haquesunny2018

Artificial Neural Network based Dynamic Voltage
Restorer for Improvement of Power Quality
Md. Samiul Haque Sunny
Electrical & Electronic Engineering
Khulna University of Engineering &
Technology, KUET
Khulna, Bangladesh
[email protected]
Eklas Hossain
Electrical Engineering & Renewable
Energy
Oregon Tech.
OR-97601, USA
[email protected]
Mikal Ahmed
Electrical & Electronic Engineering
Khulna University of Engineering &
Technology, KUET
Khulna, Bangladesh
[email protected]
Fuad Un-Noor
Electrical & Electronic Engineering
Khulna University of Engineering &
Technology, KUET
Khulna, Bangladesh
[email protected]
Abstract—Dynamic Voltage Restorer (DVR) is a custom
power device used as an effective solution in protecting
sensitive loads from voltage disturbances in power distribution
systems. The efficiency of the control technique, that conducts
the switching of the inverters, determines the DVR efficiency.
Proportional-Integral-Derivative (PID) control is the general
technique to do that. The power quality restoration capabilities
of this controller are limited, and it produces significant
amount of harmonics – all of which stems from this linear
technique’s application for controlling non-linear DVR. As a
solution, this paper proposes an Artificial Neural Network
(ANN) based controller for enhancing restoration and
harmonics suppression capabilities of DVR. A detailed
comparison of Neural Network controller with PID driven
controller and Fuzzy logic driven controller is also illustrated,
where the proposed controller demonstrated superior
performance with a mere 13.5% Total Harmonic Distortion.
Keywords—Power quality, Dynamic Voltage Restorer (DVR),
PID, Fuzzy logic, Artificial Neural Network (ANN)
I. INTRODUCTION
With advanced and complicated technologies being
implemented in today’s power systems, electrical power
quality faces many problems including voltage sags, swells,
harmonics, unbalance and flickers [1-3]. Voltage sags
associated with faults in transmission and distribution
systems, energizing of transformers, and starting of large
induction motors are considered as the most important power
quality disturbances [4-7]. Dynamic Voltage Restorers
(DVR) in the past have been deployed to counter these issues
with various techniques including static VAR compensator
(SVC) [4], different inverter configurations [8, 9], voltage
source inverter [10], and bidirectional DC-DC converter,
among others. Mahdianpoor et al. employed Posicast and
P+Resonant control method which constrained fault current
by making the DVR behave like a virtual impedance [5].
Abed et al. used Proportional-Integral (PI) controller along
with pulse width modulation to overcome voltage sags and
swells in [11], while dq0 concept was applied by Francis et
al. in [12]. Jimichi et al. proposed an isolated unidirectional
high frequency DC-DC converter to control a DVR with low
voltage ratings in [13]. Shahabadini et al.’s method uses
cascaded H-bridge multilevel converters, and reduces power
factor at load side during sags to increase compensation
capability [14]. Kumar et al. used sliding mode control for
DVR control aimed at mitigating sags in three-phase
978-1-4799-7312-5/18/$31.00 ©2018 IEEE
transmission parallel feeders [7]. This paper presents design
and implementation of a high-performance DVR – which,
unlike the past works mentioned – employs Artificial Neural
Network (ANN) control strategy for voltage sag and swell
mitigation of a power system network.
ANN loosely imitates animal brains, where numerous
neurons are connected in an intricate nexus. In ANN, a
number of nodes act as these neurons, and when a node
receives any information, it forwards it to the next one in line
after required processing. The nodes are arranged in different
layers, and each input is processed through all the layers to
produce an output. Before being able to employ ANNs in
any specific task, they need to be trained with data related to
the purpose it is supposed to serve. In this case, the ANN is
trained with fault data and the corresponding actions to
mitigate those instabilities. To achieve this, a feedforward
ANN is trained with backpropagation and Levenberg
Marquardt optimization. Bias is used during the training;
tansig, softmax, and purelin transfer functions are used in the
layers; the training data is generalized and randomized to
avoid the overfitting during the training, and consists of
previous fault history and possible disturbance conditions.
This ANN approach is guaranteed to be robust because of its
adaptive nature which comes from the ability to be trained
for all possible fault cases. The results show that the
proposed DVR works perfectly against voltage sags and
swells with only 13.5% Total Harmonic Distortion (THD),
whereas Fuzzy logic and PID control systems generated
THDs of 24.4% and 19.7%, respectively.
In this paper, section II gives the theoretical background,
section III presents the proposed method, and section IV
shows the mathematical underpinnings and the training
process of the ANN. Section V demonstrates the simulation
results: which shows the DVR performance for three phase
sag mitigation and the corresponding inverter signals.
Section VI compares the proposed method with PID and
Fuzzy controllers, where the superiority of the proposed
technique is demonstrated for different sag and swell
mitigations scenarios, and THD suppression. Finally, the
conclusion is drawn in section VII.
II. THEORITICAL BACKGROUND AND THE PROBLEM
FORMULATION
To calculate the voltage sag/swell magnitude at the point
of common coupling (PCC) in radial systems (which is the
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most prevailing one in industrial distribution networks), it is
common to use the voltage divider model. According to this
model, the power quality issues can be represented as shown
in Fig. 1, where the voltage magnitude at the PCC is given
by:
Vsag / swell = Z f / ( Z s + Z f )
(1)
Where,
Z s = The source impedance including the transformer
impedance;
Z f =The impedance between the PCC and the fault
(a)
including fault and line impedances.
Fig. 1. Block diagram of power system to represent power quality issues.
Dynamic Voltage Restorers (DVR) are complicated static
devices which work by adding the required voltage to restore
the amplitude back to stable region during a voltage sag.
Basically, this means that the device injects power into the
system in order to bring the voltage back to the level required
by the load. Injection of power is achieved by a switching
system coupled with a transformer which is connected in
series with the load. There are two types of DVR; one with
energy storage, and the other without it. Devices without
energy storage restore the voltage waveform by drawing the
required amount of current from the supply. The other type
uses the energy storage to compensate the voltage sag. The
difference between a DVR with storage, and an
Uninterruptible Power Supply (UPS) is that the DVR only
supplies the part of the waveform that has been reduced in
amplitude due to the voltage sag, not the whole waveform. In
other words, UPS is a power source, whereas DVR is just a
compensator to mitigate any disturbance in the supplied
power from a source. As can be seen from Fig. 2, the basic
DVR consists of an injection/booster transformer, a
harmonic filter, a voltage source converter (VSC), and a
control system. DVR systems are highly efficient and fast in
response. In the case of systems without storage, none of the
inherent issues with storage are relevant. Another key aspect
of DVR systems is that they can be used for harmonic
mitigation, fault current limiting, power factor correction and
reduction of transients, in addition to voltage sag mitigation.
(b)
Fig. 2. (a) Block diagram of Dynamic Voltage Restorer (DVR). (b)
Conventional DVR connected distribution system.
There are mainly three voltage sag compensation
techniques that are used by dynamic voltage restorer in order
to cater the power quality problems: pre-sag compensation,
in-phase compensation, and phase advanced compensation
technique. In the diagram shown in Fig. 3, the
V pre − sag
and
Vsag
are voltages at the point of common coupling (PCC)
before and during the sag respectively. The voltage injected
by DVR for pre-sag compensation can be written as:
Fig. 3. Phase diagram of sag compensation techniques. The vectors in
blue, red, and green present the compensation voltages for the three
differenct techniques.
| Vinj |=| V pre− sag | − | Vsag |
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(2)
θinj = tan −1 (
V pre − sag sin(θ pre − sag )
V pre − sag cos(θ pre − sag ) − Vsag cos(θ sag )
y j = f (¦i w ji xi − θ j ) − f (net j )
) (3)
Where,
The voltage injected by DVR for in-phase compensation
technique is given by:
VDVR = Vinj
(4)
| Vinj |=| V pre− sag | − | Vsag |
(5)
∠Vinj = θ inj = θ sag
(6)
net j = ¦i w ji xi − θ j
(7)
(8)
Computational output of the output node:
z l = f (¦i vlj y j − θ l ) = f (netl )
The main concept of phase advance compensation
method is to achieve required compensation by reactive
power injection only. In this method, the injected voltage and
the load current are 90 degrees apart.
Where,
netl = ¦i vlj y j − θ l
(10)
III. PROPOSED METHOD
Among the main two types of control techniques - linear
and nonlinear - the nonlinear controller is more suitable for
DVR, as it is truly a non-linear system due to the presence of
power semiconductor switches in the inverter bridge.
Frequently applied non-linear controllers are Artificial
Neural Network (ANN), Fuzzy Logic (FL), and Space
Vector Pulse Width Modulation (SVPWM). ANN control
method has adaptive and self-organization capacity. It also
has inherent learning capability that can provide improved
precision by interpolation. Because of these reasons, ANN is
chosen in the proposed system to overcome the shortcomings
of linear PID method. In Fig. 4, block diagram of the
proposed method is shown.
(9)
Error of the output node:
E=
1
1
¦ (t l − z l ) 2 = 2 ¦l(t l − f (¦iv lj y j − θ l )) 2
2 l
(11)
Hypothesis:
hθ ( x ) = θ T x =
¦
n
i=0
θ i xi
(12)
Gradient update [18]:
θ j := θ j − α
Where,
1 m
¦ (hθ ( x (i ) ) − y (i ) ) x j (i )
m i =1
(13)
xi = input node, y j = node of the hidden layer,
zl = node of output layer, w ji = weight value of network
between the input node and node of hidden layer,
vlj =
weight value of network between the nodes of hidden layer
and output layer, tl = expected value of the output node, α =
learning rate, m = total sample, θ = weight.
Fig. 4. Block diagram of the proposed DVR system to mitigate voltage
instabilities.
IV. ARTIFICIAL NEURAL NETWORK
An Artificial Neural Network (ANN) is a mathematical
model or computational model that is inspired by the
structure and functional aspects of biological neural networks
[15]. These networks are used to estimate or approximate
functions, can depend on a large number of inputs which are
generally unknown [16]. A hypothesis is made which will be
used to calculate gradient output. Output of computed
network can be explained as following [17]:
The training procedure of the ANN is depicted in Fig. 5.
Though it is difficult to set a universal set of parameters bestsuited for every system, the training can be optimized to
produce a near-perfect result for all cases, even if the
topology is different than the one considered for this work.
The time required for training can also be modified
considering the trade-offs between accuracy and the number
of nodes in the ANN; as less number of layers and nodes
reduces the training time, but also reduces accuracy.
Output of the node of hidden layer:
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compensation technique. Fig. 6 shows the simulation model,
while Table 1 shows the power system parameters used in
this work. Fig. 7 shows the waveforms of three phase supply
voltage, sag condition, required voltage to mitigate it, and
voltage after restoration by the proposed ANN driven DVR.
It can be noted that the restored waveform is identical to the
original one, and attained stability. The sag simulated here is
also a drastic one, where real systems do not usually change
in such an abrupt way. But the drastic sag is used to
underscore the robustness of the proposed ANN-based
DVR’s robustness over other methods; as for small changes
in voltage profile all methods’ responses are nearly the same.
TABLE I.
PARAMETERS FOR THE POWER SYSTEM USED IN
SIMULATION.
Component
Fig. 5. Training procedure flowchart of the ANN controller for DVR.
V. SIMULATION RESULTS AND DISCUSSION
A three-phase power system with a source, a
transmission line, two transformers at both ends of it, and a
nonlinear load has been designed to test the proposed
Fig. 6. Simulation model for sag mitigation with ANN controller.
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Details
Source
33 kV, 50 Hz
Transformer T1
5 MVA, 33/11 kV DYn11
Transformer T2
750 MVA, 11/4 kV DYn11
Line
0.675+j0.372 Ω /km, 2 km
Load
4.75 kW, 3.25 kVAR
(a)
(b)
(c)
(d)
(e)
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(f)
Fig. 7. Three phase sag mitigation based on ANN controlled DVR. (a) Instantaneous voltage at stable condition; (b) Instantantaneous voltage when sag
occurs; (c) Voltage required to mitigate voltage sag; (d) Output voltage of the inverter circuit; (e) Generated PWM for inverter; (f) Instantaneous voltage
after voltage restoration.
both by almost eliminating harmonics higher than the 4th
order (200 Hz), and the harmonics magnitudes within this
window are also less than those generated by PID and Fuzzy
controllers. Table 2 quantifies the performances of these
three methods to mitigate voltage sag and swell for both
single and three phase systems, along with the system THDs.
This comparison unequivocally proves ANN’s superiority as
it totally outperforms PID and provides significantly less
THD than both PID and Fuzzy methods. The Fuzzy
controller is clearly the least effective one among these three.
The PID controller performs only 0.4% better than ANN for
50% three-phase voltage swell restoration, and in all the
other testing criteria, the ANN controller reigns supreme.
However, this superior performance comes at the expenses
of greater cost and computational load compared to other
existing methods, but these limitations are acceptable
considering the higher accuracy.
VI. COMPARATIVE ANALYSIS
Sag restoration by DVR, using PID, Fuzzy, and the
proposed ANN controller is shown in Fig. 8. In Fig. 8a, the
output of the PID method demonstrates significant amount of
harmonics presence with noticeable waveform distortion.
Fig. 8b shows the Fuzzy controller’s output, which has less
harmonics than PID, and contains a little distorted wave
pattern all along. But the ANN method restores the voltage
without any noticeable distortion and harmonics (Fig. 8c) –
which demonstrates its superiority over the other two. To
provide a better illustration of the harmonics suppression
capabilities of these methods, the total harmonic distortion
(THD) is presented in Fig. 8d. The fundamental frequency is
50 Hz; therefore, PID and Fuzzy are both generating a large
number of harmonics - Fuzzy being more successful of these
two to suppress their magnitudes. But ANN outshines them
(a)
(b)
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(c)
(d)
Fig. 8. Restored Voltage Using (a) PID controller; (b) Fuzzy controller; (c) ANN controller; (d)THD comparison: the least THD can be seen at ANN based
DVR, the range of the harmonics is also truncated by a huge amount by this method.
TABLE II.
Controller
COMPARISON OF ANN, FUZZY, AND PID CONTROLLED DVR. THE ANN METHOD SHOWS THE BEST PERFORMANCE.
50% 3-ĭ voltage
sag restoration
99.8%
50% 1-ĭ voltage
sag restoration
99.5%
50% 3-ĭ voltage
swell restoration
99.6%
50% 1-ĭ voltage
swell restoration
99.8%
% THD
13.5%
Voltage restoration and
THD mitigation capability.
Excellent
FUZZY
98.6%
98.7%
99.2%
98.32%
24.4%
Moderate
PID
98.1%
98.4%
97%
98.2%
19.7%
Acceptable
ANN
VII. CONCLUSION
DVRs are a popular choice for enhancing power quality
in power systems, with an array of control system on offer to
drive these devices. In this paper, application of ANN to
operate DVR for providing better performance than existing
systems to mitigate voltage sag, swell, and harmonics has
been demonstrated. Problem statement and theoretical
background, structure of the proposed method, training
procedure of the ANN used have been described in detail.
Simulation results showing the DVR performance during
voltage sag have been presented. Comparison of the
proposed method with the popular PID controller, and nonlinear Fuzzy controller has been carried out, where the
proposed ANN controller appeared as the best option to
restore system voltage while mitigating THD to the greatest
extent.
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