simulation of integrated dynamic voltage restorer ... · turns off in a power system, with duration...
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IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 04, 2016 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1207
Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor
Analysis, for Power Quality Enhancement Using Feedforward Neural
Network Control System Mrs. Kalyani Sahu1 Prof. Devendra Sahu2
1P.G. Scholar 2Assistant Professor 1,2Department of Electrical & Electronics Engineering
1,2DIMAT College, CSVTU University, C.G. IndiaAbstract— Now a days, the energy storage technologies
usable and maintenance maintenance charge is decreasing
quickly. The integration of this technology into the
distribution power generation power quality is being
enhanced since these devices gives quick response and high
constance. Dynamic voltage restoration is a process and
instrument necessity to maintain, or restore and also
compensate functional electric load during voltage sag and
swell, harmonics in voltage supply. DVR is the most reliable
custom power device that can protect sensitive load and it can
be entirely resolve the power quality problem in distribution
power generation. Super capacitor is a specially designate
capacitor which have low-energy density and high- power
density, quick charge/discharge rates that area unit all ideal
characteristic for meeting high voltage low energy events like
grid intermittencies sag/swells. It combined the properties of
capacitors and batteries into one device. SCAPs are electronic
devices which are able of holding excessive amount of
electrical intrust quantity. It is also called as Ultra Capacitor
(UCAP). This paper describe the integration of SCAP-DVR
system is used to compensate the temporary voltage sags and
swells, which last form 3s to 1min. the design and control
methods of both inverter and converter have been maintain
and substantiate MATLAB simulation model. Also in this
model, neural network control system and PWM pulse
generation is used.
Key words: DC-DC converter, neural control, dynamic
voltage restorer (DVR), Supercapacitor, Voltage sag/swell
I. INTRODUCTION
Power quality means the quality of the normal voltage
supplied to your facility. It is based on the extent of diversity
of the voltage and current waveform from the ideal clean
sinusoidal waveforms of fundamental frequency. To improve
the power quality, it is necessary to know what kind of
disturbances occurred and quality initially assign to the
quality of the service delivered as ‘measured’ by the
consumers efficiency to use the energy delivered in the desire
manner. A power quality supervise system that is capable to
automatically perceive, characterize and classify disturbance
on electrical lines is thus requiredThe aim of power system is
to give electrical energy or power to consumer. Non linear
loads, utility switching and fault clearing produce disturbance
that result the quality of this delivered power. In the instant
scenario, electric power is viewed as an integral work with
given characteristics, which can be measured, forecast,
certified and improved. The word power quality indicate
different things to different people.
.
Fig. 1: Block diagram of integrated DVR-SCAP
Dynamic voltage restorer (DVR) is a power
electronic converter-based device that has been designed to
save critical loads from all supply-side disturbances and fault
other than outages. A DVR is usually build round a dc–ac
power converter that is connected in series with a distribution
line through three single phase transformers. The dc side of
the converter is connected to a dc energy-storage device, filter
and buck-boost mode of converter. The energy state of the
device is regulated by taking power from the feeder. By the
use of different devices DVR compensate the voltage sag-
swell and reduce the harmonics content. The major
contribution of this composition is in integration SCAP for a
broader frequent of application similar active/reactive power
support, renewable intermittence smoothing, voltage
sag/swell compensation and power quality conditioning to
the distribution grid. Renewable intermittence smoothing is
associate degree application which needs to bidirectional
transfer of power from the grid to the SCAPs and vice-versa
by charging and discharging the SCAP. This application
needs high active power support within the 10s-3min
constancy which may be obtained by integrating SCAPs
through a shunt active power support in the 3s-1min period
scale which can be provided integrating SCAP into the grid
through series dynamic voltage restorer (DVR). All the on
top of functionalities can also be provided by integrating the
SCAP into restraint conditioner analysis situs.
In this paper we also discuss about neural network
(NN) control system which control the PWM signal in the
integrated SCAP-DVR system. In existing system PI control
strategy is used. So replaced by the NN control in proposed
system we get the better response as compare to PI control
system.
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Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor Analysis, for Power Quality Enhancement Using Feedforward Neural Network Control System
(IJSRD/Vol. 4/Issue 04/2016/294)
All rights reserved by www.ijsrd.com 1208
II. SYSTEM DESCRIPTION
A UCAP cannot be instantly connected to the dc-link of the
inverter like a battery, as the voltage profile of the UCAP
modify as it discharges energy. Therefore, there is a need to
integrate the UCAP system through a bidirectional dc–dc
converter, which maintains a stiff dc-link voltage, as the
UCAP voltage decreases while discharging and increases
while charging. The model of the bidirectional dc–dc
converter and its controller are shown in Fig. 2,
A bidirectional dc–dc converter is need as an
interface between the UCAP and the dc-link since the UCAP
voltage deviate with the amount of energy discharged while
the dc-link voltage has to be stiff.
Fig. 2: Model of three-phase series inverter (DVR) and its
controller with integrated higher order controller.
Average current mode control, which is widely
explored in, is used to regulate the output voltage of the
bidirectional dc–dc converter in both buck and boost modes
while charging and discharging the UCAP. where the dc-link
and actual output voltage Vout is compared with the
reference voltage Vref and the error is passed through the
voltage compensator, which generates the average reference
current Iucref .When the inverter is discharging power into
the grid during voltage sag event, the dc-link voltage Vout
tends to go below the reference Vref and the error is positive;
Iucref is positive and the dc–dc converter operates in boost
mode. When the inverter is absorbing power from the grid
during voltage swell event or charging the UCAP, Vout tends
to increase above the reference Vref and the error is negative;
Iucref is negative and the dc–dc converter operates in buck
mode. Therefore, the sign of the error between Vout and Vref
determines the sign of Iucref and thereby the direction of
operation of the bidirectional dc–dc converter. The reference
current Iucref is then compared to the actual UCAP current
(which is also the inductor current) Iuc and the error is then
passed through the current compensator.
A. Dynamic Voltage Restorer (Dvr):
DVR is the most intense and effective custom power device.
The reason behind this is its lower expense, smaller size and
its immovable response towards the disturbances.
Fig. 3: Basic Circuit Diagram of DVR
The main function of DVR is to offer the desired
voltage measure in series with the help of an injection
transformer whenever voltage sag is detected. DVR is a
device which is used to sustain, restore an operational electric
load when sag, swell and other disturbance is occurs in
voltage supply. This process uses critical device such as an
automatic transfer switch and IGBT module in order to work.
B. Proposed Neural Network (NN):
A neural network is a system with input and output and is
composed of many simple and uniform procedure elements.
Neural networks are recommended for aware control as a part
of well-known construction with adaptive critic. Recently,
much investigate has been done on application of NN for
control of nonlinear dynamic procedure. At instant, most of
the duty on system control using NNs are based on multilayer
feedforward NNs with more efficient variation of this
algorithm. The neural network based controller offers very
fast implementation of proposed PWM. In this project a
multilayer neural network is used The proposed project
occupy a feedforward neural network, which receives the
command voltage and generate PWM waves for single phase
bidirectional dc/dc converter with the help of logic circuits.
So the data to be used in NNs with back propagation learning
or more effective variation of that algorithm.
III. PROBLEM IDENTIFICATION
Power quality is the capability to use the energy give forth in
the desire manner and to give sufficiently high grade
electrical services to the customer. Synchronization of the
voltage frequency section allow electrical system to execute
their intended manner while not important loss of
performance or life. The word is used to explain electric
power that drives an electrical load and the load’s capability
to perform properly. Without the right power, an electrical
device may malfunction. Fail untimely or not operate, which
effects the distribution system. There are the some power
quality problem defines-
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Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor Analysis, for Power Quality Enhancement Using Feedforward Neural Network Control System
(IJSRD/Vol. 4/Issue 04/2016/294)
All rights reserved by www.ijsrd.com 1209
A. Voltage Sag:
A decay of the normal voltage level with ranging from 10-
90% at any instant of Time and for duration of 0.5 cycle to 1
min.
Fig. 4: Voltage Sag
B. Voltage Swell:
Voltage swell is defined as temporary increase of the voltage,
external normal tolerances. Or it happens when a heavy load
turns off in a power system, with duration of more then one
cycle and typically less than a few seconds.
Fig. 5: Voltage Swell
C. Harmonics:
A harmonic frequency is a multiple of the fundamental
frequency and this frequency of the ac electric power
distribution system is 50 Hz. A harmonic frequency is any
sinusoidal frequency, Harmonic frequencies can be even or
odd multiples of the sinusoidal fundamental frequency.
IV. MODELING AND SIMULATION RESULTS
Fig. 6: Simulation model of SCAPs supported DVR system
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Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor Analysis, for Power Quality Enhancement Using Feedforward Neural Network Control System
(IJSRD/Vol. 4/Issue 04/2016/294)
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(a)
(b)
(c)
(d)
In fig.7 (a) shows source voltage and source current.
In this consideration 50% 3 phase voltage sag/swell occurring
at the utility grid, which is initiated 0.2s and it is kept until
0.4s with total voltage sag/swell duration of 0.2s. fig 7 (b), (c)
shows the load output voltage & current and inverted output
voltage and current with sag/swell and harmonics
compensation. Fig (d) shows the simulation graph of source
voltage and load voltage. In this graph results also shows the
exact output waves of the converter. All this result are
analyzed by using existing PI control system.
(a)
(b)
(c)
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Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor Analysis, for Power Quality Enhancement Using Feedforward Neural Network Control System
(IJSRD/Vol. 4/Issue 04/2016/294)
All rights reserved by www.ijsrd.com 1211
(d)
In fig.8 (a) shows source voltage and source current.
In this consideration 50% 3 phase voltage sag/swell occurring
at the utility grid, which is initiated 0.2s and it is kept until
0.4s with total voltage sag/swell duration of 0.2s. fig 8 (b), (c)
shows the load output voltage & current and inverted output
voltage and current with sag/swell and harmonics
compensation. Fig (d) shows the simulation graph of source
voltage and load voltage. In this graph results also shows the
exact output waves of the converter. The simulation results
confirm to verify the feasibility of the proposed converter. All
this result are found by using proposed neural network
control system.
By comparing the results of both control system in
fig.4 (b) and fig.5 (b), the load output voltage and current
more variation is occurring in grid, which is initiated 0.2s to
0.4s. In an NN controller the output voltage consistency is
maintained at around 300V, but in PI controller that
consistency is not maintained. And in NN control low
variation in load voltage (Vrms) as compare to PI, shown in
fig.5 (d). Conclusion is that proposed system by using NN
controller is provide greater power quality response as
compare to existing model (PI).
A. Fft Analysis:
Fig. 9: Total Harmonic Distortion of SCAP-DVR using PI
controller
Fig.9 shows the Total Harmonic Distortion (THD)
of the SCAP-DVR using PI controller. Where the Total
Harmonic Distortion of SCAP-DVR using PI controller is
11.58% at fundamental frequency (60Hz).
Fig. 10: Total Harmonic Distortion of SCAP-DVR using
Neural Network
Fig.10 shows the Total Harmonic Distortion (THD)
of the SCAP-DVR using Neural Network. Where the THD of
integrated SCAP-DVR using Neural Network is 1.54% at
fundamental frequency (60Hz). A comparative study is made
between two controller for mitigating voltage sag. The
comparative study is based on the THD of the load voltage.
From this study it is clear that the neural control is more
efficient in mitigating the voltage sag/swell as compare to PI
control.
V. CONCLUSION
The operation of a integrated SCAP-DVR system for
improving power quality, by using neural network control
system is analyzed in this paper. A higher level integrated
neural network control system is proposed which takes
decisions based on the system parameter, gives input to the
inverter and bidirectional dc-dc converter controllers to carry
out their control actions. The control strategy of the SCAP-
DVR is based on adc-dqo method for improving voltage
profile. The proposed system by using NN controller is
provide greater power quality response as compare to existing
model (PI). The THD in existing PI model is 11.48% but in
proposed NN control system THD is 1.54%, so we get reduce
harmonics in proposed model. In addition, it improves the
system efficiency to approx 98% as well as reduce voltage
sag/swell problem in critical load. The simulation of the
integrated SCAP-DVR system, power quality in distribution
grid will be enhance, also compensate sag/swell, harmonics
and result satisfy the validity of proposed NN control
strategy.
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Simulation of Integrated Dynamic Voltage Restorer-Supercapacitor Analysis, for Power Quality Enhancement Using Feedforward Neural Network Control System
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