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65
CHAPTER 4
HARMONIC ELIMINATION OF FULLY CONTROLLED
AC–DC CONVERTERS USING ARTIFICIAL NEURAL
NETWORKS
4.1 INTRODUCTION
In this chapter a shunt active power filter is developed to eliminate
the supply current harmonics using Artificial Neural Networks. For several
years, active power filters (APFs) have been recognized as advanced
techniques for harmonic compensation (Agaki 1996). The concept of the
active power filter is to detect or extract the unwanted harmonic components
of a line current, and then to generate and inject a signal into the line in such a
way to produce partial or total cancellation of the unwanted components.
APFs are very able to suppress the current harmonics and to correct power
factor, especially in fast-fluctuating loads, in comparison to other
compensation devices. A lot of recent research work investigates and tries to
improve APFs by developing new topologies with power electronics
technologies or new control laws. Active power filters could be connected
either in series or in parallel to power systems. Therefore, they can operate as
either voltage sources or current sources. The shunt active filter is controlled
to inject a compensating current into the utility system so that it cancels the
harmonic currents produced by the nonlinear load. Artificial intelligence (AI)
techniques, particularly the neural networks, are recently having significant
impact on power electronics and motor drives. Neural networks have created
a new and advancing frontier in power electronics, which is already a
66
complex and multidisciplinary technology that is going through dynamic
evolution in the recent years (Bimal K. Bose 2007). ANN techniques have
been applied with success in control of APF and are very promising in the
field. Indeed, the learning capacities of the ANNs allow an online adaptation
to every changing parameter of the electrical network, e.g., nonlinear and
time-varying loads. Most of these control constraints are quite still very
challenging with classic control methods (Djaffar Ould Abdeslam et al 2007).
Active Power Filters (APFs) have been used as effective way to
compensate harmonic currents in nonlinear loads. APFs basically work by
detecting the harmonic components from the distorted signals and injecting
these harmonic current components with a current of the same magnitude but
opposite phase into the power system to eliminate these harmonics (Hsiung
and Cheng Lin 2007). Active filter topologies have been recently discussed in
many papers (Marks and Green 2002). A comprehensive study of practical
topologies was given by Akagi (1997). Obviously, fast and precise harmonic
detection is one of the key factors to design APFs. The simplest way to
identify harmonics and generate the harmonic current is to use discrete
Fourier transformation (DFT) or Fast Fourier Transformation (FFT).
Although it has wide applications, the DFT or FFT has certain limitations in
harmonic analysis (Chan and Plant 1981). Presently, neural network has
received special attention from researchers because of its simplicity, learning
and generalization ability, and it has been applied in the field of engineering,
such as in harmonic detection since early 1900s (Pecharanin et al 1994).
In this thesis an improved scheme is proposed in which only the
actual load current is given to adaptive neural network and the reference
current is obtained. The load voltage is given to a DC regulator with a simple
PI controller, which gives the actual filter current, and which is compared
with reference current the gating pulses to Insulated Gate Bipolar Transistors
67
(IGBTs) are provided through a hysteresis band comparator. The program
developed can be used to eliminate on line harmonics. The same program is
used for eliminating the supply current harmonics in the AC–DC converters
and AC voltage controllers.
4.2 PRINCIPLE OF THE SHUNT ACTIVE POWER FILTERS
In a power system, the voltage (or current) source waveform
usually consists of a fundamental component, some harmonic components,
and random noise. Among these contents, the fundamental component
generally has a significantly dominant proportion.
The active filter (AF) technology is now mature for providing
compensation for harmonics, reactive power, and/or neutral current in ac
networks. It has evolved in the past quarter century of development with
varying configurations, control strategies, and solid-state devices. AF’s are
also used to eliminate voltage harmonics, to regulate terminal voltage, to
suppress voltage flicker, and to improve voltage balance in three-phase
systems. This wide range of objectives is achieved either individually or in
combination, depending upon the requirements and control strategy and
configuration, which have to be selected appropriately. The term active power
filter is a generic one and is applied to a group of power electronic circuits
incorporating power switching devices and passive energy-storage-circuit
elements, such as inductors and capacitors. The functions of these circuits
vary depending on the applications. They are generally used for controlling
current harmonics in supply networks at the low-to-medium voltage
distribution level or for reactive power and/or voltage control at high-voltage-
distribution level (Arrillaga et al 1997). These functions may be combined in
a single circuit or in separate active filters. Active power filtering based on the
injection method is basically performed by replacing the portion of the sine
68
wave that is missing in the current drawn by a nonlinear load. This can be
accomplished in two stages. The first stage consists of detecting the
amplitudes and phases of the AC harmonic currents (or any system quantity
associated with them), which are present in the AC line. The second stage is
the injection of the appropriate harmonic currents (or insertion of appropriate
harmonic voltages) at the appropriate frequency so as to supply the AC
harmonic currents required by the nonlinear load. Since their basic operating
principles were firmly established in the 1970s (Bird et al 1969, Sasaki and
Machida 1971), active power filters have attracted the attention of power
electronics researchers/engineers who have a concern about harmonic
pollution in power systems (Akagi et al 1986, Peng et al 1990, Akagi et al
1990). Moreover, deeper interest in active filters has been spurred by
1. the emergence of semiconductor switching devices such as
IGBTs and power MOSFETs, which are characterized by fast
switching capability and insulated-gate structure;
2. the availability of digital signal processors (DSPs), field-
programmable gate arrays (FPGAs), analog to digital (A/D)
converters, Hall-effect voltage/current sensors, and
operational and isolation amplifiers at reasonable cost
(Hirofumi Akagi 2005).
Active filters can be divided into single-phase active filters and
three-phase active filters. Research on single-phase active filters has been
carried out, and the resultant papers have appeared in technical literature.
However, single-phase active filters would attract much less attention than
three-phase active filters because single-phase versions are limited to low-
power applications except for electric traction or rolling stock. A generalized
block diagram of an active power filter is presented in Figure 4.1.
69
Figure 4.1 Generalized block diagram for active power filters
The information regarding the harmonic current, generated by a
nonlinear load, for example, is supplied to the reference-current voltage
estimator together with information about other system variables. The
reference signal from the current estimator, as well as other signals, drives the
overall system controller. This in turn provides the control for the PWM
switching pattern generator. The output of the PWM pattern generator
controls the power circuit via a suitable interface. The power circuit in the
generalized block diagram can be connected in parallel, series or
parallel/series configurations, depending on the connection transformer used.
The active harmonic source within the filtering network is basically
a static converter connected to a DC source. The converter must be controlled
to provide the proper filtering harmonic currents or voltages. This is
accomplished by shaping the DC input source into an output waveform of
appropriate magnitude and frequency through modulation of semiconductor
switches (Grady et al 1990).
70
The harmonic converter can use either a DC voltage source or a DC
current source. The DC source of a voltage converter consists of a capacitor
that resists voltage changes, while that of a current converter consists of an
inductor that resists current changes. In both cases, the DC source receives its
power from the AC power system. Converters are referred to as either
voltage-fed or current-fed according to the type of DC-side source. The basic
voltage and current source converter topologies are displayed in Figure 4.2(a)
and (b).
(a)
(b)
Figure 4.2 (a) Single-phase and three-phase current-source converter
(CSC), (b) Single-phase and three-phase voltage-source
converter (VSC)
71
In the current-source converter, a diode is placed in series with
every switch to avoid reverse breakdown of the switch when the voltage
across the switch during the OFF-period is negative. In voltage-source
converter, an inverted diode is placed across each switch to provide a path for
the current when the current cannot pass through the switch. The power
electronic circuits and devices used in both types of converter are quite
similar. Most of the existing active power filters utilize switching devices
such as Power MOSFETs and IGBTs for switching speeds up to 50 kHz.
However the most attractive device is the IGBT. It has the merit of fast
switching capability and requires very little drive power at the gate and
capable of handling high power. At present, the voltage-source converter is
more favorable than the current-source converter in terms of cost, physical
size, and efficiency.
4.3 HARMONIC ESTIMATION TECHNIQUES
One important issue that assesses and evaluates the quality of the
delivered power is the estimation or extraction of harmonic components from
distorted current or voltage waveforms. In order to provide high quality
electricity, it is essential to accurately estimate or extract time varying
harmonic components, both the magnitude and the phase angle, to mitigate
them using active power filters. There are several harmonic estimation
techniques are available among which the Fast Fourier Transform (FFT), the
Kalman filter (KF) and Artificial Neural Networks (ANN) are the most
popular. Figure 4.3 displays some of these estimation technique references.
Estimation Techniques
FFT Kalman Filter ANN
Figure 4.3 Harmonic estimation techniques
72
4.3.1 Discrete Fourier Transform (DFT) The DFT-based algorithm (fast Fourier transform (FFT)) for
harmonic measurement and analysis is a well-known technique and is widely used due to its low computational requirement. In this approach the
coefficients of individual harmonics are computed by implementing Fast
Fourier Transform on digitized samples of a measured waveform in a time window. The description of the algorithm is well documented in many
references (Cooly and Tukey 1965, Harris 1978, Oran Brigham 1988).
There are several performance limitations inherent in the FFT
application. These limitations are (Oran Brigham 1988):
1. the waveform is assumed to be of a constant magnitude during
the window size considered (stationary), 2. the sampling frequency must be greater than twice the highest
frequency of the signal to be analyzed , and
3. the window length of data must be an exact integer multiple of power-frequency cycles.
It has been reported in (Girgis 1991) that failing to satisfy these conditions will result in leakage and picket fence effects and hence inaccurate
waveform frequency analysis. Moreover, the DFT-based algorithm can cause
computational error and may lead to inaccurate results if the signal is contaminated by noise and/or the DC component is of a decaying nature
(Dash et al 1996).
As far as the active filters are concerned, and because the
transformation process takes time, the harmonic compensation will be delayed by two cycles if the FFT is used as an estimation tool (Osowski 1992). This
will influence the performance of active filtering in case that the load current
is in fluctuated state.
73
4.3.2 Harmonic Estimation Using Kalman Filter
In the Kalman filter approach (Haili Ma et al 1996) (Moreno Saiz
et al 1997), a state variable mathematical model of the signal, including all
possible harmonic components, is used. Dash and Sharaf (1989) were among
the first who utilized the Kalman filter technique to estimate the stationary
harmonic components of known frequency from unknown measurement
noise. Girgis et al (1991) generalized the work in reference to predict time-
varying harmonics too. However, it was pointed out in (Girgis et al 1991),
that the Kalman filter scheme requires more computational process to update
the state vector when estimating the time varying harmonics compared to the
stationary. Later, Haili Ma and Girgis (1991) utilized the Kalman filter
approach to identify and track the harmonic sources in power systems. A
hardware implementation of the Kalman filter to track power system
harmonics was presented by Moreno Saize et al (1997). In the Kalman filter
approach, a state variable mathematical model of the signal, including all
possible harmonic components is used. The Kalman filter technique estimates
the harmonic components by utilizing a smaller number of samples and in
relatively shorter time as compared to FFT (Girgis et al 1991). However,
Kalman filter technique suffers from being computationally demanding due to
transcendental function evaluations, which makes its unfit for on-line
applications.
4.4 HARMONIC ESTIMATION USING ARTIFICIAL NEURAL
NETWORKS
There are many available algorithms for estimation of power
system harmonic components based on learning principles. Some of ANN
algorithms are based on the back propagation learning rule (Hartana and
Richards 1990, Mori 1992, Osowski 1992), while others utilized the LMS
74
(Widrow-Hoff) learning rule (Pecharanin et al 1995, Dash 1996). Hartana and
Richards (1990) were among the first who used back propagation ANN to
track harmonics in large power systems, where it is difficult to locate the
magnitude of the unknown harmonic sources. In their method, an initial
estimation of the harmonic source in a power system was made using neural
networks. They used a multiple two-layer feed forward neural network to
estimate each harmonic amplitude and phase.
Mori et a1 (1992) have provided a basic ANN mode1 to estimate
the voltage harmonics from real measured data. In their paper, a comparison
between the conventional estimation methods for predicting the 5th harmonic
is given. Pecharanin et al (1995) presented an ANN topology, based on the
back propagation learning rule, for harmonic estimation to be used in active
power filters. They taught the neural network to map the amplitude of the 3rd
as well as the 5th harmonic from a half cycle of a distorted current waveform.
This method has a limited applicability in active filtering since it does not
consider the detection of the harmonic phase angles in which it may increase
the distortion and make the case worse if the injected signal is of the wrong
phase. The main drawback of the back propagation ANN is the requirement
of the huge data set required for training. Also, the back propagation ANN
may lead to inaccurate results because of the random-like behavior and the
large variations in the amplitude and the phase of the harmonic components
and/or in the presence of random noise (Dash et al 1997).
Osowski (1992) provided an ANN that is based on the least mean
square (LMS) learning principle to estimate the harmonic components in a
power system. Later, Dash et al (1996) utilized the ADALINE, a version of an
ANN, as a new harmonic estimation technique. The learning rule of the
method is based on the LMS introduced by Widrow-Hoff. ADALINE is an
adaptive technique. Its main advantages are speed and noise rejection. It
75
proves to be superior to the Kalman Filter technique in finding the magnitudes
and phases of the harmonics (Dash et al 1996).
4.4.1 ADAptive Llnear NEuron (ADALINE)
The ADALNE is a two layered feed-forward perceptron, having N
input units and a single output unit. This allows their outputs to take on any
value, whereas the perceptron output is limited to either 0 or 1. Both the
ADALINE and the perceptron can only solve linearly separable problems.
However, here it makes use of the LMS (Least Mean Square) learning rule,
which is much more powerful than the perceptron learning rule. The LMS or
Widrow-Hoff learning rule minimizes the mean square error and thus moves
the decision boundaries as far as it can from the training patterns. The
ADALINE is described as a combinational circuit that accepts several inputs
and produces one output. Its output is a linear combination of these inputs. An
ADALINE in block diagram form is depicted in Figure 4.4.
The input to the ADALINE is TnxxxxX ).,,.........,,( 210 , where 0x , is
called a bias term or bias input, is set to 1. The ADALINE has a weighted
vector TnwwwwW ).,.........,,( 210 and its output is simply written as
0 1 1 2 2 ..........Tn ny W X w w x w x w x (4.1)
In a digital implementation, this element receives at time k an input
signal vector or input pattern vector Tnkkkk xxxxkX ).,,.........,,()( 210 and a
desired response ),(kyd a special input used to affect learning. The
components of the input vector are weighted by a set of coefficients; the
weight vector is given by Tnkkkk wwwwkW )..........,,()( ,210 . The sum of the
weighted inputs, i.e., )()()( kXkWky T is then computed.
76
Figure 4.4 Structure of the Adaptive Linear Neuron (ADALINE)
During the training process, input patterns and corresponding
desired responses are presented to the ADALINE. An adaptation algorithm,
usually the Widrow-Hoff LMS algorithm, is used to adjust the weights so that
the output responses of the input patterns become as close as possible to their
respective desired responses. This algorithm minimizes the sum of squares of
the linear errors over the training set. The linear error e(k) is defined to be the
difference between the desired response )(kyd and the linear output )(ky , at
time or sample k.
The Widrow-Hoff learning delta rule calculates the changes to
weights of ADALINE to minimize the mean square error between the desired
signal output )(kyd and the actual ADALINE output )(ky over all k. The
weight adjustment, or adaptation, equation can be written as (Bernard and
Michael 1990).
77
( ) ( )( 1) ( )( ) ( )T
e k x kW k W kx k x k
(4.2)
where k = time index of iteration,
W (k) = weight vector at time k,
X (k) = input vector at time k,
)()()( kykyke d = error at time k
= learning parameter
Training Algorithm for an ADALINE is shown below:
Step: 0. Initialize weights (small random values are usually used).
Set learning rate α.
Step: 1. while stopping condition is false
Do Steps 2 to 6.
Step: 2. for each training pair t:S do Steps 3 to 5.
Step: 3. Set activations of input units, n........1i
ii SX
Step: 4. Compute net input to output unit:
iin W.XbYi
Step: 5. Update bias and weights, n.....1i
)( inoldnew ytbb
ioldinewi XyintWW )(
Step: 6. Test for stopping condition: If the largest weight change
that occurred in step 2 is smaller than a specified tolerance, then
stop; otherwise continue.
78
4.4.2 ADALINE as Harmonic Estimator
The ADALINE is used to estimate the time varying magnitudes of
selected harmonic in a distorted waveform. Consider a distorted signal f (t)
with the Fourier series expansion:
1
( ) sin( )N
n nn
f t A n t
(4.3)
where An and φn, are the amplitude and the phase angle of the harmonic,
respectively, N is the total number of harmonics. The discrete-time
representation of f (t) will be:
1 1
(2 ) (2 )( )N N
n n
n n
AnCos Sin nk AnCos Cos nkf kNs Ns
(4.4)
where Ns, = sampling rate = os FF , Fs= sampling Frequency, and Fo= nominal
system frequency. The proposed current ADALINE network is shown in
Figure 4.5. The input to the ADALINE input vector is chosen to be:
( ) ( )( )TSin n k Cos n kx k
Ns Ns
(4.5)
where n is the selected harmonic order, t (k) is the time and its desired output
Yd(k) is set to be equal to the actual signal, f (k). The weight vector is set to
be
1 1 1 1[ cos sin ........ cos sin ]n n n nWo A A A A (4.6)
79
The tap weight vector of the adaptive filter is denoted by
)()()( kykyke d and perfect tracking is attained when the tracking error e(k)
approaches zero. This optimum condition is realized when the estimation
variance and the lag variance contribute equally to the mean square deviation.
Therefore,
( ) ( ) ( ) ( )Td oy k y k f k W x k (4.7)
Figure 4.5 Proposed current ADALINE network
4.5 THE PROPOSED ADALINE BASED SHUNT ACTIVE
POWER FILTER
The block diagram of the proposed power line conditioner using
active power filter is shown in Figure 4.6. The proposed Modular Active
power filter connected to the electric Distribution system. The line current
signal is obtained and fed to an ADALINE which extracts the fundamental
components of the line current signal. In the controller block, fundamental
component is compared with distorted line current to generate modulating
signal. This modulating signal is used to generate Pulse-Width Modulated
(PWM) switching pattern for the switches of the active line conditioner
module (Vazquez and Salmeron 2003, Rukonuzzaman and Mutsuo Nakaoka
2002). The output current of the active filter is injected into the power line.
The injected current, equal-but opposite to the harmonic components to be
eliminated. Harmonics are suppressed by connecting the active filter modules
to the electric grid.
80
Figure 4.6 Block diagram of the proposed scheme
4.6 SIMULATION RESULTS AND DISCUSSION
The effectiveness of the proposed model is verified with a ANN
based shunt active power filter for single phase and three phase fully
controlled AC-DC converter is analyzed and results are discussed.
4.6.1 ANN Based Shunt Active Power Filter for Single Phase Fully
Controlled Converter
The simulink model for ANN based shunt active power filter for
single phase fully controlled converter and the control block is shown in
Figures 4.7 and 4.8 respectively.
81
Figure 4.7 Simulated diagram of proposed ANN based shunt active
filter connected for single phase fully controlled converter
Figure 4.8 Block diagram of the control unit for single phase fully
controlled converter
82
The converter is tested with different firing angles. First the
converter is operated with a firing angle of 18o. Figure 4.9 shows the
waveforms of the supply current, load current and filter current after the
application of the proposed active power filter. Figures 4.10 and 4.11
represent the harmonic spectrum of the supply current before and after the
application of the proposed shunt active power filter respectively. The supply
current waveform is improved and its THD is reduced to 2.14% from 16.32%.
Table 4.1 shows the comparative results of individual harmonics and %THD.
Figure 4.9 Supply current, Load current and filter current waveforms
of the converter (with filter for a firing angle of 18o)
Figure 4.10 Harmonic spectrum of the supply (Load) current (without
filter for the firing angle of 18o)
83
Figure 4.11 Harmonic spectrum of the supply current (with filter for the
firing angle of 18o)
Table 4.1 Comparative analysis of % harmonic distortion with the
proposed ANN based shunt active power filter (For firing
angle α =18o)
Sl. No Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1. 1 100.00 100.00 2. 3 11.11 1.48 3. 5 7.18 0.80 4. 7 5.28 0.68 5. 9 4.19 0.51 6. 11 3.49 0.44 7. 13 3.00 0.37 8. 15 2.65 0.34 9. 17 2.37 0.28 10. 19 2.16 0.26 11. 21 1.98 0.20 12. 23 1.84 0.18
%THD 16.32 2.14
84
In the second case the firing angle of the converter is varied to 36o.
Figure 4.12 shows the waveforms of the supply current, load current and filter
current after the application of the proposed active power filter. Figures 4.13
and 4.14 represent the harmonic spectrum of the supply current before and
after the application of the proposed shunt active power filter respectively.
The supply current waveform is improved and its THD is reduced to 2.43%
from 22.43%. Table 4.2 shows the comparative results of individual
harmonics and %THD.
Figure 4.12 Supply current, load current and filter current waveforms
of the converter (with filter for a firing angle of 36o)
Figure 4.13 Harmonic spectrum of the supply (Load) current (without
filter for the firing angle of 36o)
85
Figure 4.14 Harmonic spectrum of the supply current (with filter for the
firing angle of 36o)
Table 4.2 Comparative analysis of % harmonic distortion with the
proposed ANN based shunt active power filter (For firing
angle α =36o)
Sl. No Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1. 1 100.00 100.00 2. 3 18.36 1.93 3. 5 9.40 0.84 4. 7 5.94 0.71 5. 9 4.27 0.53 6. 11 2.42 0.46 7. 13 2.12 0.38 8. 15 2.02 0.32 9. 17 1.84 0.28 10. 19 1.72 0.23 11. 21 1.51 0.21 12. 23 1.31 0.19
%THD 22.43 2.43
86
Finally the firing angle of the converter is varied to 54o. Figure 4.15
shows the waveforms of the supply current, load current and filter current
after the application of the proposed active power filter. Figures 4.16 and 4.17
represent the harmonic spectrum of the supply current before and after the
application of the proposed shunt active power filter respectively. The supply
current waveform is improved and its THD is reduced to 3.49% from 25.11%.
Table 4.3 shows the comparative results of individual harmonics and %THD.
Figure 4.15 Supply current, load current and filter current waveforms
of the converter (with filter for a firing angle of 54o)
Figure 4.16 Harmonic spectrum of the supply (Load) current (without
filter for the firing angle of 54o)
87
Figure 4.17 Harmonic spectrum of the supply current (with filter for the firing angle of 54o)
Table 4.3 Comparative analysis of % harmonic distortion with the
proposed ANN based shunt active power filter (For firing angle α =54o)
Sl. No Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1. 1 100.00 100.00 2. 3 19.93 1.93 3. 5 9.67 0.84 4. 7 6.27 0.71 5. 9 5.78 0.53 6. 11 4.68 0.46 7. 13 3.82 0.38 8. 15 3.42 0.32 9. 17 2.69 0.28 10. 19 2.26 0.23 11. 21 1.92 0.21 12. 23 1.62 0.19
%THD 25.11 3.49
The proposed shunt active power filter is also tested with different
loads i.e. by connecting a single phase half controlled converter parallel to the
single phase fully controlled converter. The fully controlled converter is
88
operated with the same load conditions with a firing angle of 18o. The half
controlled converter is operated with a firing angle of 36o to supply a RL load
(R=180Ω and L=1H). Figure 4.18 shows the waveforms of the total supply
current, load current of fully controlled converter, load current of half
controlled converter, total load current and filter current after the application
of the proposed active power filter. Figures 4.19 and 4.20 represent the
harmonic spectrum of the load (supply) current of the half controlled and fully
controlled converter, before application of the proposed shunt active power
filter respectively. The THD value for the supply current of half controlled
converter is 23.55% and for that of fully controlled converter is about
16.53%. Figures 4.21 and 4.22 represent the harmonic spectrum of the total
supply current before and after the application of the proposed shunt active
power filter respectively. So the supply current waveform is improved and its
THD is reduced to 3.70% from 16.30%. Table 4.4 shows the comparative
results of individual harmonics and %THD.
Figure 4.18 Supply current, Load current and filter current waveforms of the parallel combination of half controlled and fully controlled converter
89
Figure 4.19 Harmonic spectrum of the supply (Load) current of half
controlled converter (without filter for the firing angle of
36o)
Figure 4.20 Harmonic spectrum of the supply (Load) current of fully
controlled converter (without filter for the firing angle of
18o)
Figure 4.21 Harmonic spectrum of the total load current (without filter)
90
Figure 4.22 Harmonic spectrum of the total supply current (with filter)
Table 4.4 Comparative analysis of % harmonic distortion with the
proposed ANN based shunt active power filter for combined
half controlled and fully controlled converter
Sl. No Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1. 1 100.00 100.00 2. 3 14.08 3.23 3. 5 5.61 1.24 4. 7 2.13 0.46 5. 9 2.05 0.49 6. 11 2.26 0.51 7. 13 1.75 0.39 8. 15 0.91 0.20 9. 17 1.09 0.23 10. 19 1.98 0.43 11. 21 2.54 0.55 12. 23 2.58 0.56
%THD 16.30 3.70
91
4.6.2 ANN Based Shunt Active Power Filter for three phase fully
controlled converter
The simulink model of ANN based shunt active filter for three
phase fully controlled converters is shown in Figure 4.23. The ANN based
control unit for shunt active power filter is shown in Figure 4.24. In the
control circuit the supply currents of all the phases are extracted separately
and processed to get the filter current. The specifications for the three phase
fully controlled converter is, Input Voltage Vs1, 2, 3= 400V, f=50Hz, Rs=0.1Ω,
Ls=0.05mH. Load connected to the converter is 1000 watts. The converter
output is simulated for the firing angles of 0o and 30o respectively for without
neural network based active power filter and for with neural network based
active power filter and the results are compared. The supply current, load
current and filter current wave forms for firing angle of 0o and 30o are shown
in Figures 4.25 and 4.26 respectively.
Figure 4.23 Simulated circuit diagram of proposed ANN based shunt
active filter connected for three phase fully controlled
converter
92
Figure 4.24 Block diagram of the control unit for three phase fully
controlled converter
Figure 4.25 Supply current, load current and filter current waveform of
ANN based shunt active power filter (for α=0o)
93
Figure 4.26 Supply current, load current and filter current waveform of
ANN based shunt active power filter (for α=300)
The harmonic spectrum for without and with ANN based shunt active power filter for the firing angle of 0o is shown in Figures 4.27 and 4.28 respectively. Similarly Figures 4.29 and 4.30 represent harmonic spectrum for without and with ANN based shunt active power filter for the firing angle of
30o. The comparative results are shown in the Tables 4.5, 4.6 and it shows the improved performance of the ANN based shunt active power filter. Table 4.7
shows the comparison total harmonic distortion for different firing angles.
Figure 4.27 Harmonic Spectrum for the supply current (without filter
for firing angle α = 0o)
94
Figure 4.28 Harmonic Spectrum for the supply current (with filter for firing angle α = 0o)
Figure 4.29 Harmonic Spectrum for the supply current (without filter for firing angle α = 30o)
Figure 4.30 Harmonic Spectrum for the supply current (with filter for firing angle α =30o)
95
Table 4.5 Comparative analysis of % harmonic distortion with the proposed ANN based shunt active power filter (For firing angle α =0o)
Sl. No Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1 1 100.00 100.00 2 5 22.63 1.95 3 7 10.11 0.84 4 11 7.87 0.65 5 13 4.48 0.37 6 17 3.60 0.30 7 19 2.14 0.18 8 23 1.66 0.14 9 25 1.16 0.11
%THD 26.77 2.29
Table 4.6 Comparative analysis of % harmonic distortion with the proposed ANN based shunt active power filter (For firing angle α =30o)
Sl. No. Harmonic order
% Harmonic Distortion without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1 1 100.00 100.00 2 5 27.63 2.13 3 7 12.43 0.93 4 11 11.70 0.85 5 13 6.55 0.47 6 17 7.31 0.53 7 19 4.04 0.29 8 23 5.04 0.36 9 25 2.27 0.21
%THD 34.54 2.62
96
Table 4.7 Comparison of total harmonic distortion for different firing
angles
Sl. No.
Firing angle in (degrees)
% Total Harmonic Distortion(%THD) without Neural Network Based
Active Filter
with Neural Network Based
Active Filter 1 0 26.77 2.29 2 15 30.08 2.49 3 30 34.54 2.62 4 45 42.67 2.71 5 60 62.81 2.78
4.7 CONCLUSION
The proposed neural network based shunt active power filter can
compensate a highly distorted line current by creating and injecting
appropriate compensation current. The simulation was carried out for single
phase and three phase fully controlled converters. In the test cases simulated
for different firing angles, the THD of the supply current is improved to less
than 5% with the proposed shunt active power filter. In addition to this the
simulation was carried out for the additional nonlinear loads (single phase
half controlled converter) and the THD in the supply current is also improved
to less than 5%. The performances reached by the proposed method are better
than those obtained by more traditional techniques as mentioned in the
introduction. It enhances the reliability of active filter. The overall switching
losses are minimized due to selected harmonic elimination. Speed and
accuracy of ADALINE results in improved performance of the Active Power
Filter.