Available online at ScienceDirect. Procedia Computer Science 58 (2015 )

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1 Available lie at ScieceDirect Prcedia Cmputer Sciece 58 (2015 ) Secd Iteratial Sympsium Cmputer Visi ad the Iteret (VisiNet 15) Applicati f Prbabilistic Neural Netwr i Fault Diagsis f Wid Turbie Usig FAST, TurbSim ad Simuli Hasmat Mali ab *, Suumar Mishra a a Departmet f Electrical Egieerig, Idia Istitute f Techlgy Delhi, New Delhi , Idia b ICE Divisi, Netaji Subhas Istitute f Techlgy, New Delhi , Idia This paper presets a itelliget diagsis techique fr wid turbie imbalace fault idetificati based geeratr curret sigals. Fr this aim, Prbabilistic Neural Netwr (PNN), which is pwerful algrithm fr classificati prblems that eeds small traiig time i slvig liear prblems ad applicable t high dimesi applicatis, is emplyed. The cmplete dyamics f a permaet maget sychrus geeratr (PMSG) based wid-turbie (WTG) mdel are imitated i a amalgamated dmai f Simuli, FAST ad TurbSim uder six distict cditis, i.e., aerdyamic asymmetry, rtr furl imbalace, tail furl imbalace, blade imbalace, acelle-yaw imbalace ad rmal peratig scearis. The simulati results i time dmai f the PMSG statr curret are decmpsed it the Itrisic Mde Frequecy (IMF) usig EMD methd, which are utilized as iput variable i PNN. The aalyzed results prclaim the effectiveess f the prpsed apprach t idetify the healthy cditi frm imbalace faults i WTG. The preseted wr reders iitial results that are helpful fr lie cditi mitrig ad health assessmet f WTG The Authrs. Published by by Elsevier B.V. B.V. This is a pe access article uder the CC BY-NC-ND licese ( Peer-review uder respsibility f rgaizig cmmittee f the Secd Iteratial Sympsium Cmputer Visi ad the Peer-review Iteret (VisiNet 15). uder respsibility f rgaizig cmmittee f the Secd Iteratial Sympsium Cmputer Visi ad the Iteret (VisiNet 15) Keywrds: TurbSim, FAST, Simuli, EMD, ANN, Wid Turbie, Fault Diagsis, Cditi Mitrig, Imbalace Faults, Curret Sigals; The wid geeratig pwer utilizati has prliferated widely i the previus decade i the wrld ad acrss the Idia. The istalled pwer idustry f i Idia is 22465MW upt December 2014, which is a w ra 5 th i the * Crrespdig authr. Tel.: address: hasmat.mali@gmail.cm The Authrs. Published by Elsevier B.V. This is a pe access article uder the CC BY-NC-ND licese ( Peer-review uder respsibility f rgaizig cmmittee f the Secd Iteratial Sympsium Cmputer Visi ad the Iteret (VisiNet 15) di: /j.prcs

2 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) wrld after Chia, USA, Germay ad Spai [1]. As a result, wid turbie idustry is beig grw up ctiuusly, ad becmes mre challegig fr pwer egieers t d cditi mitrig ad health assessmet f wid turbies (WTs). I geeral, every WT is uder shut-dw cditi fr 0.595% t 2.705% time perid f a year [2]. This shutdw cditi is due t the istallati errrs, maufacturig defects r effects f agig, asty evirmetal cditi ad perturb ladig sceari experieced by WT apparatus. There are differet types f failures ccurs i the WTGs, i.e., failure f cmpets, ctrl system, grid failure due wea cectis, failure due t high wid, lighteig, lseig f part ad icig, geeratr, turbie blades, brae system, axle bearig, hydraulic system, pitch mechaism, gear bx ad yaw system etc. [2]. The faults due t imbalace frm a majr part f all faults i WTGs [2]. The imbalace faults i blade, shaft ad furl ad aerdyamic asymmetry are cmm imbalace faults i WTGs. The mai causes f a blade imbalace are errrs i cstructi r maufacturig, icig cditi, degradati due t agig, r wear ad fatigue i WTG the perati. Due t imbalace blades ad rtatig shaft, equipmets gravitate t shift ad wear i varyig degree ver time. Fr example, effect f icig cditi ca develp a blade imbalace due t icreasig extra burde by lads WT supprtig twer, which may create fractures ad pssible t cllapses [3] the twer. The aerdyamic asymmetry is due t assrted reass, ctaiig errrs i the ctrl mechaism ad high wid shear. Fr example, due t the errr i ctrl system, the pitch agle f ay e blade is slightly chaged frm remaiig tw. This causes the aerdyamic asymmetry i the WTGs. Furl imbalace faults ca be caused by chagig i iitial r fixed rtr/tail-furl agle i degree [4-5] which create the imbalace i tail ad rtr part f the WT. Hece, a fault due t small imbalace ca surce f csequeces the WT, twers ad fially WTGs. S, effective cditi mitrig ad fault diagsis f WTGs is becme mre advatageable t reduce repairig cst ad ehace peratig life with safety f catastrphic failure cditi [6]. Geerally available techiques fr imbalace faults idetificati require additial vibrati sesrs (i.e., accelermeters) ad data acquisiti system [5]. These vibrati sesrs are placed the WT equipmet s surface, which are very difficult t access due t high height f twer durig WTGs perati. Furthermre, the cmpets ad sesrs are aturally subject t failure, ad lead extra prblems related t stability ad reliability f system ad extra csts fr maiteace. S, curret sigature based (withut mechaical sesrs) fault idetificati appraches becme mre reliable, which utilize geeratr curret measuremet apprach ly, ad data acquisiti device r sesrs is required. Measured curret sigatures have bee utilized by the WTGs ctrl system. Curret sigals are mre reliable fr cditi mitrig f WTGs ad easy accessible withut icludig the WT. As a result, Curret sigature based imbalace fault idetificati appraches have ermus ecmic prfit. I this paper, the applicati f simulatis is ivestigated t aalyze the WTs faults due t imbalace cditis usig PNN techique. The cmplete WTG mdel are desig i a itegrated dmai f Simuli [8], FAST [4], ad TurbSim [9], where TurbSim is used t prduces the wid data, cmplete wid turbie dyamics is simulated by FAST (Fatigue, Aerdyamics, Structures ad Turbulece), ad MATLAB based Simuli sftware imitates the dyamics f the electrical geeratr ad related equipmets f the WTGs. Simulati bservatis are the carried ut i six distict cditis, i.e., aerdyamic asymmetry, rtr furl imbalace, tail furl imbalace, blade imbalace, acelle-yaw imbalace ad rmal peratig scearis. The simulati results i time-dmai f the WTG utput statr curret are decmpsed it -umber f IMF usig EMD techique ad aalyzed usig PNN techique. Results represet that a variati appears at the eergy etrpy amplitude f the WT i the IMF f geeratr curret sigature i the imbalace fault cditi. This study presets iitial results that are helpful fr lie cditi mitrig ad imbalace faults idetificati f wid turbie. This article is rgaized as fllws: The itrducti ad literature review is give i Secti 1. The dyamical mdel develpmet alg with the database geerati is preseted i secti 2. The prpsed methdlgies used are represeted i Secti 3. The results are preseted ad discussed i Secti 4, ad a cclusi is give i Secti 5. b biases t target fucti w utput weight φ t 1 φ p utput des E e eergy etrpy T e electric trque yt () sigals i P e pwer w iput weight χ t 1 χ iput des

3 188 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) Brief detail f WTG mde The vigrus mdel f a 10KW wid turbie geeratig system is develped i a amalgamated dmai f thee sftware (i.e., TurbSim ad FAST f NREL ad Simuli f MATLAB), as represeted i Fig. 1. The FAST is a cmprehesive aerelastic simulatr. It is cmpetet f frecastig bth the fatigue ad extreme lads f 2 & 3 bladed hriztal axis WTs. The TurbSim (versi ) is used t geerate wid data which is utilized i FAST. The FAST (versi 7.0) is utilized t desig the dyamics f the liear WT, whereas Simuli f MATLAB (versi R2014a) is utilized t simulate the electrical geeratr ad ther equipmets f the WTGs. I the WTGs mdel, FAST perfrms as a subrutie i Simuli. Three sigals f rtatig speed (w i rad/s), electric pwer (Pe i Watt) ad electric trque (Te i Nm) are utilized t li the Simuli ad FAST mdels f WTGs. WTG mdel i cmbied simulati platfrm f FAST ad Simuli Cmplete arragemet f WTG mdel i cmbied simulati platfrm f TurbSim/FAST/Simuli. The WT mdel i FAST cmmly icrprates supprt platfrm, shaft, blades, twer ad furl. The parameters f the WT icludes: hub height f twer abve grud is 34 m, 3 umbers f blades f upwid cfigurati with rtr diameter f 2.9m, acelle ad hub mass is 260.5Kg ad 113g respectively. The meas f upwid arragemet is that the blades are upwid f the twer. A PMSG f 48-ples is imitated i Simuli dmai t cvert the mechaical eergy frm WT it electrical eergy (Pe). Three phase utput sigals f curret ad vltage i time dmai are recrded fr the further aalysis f WTGs cditi whether it is i rmal peratig cditi r faulty cditi. The Fig. 2 represets the cmplete arragemet f the WTGs mdel alg with wid velcity data WTG system imbalace fault imitati The FAST is utilized t simulate the whle dyamics f WT mdel i five imbalace fault cditis ad rmal peratig sceari. These five imbalace fault states are aerdyamic asymmetry, blade/shaft imbalace, acelleyaw agle imbalace, tail furl imbalace ad rtr furl imbalace. The Blade/shaft imbalace happe due t the mass f the WT cmpets are t uifrmly allcated w.r.t. the rtr. The mai reas fr blade imbalace are errr i cstructi, maufacturig, wear ad fatigue durig the perati f WT r ubalace icig cditi the surface f blades. Furl imbalace fault is ther e imbalace fault i WT, which are rtr-furl imbalace ad tail-furl imbalace. This type f imbalace fault is due t chage i furl agle psiti frm the required psiti due t this WT get spiig t quicly, ad turig the blades away frm the directi f the wid, either vertically r hriztally t save the system frm cllapse durig high wids. This type f imbalace attempt t twist the geeratr shaft ad i extreme cases ca lead t air-gap clsure i the geeratr, the ultimate csequece f which is a maget cllisi with the statr. The acelle-yaw system f WTs is the cmpet respsible fr the rietati f the WT rtr twards the wid. Nacelle-yaw agle imbalace is due t chage i the iitial r fixed yaw agle psiti frm the required psiti due t this rietati f WT rtr is chaged. The aerdyamic asymmetry imbalace fault i WT is due t the several reass icludig variati i pitch agle f the blades, errr i ctrl system ad high wid shear, which creates ueve distributi f the trque amg 3 blades. Fr example, whe agle f e blade pitch is chaged slightly tha ther 2 blades, the electrmagetic trque (Te) rtatig shaft is ubalaced, which creates a aerdyamic asymmetry i WTGs. Files ad its parameters f FAST sftware used fr simulatig furl imbalace, blade imbalace, ctrl errr i yaw system, aerdyamic asymmetry are listed i Table 1.

4 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) Files ad Its Related Parameters Utilized fr WTG Fault Simulati WTG fault type File ame Parameters utilized Ctrl errrs f yaw system Test17.fst NacYaw Blade imbalace SWRT_Blade.dat AdjBlMs Aerdyamic asymmetry Test17.fst BlPitch Rtr furl imbalace Test17.fst RtFurl Tail furl imbalace Test17.fst TailFurl I this paper, five imbalace faults cditis are simulated ad aalyzed with the rmal peratig cditi f WTGs. The time-series utput dataset f pwer, curret ad vltage f the WTGs are recrded fr apiece simulati. The the recrded data sets are decmpsed it IMF by usig EMD methd, which has bee explaied i fllwig secti Data set geerati fr study Fr preparig the data set, simulatis are executed fr WTGs i five imbalace fault cditis as well as healthy sceari. The imbalace i blade is created by chagig the mass desity f ay e blade, which prduces a -uifrm distributi f mass w.r.t. rtr. Three cditis are imitated with mass desity f e blade mdified by icreasig 2%, 5% ad by decreasig 3%, while mass desity f ther tw blades are remai same. The furl imbalace is simulated by adjustig the rtr/tail furl agle, which creates ueve directi f the WT frm the wid directi. I rder t imitate furl imbalace fault f WT, the rtr/tail furl agle is adjusted by 10,-5 ad 5 degree apart frm required psiti. The acelle-yaw imbalace cditi is simulated by chagig yaw agle psiti (by icreasig 10 ad 20, ad by decreasig by -10 ), which creates ueve rietati f WT rtr twards the wid. The asymmetry i aerdyamic is created by chagig the pitch agle f e blade, which geerates a uifrm trque alg the rtr. Three cditis are imitated with pitch agle f ay e blade mdified by icreasig 5 ad 10, ad by decreasig by -8, while pitch agle f ther tw blades are remai same at Each simulati rus fr 40 secds with samplig frequecy f 2000Hz. The utput ifrmati f wid speed, electric pwer, statr curret f PMSG, ad turbie shaft tque are recrded fr each case, ad are pltted i Fig. 3a- 3d fr healthy cditi. (a) (b) (c) (d) The utput ifrmati f (a) wid speed, (b) statr curret f PMSG, ad (c) electric pwer, ad (d) shaft rtatig speed 3.1. Empirical Mde Decmpsiti The EMD apprach is a data depedet, adaptive apprach. This apprach des t etail ay situati related t liearity ad statiarity f the sigal. Mai applicati f EMD apprach is t decmpse the statiary ad liear sigal y(t) it a umber f IMFs. Each IMF must satisfy fllwig 2-cditis [17-18]. 1) Fr a give dataset, bth the umber f zer crssigs ad the umber f extrema must either be equal r differ at mst by e. 2) At ay pit, the mea value f the evelpe defied by the lcal miima ad defied by lcal maxima is zer. The brief explaati f the EMD apprach is preseted step-wise fr the cmprehesi f the researcher as give bellw. 1) Lad the sigal yt (). 2) Determie the extrema (miima & maxima) f the data set yt (). 3) Cect the miima ad maxima idividually with cubic splie iterplati 4) Geerate the upper evelps em () t ad lwer evelps et () t. 5) Determie the lcal mea value f the evelpe: at () = [ em() t + et()]/2 t. 6) Determie the differece betwee rigial sigal data ad mea value f the evelpe: H 1 () t = y () t a () t. 7) If H () t satisfy the tw IMF cditi, the H () t is the 1 1 1st IMF. Else H () t is t a IMF, the 1 H () 1 t treated as a rigial sigal ad repeat the prcedure frm 1 t 6.

5 190 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) After repeated shiftig upt -time, H ( ) becme a IMF as 1 H1( ) = H1( 1) a( t). 8) Defie smallest tempral scale i yt (): Ω 1() t = H1 () t. Where Ω 1 () t is the 1 st IMF cmpet frm the rigial sigal. 9) Determie the residue: ψ 1 () t = y() t Ω 1 () t. Nw csider the ψ 1 () t as the rigial sigal data set ad repeatig the abve prcedure fr fidig the 2 d IMF. 10) The abve prcedure is repeated i -times t get IMFs f the sigal yt (). The prcedure ca be stpped whe ψ () t 1 becme a mtmic fucti frm which mre IMF ca be extracted. Fially, after cmplete decmpsiti f the sigal, the rigial sigal yt () is expressed as: L yt () = Ω l() t + ψ L() t (1) l = 1 th Where L=umber f IMFs; Ω l () t = l IMF ad ψ L () t =fial residue. All IMFs f equati (1) is suppsed t yield a meaigful lcal frequecy, ad ulie IMFs d t exhibit same frequecy at the same time, The equati (1) ca be represeted as: L l = 1 yt () A()cs[ t φ ()] t (2) l l The MATLAB cdes fr EMD decmpsiti are available at [10]. The geerated IMFs by EMD apprach the healthy ad faulty sigal are shw i Figs 4 ad its eergy distributi i Fig. 5. Frm the eergy distributis f IMFs, we ca differetiate the differece betwee the rmal ad imbalace fault cditi f WTGs. Additially, the eergy etrpies have bee calculated usig Eq. (3) are represeted i Table 2. It is see that the etrpy f the imbalace fault cditi is differ frm that f the rmal e fr IMFs. Used eergy etrpy i this paper is defied as N E = p lg p (3) e = 1 where, p = E E is the percetage value f eergy f the th IMF f EMD i the whle sigal eergy E, where N E = E. = 1 Eergy etrpies f recrded rmal ad imbalace fault sigals f WTGs Methd Nrmal perati cditi Imbalace cditi EMD (a) (b) EMD decmpsiti results: (a) rmal perati cditi ad (b) imbalace fault cditi

6 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) (a) (b) Eergy distributi f 10 IMFs: (a) rmal perati; (b) imbalace cditi Iput Variable Selecti The relevat iput variable selecti is a imprtat part fr PNN mdel frmati i imbalace fault diagsis fr WTGs. RapidMier (versi 5.2) based PCA algrithm [11-13] is used fr feature selecti as explaied i detail i referece [16]. It is fud that IMF1 t IMF10 are havig higher ra ut f geerated 17 IMFs by EMD methd, shwig these are mst ifluecig variables Prbabilistic Neural Netwr (PNN) The desig f the PNN mdel has bee described i Fig. 6, which ctais 3 layers: the iput layer, hidde layer ad utput layer. The hidde layer csists f a activati fucti applied t the distace betwee the uw iput & the traiig sample. As a example, the iput vectr α = [α 1, α 2 ] is applied t iput des χ 1 t χ 2. I the hidde layer, the etwr ctais 5 des, γ 1 t γ 5, crrespdig t five examples with weights attached t iput des. Output weights are give 1 f 2 values, 1 represets a faulty cditi whereas 2 sigifies the ppsite. Weights betwee hidde des & utput de ϕ 1 are desiged t allw ϕ 1 t cmpute the sum f all prbabilities crrespdet t the 1 st categry ly frm β 1 = (γ 1 + γ 2 )/ (γ 1 + γ 2 + γ 3 + γ 4 + γ 5 ), i.e. emulatig the Bayesia cfidece i decisi maig. Nw we exted the PNN desig t iput des (χ 1 t χ ), hidde des & p utput des (ϕ 1 t ϕ p ). The desig prcess csists f 2 mai steps: the learig stage & the recallig stage, which is explai bellw: Geerate iput weight (ω I ) betwee iput de (χ i ) & hidde de (γ ) fr every traiig sample (α ): I ωi = αi ( ) (4) Where i = 1, 2,3, 4,5,... ad = 1, 2, 3, 4,5,... I I the & iput weight ω is the by matrix = [ ω i ] x traiig samples are : α() = [ α1(), α2(), α3( )... αi(),... α ()]. Geerate utput weight (ω O ) betwee hidde de (γ ) ad utput de (φ j ) : O { 1, Categry ω 1 j = 0, Categry 2 (5) Where the umbers 1 & 2 represet the categry f the sample. where j = 1, 2, 3,....., p utput weight ω O O is give by the by p matrix = [ ω j ] xp ad the umber f traiig samples by, dimesis f χ by & dimesis f β by m. Applyig the test vectr (α test ) t the etwr: αtest = [ α1, α2... αi,... α ] Calculate the prbability f test vectr (α test ) by meas f the Gaussia activati fucti: I 2 et = ( αi ωi) (6) et γ = exp( ) (7) 2 2ν where ν is smthig factrs, ν 1 = ν 2 = ν 3 =.....= ν = ν The distace betwee the test vectr ad all the traiig samples is used fr the Gaussia fucti. Calculate the prbability f ϕ j as the sum f O j ωjγ = 1 φ =, fr categry1 (8) Nrmalize the utput prbability by dividig the sum by γ,. The utput prbability P j is: φ j Pr bpj = γ = 1 (9)

7 192 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) Stages 1 & 2 are categrized as the learig stage. Stages 3 t 6 are termed as the recallig stage. I the learig stage, the etwr creates the iput weight (ω I ) ad utput weight (ω O ). The recallig stage is where it tests the data & cmputes the prbability fr the test vectr PNN based imbalace fault idetificati mdel frmati The classifier mdel used fr imbalace fault diagsis i WTGs is desiged usig te iputs (first te IMFs selected by PCA methd).the diagsis mdel is desiged usig the afremetied traiig cases which icludes 3000 healthy ad faulty dataset. These data samples, alg with their matchig target vectrs, are stred i the PNN Data Base. The PNN is a architecture f three layers alg with 10 iputs α 1 t α 10, 2 utputs β 1 t β 2 ( healthy ad imbalace fault cditi) ad hidde des γ 1 t γ (i.e. beig equal t umber f traiig data samples). A graphical represetati fr the result is btaied usig MATLAB is shw i Fig. 7 f secti 4. The perfrmace f prpsed mdel fr imbalace fault idetificati is examied by evaluatig measures after implemetig required mdificatis. Imbalace fault idetificati accuracy (IFIA), Crrectly Classified Samples by mdel IA = (10) Ttal Number f Samples Dataset Mea Squared Errr (MSE), 1 2 MSE = ( E ) ; Where Eq = Tq OAq (11) q = 1 q Where = umber f samples i data set, T q =target value ad OA q =actual mdel utput btaied frm the traied ANN based MLP-classifier. Mea Abslute Percetage Errr (MAPE), 1 Actul Fault Type - Predicted Fault type by md el MAPE = 100 (12) = 1 Actual Fault Type The IA, MSE ad MAPE are evaluated with Eq. (10), (11) ad (12) as represeted i Table 3 f secti 4. Fur PNN mdels are desiged usig differet iput variables. Three-phase statr curret (48000x3) ad vltage (48000x3) is used as iput variable i mdel 1 ad mdel 2 respectively. I mdel 3, a cmbiati f 3-phase vltage ad curret sigature (48000x6) is used as iput variable. Thereafter, mdel 4 is desiged usig prpsed iput variables ad the perfrmace aalysis f each mdel is aalyzed as shw i table 3. The predicti accuracy f ANN i term f MAPE is represeted by A. K. Yadav et al. [15]. The maximum MAPE fr prpsed mdels at prpsed iput variables are fud t be 2.008%, shwig that after remvig less ifluecig parameters, the classificati accuracy is icreased upt 98.04% as shw i Table 3. Therefre RapidMier based PCA algrithm ca be utilized fr idetifyig the relevat iput parameters fr imbalace fault idetificati f WTGs. PNN based accuracy aalysis f fault diagsis mdel PNN mdel ad it data set MAPE MSE RMSE Successful idetificati (%) 3-phase curret sigature based PNN mdel [48000x3] phase vltage sigature based PNN mdel [48000x3] phase vltage & curret sigature based PNN mdel [48000x6] Prpsed PNN mdel [48000x10] As examples f the id f graphical results f prpsed methd fr WTGs imbalace situati that are idetified by PNN usig matlab (Matlab R2014a) [8] are shw i Figs. 7. The traiig samples fr WTGs are shw usig blue clred dts ad the ew sample after testig is represeted by usig red clred dts.

8 Hasmat Mali ad Suumar Mishra / Prcedia Cmputer Sciece 58 ( 2015 ) PNN Architecture PNN based imbalace fault diagsis. The detecti f the wid turbie imbalace fault cditi ad healthy cditi crrectly ad fast has great imprtace i the tred f imprvemet f the wid turbie perati ad its maiteace level as well as icreasig reliability f sustaiable ctiuati f pwer supply. I this paper, PNN is emplyed fr fault diagsis based geeratr curret sigals. The actual data sets, which are btaied frm simulated WTGs mdel i cmbie evirmet f three sftware (FAST, TurbSim ad Simuli) after ru fr 40 secd with samplig frequecy f 2000 Hz, are used t ivestigate perfrmace f the prpsed methd. PNN mdels are btaied, validated ad tested i rder t fid the healthy cditi f WTGs. The simulated results idicate that the PNN ca achieve bth higher diagsis accuracy ad less traiig/testig time tha ther ANN methds ad als it has better diagsis accuracy tha the cvetial methds. The future wr is fcused categrizig the differet type f imbalace faults ad predictig the WTGs perati cditi usig mst relevat iput variables. 1. The Gvermet f Idia MNRE. (2015, Feb.) Wid Pwer Prgramme Reprt [Olie]. Website accessed 15th February Available: 2. J. Ribrat, Reliability Perfrmace ad Maiteace A survey f Failures i Wid Pwer Systems, Master s thesis, KTH Schl f Electrical Egieerig, S. A. Saleh ad C. R. Mley, Develpmet ad testig f wavelet pacet trasfrm-based detectr fr ice accreti wid turbies, i Prc. IEEE Digital Sigal Prcess. Wrshp IEEE Sigal Prcess. Educati Wrshp, Ja. 2011, pp J. M. Jma ad M. L. Buhl, FAST User s Guide. Glde, CO: Natl. Reewable Eergy Lab., Jul NWTC Ifrmati Prtal (TurbSim), Accessed 01-March M. Zha, D. Jiag, ad S. Li, Research fault mechaism f icig f wid turbie blades, i Prc. Wrld N-Grid-Cected Wid Pwer Eergy Cf., Sep. 2009, pp Xiag Gg ad Wei Qia, Imbalace Fault Detecti f Direct-Drive Wid Turbies Usig Geeratr Curret Sigals, IEEE Tras. Eergy Cvers., vl. 27,. 2, pp , Ju Xiag Gg ad Wei Qia, Simulati Ivestigati f Wid Turbie Imbalace Faults, i IEEE Prc. It. Cf. Pwer System Techlgy (POWERCON), pp. 1-7, Oct MATLAB User s Guide. The MathWrs, Ic., Natic, MA 01760, < 9. B. J. Jma, TurbSim User's Guide (v ). Natial Reewable Eergy Labratry, September-2012, NWTC Ifrmati Prtal (TurbSim). Accessed 01-March E. Huag, Z. She, ad S. R. Lg, The empirical mde decmpsiti ad the Hilbert spectrum fr liear ad -statiary time series aalysis, i Prc. Ryal Sciety f Ld Ser., 1998, vl. 454, pp RapidMier Maual by Rapid-I GmbH. Olie resurce: Accessed 01-March Marius Helf ad Nils Whler. Rapid Mier: Advaced Charts, Rapid-I GmbH. 13. Rapid Mier: Data miig sftware. istall.exe. 14. K. L. Du, M.N.S. Swamy. Neural Netwrs i a Sft cmputig Framewr. A B f Spriger publicati, Verlag Ld Limited Amit Kumar Yadav, Hasmat Mali, S.S. Chadel, Selecti f mst relevat iput parameters usig WEKA fr artificial eural etwr based slar radiati predicti mdels, Reewable ad Sustaiable Eergy Reviews, Vl. 31, March 2014, PP Hasmat Mali, Suumar Mishra, Feature selecti usig RapidMier ad classificati thrugh prbabilistic eural etwr fr fault diagstics f pwer trasfrmer, i Prc. IEEE It. Aual Cf. INDICON-2014, pp. 1-6, M. Feldma, "Aalytical basics f the EMD: Tw harmics decmpsiti," Mech. Syst. Sigal Prcess., vl. 23,. 7, pp , M. Feldma, "Time-varyig vibrati decmpsiti ad aalysis based the Hilbert trasfrm," J. Sud Vib., vl. 295,. 3 5, pp , 2006.