Model Order Reduction of an ISLANDED MICROGRID using Single Perturbation, Direct Truncation and Particle Swarm Optimization

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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Model Order Reduction of an ISLANDED MICROGRID Using Single
Perturbation, Direct Truncation and Particle Swarm Optimization
Manali Patel 1, Dr. Manish Kumar Srivastava2
Research Scholar ,Dept. Of Electrical Engineering,S.I.E.T.,SHAUTS,U.P,INDIA
Head of the Department ,Dept. Of Electrical Engineering, S.I.E.T.,SHAUTS,U.P,INDIA
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ABSTRACT: In this paper we are simplify the model of an
islanded micro-grid systems using Single Perturbation
method (SP) and Particle Swarm Optimization. The used
model is of an 6th Order islanded micro-grid model. As we
know it is not possible to approximate the dynamics of any
system using slow subsystem, so we also obtain the result of
slow subsystem in this paper. In this work, we presented two
Model order reduction methods viz. Direct Truncation (DT)
and Particle swarm optimization. Thus, sixth order has been
reduced to the fourth order approximation. We have also
presented the Particle Swarm Optimization (PSO) to reduce
the model order to 2. Thus various responses have been
compared. In the results, it has been shown that even with
using 2nd order reduction using PSO, it shows the improved
response than other methods.
Keywords: singular perturbation method, Direct
Truncation, micro-grid, model order reduction.
1 INTRODUCTION
Smart grid are increasing in these days frequently, many
research have been done on many areas like , control of
grid system, safety regards of micro grids, smart
operation of grids. As we know the interconnections of
machine make the system is of higher order which create
the large computational work and due to uncountable
feedback loops , the system design become difficult. Hence
order reduction technique become very useful to simplify
the higher order system to low order system. It has also an
advantage that it has not any significant effects on the
properties of the system. Within a microgrid numerals
order reduction have been employed and also for specific
systems within it like , generators , renewable energy
sources , controller design purpose .
In this paper we used a six order model of a Islanded
micro grid system[7]. Two techniques have been
discussed to simplify the dynamics , first is the singular
perturbation method and second is Direct Truncation
method. Direct truncation gives a good model match at
high frequency , while singular perturbation methods have
superior low frequency properties .Two different
perturbation parameters have been used (Є=0)(Є≠0).
In this work, we have presented the Particle Swarm
Optimization (PSO) method for model order reduction of
6th order to 2nd Order respectively. Thus, the various
responses have been observed. It was found that it
outperforms all the methods in terms of the output
responses.
1.1 ISLANDED GRID SYSTEMS
Fig.1. Model of an Islanded Grid systems
The system of Fig. 1 is required to operate in both the grid-
connected and the islanded modes. In the grid-connected
mode, the interface converter operates as a current-
controlled VSC. Voltage magnitude and frequency of the
local load (PCC) are dictated by the grid. A -frame current-
control scheme is designed to control the power-exchange
between the DG unit and the grid of Fig. 1. The grid can
exchange real and reactive power with the potential
island. When both real and reactive power exchange
between the potential island and the grid are zero, the
system operates in a matched power state.
Otherwise, the system is in a mismatched-power
condition. When the DG unit and the local load are
islanded by opening switch S, due to power mismatch
between the load and the DG unit and in the absence of
voltage and frequency controls, the PCC voltage and
frequency deviate from their rated values.
Thus, voltage and frequency of the load can vary
significantly if the DG unit does not provide voltage and
frequency control. Therefore, to achieve uninterruptible
autonomous operation of the island, the islanding event
must be detected and subsequently voltage and frequency
must be controlled. In this case, the new controller to be
developed should regulate voltage magnitude and
frequency of the load.

The design of this new controller is based on a dynamic
model of the islanded system as discussed in the next
section. The frequency of the islanded system is controlled
using an internal oscillator in an open-loop manner.
Frequency of the internal oscillator is set at the system
nominal frequency.
1.2 Direct Truncation Model
On the other hand, the response of the fast subsystem
obtained via exact decoupling is almost identical to the
response of the original system. The exact slow-fast
decomposition is achieved by utilizing the Chang
transformation as above.
1.3 Particle Swarm Optimization (PSO)
Particle Swarm Optimization simulates the behavior of a
swarm i.e. a group of birds. It is a computational method
that optimizes a problem by running several iterations
until the optimal solution is obtained. This stochastic
optimization technique based on population was
developed by Dr.Eberhart and Dr. Kennedy in 1995.
It is a technique implemented in various applications in
order to determine an optimum solution. It simulates the
intelligent behavior of a group of birds moving from a
place to their target. The birds adjust their velocity and
speed to reach the target in accordance to their own
position as well as neighbor’s position closest to the
optimum solution. Similarly, initial solutions assumed are
moved around in a search space logically following the
PSO algorithm in accordance with the particular
application and varying various parameters to reach the
optimum solution.
 PSO learned from the scenario and used it to solve
the optimization problems [2].
 In PSO, each single solution is a bird in the search
space termed as particles.
 Initially, depending on the application a search
space is decided consisting of a number of
solutions.
 Each particle’s initial position and velocity is
assumed.
Pseudo-code:
Equation (a)
v[ ] = c0 *v[ ]+ c1 * rand() * (pbest[ ] - present[ ]) + c2 *
rand() * (gbest[ ] - present[ ])
Equation (b)
present[ ] = present[ ] + v[ ]
For each particle
Initialize particle
END
Do
For each particle
Calculate fitness value
If the fitness value is better than its peronal best
set current value as the new pBest
End
Choose the particle with the best fitness value of all
asgBest
For each particle
Calculate particle velocity according equation (a)
Update particle position according equation (b)
End
Parameters Values
No of Swarm 40
Bird Step 40
Dimension 3
PSO Parameter
C1 1
PSO Parameter
C2 1
PSO Parameter
C3 0.8
1.4 SIMUALTION PARAMETERS
Parameters Symbols Values
Resistance of VSC
Filter Rt 1.5 mΩ
Inductance of VSC
Filter Lt 300uH
VSC Rated Power 200 MVA
VSC Terminal
Voltage 600V
Switching Frequency fsw 1980 Hz
DC Bus Voltage vdc 1500 V
Load Nominal
resistance R 56 Ω
Load Nominal
Inductance L 111.9 mH
Load Nominal
Capacitance C 120uF
Load Quality Factor Q 1.83
Load Resonant
Frequency fres 273 Hz
Inductor Quality
Factor ql 753.36
System Nominal
Frequency f0 60 Hz
Transformer Voltage
ratio
0.6/13.8
kV
Transformer rating 200 MVA

Here, all the parameters has been listed of the Islanded
systems. The VSC parameters has been listed as Rt and Vt.
The system has been designed for the 200MVA systems.
The switching frequency has been used as 1980 Hz. Also,
the Load has been presented as R = 56 Ohm, L = 111.9 mH,
C = 120uF.
2 RESULTS & DISCUSSIONS
Fig.2 frequency Response of the original 6th Order system
Fig.2 Shows the frequency response of the original system.
It follows the low pass system. It shows the higher order
and lower order oscillatory behaviour. Our motive is to
follow the same response through direct truncation
method.
Fig.3 frequency Response of the reduced system transfer
function of original 6th Order system
Fig.3 Shows the frequency response of the reduced order
system. It has been shown that as per fig.3. the obtained
reduced order system follows the similar response to that
of the original system.
Now, since the original system resembles the MIMO
system. Thus, reordering the same, we get two transfer
functions H1 and H2. Thus here, both transfer function has
been presented and their step response curve has been
shown.

The transfer functions of the H1:
The transfer function of the H2:
Response of H1 and H2:
Fig.4 Step response of Original H2 Fig.5 Step response of Original H1
Step response of H
1
and H
2

the time domain response of the both systems. Thus, both
the curves has been presented here.
4 CONCLUSION
In our proposed work, we have reduced the order of the
original system by using two different methodology viz.
Direct truncation, and Particle Swarm Optimization. The
PSO represents the reduction into 2nd order and DT-
method into the 4th order model. Thus, the results, has
shown that model order reduction by the Direct
Truncation resembles the response to the original one.
Thus, it’s shown that direct truncation has been one of the
finest reduction techniques.
Also, we have used PSO for the model order reduction. It
has been shown that the model obtained by PSO is very
similar to the original model. And thus, we have also got
improved results in the form of the step plots.
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Model Order Reduction of an ISLANDED MICROGRID using Single Perturbation, Direct Truncation and Particle Swarm Optimization

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Model Order Reduction of an ISLANDED MICROGRID using Single Perturbation, Direct Truncation and Particle Swarm Optimization