power quality improvement with shunt hybrid active power...

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)

Volume 4 Issue 9, September 2015

3672

ISSN: 2278 – 1323 All Rights Reserved © 2015 IJARCET

Power Quality Improvement With Shunt Hybrid

Active Power Filter Using ANN-Based Predictive and

Adaptive Controllers Mr. CH. VNV HariKrishna Smt. CH. Sujatha Sri. P. Bala KoteswaraRao M.Tech Scholar Associate professor Assistant professor

Department of EEE Department of EEE Department of EEE

Gudlavalleru Engineering College Gudlavalleru Engineering College Gudlavalleru Engineering College

Gudlavalleru, AP, India. Gudlavalleru, AP, India. Gudlavalleru, AP, India.

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Abstract—This paper ensures that improve the dynamic performance of a shunt type Hybrid active power filter how to compensates the harmonic contents in the network. This paper is combination of adaptive and predictive control techniques for fast convergence and reduced calculations. The adaptive and predictive properties of artificial neural networks are used for fast estimate of the compensating current. Here the dc-link voltage is used in a predictive controller to generate the first estimate the compensating current then after by convergence of the algorithm by an adaptive controller ANN i.e. adaline based network. Weights in adaline are automatically adjusted to minimize the total harmonic distortion of the source current. This paper MATLAB Simulations confirms the validity of the proposed scheme for all kinds of loads i.e. balanced and unbalanced for a three-phase three-wire system. Index Terms—Adaptive, current control, nonlinear loads, shunt active power filter (SAPF), Shunt Hybrid active power filter (HAPF), total harmonic distortion (THD), voltage source inverter(VSI).

I. INTRODUCTION

In recent years power quality distortion becomes serious

problem in electrical power systems due increasing of

nonlinear loads drawing non sinusoidal currents. When source

is connected to linear load there is no effect on source then

system will be stable. Also when source is connected to non

linear load, load will produces harmonics on load side, due to

these harmonics source will become non linear. Control of ac power using thyristors and other semiconductor switches are

mostly employs to fed controlled electrical power to electrical

loads, such as adjustable speed drives, furnaces etc. these

controllers are also used in HVDC systems and renewable

electrical power generation. Harmonics in the network are not

only increase the losses but also produce unwanted

disturbance to the communication network, more voltage

and/or current stress, etc.. Different harmonic elimination

techniques are proposed, e.g., passive filter, active power line

conditioner, and also hybrid active filter, have been proposed

and used [1]–[8]. Recent technological advanced switching devices and availability of low cost controlling devices, e.g.

DSP-/field-programmable-gate-array-based system, makes

active power line conditioner a natural choice to compensate

for harmonics. Shunt-type Hybrid active power filter (HAPF)

is used to eliminate the current harmonics.

The dynamic performance of an HAPF is mainly dependent

on how quickly and accurately the harmonic contents are

extracted from the load current. Many harmonic elimination methods are available, and their performance have been

explored. Proposed techniques include traditional d−q [2] and

p−q theory [3]–[5] based approaches, Shunt active power

filter and application of adaptive filters [6], genetic algorithm, artificial neural network (ANN) etc., for quick estimation of

the compensating current [8]. Hybrid active power filter is the

combination of passive filter and active power filter. Different types of combinations are there for hybrid active power filter.

Here in this paper shunt type series connected hybrid active

filer is proposed.

Recently, Artificial Neural Networks have attracted

much different applications, including the APF. A full

“neuromimetic” strategy involving several adalines has been

reported by Abdslam et al. [15].Active power filters have been

shown to be very interesting alternative to compensate power

distribution systems. Shunt hybrid active power filter are more

suitable to compensate current harmonic components and

displacement power factor, while series topologies present

better characteristics to compensate voltage distortions.

Hybrid topology significantly improves the compensation

characteristics of simple passive filters, making active power

filter available for different high power applications, at

relative lower cost. Better performance has been observes

compared to discrete Fourier transform and fast Fourier

transform, or Kalman filtering-based approaches. Tey et al.

[16] reported a modified version. An additional PI controller

is used to regulate the dc-link voltage. The adaptive controller

can adapts for unbalance and change in working conditions..

An adaline-based harmonic compensation is reported by

Singh et al. [17]. Weights are computed online by the LMS

algorithm. Recently we have to seen lot of applications for

artificial neural networks. Here integration of Ann based

predictive and adaptive control techniques are used to

generate gating pulses for voltage source inverter.

Note that parallel implementation of predictive control



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Fig. 1. (a) HAPF to compensate for a nonlinear load. (b) Single phase shunt HAPF

techniques are reported for power controllers. These are also

applied to Active Power filter. The implementation of APF

using power balancing at the dc-link is reported by Singh et

al.[10]. The dc link voltage is used to find the magnitude of

the supply current for self supporting dc bus. However, no

detail analysis of the dynamics of the dc-link voltage is

available. This paper is an combination of predictive and

adaptive control techniques for fast convergence and reduced

caluclations. Two ANN based controllers are used for such

purpose.

The predictive controller generates immediately the first

estimate of the compensating current quickly after the change

in load is identified. The changes in voltage across the

capacitor is used for this technique. This is followed by an

Adeline based controller to fast converge to the steady value.

This paper is presented in eight sections. Section II is

presents the over view of hybrid active filter. Section III deals

with the formulation of the problem. Section IV investigates

the dynamics of the dc link voltage calculation . Predicting

algorithm is covered in Section V. Adeline-based algorithm

is presented in Section VI. Section VII presents the simulation

and experimental results. Section VIII concludes the work.

II SHUNT HYBRID ACTIVE POWER FILTER

Hybrid active power filter is a combination of active

power filter and passive filter. Different types combinations of hybrid active power filter(HAPF) is there i.e. Parallel and

series combinations. Here in this paper series combination

shunt connected Hybrid active power filter is presented

Passive filters have been extensively to compensate current

harmonics and the displacement power factor in power distribution systems. However, the poor flexibility of passive

filters to adapt to variable load compensation requirements

constitutes a major disadvantage. This results in power factor

overcompensation in the case of low load operation. Fig .(2). Show the hybrid active filter topology since passive filter is

connected in series to the active power filter through a

coupled transformer, it imposes a voltage signal at its primary terminals that forces the circulating of current harmonics

through the passive filter, improving its compensation

characteristics.

Fig. 2.Hybrid Active Power Filter Configuration

In particular , a high value of the quality factor intimates a

large band width of the passive filter , improves the

compensation characteristics of the hybrid active filter



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topology.

III.ESTIMATION OF CURRENT REFERENCE

Fig. 1(a) shows the HAPF compensating a nonlinear load.

Fig. 1(b) shows the single phase shunt HAPF schematic

diagram. A general expression for the load current (Fig. 1(b)

is

iL(t) = iα1(t) + iβ1(t) + ih(t). (1) The in phase and quadrature components of the phase current at fundamental frequency are iα1 and iβ1, respectively. All other harmonics are included in ih. The perphase source voltage and the corresponding in-phase component of the load current may be expressed as

vs(t) = Vm cos ωt (2)

iα1(t) = Iα1 cos ωt. (3)

Assuming that the APF will compensate for harmonic and reactive power the compensating current becomes

Ic(t) = iL(t) − iα1(t) = iL(t) − Iα1 cos ωt (4)

Where 𝐼𝛼1 is the peak magnitude of in phase current that the

mains supply and hence needs to estimated. Once 𝐼𝛼1 is

estimated over, the reference current for the HAPF may easily be set as per (4). iL(t) may measured using current sensors.

In our proposed scheme, estimation of Iα1 is calculated by

two stages. A single layer ANN based algorithm first

predicts the value of Iα1 following which an adaline based

ANN is used for fast convergence. Note that the inverter also

draws a small current isα(t) to maintain the dc-link voltage.

IV. CONTROL OF DC-LINK VOLTAGE IN APF

The dynamics of the dc link voltage is an indirect measure of

the performance of the APF. Whenever there is a change in

the load, the voltage across the dc link capacitor also

undergoes a corresponding change. A controller is used to

keep the voltage regulated at a desire value. In this

following section, a simple process of the dynamics of the

dc-link voltage is first carried out. Parameters that preside

over the dynamics are identified, following which an

algorithm is developed it estimate the compensating current

of the APF. To maintain the dc bus voltage to a desired

magnitude, the capacitor draws in phase i.e., in phase with the

source voltage current isα. This is in addition to the

compensating current ic. From the power balance equation

pdc = cdc vdcdvdc

dt (5)

Where pdc is the power required to maintain the voltage

Vdc across the dc link.

vsi(t)isαi(t)i = a,b,c − Rf(i2sαi(t) i=a,b,c + i2ci(t))

− 1/2 Lfi = a,b,c d/dt(i2sαi(t)+ i2ci (t)) = idc(t)vdc(t)

= pdc (6)

Where Rf and Lf are the resistance and inductance of the inductor that is connected in between the common coupling

point and the voltage source inverter. We remembers that Isα supplies the system loss at the steady state and Charges,

discharges the capacitor during transient to maintain the

dc link voltage. Considering that “power” is a scalar quantity,

(6) for a balanced three-phase system may be expressed as

3vs t isα t − 3Rf i2sα t + i2

c t

−3

2Lf

d

dt i2

sα t + i2c t = idc t vdc t = pdc (7)

Applying small perturbations in is , isα , vdc and vs , the

following new set of variables may be obtained:

is(t) = Ic + ∆ic (8)

isα(t) = Isα + ∆isα (9)

vdc (t) = Vdc + ∆vdc (10)

vs(t) = Vs + ∆vs (11)

where Ic , Isα and Vs are rms and Vdc is the dc value of the

corresponding values at the operating point. After at steady

state

3𝑉𝑠Isα − 3𝑅𝑓(𝐼2sα + 𝐼2

c ) = 0 (12)

Substituting (8)–(12) in (7), the following equation is

obtained:

3 ∆𝑣𝑠𝐼𝑠𝛼 + ∆𝑣𝑠𝑖𝑠𝛼 − 6𝑅𝑓 𝐼𝑠𝛼∆𝑖𝑠𝛼 + 𝐼𝑐∆𝑖𝑐 −

3𝐿𝑓(𝐼𝑠𝛼𝑑∆𝑖𝑠𝛼

𝑑𝑡+𝐼𝑐

𝑑∆𝑖𝑐

𝑑𝑡) = CdcVdc

dΔVdc

dt (13)

Converting the variables to s-domain and after rearranging,

(13) may be expressed as

ΔVdc(s )= KGs (s)G1(s)G2(s)

1+KGs (s)G1(s)G2(s) ΔV∗dc(s)

− G2(s)G3(s)

1 + KGs(s)G1(s)G2(s)ΔIc(s)

+ G2(s)G4(s)

1+KGs (s)G2(s)G4(s) ΔVs(s) (14)

where

K is the small signal gain. Detail derivation of (14)from(13)is available in the Appendix.

Fig. 3. Block diagram of ANN-based peak value predictor

Equation (14) confirms that the ripple in the dc-link voltage

depends on ΔV*dc, ΔIc and ΔVs . In our present problem, the



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distortion in source voltage and reference dc-bus voltage is

not considered. Therefore, (14) further modifies to

ΔVdc(s) = − −G2(s)G3(s)

1+KGs (s)G1(s)G2(s)ΔIc(s). (15)

This explores the possibility of extracting an estimate of the

compensating current from the change in Vdc . The gains of the PI controller used to regulate the dc-link voltage are

preside over by the following two inequalities(26):

Is < cdc v∗dc

3Kp Lf (16)

Is ≤ Kp Vs

2Rf Kp +Lf KI (17)

All the ac quantities in (16) and (17) are expressed as

rms value. Is, 𝑣∗dc and Vs are the source current, reference

dc-link voltage and source voltage, respectively. Equations

(16) and (17) are used to generate an initial guess of Kp and

KI and also to set their limits.

V. ANN-BASED FAST ESTIMATION OF

COMPENSATING CURRENT

An ANN-based PI controller plays a important dual role. It

ensures faster reference generation and is also accountable for

better regulating of dc-bus voltage. The block daigram of the system (i.e. ANN-tuned adaptive PI controller) is shown

in Fig. 4. To reduce computational burden, a single-layer

ANN structure issued. The input vector as expressed in

(18) is gives to the state exchanger. In our scheme, error

voltage and its gradient are chosen as the state of the

system to ensure faster corrective action

u = [𝑉∗𝑑𝑐 𝑣𝑑𝑐 ]𝑇 (18)

The task of the state generator block is to generate states

x1 andx2 as follows:

𝑥1 = 𝑣𝑒 𝑘 𝑥2 =𝛿𝑥1

𝛿𝑘 (19)

Where 𝑣𝑒 𝑘 = 𝑉∗𝑑𝑐 − 𝑣𝑑𝑐 (𝑘).The output error z(k) is

represented as

z(k)= 𝑣0 (k)− 𝑣0 (k−1). (20 )

The output vo(k) is fed to output state to estimateIα1.

Neuron cell generates controlling signal through interrelated

gathering as

u(k) = u(k−1) +Σ 𝑤𝑖2𝑖=1 𝑘 𝑥𝑖(𝑘) (21)

where wi is the weight of the system.

Here, a neuron is trained by Hebb’s rule [27], [28].

Therefore, the change of weight of the neuron cell at kth

instant may be represented as

𝑤𝑖 𝑘 + 1 = (1 − 𝑐) 𝑤𝑖(k) + η𝑟𝑖(𝑘) (22)

𝑟𝑖 𝑘 = z(k)u(k) 𝑥𝑖(𝑘 ) (23)

where ri is the progressive signal, η is Hebb’s studying ratio

(learning rate), and “c” is a constant. Substituting (22) and (23) in (21), the following equation may be obtained:

Δ 𝑤𝑖(k) i(k) = 𝑤𝑖 𝑘 + 1 − 𝑤𝑖(k)

= − c[𝑤𝑖(k) –η z(k) u(k) 𝑥𝑖(𝑘) /c] (24) Δwi(k)is the change of weight at kth step. Weights of the

neuron are tuned according to Hebb’s assumption. Hebb’s

assumption is popularly known as the covariance algorithm. Finally, for the PI controller, the weights are represented by

𝑤1 (k+1) = 𝑤1 (k) + η 𝐼𝑧(𝑘) 𝑥1 (k) (25)

𝑤2 (k+1) = 𝑤2 (k) + η𝑃𝑧 (k) 𝑥2 (k) (26)

Whenever the ANN is initiated, it starts with a set of

controller gains to generate the first estimate of the

compensating current. These initial values of controller

parameters are set by offline training of the ANN. The controller parameters are then adjusted following (28) and

(29) to regulate the dc-link voltage.

VI . ADAPTIVE CURRENT DETECTION TECHNIQUE

The ANN in Section IV provides an initial guess for any

change in system dynamics. To generate more accurate

reference for APF, load current samples are fed to the

adaline based network shown in Fig. 3. Adaline is designed

to minimize the total harmonic distortion (THD) of source

current. Uncompensated source current sample s(k) may be

represented as

𝑠 𝑘 = 𝐼𝛼1 cos(𝜔𝑘𝑡𝑠) +

𝐼𝛽1 sin (𝜔𝑘𝑡𝑠) + In cos(𝑛𝜔𝑘𝑡𝑠 +Rn=2 𝜙𝑛 ) (27)

Where 𝑡𝑠 is the step size in discrete domain. The square of

error terms for kth sample may be expressed as

ε2(k) =[ [s2 k − 2s k a(k)]

a2(k)+ 1 ] (28)

Where

a2(k)=I2α1(k)cos2 (𝜔𝑘𝑡𝑠) + I2

β1(k)sin2 (𝜔𝑘𝑡𝑠) (29)

Equation (31) may also be represented as

ε2(k) = {s2 (k)−2s k [XT k α k α−T k X(k)]}

XT k α k α−T k X(k) + 1 (30)

where the vector

α(k) = [𝐼𝛼1 𝑘 , 𝐼𝛽1 𝑘 ] (31)

and the input vector

X(k) = [cos 𝜔𝑘𝑡𝑠 , sin 𝜔𝑘𝑡𝑠]𝑇 (32)

The compensating current is then calculated according to (4).

Convergence of this method is faster than existing adaline

based schemes due to the use of less number of tuning blocks.

The orthogonal relationship between the input vectors reduces

the computational burden of the system.



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VI. SIMULATION RESULTS

Simulations have been conducted for balanced and

unbalanced loads using SIMULINK for different controller

configurations. Switching frequency of the inverter is set

at 10 kHz, and the dead time of the inverter is set at

1μs.The whole system is built in SIMULINK where the ANN routine is called whenever necessary. A 110-V 50 HZ

mains supplying a load of 3 KVA is considered. First,

simulation study is made for the case with only predictive

algorithm. Fig.(4) shows the waveform for balanced and

nonlinear load. A diode bridge feeding a highly inductive

network is treated as the nonlinear load. The figure shows the

source, load, and compensating currents in top-to-bottom

order. Load change has occurred at 5 ms. The initial estimate

of the source current is extracted from the dip in capacitor

voltage (according to the algorithm explained in Section IV).

Although a quick estimate helped, the waveform quality is poor due to the lack of any corrective mechanism in the

system .

Fig.4. (a)

Fig. 4. (b)

Fig. 4. (c)

Fig.4. (a) Source current of phase A (b) Load current of phase A (c) Compensating current of phase A.

Balanced three-phase nonlinear load is considered similar to the case with predictive algorithm. Fig. (4) show the Performance of the HAPF with predictive ANN (simulation results). Fig.4.(a) shows Source current of phase A. Fig.4.(b) shows Load current of phase A. Fig.4.(c) shows Compensating current of phase A.

Next, the adaptive algorithm is tried. Simulation is conducted to check the performance of the system for a step change in load.

Fig. 5. (a)

Fig. 5. (b)

Fig. 5. (c)

Fig.5. (a) Source current of phase A (b) Load current of phase A (c) Compensating current of phase A. Fig. (5) show the Performance of the HAPF with adaptive controller (simulation results). Fig.5.(a) shows Source current of phase A. Fig.5.(b) shows Load current of phase A. Fig.5.(c) shows Compensating current of phase A.

Now, to have the advantage of predictive and adaptive

controllers, the system is run with both the algorithms. Fig. (6)

show the simulation with both the predictive and adaptive

controllers in operation. The results have confirmed very satisfactory performance in terms of waveform quality and

response time. The THD value for the simulation result is

1.49%. Fig. (6) show the simulation daigram for the

Performance of the HAPF with predictive and adaptive

controllers Fig.6. (a) shows Source current of phase A.

Fig.6.(b) shows Load current of phase Fig.6.(c) shows

Compensating current of phase A.



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Fig.6. (a)

Fig.6. (b)

Fig.6. (c)

Fig.6. (a) Source current of phase A (b) Load current of phase A (c) Compensating current of phase A.

Fig. (7). THD value of HAPF with predictive and adaptive

controllers (simulation results).

Fig.(7). Shows the percentage of THD value of HAPF

with predictive and adaptive controller. The result of THD

have confirmed very satisfactory performance in terms of

waveform quality and response time Fig. (8). Shows the

comparison of THD values of shunt active power filter and

hybrid active power filter. This Comparision shows the

performance of HAPF is more better than shunt active power

filter.

SAPF

%THD OF

SOURSE

CURRENT

HAPF

%THD OF

SOURSE

CURRENT

predictive

controller

6.02

predictive

controller

4.97

adaptive

controller

3.73

adaptive

controller

2.95

predictive

and

adaptive

controllers

2.25

predictive

and

adaptive

controllers

1.49

Fig. (8). Comparision of THD values for SAPF and HAPF

VIII. CONCLUSION

The combination of adaptive and predictive ANN

based controller for a shunt type HAPF has been presented in

this paper to improve the dynamic performance, convergence

and reduce the computational requirement. The predictive

algorithm is derived from an ANN-based PI controller used to

regulate the dc-link voltage in the HAPF. This is followed by

an adaline based THD minimization method. Adaline is

trained by CG method to minimize THD. Use of only two

weights and two input vectors makes the convergence very

fast and simple. The system is extensively simulated in

MATLAB/SIMULINK model.

APPENDIX

Taking Laplace transformation of (13), the following

equation may be obtained:

3ΔVs(s)Isα +ΔIsα(s)(3Vs − 6Rf Isα − 3sLf Isα)

−ΔIc(s)(6Rf Ic + 3sLf Ic) = sΔVdc(s)CdcVdc. (A1)

Consider

G1(s) = − 3(Vs − 2Rf Isα + sLf Isα) (A2)

G2(s) =1

Cdc Vdc s (A3)



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G3(s) =3(2Rf Ic + sLf Ic) (A4)

G4(s) =3Isα. (A5) Substituting (A2)–(A5) in (A1), the following equation may

be derived:

ΔVs(s)G4(s) + ΔIsα(s)G1(s) − ΔIc(s)G3(s)

= ΔVdc(s)1

G2(s) (A6)

The relation between isα and vdc may be expressed as

isα = Kdv dc

dt (A7)

where K is the small-signal gain and isα is the part of the source current used to stabilize the dc-bus voltage.

Instantaneous dc link voltage is compared with the reference

dc voltage, and the error voltage in Laplace domain may be

expressed as

Ve(s) = V∗dc

s − Vdc(s). (A8)

A PI controller may be used to maintain the capacitor voltage.

Therefore, the output of the PI controller may be expressed as

Vo(s) = (KP + KI

s )Ve(s). (A9)

Thus, (A7) may be modified as

ΔIsα(s) = KGs(s)( ΔV ∗dc

s − ΔVdc(s)) (A10)

where

(KP + KIs) = Gs(s). (A11)

Substituting values of ΔIsα(s) in (A1), (14) is obtained (in Section IV).

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