16
Leonardo Electronic Journal of Practices and Technologies ISSN 1583-1078 Issue 18, January-June 2011 p. 49-64 [ 49 http://lejpt.academicdirect.org Adaptive Neuro-Fuzzy Inference System based DVR Controller Design Samira DIB, Brahim FERDI * , Chellali BENACHAIBA Faculty of the sciences and technology, Department of Technology, Bechar University B.P 417 BECHAR (08000), Algeria E-mail: [email protected] * Corresponding author: Phone: (213) 0558111227 Received: 8 October 2010 / Accepted: 13 May 2011 / Published: 25 June 2011 Abstract PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions. Keywords Dynamic Voltage Restorer (DVR); Sugeno Fuzzy Controller; Adaptive Neuro-Fuzzy Inference System (ANFIS); Voltage Sag; Voltage Swell; Voltage Unbalance.

Adaptive Neuro-Fuzzy Inference System based DVR …lejpt.academicdirect.org/A18/049_064.pdf(Vinj>0), the DVR is injecting a compensation voltage through the voltage injection transformer

Embed Size (px)

Citation preview

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64 [

49 http://lejpt.academicdirect.org

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI*, Chellali BENACHAIBA

Faculty of the sciences and technology, Department of Technology, Bechar University

B.P 417 BECHAR (08000), Algeria E-mail: [email protected]

* Corresponding author: Phone: (213) 0558111227

Received: 8 October 2010 / Accepted: 13 May 2011 / Published: 25 June 2011

Abstract

PI controller is very common in the control of DVRs. However, one

disadvantage of this conventional controller is its inability to still working

well under a wider range of operating conditions. So, as a solution fuzzy

controller is proposed in literature. But, the main problem with the

conventional fuzzy controllers is that the parameters associated with the

membership functions and the rules depend broadly on the intuition of the

experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System

(ANFIS) based controller design is proposed. The resulted controller is

composed of Sugeno fuzzy controller with two inputs and one output.

According to the error and error rate of the control system and the output data,

ANFIS generates the appropriate fuzzy controller. The simulation results have

proved that the proposed design method gives reliable powerful fuzzy

controller with a minimum number of membership functions.

Keywords

Dynamic Voltage Restorer (DVR); Sugeno Fuzzy Controller; Adaptive

Neuro-Fuzzy Inference System (ANFIS); Voltage Sag; Voltage Swell;

Voltage Unbalance.

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

50

Introduction

Due to the increased use of a large numbers of sophisticated electrical and electronic

equipment, such as computers, programmable logic controllers, variable speed drives, and so

forth, proliferation of highly sensitive end-user devices is starting to draw attention of both

end customers and suppliers to the question of power quality [1]. Faults at either the

transmission or distribution level may cause voltage sag or swell in the entire system or a

large part of it. Also, under heavy load conditions, a significant voltage drop may occur in the

system. Voltage sags can occur at any instant of time, with amplitudes ranging from 10 - 90%

and a duration lasting for half a cycle to one minute [2]. Further, they could be either balanced

or unbalanced, depending on the type of fault and they could have unpredictable magnitudes,

depending on factors such as distance from the fault and the transformer connections. Voltage

swell, on the other hand, is defined as a sudden increasing of supply voltage up 110% to

180% in RMS voltage at the network fundamental frequency with duration from half a cycle

to 1 minute [2]. Voltage swells are not as important as voltage sags because they are less

common in distribution systems. Voltage sag and swell can cause sensitive equipment (such

as found in semiconductor or chemical plants) to fail, or shutdown, as well as create a large

current unbalance that could blow fuses or trip breakers. These effects can be very expensive

for the customer, ranging from minor quality variations to production downtime and

equipment damage [3]. There are many different methods to mitigate voltage sags and swells,

but the use of a DVR is considered to be the most cost efficient method [3].

The most common choice for the control of the DVR is the so called PI controller

since it has a simple structure and it can offer relatively a satisfactory performance over a

wide range of operation. The main problem of this simple controller is the correct choice of

the PI gains and the fact that by using fixed gains, the controller may not provide the required

control performance, when there are variations in the system parameters and operating

conditions. To solve these problems fuzzy logic control appears to be the most promising, due

to its lower computational burden and robustness. Also, a mathematical model is not required

to describe the system in fuzzy logic based design. But, the main problem with the

conventional fuzzy controllers is that the parameters associated with the membership

functions and the rules depend broadly on the intuition of the experts. If it is required to

change the parameters, it is to be done by trial and error only. There is no scientific

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

51

optimization methodology inbuilt in the general fuzzy inference system [4]. To overcome this

problem, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. In ANFIS, the

parameters associated with a given membership function are chosen so as to tailor the

input/output data set.

This paper introduces Dynamic Voltage Restorer (DVR) and its operating principle,

also presents the proposed Sugeno fuzzy controller which was generated using ANFIS

method design. Then, simulation results using MATLAB-SIMULINK were illustrated and

discussed.

Dynamic Voltage Restorer (DVR)

A Dynamic Voltage Restorer (DVR) is a series connected solid state device that

injects voltage into the system in order to regulate the load side voltage. The DVR was first

installed in 1996 [4]. It is normally installed in a distribution system between the supply and

the critical load feeder. Its primary function is to rapidly boost up the load-side voltage in the

event of a disturbance in order to avoid any power disruption to that load [6]. There are

various circuit topologies and control schemes that can be used to implement a DVR [7, 8]. In

addition to its main task which is voltage sags and swells compensation, DVR can also added

other features such as: line voltage harmonics compensation, reduction of transients in voltage

and fault current limitations [9]. The general configuration of the DVR consists of a voltage

injection transformer, an output filter, an energy storage device, Voltage Source Inverter

(VSI), and a Control system as shown in Figure 1.

Figure 1. DVR general configuration

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

52

Voltage Injection Transformer

The basic function of this transformer is to connect the DVR to the distribution

network via the HV-windings and couples the injected compensating voltages generated by

the voltage source converters to the incoming supply voltage. The design of this transformer

is very crucial because, it faces saturation, overrating, overheating, cost and performance. The

injected voltage may consist of fundamental, desired harmonics, switching harmonics and dc

voltage components. If the transformer is not designed properly, the injected voltage may

saturate the transformer and result in improper operation of the DVR [10].

Output Filter

The main task of the output filter is to keep the harmonic voltage content generated by

the voltage source inverter to the permissible level (i.e. eliminate high frequency switching

harmonics).It has a small rating approximately 2% of the load VA [11].

Voltage Source Inverter

A VSI is a power electronic system consists of switching devices (IGCTs, IGBTs,

GTOs), which can generate a sinusoidal voltage at any required frequency, magnitude, and

phase angle. In the DVR application, the VSI is used to temporarily replace the supply

voltage or to generate the part of the supply voltage which is missing [12].

DC Energy Storage Device

The DC energy storage device provides the real power requirement of the DVR during

compensation. Various storage technologies have been proposed including flywheel energy

storage [13], super-conducting magnetic energy storage (SMES) [14] and Super capacitors

[15, 16]. These have the advantage of fast response. An alternative is the use of lead-acid

battery [17, 18]. Batteries were until now considered of limited suitability for DVR

applications since it takes considerable time to remove energy from them [19]. Finally,

conventional capacitors also can be used [20, 21].

Control System

The aim of the control system is to maintain constant voltage magnitude at the point

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

53

where a sensitive load is connected, under system disturbances. The control system of the

general configuration typically consists of a voltage correction method which determines the

reference voltage that should be injected by DVR and the VSI control which is in this work

consists of PWM with PI controller. The controller input is an error signal obtained from the

reference voltage and the value of the injected voltage (Figure 2). Such error is processed by a

PI controller then the output is provided to the PWM signal generator that controls the DVR

inverter to generate the required injected voltage.

Figure 2. Classical PI controller

Operating Principle of DVR

The basic function of the DVR is to inject a dynamically controlled voltage Vinj

generated by a forced commutated converter in series to the bus voltage by means of a voltage

injection transformer. The momentary amplitudes of the three injected phase voltages are

controlled such as to eliminate any detrimental effects of a bus fault to the load voltage VL.

This means that any differential voltages caused by disturbances in the ac feeder will be

compensated by an equivalent voltage. The DVR works independently of the type of fault or

any event that happens in the system. For most practical cases, a more economical design can

be achieved by only compensating the positive and negative sequence components of the

voltage disturbance seen at the input of the DVR (because the zero sequence part of a

disturbance will not pass through the step down transformer which has infinite impedance for

this component).

The DVR has two modes of operation which are: standby mode and boost mode. In

standby mode (Vinj=0), the voltage injection transformer’s low voltage winding is shorted

through the converter. No switching of semiconductors occurs in this mode of operation,

because the individual inverter legs are triggered such as to establish a short-circuit path for

the transformer connection. The DVR will be most of the time in this mode. In boost mode

(Vinj>0), the DVR is injecting a compensation voltage through the voltage injection

transformer due to a detection of a supply voltage disturbance.

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

54

Voltage Reference Calculation Method

There are lots of methods for DVR voltage correction generating reference voltage

that DVR must inject it into the bus voltage [22-27]. The strategy of voltage reference

calculation used in this work is shown in Figure 3.

Figure 3. SIMULINK model of SRF method for voltage reference calculation

Figure 3 shows the basic control scheme and parameters that are measured for control

purposes. When the supply voltage is at its normal level the DVR is controlled to reduce the

losses in the DVR to a minimum. When voltage sags/swells are detected, the DVR should

react as fast as possible and inject an ac voltage into the grid. It can be implemented using the

synchronous reference frame (SRF) technique based on the instantaneous values of the supply

voltage. The control algorithm produces a three phase reference voltage to the PWM inverter

that tries to maintain the load voltage at its reference value. The voltage sag/swell is detected

by measuring the error between the d-voltage of the supply and the d-reference value. The d-

reference component is set to a rated voltage. The MATLAB/Simulink environment is a

useful tool to implement this method (SRF) because it has many tool boxes that can be used

easily. The SRF method can be used to compensate all type of voltage disturbances, voltage

sag/swell, voltage unbalance and harmonic voltage, but in this work we have studied only

voltage sag/swell. The difference between the reference voltage and the injected voltage is

applied to the VSI to produce the load rated voltage, with the help of pulse width modulation

(PWM) through the PI controller.

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

55

ANFIS based controller design

This section introduces the basics of ANFIS network architecture and its hybrid

learning rule. Inspired by the idea of basing the fuzzy logic inference procedure on a feed

forward network structure, Jang [29] proposed an Adaptive Network-based Fuzzy Inference

System (ANFIS) or semantically equivalently, the Adaptive Neural Fuzzy Inference System,

whose architecture is shown in Figure 4. He reported that the ANFIS architecture can be

employed to model nonlinear functions, identify nonlinear components on-line in a control

system, and predict a chaotic time series.

It is a hybrid neuro-fuzzy technique that brings learning capabilities of neural

networks to fuzzy inference systems. The learning algorithm tunes the membership functions

of a Sugeno-type Fuzzy Inference System using the training input-output data. The ANFIS is,

from the topology point of view, an implementation of a representative fuzzy inference

system using a back propagation (BP) neural network-like structure. It consists of five layers.

The role of each layer is briefly presented as follows: let denote the output of node i in

layer l, and xi is the ith input of the ANFIS, i = 1, 2,...,p. In layer 1, there is a node function M

associated with every node:

)x(MO ii1i = (1)

The role of the node functions M1, M2 ...Mq here is equal to that of the membership

functions μ(x) used in the regular fuzzy systems, and q is the number of nodes for each input.

Gaussian shape functions are the typical choices. The adjustable parameters that determine the

positions and shapes of these node functions are referred to as the premise parameters. The

output of every node in layer 2 is the product of all the incoming signals:

)x(M AND)x(MO jjii2i = (2)

Each node output represents the firing strength of the reasoning rule. In layer 3, each

of these firing strengths of the rules is compared with the sum of all the firing strengths.

Therefore, the normalized firing strengths are computed in this layer as:

∑=

i2i

2i3

i OOO (3)

Layer 4 implements the Sugeno-type inference system, i.e., a linear combination of the

input variables of ANFIS, x1, x2, ...xp plus a constant term, c1, c2, ...,cp, form the output of each

IF −THEN rule. The output of the node is a weighted sum of these intermediate outputs:

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

56

∑ =+=

p

1j jjj3i

4i cxPOO (4)

where parameters P1, P2, ...,Pp and c1,c2, ...,cp, in this layer are referred to as the consequent

parameters. The node in layer 5 produces the sum of its inputs, i.e., defuzzification process of

fuzzy system (using weighted average method) is obtained:

∑= i4i

5i OO (5)

The flowchart of ANFIS procedure is shown in Figure 5. ANFIS distinguishes itself

from normal fuzzy logic systems by the adaptive parameters, i.e., both the premise and

consequent parameters are adjustable. The most remarkable feature of the ANFIS is its hybrid

learning algorithm. The adaptation process of the parameters of the ANFIS is divided into two

steps. For the first step of the consequent parameters training, the Least Squares method (LS)

is used, because the output of the ANFIS is a linear combination of the consequent

parameters. The premise parameters are fixed at this step. After the consequent parameters

have been adjusted, the approximation error is back-propagated through every layer to update

the premise parameters as the second step. This part of the adaptation procedure is based on

the gradient descent principle, which is the same as in the training of the BP neural network.

The consequence parameters identified by the LS method are optimal in the sense of least

squares under the condition that the premise parameters are fixed [30].

Figure 4. Structure of ANFIS

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

57

Figure 5. ANFIS procedure

The MATLAB/SIMULINK implementation of the Sugeno fuzzy controller for one

phase is shown in figure 6.

Figure 6. SIMULINK model of the proposed controller

Simulation Results and Discussions

The input-output data pairs for training the ANFIS were generated using the

conventional PI controller. ANFIS structure with Sugeno model containing 9 rules (Tables I)

have been considered. Hybrid learning algorithm method was used to adjust the parameter of

membership function. All the variables’ fuzzy subsets for the inputs ε and ∆ε are defined as

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

58

(M1, M2, M3) with triangular membership function. The membership functions and initial

universes of the inputs generated by ANFIS training are illustrated in Figure 7 and 8. The

output variable Y given by ANFIS training is a vector of constants. Y= [y1, y2, y3, y4, y5, y6,

y7, y8, y9] where, y1=-1397, y2=-1397, y3=-1397, y4=-38.58, y5=-38.58, y6=-38.58, y7=1319,

y8=1320, y9=1320. The control rules are illustrated in Table 1.

Figure 7. Membership function curves of the inputs ε

Figure 8. Membership function curves of the input ∆ε

Table 1. Fuzzy control rules

ε / ∆ε M1 M2 M3 M1 y1 y2 y3 M2 y4 y5 y6 M3 y7 y8 y9

A DVR is connected to the system through a series transformer with a capability to

insert a maximum voltage of 80% of the phase to ground system voltage. In the following

simulations, the main characteristics of the DVR are set as: voltage source full-bridge IGBT

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

59

based inverter controlled with PWM signal generator with commutation frequency of 12kHz,

capacitor energy storage bank 8.8 mF, coupling transformer ratio 1:1, nominal dc link voltage

850V, LC output filter values C=80 µF in series with a damping resistance Rd = 0.1 Ω, L = 1

mH, source voltage 220Vrms and source frequency of 50 Hz. The load is 80 kVA with 0.92

p.f., lagging. The tuning of the PI is made such to have high transient speed and to have very

low tracking error for the fundamental (50 Hz), with Kp = 250 and Ki = 135.

A case of Three-phase 50% balanced voltage sag is simulated and the result is shown

in Figure 9. Voltage sag is initiated at 200 ms and it is kept until 300 ms, with total voltage

sag duration of 100ms. As a result of the control of DVR by the proposed fuzzy controller; the

load voltage is kept at 1.00 p.u throughout the simulation, including the voltage sag period.

We can notice that during normal operation, the DVR is doing nothing but once voltage sag is

detected, it quickly injects necessary voltage components to smooth the load voltage.

0.15 0.2 0.25 0.3 0.35-400

-200

0

200

400S O U R C E V O L T A G E S

V s

(

V )

0.15 0.2 0.25 0.3 0.35-400

-200

0

200

400I N J E C T E D V O L T A G E S

V i

n j

( V

)

0.15 0.2 0.25 0.3 0.35-400

-200

0

200

400L O A D V O L T A G E S

V L

(

V )

T I M E ( s )

Figure 9. Simulation result of DVR response to a balanced voltage sag

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

60

0.15 0.2 0.25 0.3 0.35-500

0

500S O U R C E V O L T A G E S

V s

(

V )

0.15 0.2 0.25 0.3 0.35-400

-200

0

200

400I N J E C T E D V O L T A G E S

V i

n j

( V

)

0.15 0.2 0.25 0.3 0.35-500

0

500L O A D V O L T A G E S

V L

(

V )

T I M E ( s )

Figure 10. Simulation result of DVR response to a balanced voltage swell

0.15 0.2 0.25 0.3-500

0

500S O U R C E V O L T A G E S

V s

(

V )

0.15 0.2 0.25 0.3-400

-200

0

200

400I N J E C T E D V O L T A G E S

V i

n j

( V

)

0.15 0.2 0.25 0.3-500

0

500L O A D V O L T A G E S

V L

(

V )

T I M E ( s )

Figure 11. Simulation result of DVR response to unbalanced voltages

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

61

For the case of balanced voltage swell compensation represented by Figure 10, the

load voltage is kept at the nominal value with the help of the DVR. Similar to the case of

voltage sag, the DVR reacts quickly to inject the appropriate voltage component (negative

voltage magnitude) to correct the supply voltage.

At the end Three-phase unbalanced voltages condition is investigated to confirm the

performance of the DVR under the proposed fuzzy controller. According to figure 11, the

DVR is able to produce the required voltage components for different phases rapidly and help

to maintain a balanced and constant load voltage at 1.00pu. As a result, the performance of

DVR under the proposed fuzzy controller in mitigating voltage sags/swell and voltage

unbalance is evident. In addition the proposed fuzzy controller has the minimum number of

membership functions for the inputs (3) and do not need gains which make its implementation

practically very easy with a minimum cost.

Conclusions

DVRs are effective custom power devices for voltage sags and swells mitigation.

They inject the appropriate voltage component to correct rapidly any anomaly in the supply

voltage to keep the load voltage balanced and constant at the nominal value. In the present

paper a reliable controller with high performance for dynamic voltage restorers was proposed.

The proposed controller is generated by ANFIS training according to a given input output

data. Compared to the traditional fuzzy controller, the proposed one is the simplest (9 rules

only) and the most cost efficient controller. In addition this controller has no gains to adjust

and solve the problem of traditional fuzzy controller gains tuning.

References

1. Lim P.K, Dorr D.S., Understanding and resolving voltage sag related problems for

sensitive industrial customers, Power Engineering Society Winter Meeting, 2000. IEEE,

4, p. 2886-2890.

2. IEEE Std. 1159 - 1995, Recommended Practice for Monitoring Electric Power Quality.

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

62

3. Youssef K., Industrial power quality problems Electricity Distribution, IEE Conf. Pub1

No. 482, 2001, 2, p. 5.

4. Li B. H., Choi S.S., Vilathgamuwa D. M., Design considerations on the line-side filter

used in the dynamic voltage restorer, IEE Proceedings - Generation, Transmission, and

Distribution, 2001, 148, p. 1-7.

5. Mitra P., Maulik S., Chowdhury S.P., Chowdhury S., ANFIS Based Automatic Voltage

Regulator with Hybrid Learning Algorithm, International Journal of Innovations in

Energy Systems and Power, 2007, 3(2), 397-401.

6. Sng E. K.K., Choi S.S., Vilathga-muwa D.M., Analysis of Series Compensation and

DC-Link Voltage Controls of a Transformerless Self - Charging Dynamic Voltage

Restorer, IEEE Transactions on Power Delivery, July 2004, 19, p. 1511-1518.

7. Wang Jing, Xu Aiqin, Shen Yueyue., A Survey on Control Strategies of Dynamic

Voltage Restorer, 13th International Conference on Harmonics and Quality of Power

(ICHQP), Sept. 28 2008-Oct. 1, 2008, p. 1-5, Wollongong, NSW.

8. Nielsen J.G., Blaabjerg F., A Detailed Comparison of System Topologies for Dynamic

Voltage Restorers, IEEE Transactions on Industry Applications, 2005, 41(5), 1272-

1280.

9. Young-Hoon Cho, Seung-Ki Sul., Controller Design for Dynamic Voltage Restorer

with Harmonics Compensation Function, Industry Applications Conference, 2004. 39th

IAS Annual Meeting. Conference Record of the 2004 IEEE, 7 Oct. 2004, 3(3), p. 1452 -

1457.

10. Mahesh S.S., Mishra M.K., Kumar B.K., Jayashankar V., Rating and Design Issues of

DVR Injection Transformer, Applied Power Electronics Conference and Exposition

(APEC) 2008. Twenty-Third Annual IEEE, Austin, TX, p. 449-455.

11. Hyosung Kim, Jang-Hwan Kim, Seung-Ki Sul., A design consideration of output filters

for dynamic voltage restorers, Power Electronics Specialists Conference, 2004. PESC

04.2004 IEEE 35th Annual, 20-25 June, 2004, 6, p. 4268 - 4272.

12. Bollen M.H.J., Understanding Power Quality Problems, New York: IEEE Press, 2000

13. Bingsen Wang, Giri Venkataramanan, Dynamic Voltage Restorer Utilizing a Matrix

Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 18, January-June 2011

p. 49-64

63

Converter and Flywheel Energy Storage, IEEE Transactions On Industry Applications,

2007, p. 208-215.

14. Zhan C., Ramachandaramurthy V.K., Arulampalam A., Fitzer C., Kromlidis S., Barnes

M., Jenkins N., Dynamic voltage restorer based on voltage space vector PWM control,

in Proc. Applied Power Electronics Conference and Exposition, 2001, p. 1301- 1307.

15. Jong Hee Han, II Dong Seo, lin Geun Shon, Hee Jong Jeon., Development of On-line

type Dynamic Voltage Compensation System Using Supercapacitor, The 7th

International Conference on Power Electronics October 22-26, 2007 / EXCO, Daegu,

Korea.

16. Yan Li, Yun-Ling Wang, Bu-Han Zhang, Cheng-Xiong Mao, Modeling and Simulation

of Dynamic Voltage Restorer Based on Super Capacitor Energy Storage, International

Conference on Electrical Machines and Systems (ICEMS), 17-20 Oct. 2008, Wuhan,

China, p. 2064-2066.

17. Zhan C.-J., Wu X.G., Kromlidis S., Ramachandaramurthy V.K., Barnes M., Jenkins N.,

Ruddell A.J., Two electrical models of the lead-acid battery used in a dynamic voltage

restorer, IEEE Proc-Gener.Transm. Distrib, 2003, 150(2), 175-185.

18. Jayaprakash P., Singh B., Kothari D.P., Chandra A., Kamal-Al-Haddad, Control of

Reduced Rating Dynamic Voltage Restorer with Battery Energy Storage System,

International Conference on Power System Technology and IEEE Power India

Conference, 12-15 Oct. 2008, p. 1-8, New Delhi, India.

19. Zhan C., Barnes M., Ramachandarmurthy V.K., Jenkins N., Dynamic Voltage Restorer

With Battery Energy Storage For Voltage Dip Mitigation, Power Electronics and

Variable Speed Drives, 18-19 September 2000, Conference Publication No. 475 0 IEE

2000.

20. Takushi Jimichi, Hideaki Fujita, Hirofumi Akagi., Design and Experimentation of a

Dynamic Voltage Restorer Capable of Significantly Reducing an Energy-Storage

Element, IEEE Transactions on Industry Applications, 2008, 44(3), p. 817-825.

21. Arindam Ghosh, Amit Kumar Jindal, Avinash Joshi, Design of a Capacitor-Supported

Dynamic Voltage Restorer (DVR) for Unbalanced and Distorted Loads, IEEE

Transactions On Power Delivery, 2004, 19(1).

Adaptive Neuro-Fuzzy Inference System based DVR Controller Design

Samira DIB, Brahim FERDI and Chellali BENACHAIBA

64

22. Yuan Chang, Liu Jinjun, Wang Xiaoyu,Wang Zhaoan, Gan Weiwei., Advanced Phase

Shift Control of Capacitor Supported Dynamic Voltage Restorer, Power Electronics

Specialists Conference (PESC), 15-19 June 2008, p. 1715-1718.

23. Singh B, Jayaprakash P, Kothari D P, Chandra A, Kamal-Al-Haddad., Indirect Control

of Capacitor Supported DVR for Power Quality Improvement in Distribution System,

Power and Energy Society General Meeting - Conversion and Delivery of Electrical

Energy in the 21st Century, 2008 IEEE, 20-24 July 2008, p. 1-7.

24. Meyer C., Romaus C., De Doncker R. W., Optimized Control Strategy for a Medium-

Voltage DVR, 36th IEEE Power Electronics Specialists Conference (PESC), 16-16 June

2005, p. 1887-1893.

25. Hyosung Kim, Sang-Joon Lee, Seung-Ki Sul., A Calculation for the Compensation

Voltages in Dynamic Voltage Restorers by use of PQR Power Theory, Applied Power

Electronics Conference and Exposition (APEC) , Nineteenth Annual IEEE, 2004, 1, p.

573-579.

26. Ming Hu, Heng Chen, Modeling and Controlling of Unified Power Quality Component,

in APSCOM-00, 2000, 2, p. 431-435.

27. Liu J.W., Choi S.S., Chen S., Design of Step Dynamic Voltage Regulator for Power

Quality Enhancement, IEEE Transactions on Power Delivery, 2003, 18, p. 1403-1409.

28. Boonchiam P., Mithulananthan N., Understanding of Dynamic Voltage Restorers

Through MATLAB Simulation, Thammasat Int. J. Sc. Tech., 2006, 11(3), Available at:

http://www.tijsat.tu.ac.th/issues/2006/no3/2006_V11_No3_1.PDF (Accessed January

2010).

29. Jang J.S.R., ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE

Transactions on Systems, Man, and Cybernetics, 1993, 23(03), p. 665-685.

30. Alavandar S., Nigam M. J., Adaptive Neuro-Fuzzy Inference System based control of six

DOF robot manipulator, Journal of Engineering Science and Technology Review 1,

2008, p. 106-111.