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Fuzzy Logic Controlled Dynamic Voltage Restorer for Voltage
Sags/Swells Mitigation
M. Nagaraju1, K. Srujana Reddy2
1Assistant
2Student
professor, Electrical Engineering, SIET, Telangana, India
M.Tech, Electrical Engineering, SIET, Telangana, India
Abstract : Power quality is one of major concerns
in the present era. It has become important,
especially, with the introduction of sophisticated
devices, whose performance is very sensitive to the
quality of power supply. Power quality problem is
an occurrence manifested as a nonstandard
voltage, current or frequency that results in a
failure of end use equipment. One of the major
problems dealt here is the voltage sag and voltage
swell. To solve this problem, custom power devices
are used. One of those devices is the Dynamic
Voltage Restorer (DVR), which is the most
efficient and effective modern custom power
device used in power distribution networks. Its
appeal includes lower cost, smaller size, and its
fast dynamic response to the disturbance. The
control for DVR based on Fuzzy Logic Control is
discussed. The proposed control scheme is simple
to design. Simulation results carried out by
MATLAB/SIMULINK verify the performance of
the proposed method.
Key words: Dynamic Voltage restorer, Volatge sag
and Voltage swell , Fuzzy Logic Control.
I.
INTRODUCTION
Nowadays, modern industrial devices are mostly
based on electronic devices such as programmable
logic controllers and electronic drives. The electronic
II.
PO W ER Q UA LI TY PR OB LEM S
2.1 Sources and effects of over quality problems:
devices are very sensitive to disturbances and become
less tolerant to power quality problems such as
voltage sags, swells and harmonics. Voltage dips are
considered to be one of the most severe disturbances
to the industrial equipment. Voltage support at a load
can be achieved by reactive power injection at the
load point of common coupling. The common method
for this is to install mechanically switched shunt
capacitors in the primary terminal of the distribution
transformer. The mechanical switching may be on a
schedule, via signals from a supervisory control and
data acquisition (SCADA) system, with some timing
schedule, or with no switching at all. The
disadvantage is that, high speed transients cannot be
compensated. Some sags are not corrected within the
limited time frame of mechanical switching devices.
Transformer taps may be used, but tap changing
under load is costly another power electronic solution
to the voltage regulation is the use of a dynamic
voltage restorer (DVR). DVRs are a class of custom
power devices for providing reliable distribution
power quality. They employ a series of voltage boost
technology using solid state switches for
compensating voltage sags/swells. The DVR
applications are mainly for sensitive loads that may
be drastically affected by fluctuations in system
voltage.



III.
Voltage transients: They are temporary,
undesirable voltages that appear on the
power supply line. Transients are high overvoltage disturbances (up to 20KV) that last
for a very short time.
Harmonics: The fundamental frequency of
the AC electric power distribution system is
50 Hz. A harmonic frequency is any
sinusoidal frequency, which is a multiple of
the fundamental frequency. Harmonic
frequencies can be even or odd multiples of
the sinusoidal fundamental frequency.
Flickers: Visual irritation and introduction
of many harmonic components in the
supply power and their associated ill effects.
DYNAMIC VOLTAGE RESTORER
Fig. 2.1 Single line diagram of power supply system.
Power distribution systems, ideally, should provide
their customers with an uninterrupted flow of energy
at smooth sinusoidal voltage at the contracted
magnitude level and frequency. However, in practice,
power systems, especially the distribution systems,
have numerous nonlinear loads, which significantly
affect the quality of power supplies. A nonlinear
loads, the purity of the waveform of supplies is lost.
This ends up power quality problems.
While power disturbances occur on all electrical
systems, the sensitivity of today’s sophisticated
electronic devices make them more susceptible to
the quality of power some sensitive devices, a
momentary disturbance can cause scrambled,
interrupted
communications, a frozen mouse,
system crashes and equipment failure etc., A power
voltage spike can damage valuable components.
Power Quality problems encompass a wide range of
disturbances such as voltage sags/swells, flicker,
harmonics distortion, impulse transient, and
interruptions.
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


Voltage dip: A voltage dip is used to refer
to short-term reduction in voltage of less
than half a second.
Voltage sag: Voltage sags can occur at any
instant of time, with amplitudes ranging
from 10 – 90% and a duration lasting for
half a cycle to one minute.
Voltage swell: Voltage swell is defined as
an increase in rms voltage or current at the
power frequency for durations from 0.5
cycles to 1 min.
Voltage 'spikes', 'impulses' or 'surges':
These are terms used to describe abrupt,
very brief increases in voltage value.
Fig: schematic diagram of Dvr
Among the power quality problems (sags,
swells, harmonics...) voltage sags are the most severe
disturbances. In order to overcome these problems the
concept of custom power devices is introduced
recently. One of those devices is the Dynamic Voltage
Restorer (DVR), which is the most efficient and
effective modern custom power device used in power
distribution networks. DVR is a recently proposed
series connected solid state device that injects voltage
into the system in order to regulate the load side
voltage. It is normally installed in a distribution system
between the supply and the critical load feeder at the
point of common coupling (PCC). Other than voltage
sags and swells compensation, DVR can also added
other features like: line voltage harmonics
compensation, reduction of transients in voltage and
fault current limitations.
IV.
FUZZY LOGIC CONTROL
Fuzzy relational models encode associations
between linguistic term defined in the system’s input
and output domains by using fuzzy relations. The
individual elements of the relation the strength of
association between the fuzzy sets. As a simple
example, assume a static model with one input x ∈ X
and one output y ∈ Y. Denote A a collection of M
linguistic term (fuzzy sets) defined on domain X, and
B a collection of N fuzzy sets defined on Y:
𝐴 = {𝐴1 𝐴2 , … , 𝐴𝑀 },
𝐵 = {𝐵1 𝐵2 , … , 𝐵𝑁 },
As depicted in Figure 5.6, a fuzzy relation 𝑅 =
[𝑟𝑖𝑗 ] 𝜖 [0,1]𝑀 𝑥 𝑁 defines a mapping:
R:A→B , where each Airelated to each Bj , with a
strength given by the element rij of the relation.
Figure 5.6. Fuzzy relation as a mapping from input to
output linguistic terms.
It should be stressed that the relation R in
fuzzy
relational
models
is
different
from the relation, encoding fuzzy if-then rules. The
latter relation is a multidimensional membership
function defined in the product space of the input and
output
domains. Each element of this relation represents the
degree
of
association
between
the individual crisp elements in the antecedent and
consequent
domains.
In
fuzzy
relational models, however, the fuzzy relation
represents
associations
between
the
individualfuzzysets defined in the input and output
domains of the model. It is, in fact, a
table storing the rule base in which all the antecedents
are
related
to
all
the
consequents
with different weights.
The inference in fuzzy relational models
proceeds as follows. For a crisp input x, a fuzzy set X,
given by
𝑋 = [𝜇𝐴1 (𝑥), 𝜇𝐴2 (𝑥), … , 𝜇𝐴𝑀 (𝑥)],
Represents the degree to which x is compatible with
the input term. The corresponding output fuzzy se
fuzzy set Y = [𝜇1 , 𝜇1 , … , 𝜇𝑁 ] is derived by the max-t
composition:
Y = X o R.
The crisp output of the fuzzy relational model yois
calculated using the weighted mean:
𝑦𝑜 =
∑𝑁
𝑗=1 𝜇𝑗 𝑏𝑗
∑𝑁
𝑗=1 𝜇𝑗
,
Where bj= cog(Bj) are the centroids of fuzzy sets Bj .
In the MIMO case, the sets X and Y are
multidimensional fuzzy sets. The main advantage of
the relational model is that the input-output mapping
can be fine- tuned without changing the consequent
fuzzy sets (linguistic terms). In the linguistic model,
the outcomes of the individual rules are restricted to
the grid given by the centroids of the output fuzzy
sets, which is not the case in the relational model.
For this additional degree of freedom, one
pays by having more free parameters (elements in the
relation), which poses problem in identification.
Moreover, if no constraints are imposed on these
parameters, several elements in arrow of R can be
nonzero, which may hamper the interpretation of the
model. Furthermore, the shape of the output fuzzy
sets has no influence on the resulting defuzzified
value, since only centroids of these sets are
considered in defuzzification.
It is easy to verify that if the antecedent
fuzzy sets from a partition and the bounded-sumproduct composition is used, a relational model can
be computationally replaced by an equivalent model
with singleton consequents. If also the consequent
membership functions from a partition, a single ton
model can be expressed as an equivalent relational
model by computing the membership degrees of the
singletons in the consequent fuzzy sets Bj .These
membership degrees the become elements of the
fuzzy relation:
Figure 5.7. An input-output mapping of a fuzzy
relational model
V.
RESULTS AND DISCUSSIONS
The first simulation was done with no
DVR and a voltage sag of 20% is introduced into
the system (ie., by reducing supply voltage) for 0.2
sec. Here DVR is injecting the voltage to nullify the
sag and then controller will calculate the required
delay delta so that DVR can obtain required injected
voltage so as to maintain flat voltage profile.
the required voltage into each phase so as to
maintain flat voltage profile.
Figure 6.1 Voltage Response with PI Control for
Sag in Grid Voltage
Figure 6.2. Voltage Response with Fuzzy Logic
Control for Sag in Grid Voltage
A voltage swell of 20% was introduced
into the system (ie., by increasing supply voltage)
for 0.2 sec. Here DVR is injecting the negative
voltage to nullify the swell and then controller will
calculate the required delay delta so that DVR can
obtain required injected voltage so as to maintain
flat voltage profile.
Figure 6.3. Voltage Response with PI Control for
10% increase in Grid Voltage
Figure 6.4. Voltage Response with Fuzzy Logic
Control for 10% increase in Grid Voltage
Here also an unbalance in voltage is
created into the system. DVR is properly injecting
Figure6.5. Voltage Response with PI Control for
harmonic in Grid Voltage
Figure 6.6. Voltage Response with Fuzzy Logic
Control for harmonic in Grid Voltage Over fuzzy
logic control show the better time response
characteristic than proportional plus integral
control.
Figure 6.7. Comparing the performance of PI
Control and Fuzzy Logic Control for
Voltage Sag and Swell
VI.
CONCLUSION
This paper has presented the power quality
problems such as voltage dips, swells, distortions and
harmonics. Compensation techniques of custom
power electronic devices DVR was presented. The
design and applications of DVR for voltage sags and
comprehensive results were presented. A PWMbased control scheme was implemented. As opposed
to fundamental frequency switching schemes already
available in the MATLAB/ SIMULINK, this PWM
control scheme only requires voltage measurements.
This characteristic makes it ideally suitable for lowvoltage custom power applications.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
O. Anaya-Lara, E. Acha,
“Modeling and Analysis of Custom Power
Systems by PSCAD/EMTDC,” IEEE Trans.,
Power Delivery, PWDR vol-17 (1), pp. 266272, 2002.
Bingsen Wang,
GiriVenkataramanan and Mahesh Illindala,
“Operation and Control of a Dynamic
Voltage Restorer Using Transformer
Coupled H-Bridge Converters”, IEEE
transactions on power electronics, VOL. 21,
NO. 4, JULY 2006
H. Hingorani “Introducing custom
power” IEEE spectrum, vol.32 no.6 June
1995 p 4148.
Ray Arnold “Solutions to Power
Quality Problems” power engineering
journal 2001 pages: 65-73.
John Stones and Alan Collinsion
“Introduction to Power Quality” power
engineering journal 2001 pages: 58 -64.
Gregory F. Reed, Masatoshi
Takeda, "Improved power quality solutions
using advanced solid-state switching and
static compensation technologies," Power
Engineering Society 1999 Winter Meeting,
IEEE
G. Venkataramanan and B.
Johnson, “A pulse width modulated power
line conditioner for sensitive load centers,”
IEEE Trans. Power Delivery, vol. 12, pp.
844–849, Apr. 1997.
[8]
N.G. Hingorani and L. Gyugyi,
“Understanding FACTS: Concepts and
Technology of Flexible AC Transmission
Systems”, 1st edition, The Institute of
Electrical and Electronics Engineers, 2000.
[9]
F. Z. Peng, H. Akagi, and A.
Nabae, “Compensation characteristics of the
combined system of shunt passive and series
active filters,” IEEE Trans. Ind. Applicat.,
vol. 29, pp. 144–151, Jan./Feb. 1993.
[10]
M.H.Haque “Compensation of
distribution system voltage sag by DVR and
DSTATCOM” Power Tech Proceedings,
2001 IEEE Porto , Volume: 1 , 10-13 Sept.
2001 Pages:5 pp. vol.1
[11]
S. S. Choi, B. H. Li, and D.
D.Vilathgamuwa,
“Dynamic
voltage
restoration with minimum energy injection,”
IEEE Trans. Power Syst., vol. 15,pp. 51–57,
Feb. 2000
[12]
P.W. Lehn and MR. Iravani,
“Experimental Evaluation of STATCOM
Closed Loop Dynamics,” IEEE Trans.
Power Delivery, vol. 13, no.4, October
1998, pp.1378-1384.
[13]
M. H. J. Bollen, “Understanding
Power Quality Problems—Voltage Sags and
Interruptions” Piscataway, New York: IEEE
Press, 2000.
[14]
Mohan, T. M. Undeland, andW. P.
Robbins,
Power
Electronics:
Converters,Applications and Design . New
York: Wiley, 1995
[15]
F.A.L. Jowder, “Design and
analysis of dynamic voltage restorer for
deep
voltage
sag
and
harmonic
compensation”,
Published
in
IET
Generation, Transmission & Distribution
Received on 17th October 2008, Revised on
2nd March 2009