International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
Ant Colony Optimization Based Hybrid Active
Power Filter for Harmonic Compensation
Akhilesh Kumar Tiwari
Satya Prakash Dubey
Department of Electrical Engineering
Rungta College of Engineering and Technology
Bhilai, INDIA
akhileshtiwari@india.com
Department of Electrical Engineering
Rungta College of Engineering and Technology
Bhilai, INDIA
spd1020@yahoo.com
Abstract— This paper presents an efficient technique for
harmonic compensation using Ant Colony Optimization (ACO)
algorithm for power quality improvement under various loading
conditions. ACO employs a set of ants which randomly select the
components according to the previous pheromone level
distribution. As the time will progress ant will converge to set of
components thus completing the optimisation. The performance
of the Hybrid Active Power Filter (HAPF) with the proposed ant
colony optimization algorithm is found to be considerably
effective and adequate to compensate harmonics. The results
show that filter obtained by ACO has better performance and
simple structure.
Keywords— Hybrid active power filter, Instantaneous reactive
power theory, Harmonic compensation, low pass filter, THD, Ant
Colony Optimization (ACO)
I. INTRODUCTION
The capacious application of nonlinear loads in industries,
commercial and for domestic purpose causes power quality
troubles such as harmonic current, low power factor,
unbalance in voltage, sag & swell, reactive power burden etc.
Some of the paradigms of nonlinear loads are rectifiers;
variable speed drives both AC & DC, uninterrupted power
supplies, arc furnaces, electronic ballast, programmable logic
controllers etc. All these devices may degrade power quality
by injecting harmonic current into the power system by
absorbing enormous reactive power, as they are extracting non
sinusoidal current from utilities. This anomaly can cause many
problems such as resonance, excessive neutral currents, low
power factor etc. This resulted in enforcement of stringent
harmonic standards like IEEE 519-1992 & IEC 61000-3 [1-2].
Harmonics in power system involve additional power loss,
malfunctioning of protective relays and switchgears [3].
Elimination of harmonics in power system can be done in two
ways; primarily by providing a low impedance path to ground
for harmonic signal by adopting passive tuned filter,
furthermore by injecting compensating signals which are in
phase opposition with the harmonic signal present in the
system by adopting active filter [4].
Customary passive filter consists of resistance, inductance,
capacitance element configured and tuned to control the
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particular harmonic frequencies. The single tuned “notch”
filter is the most prudent and common in use. It is connected
in parallel with the system & is series tuned to filter out the
specific harmonic current by tendering low impedance path to
it. The flaws of conventional passive filters such as – massive
structure, its resonance phenomenon, dependency on source
impedance and fixed compensation characteristics increased
losses etc. On the contrary, the active power filter can solve
the above problems and is generally used to compensate
harmonic currents and to revamp power factor [2, 5].
APF are pondered as favourable solution for eradication of
harmonic current distortion and reducing reactive power
requirement, due to smaller size, no dependency on power
system impedance. It can be procured dynamically in case of
APF despite of various advantages of APF, the complexity
and cost have been always been drawbacks [6]. The
combination of passive element with active power filter results
in a hybrid configuration that brings down the cost of active
power filter drastically [7]. Certainty of hybrid active power
filter banks on the calculation of harmonic current and
generation of reference current. In this paper, a three phase
three wire ACO controlled shunt hybrid active power filter is
proposed to facilitate the calculation of reference currents.
ACO controller is used to generate fundamental from nonideal voltage source. As such numerous schemes are available
to generate reference current for the control of active filters
such as Fast Fourier Transform (FFT), Kalman filter, artificial
neural network (ANN) [8], genetic algorithm (GA) and
particle swarm optimization (PSO). The extraction by FFT
leads to incorrect results if the signal is contaminated by noise
and/or the DC component of decaying nature. Kalman filter
technique suffers from being computationally demanding due
to transcendental function evaluations, which makes it unfit
for on-line applications such as active power filtering. The
ANNs, based on back propagation learning rule, are trained
to estimate the harmonic components. This approach requires
too much data- for training of ANN and lead to inaccurate
results in presence of random noise filters obtained by GA are
always complex and require lengthy computations. Though
GA is capable of optimization of filters, sometimes the
resulted filters are unnecessarily complicated and difficult to
implement [9]. The limitations of the conventional PSO are
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
that it may be influenced by premature convergence and
stagnation problem [10].
IV. PROPOSED CONTROL ARCHITECTURE
Vα
In this paper, an ACO approach for digital filter design is
implemented. In this approach, ants in colony randomly select
components according to previous pheromone distribution and
release pheromone on the components they select. As time
passes by, the ants will eventually converge on a set of
components, thus completing the optimization [9].
Vα & Vβ
calculation
Vβ
Ant
colony
optimization
based
LPF
Iα & Iβ
calculation
Compute
Instantaneous power
block
Pf
Ploss
The rest of this paper is organized as follows: Section II
describes the system configuration. Section III defines
instantaneous power theory based control strategy. Section IV
demonstrates proposed control architecture. And a conclusion
is given in Section VI.
+
Vdc ref
Vdc
Estimate loss
component to restore
energy of DC bus
Hysteresis current
controller
II. SYSTEM CONFIGURATION
Fig. 1 shows the basic hybrid APF scheme including nonlinear
loads on a three-phase supply system. The load may be either
single phase or three-phase may be balanced or unbalanced
connected to the
supply
mains .This load draws nonsinusoidal currents from supply mains. A voltage source
inverter [11] is used as a power quality compensator to
compensate necessary harmonics and reactive power
generated by the nonlinear load.
Figure 1: Basic scheme of protection
III. INSTANTANEOUS POWER THEORY BASED CONTROL
STRATEGY
This theory has been entrenched by Akagi et al. [11] and
Gyugyi [11], that a voltage source inverter (VSI) can
instantaneously supply reactive power and compensate
harmonics of the nonlinear loads. A detailed mathematical
formulation of reactive power theory is given in [12], [13]. In
[12], Akagi et. al. have explicated the compensation of
reactive power and harmonics of a nonlinear load using the
current-controlled voltage-source inverter (CC-VSI). Hence,
the CC-VSI (active filter) is identified as an example for
utilizing the mathematical model. All calculation is done in
respect of load current in conventional p-q theory.
Hence, in the proposed method, ACO based LPF is used for
extracting fundamental component from the source current
instead of load current for non-ideal mains supply and only
real power due to fundamental component of current is
calculated.
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P
Current calculation
from fundamental real
power
Vdc
VSI
bridge
Three phase AC
mains
Three phase non linear load
Figure 2: Proposed control scheme of the APF using ACO
P = Pf + Ploss
(1)
Paramount component extraction is fast and concrete even in
case of distortion in the supply mains. The source not only
supplies active power component of load currents but also loss
components of current to maintain the average voltage of the
dc bus capacitor to a constant value. The loss component of
supply current feeds losses in the Inverter Bridge such as
switching losses, leakage current of capacitor, etc. under
steady state conditions and to regulate the stored energy on the
dc bus of the APF under transient conditions imposed on the
system. This component of power is figured by using average
dc bus voltage (Vdc) and required reference value of dc bus
voltage (Vdc ref). Equation (1) shows net real power from
source which is sum of fundamental real power and small
amount of loss power.
The APF draws the required currents from the ac mains to
feed harmonics and reactive power current and causes
sinusoidal unity power-factor supply currents under all
operating conditions.
A. Ant Colony Optimization based extraction circuit
ACO takes inspiration from the foraging behaviour of some
ant species. These ants deposit pheromone on the ground in
order to mark some favourable path that should be followed
by other members of the colony. As time passes by, most ants
will take a nearly optimal path in unison [14]. Finally these
two-phase components of fundamental source voltage and
current are used to calculate the real power transferred from
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
source to load into three-phase system. Further, alpha and beta
axes source reference currents are estimated and by doing
inverse Clark’s transform three phase reference source current
can be estimated. The block diagram of proposed ant colony
optimization based LPF is shown in Fig. 3
they follow the trail and reinforce it if they eventually find
food [14].
Visibility = 1/distance between cities
(5)
Pheromone update rule = (1-reducing rate)* pheromone +
temporary value + previous value
(6)
The pheromone level will be updated, in each of the iteration
and according to the pheromone level the ant will choose the
shortest path between the nest and the food. The pheromone
level decides the probability to choose a particular path.
Figure 3: ACO based extraction circuit
A digital IIR filter is characterized by,
N
H ( z ) =  h ( n ) z − n n=0, 1… N
(2)
n=0
Where N is the order of the filter which has (N+1) number of
filter’s impulse response coefficients, h(n). The values of h(n)
will determine the type of the filter, e.g., low pass, high pass,
band pass etc. The values of h(n) are to be determined in the
design process and N represents the order of the polynomial
function.
Ideal filter have the magnitude of one in its passband and zero
for the stopband. With the error between frequency response
of ideal filter and the designed filter the error function is
derived. For the whole successive iterations the coefficients of
the filter gets updated and the error is calculated with the use
of error function. The frequency response of the IIR digital
filter can be calculated as
Figure 4: Self adaptive behavior of a real ant colony
N
H (e jwk ) =  h ( n )e − jwk n n=0, 1… N
(3)
n =0
An error function given by (4) is the approximate error used in
our algorithm for filter design
E ( w ) = G ( w )[ H d ( e
jw
) − H i (e
jw
)]
(4)
Where Hd(ejw ) is the frequency response of the designed
approximate filter; Hi(ejw) is the frequency response of the
ideal filter; G(ω) is the weighting function used to provide
different weights for the approximate errors in different
frequency bands.
ACO execution is encouraged by the collective behaviour of
deposit and monitoring of slopes that is contemplated in
insect’s colonies, such as ants. Figure shows an instance of the
ability of ants to find the shortest path between food and their
nest.
It is illustrated through the example of the appearance of an
obstacle on their path. Ants acquaint indirectly through
dynamic changes in their environment (pheromone trails).
Pheromones are chemical substances that are laid down by
ants. Thus, when other ants find the path taken by the former
ant, they are no more likely to ‘walk randomly’, but rather
978-1-4673-9939-5/16/$31.00 ©2016 IEEE
a) Ants go in search of food.
b) Ants follow a path between nest and food source.
They choose, with equal probability, whether to
shortest or longest path.
c) The majority of ants have chosen the shortest path.
The parameters for ACO are promulgated in table 1
Table 1 : Configuration of parameters
Parameter
Value
Number of ants
30
Decay rate of pheromone
0.1
Maximum iteration
100
Filter order
10
hmin(minimum value of filter coefficient)
-1
hmax(minimum value of filter coefficient)
1
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
Fig. 6 and 7 describes the performance of proposed ACO
architecture. Error function is truncating rapidly in ACO LPF
block which demonstrate the better design process
Figure 2 : ACO filter responses (a) frequency response (b) error convergence
plot
V. SIMULATION RESULT
For simulation of ACO based HAPF a model in MATLAB \
SIMULINKTM is developed. ACO based LPF endmost
coefficients are loaded in biquad filter design block of matlab
and then implemented in Simulink model. The complete active
filter system is composed using three phase source, a PWM
voltage source inverter and R-L load with three phase diode
rectifier. Various simulations are carried out to verify the
performance of the ACO based HAPF. Simulations have been
done with 3-phase balanced source 400V phase to phase and
50Hz frequency.
A. Balanced R L load
Fig. 8 shows the performance of the HAPF under balanced RL
load. The applied load in this case is R = 50Ω and L= 80mH.
The HAPF is switched on at 0.06 s. The instant the filter is
switched-on; the source current becomes sinusoidal with THD
as per IEEE standard. Under balanced load condition, THD of
source current reduce from 30% to 3.87%.
Figure 6: Flow chart of ACO procedure
978-1-4673-9939-5/16/$31.00 ©2016 IEEE
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
Figure 4 : For unbalanced load condition (a) 3-phase voltage (b) 3-phase load
current (c) 3-phase source current (d) Harmonic spectrum & THD
C. Dynamic load
Figure 3 : For balanced load condition (a) 3-phase voltage (b) 3-phase load
current (c) 3-phase source current (d) Harmonic spectrum & THD
Fig. 10 shows the performance of the HAPF control scheme
and system when the load is dynamic in nature. The filter is
switched on at 0.06 sec. In this case load is suddenly increased
under running condition at t = 0.12 sec. The THD of source
current is found 4.76% that is as per IEEE standard.
B. Unbalanced RL load
Fig. 9 shows the performance of the HAPF when the load is
unbalanced. The unbalancing is done by adding load in
parallel with 2 phases. The HAPF is switched on at 0.06 s. The
THD of source current becomes 4.68 %.
Figure 5 : For unbalanced load condition (a) 3-phase voltage (b) 3-phase load
current (c) 3-phase source current (d) Harmonic spectrum & THD
978-1-4673-9939-5/16/$31.00 ©2016 IEEE
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016
interlaced filters those who have multiple passbands, if proper
specifications are given.
D. Distorted mains
Fig. 11 shows the performance of the HAPF when the supply
from the mains is distorted. The values of the phase voltages
are 220V, 210V and 190V respectively for phase a, b and c.
The filter is switched on at 0.06 sec. The THD of source
current is found 2.96 % that is as per IEEE standard.
Figure 6 : For distorted mains supply condition (a) 3-phase voltage (b) 3phase load current (c) 3-phase source current (d) Harmonic spectrum & THD
VI. CONCLUSION
This paper presents an ACO based approach for HAPF design
to satisfy a set of specifications. The ability of the proposed
approach to optimize filter component values simultaneously
is advantageous in filter design.
Results show that the obtained filter satisfies design
requirements. Moreover, the results show better performance
and simpler structure. Further, the ACO approach for filter
design is customary acclimatized and can be used to optimize
978-1-4673-9939-5/16/$31.00 ©2016 IEEE
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