Crcuts and Systems, 2016, 7, 2551-2567 Publshed Onlne July 2016 n ScRes. http://www.scrp.org/journal/cs http://dx.do.org/10.4236/cs.2016.79221 Intellgent-Based Maxmum Power Extracton on Grd-Integrated Multlevel Inverter-Fed Wnd-Drven Inducton Generators B. Meenaksh Sundaram 1, B. V. Mankandan 2, H. Habeebullah Sat 3 1 Department of Electrcal and Electroncs Engneerng, Sethu Insttute of Technology, Karapatt, Taml Nadu, Inda 2 Department of Electrcal and Electroncs Engneerng, Mepco Schlenk Engneerng College, Svakas, Taml Nadu, Inda 3 Department of Electrcal and Electroncs Engneerng, Anna Unversty, BIT Campus, Truchrappall, Taml Nadu, Inda Receved 28 March 2016; accepted 15 Aprl 2016; publshed 27 July 2016 Copyrght 2016 by authors and Scentfc Research Publshng Inc. Ths work s lcensed under the Creatve Commons Attrbuton Internatonal Lcense (CC BY). http://creatvecommons.org/lcenses/by/4.0/ Abstract Ths paper presents an ntellgent controller employng Adaptve Neuro-Fuzzy Inference System (ANFIS) for extractng maxmum power from the wnd energy converson system even durng the change n the wnd speed condtons wth mproved qualty of power. The proposed nducton generator wth multlevel nverter along wth ntellgent controller based Maxmum Power Pont Trackng (MPPT) technque ams at ntegratng wnds system wth mproved maxmum power njecton and mnmum harmonc ssues. The proposed method wll mprove the power qualty whch s delvered to the grd n terms of harmonc, and nject the maxmum power to the grd. To valdate the effectveness of the proposed control strategy, ANFIS controller, Fuzzy Inference System (FIS) and wthout MPPT controller have been presented and tested usng MATLAB/Smulnk envronment. Keywords Wnd Energy, Multlevel Inverter, MPPT Technque, ANFIS, FIS, Harmoncs How to cte ths paper: Meenaksh Sundaram, B., Mankandan, B.V. and Habeebullah Sat, H. (2016) Intellgent-Based Maxmum Power Extracton on Grd-Integrated Multlevel Inverter-Fed Wnd-Drven Inducton Generators. Crcuts and Systems, 7, 2551-2567. http://dx.do.org/10.4236/cs.2016.79221
B. Meenaksh Sundaram et al. 1. Introducton In the last decade, great ncrease has been wtnessed by the use of renewable energy due to the exhauston of fossl fuels and dfferent polces of ndustral countres wth the am of reducng ar polluton [1]. Especally, Wnd Energy Converson Systems (WECS) are consdered as one of the most cost-effectve solutons than all other renewable sources. In present days, power electroncs researchers have been workng n multlevel nverters, because of the followng features such as: hgher voltage operatng capablty, reduced rate of change of voltage (dv/dt), lower common mode voltages, reduced harmonc content, producng near snusodal current, flter of less operaton, reduced swtchng and conducton loss by operatng the power swtches by fundamental swtchng whch results n ncreased converson effcency. The major problem to harvest more energy from the wnd to the grd s the lmtaton mposed by the ratngs of currently avalable swtchng devces n the converter. The ratngs of the semconductor devces used n the conventonal two-level or three-level VSI topologes do not support the hgher power ratngs necessary for the grd nterface of such large machnes. The use of multlevel VSI topology for dstrbutng voltage stress and power losses between a numbers of devces has been well reported [2] [3]. Ths has motvated desgners to go for medum-voltage converters as these are more compact than low-voltage converters for power larger than 1.5 MW [4]. Hence, multlevel nverters are consdered as the nterface solutons for modern hgh-power wth good qualty of power demandng wnd-turbne applcatons [5]. In general, there are two operatonal modes (fxed speed and varable speed) performed n wnd energy converson system. The varable speed operaton has more features such as: reducton n mechancal structure stresses, nose and beng easy to control actve and reactve power [6]-[8]. In fact, varable speed operaton ncreases the system effcency and reduces generated power fluctuatons [9]. In the operatng wnd speed range, the turbne shaft s rotatonal speed should be adjusted optmally wth respect to the varable wnd speed to extract the maxmum power [10]. The features of MPPT are smple and quck trackng under changng condton of output power fluctuatons [11] [12]. MPPT algorthms can be broadly classfed nto sensor based and ntellgent based (wthout sensor) [13]. The algorthms wthout sensors track the maxmum power pont by montorng the power varaton. These algorthms are Perturbaton and Observaton (P&O) and Incremental Conductance [14] [15]. Nether P&O nor Incremental Conductance algorthms requre any addtonal sensors to measure wnd or rotor speed. But, they have poor dynamc characterstcs because they are not usually senstve to varatons n wnd speed [16] [17]. Therefore, they are senstve to modelng uncertantes and may become neffectve n some cases [18]. Most common methods to acheve MPPT n wnd turbnes are Tp-Speed Rato (TSR) algorthm, Hll-Clmb Searchng (HCS) algorthm and the Optmal Torque Control (OTC) [19]. TSR control s used to fx an optmal TSR based on rotor speed. HCS control extracts MPP by searchng the peak output power of the wnd turbne [20]. The computatonal ntellgence technques such as fuzzy logc controller, neuro-fuzzy controller and adaptve control technques are the recent technques used for the MPPT control [21] [22]. In [23], Wlcoxon Radal Bass Functon Network (WRBFN) wth HCS, MPPT strategy has been proposed for a varable speed wnd turbne systems. Two serous problems assocated wth the above sad controllers are speed effcency trade off and poor qualty output under rapd wnd change. Ths can sgnfcantly deterorate the performance of the grd connected wnd energy system. In ths work, ANFIS based approach for controllng the rectfer output and current controller for dervng gatng sgnals for multlevel nverter are presented. The proposed scheme provdes a better snusodal njecton of current nto the grd and extracts maxmum power from the wnd energy converson system. It s verfed wth the comparson of other technques such as fuzzy nference system and wthout a controller. The paper s organzed as follows: mathematcal modelng of wnd energy converson wth the grd connected system s presented n Secton 2. Secton 3 explans the FIS control strategy. ANFIS based control strategy s dscussed n Secton 4. Results and dscussons are outlned n Secton 6. 2. Mathematcal Model of Wnd Energy Converson wth Grd Connected System The electrcal behavor of the wnd turbne s examned by a smplfed aerodynamc model. In the proposed grd connected wnd power generaton system, the Inducton Generator [IG] s used because, the advantages of smplcty and absence of separate feld crcut can operate wth constant and varable speed operatonal modes and naturally, t protects aganst short crcut. The structure of the proposed control technque of wnd energy system s llustrated n Fgure 1. In the proposed wnd energy system model, the AC-DC-AC converter has been em- 2552
B. Meenaksh Sundaram et al. Fgure 1. Grd connected wnd energy system wth the proposed control system. ployed to enhance the operatng performance of the system. The output of varable voltage varable frequency from IG s fed to the controlled rectfer. ANFIS based control scheme has been employed to mprove the output performance of the rectfer. The multlevel nverter gate drve pulse s generated by the PI current controller. PI controller s one of the control theory based control technques and the performance of the controller depends on the controller gan. The nputs of PI controller are the error sgnal of DC bus voltage and nverter output current. The controlled PWM sgnal s obtaned by a PI current controller, whch s appled to the multlevel nverter gate termnals for extractng maxmum power and to nject pure snusodal current nto the grd. So, the converson performance of the multlevel nverter gets mproved. The detaled descrpton of ANFIS based control s descrbed n Secton 4. The DC lnk voltage s fed to the grd through multlevel nverter and thereby, total harmonc dstorton can be reduced. The mechancal power generated by the wnd energy system s derved [24] and presented as follows, ( λβ, ) P = C P (1) mech p wnd where C p s power coeffcent; λ denotes tp speed rato; β s the ptch angle; P wnd represents the avalable power from wnd energy system. The avalable power of the wnd energy system s llustrated as, 1 3 Pwnd = ρsvwnd (2) 2 where ρ s the ar densty (kg/m 3 ); S denotes area of wnd wheel (m 2 ); V wnd s the wnd speed (m/s). In IG model, the output power extractng equaton s needed to analyze the energy converson of wnd system. The real and reactve power flow of IG s derved n terms of voltage, flux, synchronous speed and stator resstance of the system. The real and reactve power flow equatons of IG are gven below, 2553
B. Meenaksh Sundaram et al. Real power, Reactve power, where ω s s the synchronous speed; M s the magnetzng nductance (H); ψ s the stator flux; s 3 ωψ s sm Ps = qr (3) 2 L s s dr s s 3 ωψ s s Q = ( ψ M ) (4) 2 L L s s the stator per phase nductance (H); qr and dr are the q-axs and d-axs rotor current, respectvely. The Total Harmonc Dstorton (THD) of the system s expressed as follows, Voltage THD, Current THD, where V n and I n are the current respectvely. The real and reactve power flow of the wnd energy system to represented as follows, where V s s the stator voltage of IG; the reactance of the lne. Wnd Power Characterstcs V THD I THD 2 Vn = (5) V 1 2 In = (6) I 1 th n harmonc voltage and current; V 1 and I 1 are the fundamental voltage and Q grd P V b and grd th b bus of the grd connected system can be VV s bsnθb = (7) X b 2 Vs VV s bcosθb = (8) X b θ b are the voltage magntude and angle of th b bus; X b s Fgure 2 llustrates the mechancal power of a wnd turbne versus the rotor speed at dfferent wnd veloctes. Each and every pont of the blade receves dfferent values of wnd speed. The mechancal power developed by the wnd turbne not only depends on the wnd speed (whch s dffcult to measure) and also depends on the ar densty and the turbne performance coeffcent. Accordng to the deal gas law, the densty of a gas s proportonal to ts pressure and nversely proportonal to ts temperature as n (9). M P ρ = R T where P s the absolute pressure, M s the molar mass, R s the gas constant (8.314472 J K 1 mol 1 ), and T s the absolute temperature. If the pressure ncreases by 10%, the temperature decreases by 15% and the ar densty wll ncrease about 30%. The power co-effcent and the effcency of wnd turbne are the functon of the tpspeed rato. In general, wnd turbne must be operatng at the maxmum value of power co-effcent at all wnd speeds. The above sad problem has been elmnated by FIS and ANFIS based MPPT control strategy. (9) 2554
B. Meenaksh Sundaram et al. Fgure 2. Change n rotor speed wth mechancal power. 3. FIS Based Control Strategy for Rectfer Control Fuzzy Inference System s a multdscplnary computng technque based on the concepts of fuzzy set theory, fuzzy f then rules and fuzzy reasonng. The applcatons of FIS n a wde varety of areas lke automatc control, decson analyss and tme seres predcton. Wth crsp nput and outputs, a fuzzy nference system mplements a non lnear mappng from ts nput space to output space. Ths mappng s accomplshed by a number of fuzzy f then rules, each of whch descrbes the behavor of the mappng. In ths paper, FLC has been employed for extracton of maxmum power at dfferent wnd speed to operate the wnd turbne at maxmum torque condton. The nputs of the FLC are error and change n error. The error value has been derved from the electrcal parameters of the nducton generator (voltage and current). Fgure 3 ndcates the structure of the nput membershp functon used n FLC for the proposed work. Here, the trangular and trapezodal membershp functons were utlzed. The error value les n the range of 1 to 1, whch s represented as lngustc terms (Negatve Bg, Negatve Small, Zero, Postve Small, Postve Bg). The proposed FLC tuned by 5 5 rules for provdng the requred duty cycle (ΔD) for controllng the operaton of the rectfer. The rule based MPPT algorthm s presented n Table 1. The result of the defuzzfcaton has to be a numerc value whch determnes the change of duty cycle of the PWM sgnal used to drve the Swtch. Cluster 1: When nducton generator voltage and current are negatve bg or negatve small, the frng angle of the rectfer should be negatve bg or negatve small n order to mantan the DC lnk voltage at the rated value. Cluster 2: When nducton generator voltage and current are zero, the frng angle of the rectfer should be zero n order to mantan the DC lnk voltage at the rated value. Cluster 3: When nducton generator voltage and current are postve small or postve bg, the frng angle of the rectfer should be postve bg or small n order to mantan DC lnk voltage at the rated value. There are many methods to calculate the crsp output of the system. Centre of Gravty (CoG) method s used n the proposed system because t gves better results. In the present work, the CoG s expressed mathematcally as D gven n (10). D = 4 n= 1 [ ] F[ ] Y 4 n= 1 where Y[] s the th member of the output vector and F[] are the multplyng coeffcents of the output mem- [ ] Y (10) 2555
B. Meenaksh Sundaram et al. Fgure 3. Input membershp functon of FLC. Table 1. Fuzzy rule base table. E NB NS ZE PS PB ΔE NB 1 0.5 0.5 0 0 Cluster 1 NS 0.5 0.5 0.5 0 0.5 ZE 0.5 0.5 0 0.5 0.5 Cluster 2 PS 0.5 0 0.5 0.5 1 PB 0 0 0.5 0.5 1 Cluster 3 bershp functon as shown n Table 1. D s the change of duty cycle and ths number represents a sgned number whch s added or subtracted from the present duty cycle to generate the next system response for reachng the MPP as gven by (11). D = D + D (11) new Fuzzy system output presented n surface plot whch s shown Fgure 4 for a change n duty cycle of rectfer for an nputs of dfferent error and change of error. 4. ANFIS Based Control Strategy for PWM Pulses Appled to Rectfer old ANFIS holds the benefts of both neural network and fuzzy logc controller. In ANFIS, the fuzzy nference system s mpled through the structure and neurons of the feed forward adaptve neural network. In the proposed control, the data set s developed by neural network n terms of voltage ( V IG ), current ( I IG ) as nputs and PWM control pulses ( I g ) as output. Fuzzy control rules are desgned by usng the generated data set. The adaptve system s developed through tranng and testng of nput-output data. In the proposed approach, the voltage and the current of the IG are generated n the form of vector and the data are appled to the neural network. The testng of ANFIS s executed for provdng exact output as same as speed control of IG. The structure of ANFIS system conssts of fve layers whch are categorzed as the nput layer, nput membershp functon layer, rule layer, output membershp functon layer and output layer. The frst order two nput Sugeno fuzzy model s employed and t s gven n Fgure 5. The typcal fuzzy f-then rule set for the frst order Sugeno fuzzy nference model can be stated as follows, IF x1s A1AND x2s B1 THEN f1 = pv 1 IG + qi 1 IG + r1 IF x s A AND x s B THEN f = pvig + q IIG + r 1 2 2 2 2 2 2 2 where A and B are the antecedent fuzzy sets and parameters p, q, r are the fuzzy desgn parameters calculated durng the tranng process ( = 1, 2,, n). It s shown n Fgure 7. The mechansm for obtanng the control pulses of rectfer for a gven nput vector [ VIG, I IG ] through ANFIS structure s shown n Fgure 6. The PWM control sgnal generaton for rectfer based on the weght functon s gven below; 2556
B. Meenaksh Sundaram et al. Fgure 4. Surface plot response of FLC. Fgure 5. Two nput frst order Sugeno fuzzy model wth two rules. Fgure 6. Structure of ANFIS. wf + wf 1 1 2 2 Ig = = wf 1 1+ wf 2 2 w1 + w2 The crcular nodes represent nodes that are fxed; whereas the square nodes are nodes that have parameters to be learnt. The structure of the ANFIS and each layer are explaned below. Layer 1: In ths layer, the adaptve nodes wth node functons are ncluded; the node functon s defned and gven n Fgure 6. In Fgure 6, V IG s the nput to the node & A s the lngustc label related to that node functon. The layer1 membershp functon O ndcates the extent to whch the specfed I IG satsfes the quantfer A. Generally, a bell-shaped functon µ A ( V ) IG, whch has a maxmum of 1 and a mnmum of 0, s consdered. It s represented below. (12) 2557
B. Meenaksh Sundaram et al. µ ( V ) 2b 1 VIG c = = exp a 1+ a A IG 2b VIG c where the parameter set {,, } known as premse parameter. Smlarly, the value of ( I ) a b c whch modfes the membershp functons on the lngustc label A s µ B IG s selected. Layer 2: Every node n ths layer s fxed. Ths s where the t-norm s used to AND, the membershp grades (for example the product). The frng strength of the rule s calculated as ( ) ( ) layer 2 A IG B IG (13) O = w = µ V µ I, = 1, 2 (14) th Layer 3: The node of ths layer s used to determne the rato between the frng strength of the and the sum of frng strength of all rules. It s referred as normalzed frng strength. layer 3 w O = w =, 1, 2 w + w = 1 2 Layer 4: The nodes n ths layer are adaptve and perform the consequent of the rules, ( ) layer 4 IG IG th rule (15) O = w f = w pv + q I + r (16) where p, q, r are the parameter set of the layer 3. The term consequent parameters are used to ndcate the parameters of ths layer. Layer 5: By takng the summaton of all the nputs by the sngle node that exsts n ths layer ndcated as Σ, the overall output s calculated. Overall output, w s the output layer 3 and { } O = wf = layer 5 The overall output I g can be expressed as a lnear combnaton of the consequent parameters for the case of fxed premse parameters. The output of Fgure 6 can be represented more precsely as, g 1 1 2 2 wf w (17) I = wf + w f (18) In the proposed control system, the ANFIS s traned by gvng the voltage vector and the current vector of IG as nputs whch determne the desred PWM control pulses for rectfer. Thereby, maxmum power can be extracted from IG. Fgure 7 shows the tranng data for the ANFIS controller. Intally an nput-output membershp functon and 25 fuzzy rule set have to be nvoked from the grd partton of ANFIS concept. Fgure 7. Tranng data for ANFIS controller. 2558
B. Meenaksh Sundaram et al. After generatng the ntal nput membershp functon and fuzzy rules based on the tranng data, fuzzy nference system s traned by the hybrd learnng algorthm of neural network. One hundred epochs have been consdered for tranng and Fgure 8 shows tranng error at the end of tranng. From the tranng error plot, t s evdent that the fuzzy nference system has been well traned wth help of neural network wth mnmum error of 1.24. Fgure 9 shows the testng of traned data wth test data. The proposed ANFIS model structure s shown n Fgure 10. Fgure 8. Tranng error plot. Fgure 9. Testng of traned data wth test data. Fgure 10. ANFIS model structure for grd connected wnd energy system. 2559
B. Meenaksh Sundaram et al. The structure conssts of fve layers. Frst layer s the nput layer and the nputs are error and the rate of change of error. Next layer s the nput membershp functon layer where nputs are dstrbuted wth fve fuzzy sets. Thrd layer s the rule layer where the nputs and outputs are lnked wth AND operator. Fourth layer s the output membershp functon layer where the output s dstrbuted wth ten constant values. Last layer s the output layer whch sums up all the nputs comng from the prevous layer and transforms the fuzzy classfcaton results n to a crsp value. 5. Results and Dscusson The proposed MPPT based control technque has been mplemented n MATLAB envronment and the performances are evaluated. The proposed MPPT controller performance s tested wth an Inducton Generator model wth ratng 480 V, 275 KW. IG current and voltage are appled to ANFIS and the control output s obtaned. Based on the control output, the PWM pulse s generated to control the operaton of the rectfer. The PI current controller provdes swtchng sgnals for a multlevel nverter to reduce the harmonc dstorton n the output. The operaton of the PI current controller s based on the DC lnk voltage, grd current, and grd voltage. The voltage from the IG, rectfer voltage, nverter output voltage and current njected to the grd and current harmoncs are evaluated at dfferent wnd speed. The output voltage of the Inducton Generator and the current njected by the nverter to the grd system are shown n Fgure 11 and Fgure 12. For the consdered wnd speed of 6 m/sec, real power njected nto the grd and the reactve power consumpton have been llustrated n Fgure 13 and Fgure 14. Fgure 15 and Fgure 16 descrbe the output voltage of the controlled rectfer and multlevel nverter. The harmoncs are evaluated at dfferent wnd speed such as 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s, respectvely. The performance of the proposed (ANFIS and PI) control strategy s compared wth the exstng control strategy. The maxmum power extracton wth the change n wnd speed of 6 m/s and 14 m/s has been tested for dfferent control strateges. It s shown n Fgure 17. It s found that the proposed control strategy has extracted maxmum power compared to the exstng strategy. The Maxmum Power extracted at dfferent wnd speed s tabulated n Table 2. The measurement of harmoncs s used to analyze the performance of the proposed control strategy [25]. The current harmonc spectrum of the wnd speed of 6 m/s has been analyzed for dfferent control strateges and they are presented n Fgures 18(a)-(c). Smlarly, the current THD for dfferent wnd speed s analyzed and tabulated n Table 3. Fgure 11. Output voltage of the nducton generator. 2560
B. Meenaksh Sundaram et al. Fgure 12. Injected nverter current to the grd. Fgure 13. Real power njected to the grd at a wnd speed of 6 m/sec. Table 2. Maxmum power at dfferent wnd speed. Wnd speed (m/sec) Maxmum power (KW) Wthout MPPT strategy Fuzzy control strategy Proposed ANFIS based control strategy 6 78.45 80.75 82.5 8 128.8 132.54 135.7 10 177.59 182.34 189.6 12 215.49 220.65 229.8 14 260.89 265.25 274.5 2561
B. Meenaksh Sundaram et al. Fgure 14. Reactve power consumpton at a wnd speed of 6 m/sec. Fgure 15. Output voltage of the controlled rectfer. Table 3. Current harmoncs at dfferent wnd speed. Current THD n % Wnd speed (m/sec) Wthout MPPT strategy Fuzzy control strategy Proposed ANFIS based control strategy 6 10.94 9.65 4.62 8 10.93 9.16 4.60 10 9.63 9.10 4.50 12 8.89 8.39 3.69 14 8.31 8.20 3.17 2562
B. Meenaksh Sundaram et al. Fgure 16. Output voltage of the multlevel nverter. Fgure 17. Maxmum power at wnd speed of 6 m/s and 14 m/s for dfferent control strateges. 2563
B. Meenaksh Sundaram et al. (a) (b) (c) Fgure 18. (a) Current Harmoncs (THD) at 6 m/s for wthout control strategy; (b) Current Harmoncs (THD) at 6 m/s for fuzzy control strategy; (c) Current Harmoncs (THD) at 6 m/s for proposed control strategy. The bar chart s used to analyze the devaton of the proposed control strategy whch s represented n Fgure 19. In Fgure 20, the current THD devaton has been compared wth dfferent control strateges. The proposed control strategy provdes the current harmoncs THD of 4.62%, fuzzy based control strategy has provded 9.65% 2564
B. Meenaksh Sundaram et al. Fgure 19. Current THD n percentage. Fgure 20. Current THD devaton n percentage. and wthout an MPPT control strategy has provded 10.94% at a wnd speed of 6 m/s. Hence, the power qualty parameters of current njected nto the grd connected system have drastcally mproved. Also, the proposed control strategy performs well at dfferent wnd speed wth reduced current harmoncs compared to the exstng control strategy. 6. Concluson The performance of the multlevel nverter based grd-connected wnd energy system has been evaluated by usng the proposed ANFIS-based power extracton controlled strategy. The output current of multlevel nverter has been njected to the grd, and maxmum power extracted from the wnd and current harmoncs have been nvestgated for dfferent wnd speeds. The proposed control strategy has extracted the maxmum output power of 274.5 KW wth reduced current harmoncs of 3.17% at a wnd speed of 14 m/s. The current THD devaton of the proposed strategy has been compared wth the exstng control strategy. The comparatve analyss hghlghts that the proposed control strategy has less harmoncs and extracts maxmum power compared to the exstng control strategy. 2565
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