restaurador dinamico de voltage

Adapt ive Neuro-Fuzz y Inference System based DVR Control ler Desi gn

Samir a DIB, Bra him FERDI *, Che ll al i BENACHAIBA

Facul ty of the sciences and technology, Department of Technol ogy, Bechar Uni versit yB.P 417 BECHAR (0 8000 ), Alger iaE-mai l: ferdi_brahi m@yahoo. com

* Cor responding aut hor: Phone: (213) 0558111227

Recei ved: 8 Oct ober 2010 / Acce pt ed: 13 May 2011 / Publ ished: 25 June 2011

Abs tractPI cont roll er is very common in the contr ol of DVRs . However, one dis advant age of t hisconvent ional cont roll er is it s inabil ity to still working well under a wider range of operatingcondi ti ons. So, as a soluti on fuzzy control ler is proposed in l it erat ure. But , the main problemwith the conventional f uzzy control lers is that t he paramet er s associated with the member shipfunct ions and t he rul es depend br oadly on t he int ui ti on of the expert s. To over come thi sprobl em, Adap ti ve Neuro-Fuzzy I nf eren ce System (ANFIS) ba sed controller desig n isproposed. The r esulte d controll er i s composed of Sugeno f uzzy controller wi th t wo i nput s andone output. Accor ding to the er ror and er ror ra te of the control system and the output data,ANF IS generat es t he appropr iate fuzzy contr ol ler. The simulat ion result s have proved that theproposed desi gn met hod gives rel iable powerf ul fuzzy control ler wi th a minimum number ofmembe rshi p functions.

Keyword sDynamic Voltage Res torer (DVR); Sugeno Fuzzy Controll er ; Adaptive Neuro-Fuzzy I nf erenceSys tem (ANF IS); Vol tage Sag; Volt age Swel l; Voltage Unbalance.

Int roduct ion

Due t o the increased use of a l ar ge numbers of sophisticated el ectr ical and e lect ronic equipment, suchas comput er s, programmable logic controll er s, variable speed dr ives, and so f orth, prol iferat ion of highlysensi tive end-user devi ces is starting to draw at tention of both end cust omers and suppli er s to the question ofpower quality [ 1] . Faults at ei ther the t ransmission or distribut ion level may cause voltag e sag or swell i n theent ire syst em or a large part of it . Also, under heavy load conditions, a s ignifi cant vol tage drop may occur inthe s ystem. Voltage sags can occur at any i nstant of ti me, wi th amplitudes ranging from 10 - 90% and adur ation last ing for half a cycl e to one minute [2] . Fur ther, they could be ei ther bal ance d or unbal anced,

Dynamic Voltage Res torers http:// lejpt.academicdirect .org/A18/0 49_064.htm

1 de 14 23/ 02/2015 12:15 a. m.

[email protected]

http://lejpt.academicdirect.org/A18/049_064.htm

dependi ng on the type of faul t and they could have unpr edictabl e magnitudes, depending on factors such asdistance fr om the f ault and the transformer connections. Voltage swel l, on the ot her hand, is defined as asudden incr easing of supply vol tage up 110% t o 180% i n RMS voltage at the network f un dament al f requen cywith durati on from half a cycle t o 1 mi nute [2]. Volt age swel ls are not as import ant as vol tage sags becausethey ar e less common in distr ibut io n systems. Vol tage sag and swell can cause sensi ti ve equipment (such asfound i n semiconductor or chemi cal pl ants ) to f ai l, or shut down, as wel l as create a large curr ent unbalancethat could bl ow fus es or tr ip breakers. These effects can be very expensive for t he customer, rangi ng fromminor quali ty var iations to production downti me and e quipment damage [3]. There are many di ff erentmet hods t o mi tigate vol tage sags and swel ls , but the use of a DVR i s consider ed to be the most cost eff icientmet hod [3].

The most common choice fo r the cont rol of t he DVR is the so cal led PI controller since it has a s implestructure and it can of fe r relatively a sat isfact ory perf ormance over a wide range of operati on. The ma inprobl em of th is s imple controller is the correct choice of the PI gains and t he fact that by using fi xed gains,the contr ol ler may not pr ovide the requir ed control performance, when there are var ia tions in the systempar amet er s and oper ating condit ions. To solve t hese probl ems fuzzy logic cont rol appear s to be the mostpromi si ng, due to its l ower computational burden and robust ne ss . Also, a mathematical model i s not requiredto descri be t he sys te m in f uz zy logic based design. But , the main problem wit h the conventi onal f uzzycontr ollers is that t he parameter s associat ed wit h the me mbership funct ions and the rul es dep end br oadly onthe i ntuiti on of the experts. If it i s requir ed t o change the parameters, i t is t o be done by trial and err or onl y.There is no sci enti fi c opti mizati on methodology i nbuilt in the genera l fu zzy inference syst em [4]. Toovercome this problem, Adapti ve Neuro-Fuz zy Inf er ence System ( ANF I S) is used. I n ANFI S, the paramet er sassociated wi th a given membershi p functi on are c hose n so as to tai lor the input/output data set.

Thi s paper intr oduces Dynamic Vol tage Restore r (DVR) and its operating pr inci pl e, also pr esents t heproposed Sugeno fuzzy contr ol ler which was generated us ing ANFIS me thod des ign. Then, simul at ion result susing MATLAB- SI MULI NK were illu st ra ted an d di scussed.

Dynamic Voltage Restorer (DVR)

A Dyn amic Vol tage Res torer (DVR) is a series connected so li d state device that inject s volt age intothe system in order to regulate t he load si de vol tage. The DVR was fi rst inst al led in 1996 [4]. It is nor mallyins talled in a di stribution sys tem between the supply and t he cri ti cal load f eeder. Its pri mary f unct ion is t orapidly boost up the load-side voltage in the event of a di stur bance in order to avoid any power di srupti on tothat load [6] . There ar e various ci rcui t topologies and contr ol schemes t hat can be used to i mplement a DVR[7, 8]. I n addition to it s main task which is voltage s ags and swel ls compe nsation, DVR can also added ot herfeatures such as: l ine voltage harmonics compensation, reduction of t rans ient s in vol ta ge and fault cur rentlimit ations [9]. The general configur at ion of t he DVR consi st s of a vol tage injecti on t ransformer , an o ut putfilter, an energy s torage device, Voltage Source Invert er (VSI) , and a Control system as shown in Figur e 1.


2 de 14 23/ 02/2015 12:15 a. m.


Figure 1. DVR general c on figurati on

Vol tage Inj ection Transformer

The basic function of t hi s transf or mer is to connect the DVR to the dis tr ibution networ k vi a theHV- wind ings and coupl es t he i njected compensating vol tages generated by the vol tage source conver ters t othe i ncoming supp ly vol tage. The design of thi s transformer is very crucial because, i t faces saturation,overr ating, overheating, cost and per formance. The injected vol ta ge may consi st o f fundamental, des iredhar moni cs , swit ching harmonics and dc voltage components. If the tr ansf or mer is not designed pr op er ly, theinj ected volt age may saturate t he t ransformer and result in improper oper at ion of the DVR [10].

Out put FilterThe main task of the output f il ter is t o keep the harmonic voltage content generated by t he vol tage

source inverter t o the permissible level (i .e. el imin ate high f requency swi tching harmoni cs).It has a smallrat ing appr oximat el y 2% of th e load VA [11] .

Vol tage Source InverterA VSI i s a power el ectr onic system consists of switching devices (I GCTs, IGBTs, GTOs), which can

gener at e a sinusoidal voltage at any required f requency, magnitude, and phase angle. In t he DVR appli cati on,the VSI i s used to temporar il y repl ace the supply voltage or to generat e the part of the supply voltage which i smissing [12].

DC Ener gy Storage DeviceThe DC en er gy storage device provides t he real power requir ement of t he DVR dur ing compensation.

Var ious s torage technologies have been pr oposed i ncludi ng f lywheel ener gy s torage [ 13], super -conduct ingmagneti c energy storage (SMES) [14] and Super capacit or s [15, 16] . These have the advantage of fastresponse. An al ternat ive is the use of lead-aci d batt ery [17, 18] . Batter ies were unt il now considered oflimit ed sui tabili ty for DVR applicati ons si nce it t ak es consi derabl e time t o remove energy from them [19] .


3 de 14 23/ 02/2015 12:15 a. m.


Final ly, conventional capacit ors al so can be used [ 20, 21].

Contr ol Sys temThe aim of th e control syst em i s to maint ain cons tant vol tage magnitude a t the point where a sensitive

load is connected, under system disturbances. The c ontr ol system of the general c onfi guration t ypical lyconsi sts of a voltage cor rect ion method which determi nes the re fere nce volt age th at shoul d be i njected byDVR a nd t he VSI contr ol whi ch is in t his work con si sts of PWM with PI contr oller. The contr ol ler input is anerror s ignal obtained from the reference voltage an d the value of the inj ected volt age (Figur e 2). Such error i sprocessed by a PI contr oller then the out put is provi ded to t he PWM signal generator that control s the DVRinver ter to generate the requir ed i njected volt age.

Figure 2. Class ical PI controller

Operating Principle o f DVRThe basic f unction of t he DVR is to inj ect a dynamica ll y controlled voltage Vinj gener at ed by a forced

commu ta ted conver te r in ser ies to the bus vol tage by means of a v ol tage i nj ecti on t ransformer . Themomen tary amplitudes of t he three i nj ected phase volt ages are controlled such as to eli mi nate any det riment aleffects of a bus fault to t he load volt age VL. Thi s means th at any different ial volt ages caused by disturbancesin the ac f eeder will be compensate d by an equi valent v ol tage. The DVR works independentl y of t he type offault or any event that happens i n the syst em. For most pract ical cases, a more economi ca l desi gn can b eachieved by onl y compensati ng t he pos it ive and negative s equence components of the volt age dist ur banceseen at the input of the DVR (because the zer o sequ en ce part of a dis turbance wil l not pass through t he s tepdown tr ansformer which has infinite i mpedance f or this component).

The DVR h as t wo modes of op er atio n which are: standby mode and boos t mo de. In s tandby mode(Vinj =0), the vol ta ge i nj ection t ra nsformer’s l ow vol ta ge windi ng is shorte d through the co nver ter. Noswi tching of semi conductors occ urs in t hi s mode of operat ion, because the individual in vert er legs aretrigger ed such as to es tabl ish a short- circui t path for the transformer connect ion. The DVR wil l be most of thetime in this mode. In boost mode ( Vinj >0), the DVR i s in je ct ing a compensati on voltage thr ough t he vol tageinj ecti on transformer due t o a detect ion of a supply voltage di st urbance.

Vol tage Ref erence Calculati on Met hod

There are lot s of methods for DVR vol tage cor rect ion generati ng r efer ence vol tage that DVR mustinj ect it into the bus volt age [22- 27]. The str at egy of vol tage ref er ence calculati on used in this work i s shownin Figure 3.


4 de 14 23/ 02/2015 12:15 a. m.


Figure 3. SIMULINK model of SRF met hod for voltage referenc e calc ul ation

Figur e 3 shows the basi c control scheme and par amet er s that are measured fo r cont rol purposes.When the supply voltage i s at its normal level the DVR is control led to red uce the losses in the DVR to aminimum. When volta ge sags/swel ls are det ected, t he DVR s hould react as f ast as possi ble and inject an acvol tage i nt o the gr id. It can be implemented using the synchr onous re ference fr ame (SRF) technique based onthe i nstant aneous values of t he suppl y volt age. The control a lgorit hm produces a three phase refe rencevol tage t o the PWM invert er tha t tr ies to mai ntain the load vol tage at it s refere nce value. The vol tagesag/swell i s detected by measuring the er ror betwee n the d- voltage of the suppl y and the d- refere nce value.The d-ref erence component i s set to a r at ed vol tage. The MATLAB/Simul ink environment is a useful tool toimp lement t hi s method ( SRF) bec ause i t has many t ool boxes that can be used eas il y. The SRF method can beused to compe nsat e al l type of volt age disturbances , volt age sag/ swell, vol tage unbalance and har monicvol tage, but in thi s work we have s tudi ed o nl y voltag e sag/ swel l. The dif ference between the referencevol tage and t he i nj ected volt age is appli ed t o the VSI to produce the load rated volt age, with the help of pulsewidth modulat ion (PWM) through the PI c on troller.

ANFIS bas ed control ler desi gnThi s section introduces the basics of ANFIS network archi tecture and it s hybr id l earning rule. Inspir ed

by the idea of basi ng the fuzzy l ogic i nf erence procedure on a feed f orward net work str ucture, Jang [ 29]proposed an Ada ptive Networ k-based Fuzzy In ferenc e Syst em ( ANFIS) or semant ical ly equival entl y, t heAdapt ive Neur al Fuz zy I nfer ence Sys tem, whose archi tect ur e is shown in Fi gu re 4. He r epor ted that t heANF IS architectur e can be employed to model nonli near f unctions , identify non li near component s on-l ine in acontr ol sys tem, and predi ct a chaot ic time seri es.

It is a hybri d neur o- fuzzy techni que that bri ngs le arning capabilit ies of neural networks t o fuzzyinf erence sys tems. The learni ng algorit hm t unes t he membershi p functi ons of a Sug eno-type Fuzzy Inf er enceSys tem usin g the trai ni ng input -output data. The ANFIS is , from t he topol ogy point of view, animp lement at ion of a representat ive fuzzy inference syst em using a back pr opagat ion (BP) neural network-li ke

structure. It consists of five layers. The role of each l ayer is br iefly presented as fol lows : let denote t heout put of node i in layer l, and xi is the ith input of the ANFIS, i = 1, 2,.. .,p . In layer 1, t here is a node f unction


5 de 14 23/ 02/2015 12:15 a. m.


M associated wi th every n ode:(1)

The r ole of the node functi ons M1, M2 ...Mq h er e is equal to that of the membership funct ions μ( x)used in the r egular f uzzy systems, and q is the number of no des for each input . Gaussian shape f unct ions arethe t ypical choices. The adjustable paramet er s that deter mine t he positions and s hapes of these node functi onsare r eferred to as the premis e parameters . The output of every node i n layer 2 is the product of all theincoming signal s:

(2)Each node out put repr esents t he firing strength of the reasoning rule. In l ayer 3, each of these firing

strengt hs of the rules is compared wi th t he sum of al l th e fi ring s tren gths. Theref or e, the n ormalized firingstrengt hs are computed in thi s la yer as:

(3)

Layer 4 implements the Suge no-t ype inference syst em, i. e. , a li near combi nati on of the input variable sof ANFI S, x1, x 2, ...x p pl us a const ant term, c1, c 2, ...,c p, form the output of each IF −THEN r ule. The out putof the node is a weighted sum of th ese intermediate out pu ts:

(4)

where par amet er s P1, P 2, ...,P p and c1,c2, ...,c p, in this layer are ref er red to as the consequent parameters.The node in l ayer 5 produces the sum of i ts i nput s, i .e., def uzzificati on process of fuzzy syst em (usingwei ghted average method) is obt aine d:

(5)The f lowchart o f ANFI S procedure is s hown i n Fi gure 5. ANFI S di stinguishes it se lf from normal f uzzy

logic sys tems by the ad aptive paramet er s, i .e., both the premise and consequent parameter s are adjustable.The most remarkable f ea ture of the ANFIS is i ts hybri d learning algorit hm. The adaptation process of thepar amet er s of t he ANFIS is divi ded in to t wo s teps. For the firs t st ep of the conseq uent paramet ers trai ni ng,the Least Squares met hod (LS) i s used , because the ou tput of the ANFIS is a linear combinat ion of t heconsequent parameters. The pr emise parameters a re f ixed at th is step. Aft er t he consequent parameters havebeen ad just ed, the appr oximat ion er ror is back- pr opag at ed thr ou gh every l ayer to update t he premisepar amet er s as t he second step. This par t of t he adaptat ion proc edure is based on the gradient descentprinciple, which is the same as in the trai ni ng of th e BP neural ne twork. The consequence par amet ersident if ied by the LS method are optimal in the sense of l east squar es und er the condition t hat the premisepar amet er s are fixed [30].


6 de 14 23/ 02/2015 12:15 a. m.


Figure 4. Structure of ANFI S

Figure 5. ANFIS proce du re

The MATLA B/ SI MULI NK impleme nt at ion of the S ugeno fu zzy cont roller f or one phase i s shown in


7 de 14 23/ 02/2015 12:15 a. m.


figur e 6.

Figure 6. SIMULINK model of the proposed cont roll er

Simul at ion Result s and Discussions

The i nput-out put data pairs f or t raining the ANFIS were gener at ed usi ng t he conventiona l PIcontr oller. ANFIS structu re with Sugeno model containing 9 rules (Tables I) have been considered. Hybridlearning algori thm method was used to a dj ust the para meter of membership functi on. All the vari ables’ fuzzysubsets f or t he input s ε and ∆ε are def ined as (M1, M2, M3) with triangul ar membershi p function. Themembe rshi p functions and init ial universes of t he inputs generated by ANFIS training ar e illust rated in Figure7 and 8. The output var iabl e Y gi ven by ANFIS training is a vector of const ants. Y= [y 1, y 2, y 3, y 4, y 5, y 6, y 7,y8, y 9] whe re, y 1=-1397, y 2=-1397, y 3=-1397, y 4=-38. 58, y 5=-38. 58, y 6=-38. 58, y 7=1319, y 8=1320,y9=1320. The cont rol rul es ar e illustr ated in Table 1.

Figure 7. Membership func ti on curves of the i nput s ε


8 de 14 23/ 02/2015 12:15 a. m.


Figure 8. Membership func ti on curves of the i nput ∆ε

Table 1. Fuzzy cont rol rulesε / ∆ε M1 M2 M3M1 y1 y2 y3M2 y4 y5 y6M3 y7 y8 y9

A DVR i s connected to t he system through a seri es transformer with a capabi li ty to insert a maximumvol tage of 80% of the phase to ground system volt age. In the following si mulati ons, t he mai n characteri sticsof the DVR are se t as: volt age source f ull-bridge IGBT based invert er controlled with PWM signal genera to rwith commut at io n fr equency of 12kHz, capaci tor ener gy s torage bank 8. 8 mF, coupli ng transformer r at io 1:1,nomin al dc link vol tage 850V, LC output f il ter values C=80 µF i n seri es wit h a damping resistance Rd = 0. 1Ω, L = 1 mH, source v oltage 220Vrms and source fr equency of 50 Hz. The l oad is 80 kVA wi th 0.92 p. f. ,laggi ng. The tuning of the PI i s made such to have high transient s peed and to have very low tr acki ng error f orthe f undament al ( 50 Hz) , wi th Kp = 250 an d Ki = 135 .

A case of Three-phase 50% bal anced voltage sag is simul ated and t he resul t is shown in Fi gure 9.Vol tage sag i s init iated at 200 ms and it is kept until 300 ms, wit h total voltage sag durati on of 100ms. As aresul t of the control of DVR by t he proposed fuzzy co nt roll er ; the load voltage i s kept at 1. 00 p.u thr oughoutthe s imulatio n, including t he voltage sag period. We can noti ce that duri ng nor mal operation, t he DVR i sdoi ng not hing but once voltage sag is det ected, i t quickl y inject s necessary voltage components to smooth theload voltage.


9 de 14 23/ 02/2015 12:15 a. m.


Figure 9. Simul ation resu lt of DVR response t o a balanced volta ge s ag

Figure 10. Simulation resul t of DVR response to a balanced voltag e swell


10 de 14 23/ 02/2015 12:15 a. m.


Figure 11. Simulation resul t of DVR response to unbalanced voltag es

For the case of bal anced voltage swell compensation r epresent ed by Fi gure 10, t he l oad voltage is keptat the nomi nal valu e wi th the hel p of t he DVR. Si milar to the case of voltage sag, th e DVR reacts q uickly toinj ect the appropri ate volt age component (negat ive volt age magnit ud e) t o corr ect the supply voltage.

At the end Three- phase unbalanced vol tages condit ion is i nvesti gated to confi rm t he per formance ofthe DVR u nd er the proposed fuzzy cont roll er . According to figur e 11, the DVR is abl e to pro duce t he r equi redvol tage component s fo r di ff er ent phases r apidly and hel p to maintai n a balanced and const ant load vol tage at1.00pu. As a resu lt , the perf ormance of DVR under t he proposed fuzzy controll er i n mi tigati ng vol tagesags/ swell an d voltage unba lance is evi dent . In addit ion the pr oposed fuzzy contr ol ler has the mi ni mumnumbe r of membership func ti ons for the inputs ( 3) and do not need gains whi ch make it s implementationpract ically ver y easy wit h a mi nimum cost .

Concl usions

DVRs ar e ef fect ive custom power devices f or vol ta ge sags and swel ls mitigat ion. They inject theappropriate voltage component to corr ect rapi dl y any anomaly in the supply voltage to keep the load vol tagebal anced and constant at the nominal value. I n the pr esent pape r a reliable contr ol ler with high perfor mancefor dynamic vol ta ge r estorers was proposed. The proposed cont roll er i s generated by ANFIS t ra iningaccordi ng t o a gi ven input output dat a. Compared to the t radi ti onal f uzzy control ler, t he proposed one is t hesimpl es t (9 rules onl y) and the most cost eff icient controller . In additi on t his cont roller has no gains to adj ust


11 de 14 23/ 02/2015 12:15 a. m.


and solve t he problem of traditional fuzz y controller gai ns tuning.

References

1. Lim P .K, Dorr D.S., Underst anding and resol vi ng vol tage sag rel ated probl ems for sens it iveindus tr ial customers , Power Engineeri ng Society Winter Meeting, 2000. IEEE, 4 , p. 2886-2890 .

2. IEEE St d . 1159 - 1995, Recommended Prac ti ce f or Monit or ing Elec tric Power Quality.

3. Youssef K., Indus tr ial power qualit y pr oblems Elect ricity Distr ibut ion , IEE Con f. Pub1 No. 482,2001, 2, p. 5.

4. Li B. H., Cho i S.S., Vi lathgamuwa D. M., Design cons iderat ions on the li ne-side fi lter used in t hedynamic v oltage res torer, IEE Pr ocee dings - Generati on, Transmis si on, and Distri bution, 2001, 148, p.1-7.

5. Mitra P., Maulik S., Chowdhury S.P. , Chowdh ur y S. , ANFIS Based Aut omat ic Vol tage Reg ul ator withHybri d Lear ning Algorit hm , Inter nati onal Journal of Innovati ons in Energy Systems and Power, 2007,3(2), 397-401.

6. Sng E. K. K., Ch oi S .S., Vil at hga-muwa D.M., Analysis of Series Compensati on and DC- Li nk Vol tageControls of a Trans former less Sel f - Chargi ng Dynamic Vol tage Restorer , IEEE Tr an sact ions onPower Deliver y, July 2004, 19, p. 1511- 1518.

7. Wang Ji ng, Xu Aiqin, Sh en Yue yue. , A Survey on Control Strat egies of Dynamic Vol tage Res torer ,13t h Internat ional Conf erence on Harmon ics and Qual it y of Power (ICHQP), Sept . 28 2008- Oct. 1,2008, p. 1-5, Wollongong, NSW.

8. Nielsen J.G., Blaabje rg F., A Det ai led Comparis on o f Sy st em Topologie s for Dynami c Volt ag eRestorers , IEEE Tr an sact ions on Industry Applicati ons, 2005, 41(5), 1272-1 280.

9. Young-Hoon Ch o, Seung-Ki Sul., Controller Design f or Dynamic Vol tage Restore r wi th HarmonicsCompe nsat ion Function , I ndus tr y Applicat ions Conference, 2004. 39t h IAS Annual Meeti ng.Conference Record of th e 2004 IEEE, 7 Oct . 2004, 3( 3) , p. 1452 - 1457.

10. Mahesh S. S. , Mi shra M.K ., Ku mar B. K. , Ja yash ankar V. , Rat ing and Desi gn I ssue s of DVR I njectionTrans former , App li ed Power Elect roni cs Confe rence and Expositi on (APEC) 2008. Twenty-Thi rdAnnual IEEE, Austin , TX, p. 449-455.

11. Hyosung Kim, Jang -Hwa n Kim, Seung-Ki Su l. , A des ign cons iderat ion of out put fi lters for dynamicvol tage res torers , Power Electroni cs Specialis ts Conference, 2004. PESC 04.2004 IEEE 35th Annual ,20- 25 June, 2004, 6, p. 4268 - 4272.

12. Bol len M. H. J., Underst anding Power Quali ty Probl ems , New York : IEEE Pr es s, 2000

13. Bingsen Wang, Gir i Venkataramanan, Dynamic Voltage Res torer Ut il izing a Matr ix Convert er and


12 de 14 23/ 02/2015 12:15 a. m.


Flywhee l Energy Sto rage , IEEE Tr an sact ions On Industry Applicati ons, 2007, p. 208-215.

14. Zhan C. , Ra machanda ramu rt hy V.K., Arulampal am A., Fit zer C., Kromlidi s S. , Barn es M., Jen kins N.,Dynamic v oltage res torer base d on voltage space vector PWM cont ro l, in Proc . Appl ied Po werElect ronics Conference an d Exposi tion , 2001, p. 1301- 1307.

15. Jong Hee Ha n, I I Do ng Seo, lin Geun Shon, Hee Jong Jeon., Devel opment of On-l ine type Dyn amicVol tage Compe nsat ion Syst em Usi ng Super capaci tor , The 7th Int erna tional Conference on PowerElect ronics Oct ober 22-26 , 2007 / EXCO, Daegu, Korea.

16. Yan Li, Yun-Ling Wang, Bu-Han Zhang , Cheng-Xion g Mao, Model ing and Si mulati on of Dyna mi cVol tage Res torer Based on Super Capacitor Energy Storage , Inter nati onal Conference on Ele ct ri calMachi nes and Systems (I CEMS), 17-20 Oct . 2008, Wuha n, China, p. 2064- 2066 .

17. Zhan C. -J., Wu X. G., Krom li dis S., Ramachan daramu rthy V.K., Bar nes M., Jenk ins N. , Ruddel l A. J. ,Two e lect rical models of the le ad-aci d batt ery used i n a dynami c volt age re st orer , I EEEProc- Gener. Tr ansm. Dist rib, 2003, 150(2 ), 175-185.

18. Jayaprakash P., Singh B., Kothari D.P., Chand ra A., Kamal-Al- Haddad, Control of Reduced Rati ngDynamic Voltage Res torer wi th Bat tery Energy St orag e Syst em , Inter nati onal Conference on PowerSys tem Tech nology and I EEE Power Indi a Conference , 12-15 Oc t. 2008, p. 1-8, New Del hi, Indi a.

19. Zhan C. , Ba rnes M., Ramachandarmu rthy V.K., J enki ns N., Dynamic Voltage Res torer Wi th Bat teryEnergy St or age For Volt age Dip Mi ti gati on , Power Elect roni cs and Variabl e Speed Dr ives , 18-19September 2 000, Conference Public at ion No. 475 0 IEE 2000.

20. Takushi Jimichi, Hide aki Fujita, Hi rofumi Akagi., Design and Experi mentat ion of a Dyn amic Vol tageRestorer Capabl e of Signi fi cant ly Reducing an Energy-St orage Element , IEEE Tr an sact ions onIndus tr y Applicatio ns , 2008, 44(3) , p. 817-825.

21. Arindam Ghosh , Amit Kumar Jinda l, Avinash Jos hi , Design of a Capacitor -Sup port ed DynamicVol tage Res torer (DVR) for Unbalanced a nd Distort ed Loa ds , IEEE Tr an sact ions On Power Deli very,2004, 19(1).

22. Yuan Chang, Liu J injun, Wang Xiaoyu,Wang Zhaoan, Gan Weiwei ., Advanced Phase Shif t Control ofCapacit or Support ed Dynamic Vol tage Resto rer , Power Elect roni cs Specialis ts Conference (PESC),15- 19 June 2008, p. 1715- 1718.

23. Singh B, Jaya prakash P, Kot hari D P, Chandr a A, Kamal-Al- Haddad., Indirect Cont rol of CapacitorSupport ed DVR f or Power Quality I mproveme nt i n Di st ri bution System , Power a nd Energy Societ yGener al Meeting - Conversion and Deli very of Electrical Energy in t he 21st Centur y, 2008 IEEE, 20-24Jul y 2008, p. 1-7.

24. Meyer C., Romaus C., De Doncker R. W., Opt imized Control Str at egy for a Medi um-Vol tage DVR ,36t h IEEE Power Elect roni cs Speci alists Conference (PESC) , 16-16 June 2005, p. 18 87-1893.

25. Hyosung Kim, Sang -Joo n Lee, Seung-Ki Sul. , A Cal culati on for the Compensat ion Volt ages i nDynamic Voltage Res torers by use of PQR Power Theory , App li ed Power Elect roni cs Confe rence and


13 de 14 23/ 02/2015 12:15 a. m.


Expos it ion (APEC) , Ninet ee nt h Annual IEEE, 2004, 1, p. 573-579 .

26. Ming Hu, He ng Chen, Model ing and Controll ing of Uni fied Power Quali ty Compone nt , inAPSCOM- 00 , 2000, 2, p. 431- 435.

27. Liu J .W., Cho i S.S. , Chen S., Design of Step Dynami c Volt age Regulato r for Power Qual it yEnhancement , IEEE Tr ansact ions on Power Deli very, 2003, 18, p. 1403-14 09.

28. Boonchi am P., Mit hulananthan N., Underst anding of Dynami c Volt age Re st orers Through MATLABSimul at ion , Tha mmasat I nt . J. Sc. Tech., 2006, 11(3), Avail able at: http: // www. tijsat .t u.ac.th/issu es/2006/no3/2006_V11_No3_1. PDF (Accessed Januar y 2010).

29. Jang J. S.R. , ANFIS: Adaptive -Net work-based Fuz zy Inf erence Sys tems , IEEE Tr an sact ions onSys tems, Ma n, and C yber netics, 1993, 23(03) , p. 665-685.

30. Alavandar S., Nigam M. J., Adapt ive Neuro- Fuzz y Inference System based control of si x DOF robotmanipulat or , Journal of Engineer ing Sc ience and Technology Review 1, 2008, p. 106- 111.


14 de 14 23/ 02/2015 12:15 a. m.

http://www.tijsat.tu.ac.th/issues


restaurador dinamico de voltage

Documents