. Genetic
Algorithm (GA) and Particle Swarm optimization (PSO) algorithms
have been applied to optimize the controller parameters. Two
objective functions namely Integral Square Error (ISE) and Integral
of Time-multiplied Absolute value of the Error (ITAE) are
considered for optimization. The effectiveness of an objective
function is considered based on the variation in tie line power and
change in frequency in both the areas. MATLAB/SIMULINK was
used as a simulation tool. Simulation results reveal that ITAE is a
better objective function than ISE. Performances of optimization
algorithms are also compared and it was found that genetic algorithm
gives better results than particle swarm optimization algorithm for
the problems of AGC.
INTRODUCTION
a large volume of work has already been reported in the
field of automatic generation control [1]-[11]. In most of
the previous works on interconnected systems, tie-line
bias control strategy has been widely accepted by utilities. In
this method, Area Control Error (ACE) is calculated through
feedback for each area and control action is taken to regulate
ACE to zero. Thus, the frequency and the interchanged power
are kept at their desired values. A bias constant is used for
each area to give relative importance to the frequency error
with respect to the tie-line power error. ACE for ith (i = 1, 2)
area is defined by utilities as [6]:
APPLICATION OF PARTICLE SWARM OPTIMIZATION
ALGORITHM
Particle swarm optimization (PSO) [7], [8] is a population
based stochastic optimization technique inspired by social
behavior of bird flocking or fish schooling. The system is
initialized with a population of random solutions and searches
for optima by updating generations. In PSO, the potential
solutions, called particles, fly through the problem space by
following the current optimum particles. In PSO system, each
individual adjusts its flying according to its own flying
experience and its companion’s flying experience. Each
particle keeps track of its coordinates in the problem space
which are associated with the best solution (fitness) it has
achieved so far. This value is called ‘pbest’. Another "best"
value that is tracked by the particle swarm optimizer is the
best value, obtained so far by any particle in the population.
This best value is a global best and called ‘gbest’.
V.ILLUSTRATIVE SYSTEM EXAMPLE
In the literature a lot of works concerning AGC have
already been reported considering conventional controllers.
Although, many studies pertaining to thermal plants are
available, only few works deal with the area of hydrothermal
systems provided with reheater and electric governor.
In the present work, investigations have been carried out on
an interconnected hydrothermal system provided with reheat
type of turbine and electric governor as shown in Fig. 3. The
system parameters are given in Appendix. MATLAB
(/Simulink) [13] is used as a simulation tool to obtain dynamic
responses for ΔF1, ΔF2 and Ptie for 1% step load perturbation
in thermal area.