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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2006
All Rights Reserved © 2015 IJARECE
A Novel Intelligent Controlling Technique for
Distributed power flow Controller for Power
Quality Improvement
D.Radhika1, Dr. B.V.Sanker Ram
2
Abstract – Non linear loads are the cause for most of the
power quality issues. Insufficient reactive power causes
Voltage Sag as well excess reactive power causes the Swell.
To control the voltage to a desired value, regulation of
reactive power is required. The existing UPFC approach for
the improvement of voltage profile is expensive. In this
paper a novel intelligent control scheme is proposed for the
new D-FACTS device, Distributed Power Flow Control for
the improvement of voltage profile of a IEEE Standard 4
bus system. Firstly conventional PI controller is used
inconjuction with DPFC, connected to a load bus. To
improve the efficiency of controlling action of DPFC, first a
fuzzy controller is proposed and later the results are
compared with Neuro controller. The addition of Neuro
controller with the DPFC improves the Voltage Profile in a
better way. The entire circuit is simulated using MATLAB.
Keywords: DPFC, PI Controller, Power Quality, Fuzzy
Logic, Neural Network, Voltage Sag, Voltage Swell.
I. INTRODUCTION
Electrical power is perhaps the most essential
raw material used by commerce and industry today. It is
required as a continuous flow. The most obvious power
defects are complete interruption (which may last from a
few seconds to several hours) and voltage dips or sags
where the voltage drops to a lower value for a short
duration. Naturally, long power interruptions are a
problem for all users, but many operations are very
sensitive to even very short interruptions. Voltage control
in an electrical power system is important for proper
operation for electrical power equipment to prevent
damage such as overheating of generators and motors, to
reduce transmission losses and to maintain the ability of
the system to withstand and prevent voltage collapse. In
general terms, decreasing reactive power cause voltage to
fall while increasing it cause voltage to rise [13]. While
the majority of voltage dips and interruptions originate in
the transmission and distribution system [12]. A voltage
collapse occurs when the system try to serve much more
load than the voltage can support [13]. Due to the use of
the different types of sensitive electronic equipments, PQ
issues have drawn substantial attention from both utilities
and users [3].
In this paper, a distributed power flow controller
is introduced to mitigate voltage waveform deviation and
improve power quality within seconds. The DPFC
structure is derived from the UPFC structure that is
included one shunt converter and several small
independent series converters as shown in Fig.1 [7].
Fig. 1: The DPFC Structure
A DPFC is connected to a 4 bus transmission system as
shown in Fig. 2. The DPFC consists of one shunt and
several series-connected converters. The shunt converter
is similar as a STATCOM, while the series converter
employs the D-FACTS concept, which is to use multiple
single-phase converters instead of one large rated
converter [1]. Within the DPFC, there is a common
connection between the ac terminals of the shunt and the
series converters, which is the transmission line.
Therefore, it is possible to exchange the active power
through the ac terminals of the converters.
DPFC consists of three types of controllers; they are
central controller, shunt control, and series control. The
central control generates the reference signals for both
the shunt and series converters of the DPFC[1].
Fig. 2 Simulated IEEE Four Bus System
All the reference signals generated by the central control
are at the fundamental frequency. Each series converter
ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2007
All Rights Reserved © 2015 IJARECE
has its own series control. The controller is used to
maintain the capacitor dc voltage of its own converter
and to generate series voltage at the fundamental
frequency that is prescribed by the central control. The
shunt converter‟s fundamental frequency control aims to
inject a controllable reactive current to grid and to keep
the capacitor dc voltage at a constant level [1].
II. CONVENTIONAL CONTROL OF DPFC
This paper proposed a Conventional PI controller for the
improvement of the performance of the DPFC connected
to a 4 bus system.
Fig.3 Distributed Power Flow Controller with PI Contol
The performance with DPFC and without DPFC is
observed and it is found that with the conventional
controller the voltage of the bus is not reached to nominal
value after clearance of sag and swells.
Fig. 4 Voltage response of DPFC with PI and without PI
III. DPFC WITH FUZZY CONTROL
Modeling and control of dynamic systems belong to
the fields in which fuzzy set techniques have received
considerable attention, not only from the scientific
community but also from industry. Many systems are
not amenable to conventional modeling approaches
due to the lack of precise, formal knowledge about the
system, due to strongly nonlinear behavior, due to the
high degree of uncertainty, or due to the time varying
characteristics. To improve the efficiency of DPFC in
the system an intelligent control strategy, namely
fuzzy control mechanism is proposed for the power
system engineer. The controlled strategy implemented
by the engineers are prepared as set of rules that are
simple to carry out manually but difficult to
implement by using conventional control strategy.
In this proposed fuzzy controller approach
the inputs are error (e) and change in error (∆e)
generates required control signal. The design procedure
of FLC consists of the following modules: 1)
Fuzzification 2) Fuzzy Rule-base 3) Fuzzy Inference
Engine (Decision Making Logic) and 4) Defuzzification.
A Fuzzy controller operates by repeating a cycle of
following four steps. First, measurements are taken of all
variables that represent relevant conditions of the
controlled process (Universal Discourse). Next, these
measurements are converted into appropriate fuzzy sets
to express measurement uncertainties (Fuzzification).
Fuzzy models can be seen as logical models which use
"if-then" rules to establish qualitative relationships
among the variables in the model. Fuzzy sets serve as a
smooth interface between the qualitative variables
involved in the rules and the numerical data at the inputs
and outputs of the model. The rule-based nature of fuzzy
models allows the use of information expressed in the
form of natural language statements and consequently
makes the models transparent to interpretation and
analysis. At the computational level, fuzzy models can be
regarded as flexible mathematical structures that can
approximate a large class of complex nonlinear systems
to a desired degree of accuracy. The fuzzified
measurements are the used by the inference engine
(Decision Making Logic) to evaluate the control rules
stored in fuzzy rule base. The result of this evaluation is a
output fuzzy set (or several fuzzy sets) defined on the
universe of possible actions and the degree of
membership of the output fuzzy set can be calculated by
using Root Sum Square Method. This fuzzy set is then
converted, in the final step of the cycle, into a crisp
(single) value that, in some sense, is the best
representative of the fuzzy set (Defuzzification). The
defuzzified value represents the actions taken by the fuzzy
controller in individual control cycles. Now, in the
following Sections we develop the various components
of FLC to solve power quality problem. Fuzzy controller
is developed to generate required control signal it
receives the input signal from the system and processing
the data in different stages and generate required
output[15].
Fig. 5 Fuzzy Logic Controller
ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2008
All Rights Reserved © 2015 IJARECE
III.I. Fuzzy Control Algorithm
The following algorithm is proposed for designing of
Fuzzy logic controller to mitigate power quality problem
of four bus system.
Step 1: Select the input variables to enhance power
quality issues like swell, sag. Error and change in
error are selected as fuzzy inputs and the output
of the FLC is proposed as the phase angle of
injected current(D-STATCOM) and phase angle of
injected voltage(D-SSSC).
Step 2: Selected input and out variables are then
partitioned in to 2 regions and their membership
functions have been defined.
Step 3: With the knowledge of the power quality issue
like voltage swell and voltage sag (through the
method, “Expert Experience and Control
Engineering Knowledge”), 4 rules are framed to
decide the membership function value of the
output variable. The rules are then tabulated in the
Decision-Table.
Step 4: Fuzzy output sets are formed and the strengths
of each of the output membership function is
estimated by Root Mean Square method.
Step 5: By applying Defuzzification, Crisp output is
obtained.
Step 6: With the crisp value, output signal is generated.
Fig. 6 Fuzzy Control logic of DPFC
IV. ANN CONTROL OF DPFC
Neural networks represent a class of very powerful,
general-purpose tools that have been successfully applied
to prediction, classification and clustering problems. The
popularity of neural networks is based on their
remarkable versatility, abilities to handle both binary and
continuous data, and to produce good results in complex
domains. When the output is continuous, the network can
address prediction problems, but when the output is
binary, the network works as a classifier.
An artificial neural network consists of a number of very
simple and highly interconnected processors, also called
neurons, which are analogous to the biological neurons in
the brain. The neurons are connected by weighted links
passing signals from one neuron to another. Each neuron
receives a number of input signals through its
connections; however, it never produces more than a
single output signal. The output signal is transmitted
through the neuron‟s outgoing connection[13].
Fig. 7 Architecture of Artificial Neural Network
A typical ANN is made up of a hierarchy of layers, and
the neurons in the networks are arranged along these
layers. The neurons connected to the external
environment form input and output layers. The weights
are modified to bring the network input/output behavior
into line with that of the environment. Increasing the
complexity of an ANN, and thus its computational
capacity, requires the addition of more hidden layers, and
more neurons per layer.
Each neuron is an elementary information-processing
unit. It has a means of computing its activation level
given the inputs and numerical weights. To build an
artificial neural network, we must decide first how many
neurons are to be used and how the neurons are to be
connected to form a network. In other words, we must
first choose the network architecture. Then we decide
which learning algorithm to use. And finally we train the
neural network, that is, we initialize the weights of the
network and update the weights from a set of training
examples[13].
The neuron computes the weighted sum of the input
signals and compares the result with a threshold value, θ.
If the net input is less than the threshold, the neuron
output is -1. But if the net input is greater than or equal to
the threshold, the neuron becomes activated and its
output attains a value +1.
The neuron uses the following transfer or activation
function[13]
Xk+1 = Xk – [JTJ + µI]
-1J
Te
Where J is the Jacobian matrix that contains first
derivatives of the network errors with respect to the
weights and biases and e is the vector of network errors. J
can be computed through back propagation technique.
This function uses the Jacobian for calculations, which
assumes that performance is a mean or sum of squared
ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2009
All Rights Reserved © 2015 IJARECE
errors. Therefore, networks trained with this function
must use either the mse or sse performance function.
Levenburg marquardt algorithm can train any network as
long as its weight, net input, and transfer functions have
derivative functions.
Training stops when any of these conditions occurs:
The maximum number of epochs (repetitions)
is reached.
The maximum amount of time is exceeded.
Performance is minimized to the goal.
The performance gradient falls below min_grad.
mu exceeds mu_max.
Validation performance has increased more
than max_fail times since the last time it decreased
(when using validation).
Fig. 8 ANN Control of Shunt Compensator
Fig. 9 ANN Control of Series Compensator
V. RESULTS
In this paper a comparison is made on the performance of
DPFC with PI, Fuzzy and Neural Network Controlling
techniques for the improvement of Power Quality issues,
voltage sag and swell. The proposed controllers are
evaluated by computer simulation in
MATLAB/SIMULINK. The proposed intelligent
controllers improved the power quality of the
transmission system at load bus. The results are taken on
2% tolerance base.
Fig. 10 Voltage Response at load bus with different controllers
Fig. 11 Voltage Response at load bus for sag clearance with different
controllers
Fig. 12 Voltage Response at load bus for swell clearance with different
controllers
ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2010
All Rights Reserved © 2015 IJARECE
Fig. 13 Power Response at load bus for sag created
Fig. 14 Power Response at load bus for swell created
VI. CONCLUSION
To improve the power quality of the transmission and
distribution system there are many methods available.
This paper has presented a novel intelligent control
technique for DPFC to improve the power quality. The
Conventional PI controller cannot improve the voltage
profile at a faster rate to the nominal value. The
limitations of the conventional control are rectified first
by the proposed fuzzy control and observation has made
that ANN control has improved the voltage profile at a
faster rate than Fuzzy control. The experimental results
of conventional and intelligent control techniques are
presented with different comparisons of voltage sag,
voltage swell and reactive power at load bus. It is shown
that the DPFC has improved the voltage profile at a faster
rate with ANN control.
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[2] Mr. Sandeep R. Gaigowal and Dr. M. M. Renge, “ Some studies of Distributed Series FACTS Controller to control active power flow through Transmission Line,‟‟ 2013,International Conference on Power, Energy and Control (ICPEC), pp 124-128.
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[4] Divya Nair, Ashwini Nambiar, Megha Raveendran and Neenu P. Mohan “Mitigation of Power Quality Issues using Dstatcom,” Emerging trends in Electrical Engineering and Energy management(ICETEEEM), 2012, pp 65-69.
[5] Santosh Kumar Gupta and Shelly Vadhera, “Performance of Distributed Power Flow Controller on System Behaviour under Unbalance Fault Condition,” Energy and Systems (SCES), 2014, pp 1-5.
[6] K.H.Kuypers, R.E.Morrison and S.B.Tennakoon,“Power Quality Implications Associated with a Series FACTS Controller,” IEEE Harmonics and Quality of Power , 2000, pp 176-181, volume1.
[7] Ahmad Jamshidi, S. Masoud Barakati and Mohammad Moradi Ghahderijani, „‟ Power Quality Improvement and Mitigation Case Study Using Distributed Power Flow Controller,‟‟Industrial Electronics (ISIE),2012 pp 464 - 468.
[8] Shital B.Rewatkar, Y.C.C.E Nagpur and Shashikant G.Kewte,‟‟ Role of Power Electronics based FACTS Controller SVC for mitigation of Power Quality Problems, „‟ Emerging trends in Engineering and Technology,(ICETET),2009,pp731–735.
[9] Akhib Khan Bahamani,Dr. G.V.Siva Krishna Rao, Dr. A.A. Powly Thomas and Dr. V.C. Veera Reddy, „‟ Modelling and Digital Simulation of DPFC System using Matlab Simulink,‟‟ International Journal of Engineering Trends in Electrical and Electronics (IJETEE ), vol. 6, Issue 1,August,2013.
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ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)
Volume 4, Issue 7, July 2015
2011
All Rights Reserved © 2015 IJARECE
[14] Akwukwaegbu I. O, Okwe Gerald Ibe,‟‟ Concepts of Reactive
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Authors’ information
Radhika Dora was born in 1978. She received
her B.Tech degree in Electrical and Electronics Engineering from JNTU Ananthapur in the Year
2000 and M.Tech in Electrical Power Engineering
from JNTU Hyderabad in 2007. She is pursuing Ph.D from JNTU Hyderabad in Electrical and
Electronics Engineering. Currently she is working
as Associate Professor in the Department of Electrical and Electronics Engineering in Geethanjali College of
Engineering and Technology, Cheeryal, Keesara, Hyderabad. Her major
interested areas are Power Quality, Power Converters, Power Electronics etc.
. Dr. Sanker Ram B.V did his B.E in Electrical
Engineering in the year 1982 from OU college of
Engineering. Obtained M.Tech in Power Systems in 1984 from OU College of Engineering and Ph.D in
2003 from JNTU. He joined in JNTU in 1985 and
worked in various Capacities. He has 70 technical papers to his credit in various international and
national journals and conferences. He has guided 12 research scholars
for Ph.D and 6 Candidates are still pursuing their research. His areas of interest include FACTS, Power Electronic Applications to Power
Systems, Power Systems Reliability.