Iaetsd estimation of damping torque for small-signal

Estimation of Damping Torque for Small-Signal
Stability of Single Machine Infinite Bus System
M.Venkateswara Rao A.Krishna veni
Associate professor PG scholar
mvr.venki@gmail.com, krishnaveni203@gmail.com
Department of Electrical and Electronics Engineering, GMRIT, Rajam, A.P. INDIA.
Abstract— This paper discusses the
Estimation of damping torque coefficient
for Small-signal stability of infinite bus
system. This damping torque coefficient is
used to identify the angle stability of a
system. Initially a mat lab coding was
utilized to generate the time domain
responses of rotor angle, rotor speed and
electromagnetic torque under various
loading conditions. The particle swarm
optimization (PSO) technique is then used
for accurate estimation of damping
torque coefficient. The mat lab coding
results using PSO, under various loading
conditions shows the effectiveness of the
proposed control strategy.
Index terms – Damping torque
coefficient, Particle swarm optimization,
Small-signal stability, and Synchronizing
torque coefficient.
I. INTRODUCTION
The power system instability can be
demonstrated in many different ways
depending on the system configuration and
working mode. Since power system works
on synchronous generators, an essential
condition for system operation is that all
synchronous machines remain in
synchronism [1-3]. The small signal stability
is the ability of the power system to
maintain synchronism when subjected to a
small disturbance [1]. The operating
condition of the power system changes with
respect to time because of the dynamic
nature of the system. The rotor angle
stability can be analyzed from the
Synchronizing torque coefficient SK and
Damping torque coefficient DK . For stable
operation of the system, both synchronizing
and damping torque coefficients must be
positive.
The electromagnetic torque deviation is
split into Synchronizing torque and
Damping torques. The Synchronizing torque
is responsible for restoring the rotor angle
excursion and the Damping torque damps
out the speed deviations [4, 5]. In general
the synchronizing and damping torques are
expressed in terms of Synchronizing torque
coefficient SK and Damping torque
coefficient DK . These SK , DK can be calculated
frequently for stability assessments.
Various computational techniques like
Simulation Annealing (SA) algorithm,
Evolutionary programming (EP), Genetic
Algorithm (GA) and Differential Evolution
(DE) are employed for optimization problem
[7, 8]. These techniques need more
parameters, high calculation time and not
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easy to implement when compared to
particle swarm optimization. Particle swarm
optimization (PSO) was developed by
Kennedy and Eberhart. This has appeared as
a promising algorithm for handling the
optimization problems [20]. PSO is a robust,
non-linear and population based stochastic
optimal technique which can generate high
quality solutions within shorter calculation
time. The single-machine power system
modeling and small-signal stability studies
are carried out using Eigen analysis based
technique With PSO (particle swarm
optimization) optimal strategy. The
suggested control technique is based on
estimation of damping torque coefficient DK
of a synchronous machine from the time
responses of the rotor angle )(tr , rotor
speed )(t and electromagnetic torque
)(tTe . Thus PSO has been chosen to
coordinate the operation in estimating
Damping torque coefficient DK for stability
analysis [17-19].
II. POWER SYSTEM MODEL
A simplified block diagram model of
the small signal performance is shown in
fig1 [1]. In this work, the proposed method
has been tested on a system comprising a
single machine connected to infinite bus
system through a transmission line.
Normally, for small signal stability study a
second-order model is considered for the
synchronous generator. The single machine
infinite bus system model is linearized at a
particular operating point to obtain the
linearized power system model. This model
is represented with some variables, such as
electrical torque, mechanical torque, and
rotor speed and rotor angle.
Figure1. Block diagram model of small
signal performance.
In the classical generator model, the
acceleration circuit dynamic equations are:
)(
2
1
rDem
r
KTT
Ht





(1)
r
t





0
(2)
Where mT , eT are mechanical torque,
electromagnetic torque and 0 = 02 f .
From the block diagram, the following state-
space form is developed.
BUAXX 
The elements of the system matrix A are
function of DK , H , TX and the initial
operating conditions.
The perturbation matrix B depends on the
system parameters only. From the block
diagram of figure1, we have
(3)
m
r
sD
r
THH
K
H
K
dt
d
























 









0
2
1
0
22
0




 



 mrDS TKK
HSS



2
10
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And the characteristic equation is
(4)
Therefore, the damping ratio is
(5)
T
B
t
jX
jEE
I
)sin(cos'
 

(6)
EdT XXX  '
(7)
0
'
cos 
T
B
S
X
EE
K 
(8)
III. SMALL SIGNAL STABILITY
ASSESSMENT USING MODAL
MATRICES
The power system experiences small
disturbances by small changes in loads.
Then the system will be driven to an infinite
state 00 )( XtX  at time 0t =0. The system
responds according to the state equations.
The linearized state equations can be used to
find the Eigen values i of the system matrix
A , where iii j  are the distinct
eigenvalues corresponding to a set of right
and left eigenvectors. Here i is a damping
factor and i is Damped angular frequency.
The right and left Eigen vectors are
orthogonal and are usually scaled to be
orthogonal. Real eigenvalues indicates
modes which are aperiodic and complex
eigenvalues indicates modes which are
oscillatory. The uses of right and left
eigenvectors are for identifying the
relationshipbetween the states and the
modes is that the elements of the
eigenvectors are dependent on units and
scaling associated with the state variables.
To overcome this participation matrix which
combines the right and left eigenvectors are
used. The participation factor provides a
measure of association between the state
variables and the oscillatory modes.
IV. SMALL-SIGNALSTABILITY
ASSESSMENT USING
SYNCHRONISING AND DAMPING
TORQUES.
The electromagnetic torque ( eT ) deviation
of a machine can be expressed as its speed (
 ) and angle ( ) deviations, which are
called damping and synchronizing torques.
The synchronizing and damping torques are
expressed in terms of its synchronizing
torque coefficient ( SK ) and damping torque
coefficient ( DK ). Then the electromagnetic
torque deviation will be expressed as:
)()()( 0 tKtKtT SDe  
(9)
Where )(tr = change in rotor speed
)(t = change in rotor angle
V. OVER VIEW OF PARTICLE
SWARM OPTIMIZATION
PSO is one of the evolutionary based
optimization techniques [Fukuyama, 1999;
Kennedy and Eberhart, 1995]. This method
is introduced based on the research of bird
and fish flock movements behavior. Due to
its many advantages like its simplicity and
easy implementation, the algorithm can be
widely used in function optimization.
PSO ALGORITHM:
The particle swarm optimization consists
of ‘n’ particles and the particles position
stands for the potential solution in D-
0
22
02

H
K
S
H
K
S SD 
022
1


HK
K
S
D

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15

dimensional space. Each particle can be
shown by its current speed and position.
Particles change its position according to
it’s:
1. Current position
2. Current velocity
3. Distance between its current
position and pbest
4. Distance between its current
position and gbest
Velocity of each particle can be modified
based on the following equation.
)()( 2211
1 k
id
k
id
kk
id
k
id
kk
id
k
id xgbestrcxpbestrcwvv 
By using velocity equation, a certain
velocity which gradually gets close local
best and global best can be calculated. The
current position of the particle can be
modified by the following equation.
11 
 k
id
k
id
k
id vxx
Where, pbest represents the D-dimension
quantity of the individual “i” at its most
optimist position at its “k” times and gbest
represents the D-dimension quantity of the
individual “i” at its most optimist position at
its “k” times. The speed of the particle at its
each direction is confined in between –
vdmix and +vdmax. If vdmax is too big,
solution is far from the best and if vdmax is
too low, it means that the solution will be
local optimism. C1, C2 represents speeding
figures which lies between 0 to 2. r1, r2
represents random fiction, and 0-1 is a
random number.
VI. ESTIMATION OF LOWER AND
UPPER LIMITS OF DAMPING
TORQUE COEFFICIENT.
For the linearized system model presented in
figure1, the eigenvalues of the local system
can be evaluated. The proposed method is
aiming to search for the optimal damping
torque coefficient, such that the damping
ratio can be maximized.
Where  =damping ratio.
For stable operating condition of the system
the Damping ratio must be in [0.4, 0.7].
Hence the Corresponding values of DK will
be [35.712, 63.125]. The control parameters
can be tuned through the optimization
algorithm. The proposed algorithm will be
as follows.
VII. IMPLEMENTATION OF PSO FOR
OPTIMAL ESTIMATION OF
DAMPING TORQUE
Step1. Read the system input data, PSO
parameters.
Step2. Initialize population of particle ( DK )
with random velocities and positions.
Step3. Evaluate fitness values using the
objective function.
Step4. Each particle has its own best
position called local best and the best
position among all the particles is called
global best.
Step5. Update the velocity of particle using
)()( 2211
1 k
id
k
id
kk
id
k
id
kk
id
k
id xgbestrcxpbestrcvv 
022
1


HK
K
S
D

022
1


HK
K
S
D

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Check the updated velocity, within the limits
or not
maxmin
iii vvv 
Step6. Update the position of particle using
11 
 k
id
k
id
k
id vxx
Step7. Evaluate fitness values of the new
particles. Update the local best values as
current fitness values if these values are
better than previous values, update it. Then
find new global best values.
Step8. Repeat the procedure until the
stopping criteria is reached.
VIII. RESULTS AND DISCUSSIONS
In this work the optimal values of DK are
obtained using PSO and the rotor speed,
rotor angle and electromagnetic torque
responses are generated and are compared
with those obtained in [1]. In which the
damping torque coefficient is chosen
randomly [-10, 0, 10]. In addition the same
responses are generated for different loading
conditions using mat lab coding. It is
observed that the steady state stability of the
system is improved. These responses are
shown in figure2-a to figure4-c. The rotor
responses are obtained for various
conditions:
1. Nominal operating condition (P=0.9,
Q=0.3).
2. Light operating condition (20% of
the nominal values).
3. Heavy operating condition (50%
higher than the nominal operating
condition).
The following responses show the rotor
characteristics under various loading
conditions.
The following responses show the rotor
characteristics under various loading
conditions.
Figure 2-a. Rotor speed response for
P=0.9,Q=0.3.
Figure 2-b. Rotor Angle response for P=0.9,
Q=0.3.
Figure 2-c. Torque response for P=0.9,
Q=0.3.
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Figure 3-a. Rotor speed response for P=1.35,
Q=0.45.
.
Figure 3-b. Rotor Angle response for
P=1.35, Q=0.45
Q=0.45.
Figure 4-a. Rotor speed response for P=0.72,
Q=0.24
Figure 4-b. angle response for P=0.72,
Q=0.24.
Q=0.24.
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CONCLUSION
In this project the steady state performance
improvement is obtained by the accurate
estimation of damping torque coefficient
using particle swarm optimization
technique. The matlab programming results
using PSO under various loading conditions
shows the effectiveness of the proposed
technique. Compared to normal operating
conditions under heavy and light operating
conditions the peak over shoots are very
high which are reduced using PSO
technique. The effectiveness of the proposed
controller is to provide good damping of low
frequency oscillations. It can be concluded
that the proposed PSO controller extends the
power system stability limit by enhancing
the system damping.
APPENDIX:
Input data:
1. Generator parameters:
85.0,003.0,3.0
,76.1,81.1,8,5.3
'
'
0


sqsdad
qdd
KKRX
XXTH
2. Transmission line parameters:
.15.0,65.0,0  Lee XXR
Table1. (Lower, Upper limits of control
parameter):
PARAMETER DK
Lower limit 35.712
Upper limit 63.125
Table2.PSO parameters:
Table3 (Loading conditions):
Table4. (Optimal value of control
parameter)
REFERENCES
[1] Kundur, P.: “power system stability and
control”, McGraw-Hill, 1994.
[2] Hsu Y.Y., Chen, C.L.:“Identification of
optimum location for stabilizer
Application using participation
factors”. In: IEEE Proc. Gen., Trans. &
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238-244.
[3] Demello F.P., concordia. C,“Concepts
of synchronostability as affected by
excitation control”. In: IEEE Trans.
Population size 10
Maximum number of generations 50
Acceleration coefficients(C1,C2) 1.4
Inertia weight 1
S.no Cases P(p.u) Q(p.u)
1 Nominal operating
condition 0.9 0.3
2 Heavy operating
condition (50% higher
than the nominal load)
1.35 0.45
3 Light operating
condition(20% of the
nominal operating
condition)
0.72 0.24
DK 57.8
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Power Apparatus and Systems,
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