06153364

1638 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013

A Twofold Daubechies-Wavelet-Based Module forFault Detection and Voltage Regulation in SEIGs for

Distributed Wind Power GenerationK. Lakshmi Varaha Iyer, Student Member, IEEE, Xiaomin Lu, Student Member, IEEE, Yasir Usama,

Vamsi Ramakrishnan, and Narayan C. Kar, Senior Member, IEEE

Abstract—As Canada and the world move rapidly toward in-creased reliance on wind power generation, self-excited inductiongenerators (SEIGs) will play an important role in distributedwind power generation (DWPG). Understanding the significanceand prospects of SEIGs in DWPG, first, this paper elucidatesthe significance of fault detection (FD) and voltage regulation(VR) in the aforementioned application. A comprehensive analy-sis of VR and faults on niche industrial 7.5-hp copper-rotorSEIG and conventional 7.5-hp aluminum-rotor SEIG is performedthrough numerical simulations, and the calculated results arevalidated through experimental investigations. Second, a twofoldDaubechies-wavelet-transform-based module is designed for thefollowing: 1) FD and 2) VR, respectively. A discrete-wavelet-transform-based algorithm is proposed and implemented on alow-cost embedded system to provide an economical solutionfor the aforementioned issues. Thereafter, the aforementionedschemes are tested, and results are investigated.

Index Terms—Aluminum-rotor machine, copper-rotor ma-chine, fault detection (FD), self-excited induction generator(SEIG), voltage regulation (VR), wavelet transforms (WT).

I. INTRODUCTION

CANADA’s centralized energy infrastructure is becom-ing more challenging as the demand for clean, reliable,

and affordable electricity generation grows. North America’scentralized grid system stressed to its limits [1] has becomevulnerable and increasingly brittle [2]. Overreliance on large,polluting, and expensive generation and transmission is nolonger an option that Canadians would endorse. Rapidly,centralized generation is being supplemented or replaced bydistributed generation (DG), a new way of thinking aboutelectricity generation, transmission, and distribution [3]. Windpower is becoming increasingly popular and is the fastestgrowing generation form because of its promising potential fordevelopment. Distributed wind power generation (DWPG) has

Manuscript received May 2, 2011; revised December 8, 2011; acceptedJanuary 15, 2012. Date of publication February 16, 2012; date of currentversion November 22, 2012.

K. L. V. Iyer, X. Lu, and N. C. Kar are with the Centre for Hybrid AutomotiveResearch and Green Energy, University of Windsor, Windsor, ON N9B 3P4,Canada (e-mail: [email protected]; [email protected]; [email protected]).

Y. Usama is with the SNC–Lavalin, Toronto, ON M9B 6E2, Canada (e-mail:[email protected]).

V. Ramakrishnan is with Robert Bosch Engineering, Coimbatore 641035,India (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIE.2012.2188258

become a growing market area, as people are trying to reducetheir dependence on the conventional power grid.

Case studies from [4] and [5] show some examples ofisolated wind farms in Canada. Ramea Island, about 10 kmoff the south coast of Newfoundland, Canada, which is thehome to approximately 700 inhabitants, used one or more ofthe three installed 925-kW diesel generators, with an averagefuel efficiency of around 4 kWh/L, to meet its electricity re-quirements. The latest island wind farm installed there producesaround 10%–13% of the 4.3 GWh consumed annually by thecommunity, thus reducing the amount of fuel purchased fordiesel generators. The Yukon Energy Corporation of Canadainstalled a 150-kW wind energy generation system on HaeckelHill, a shoulder of Mt. Sumanik, at an altitude of 1430 m,approximately 750 m above the valley floor where the terri-tory’s capital, i.e., Whitehorse, is located. The Whitehorse grid,which is isolated from Canada’s national electrical grid, alsohosts 0.8-MW wind turbine capacity, provided at Haeckel Hill.The rolling prairies of Alberta, between Calgary and Red Deer,are one of the most productive agricultural areas in WesternCanada. A wheat farmer, who wanted independence from theelectric utility, purchased a 10-kW wind turbine to supplyall of his power requirements. The Trochu Wheat Farm wasalready connected to a power grid, but the farmer’s goal wasa stand-alone system that would survive inflation and have lessenvironmental impact in comparison to the coal used to produceelectricity for the grid. The farm’s wind energy system alsosupplies power to a residence for a family of four, a machineshop, a water well, and yard lights.

These wind turbines use permanent-magnet or self-excitedinduction generators (SEIGs) to supply small demands ef-fectively. An SEIG is an ideally suited electricity-generatingsystem for DWPG as it becomes tedious and highly expensiveto lay transmission lines over or under water, through mountain-ous areas and across long distances. A stand-alone SEIG drivenby wind turbine is capable of supplying power to domestic,industrial, and agricultural loads, particularly in the remote andhilly areas where conventional grid supply is not available.Installation of SEIG reduces the high maintenance and installa-tion costs as large amounts of metal and raw material use canbe minimized and reduces the infrastructure and transmissionlosses which occur when regular power grids or transmissionlines are installed. Self-excitation in induction machines withcapacitors at their stator terminals, although known for more

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IYER et al.: TWOFOLD DAUBECHIES-WAVELET-BASED MODULE FOR FD AND VR IN SEIGs FOR DWPG 1639

than half a century, is still a subject of considerable attention.Interest in this topic is primarily due to the application of SEIGsin autonomous wind power generation. Over the years, SEIGhas emerged as an alternative to the conventional synchronousgenerator for such applications [6].

Most of the SEIGs use less efficient aluminum rotors becausefabrication by pressure die casting is a well established andeconomical method. Conventional wisdom states that copperconductors are the most reliable and outperform their aluminumcounterparts, since the electrical conductivity of copper is 60%more than aluminum. Recent developments in the die castingprocess that produces copper rotors can easily increase themotor efficiency by up to 2.1% [7]. Air pollution would alsodecrease as a direct result of reduced energy consumption. Useof copper rotors can also reduce motor operating temperaturesby 5 ◦C–32 ◦C [8]. As a general rule, for every 10 ◦C increasein the motor operating temperature, the insulation life of themotor is halved. Therefore, these data indicate that the lifetimeof motors using copper rotors may be extended by 50% or more,with proper maintenance.

The nameplate efficiency of a practical in-service 15-hp1800 r/min aluminum-rotor induction machine today is about89.5%, which is below the 1997 Energy Policy Act standard of91%. As demonstrated by many other researchers, the adoptionof copper rotors should bring efficiencies to the 94%–96%range exceeding the requirements of today’s premium effi-ciency motor, nominally 93% [9]. In addition, analyses bymotor manufacturers have shown that copper rotors can beemployed to reduce overall manufacturing costs at a givenefficiency or to reduce motor weight, depending on whichattribute the designer chooses to emphasize. The potentialenergy savings achievable through the use of copper rotors issubstantial. The U.S. Department of Energy reports that motorsabove 1/6 hp use about 60% of all electricity generated in theUnited States and the medium-power motors (1–25 hp) arethe favored candidates for conversion to copper rotors [10].In Canada alone, 1% increase in the motor electrical energyefficiency would save roughly $200 million and, as a result,0.5 million barrels of oil annually. As Canada and the worldmove rapidly toward increased dependence on wind powergeneration, aluminum and copper can play an important role inthe rotor construction of SEIG [11]. Hence, both the aluminum-and copper-rotor induction machines have been used here in theinvestigations.

Major challenges in an SEIG are its poor voltage and fre-quency regulation, since there is no separate dc excitationsystem. As a load is applied, the reactive power supplied bythe capacitance of the parallel combination of the excitationcapacitance and the connected load must match the reactivepower demanded by the machine as dictated by the magnetizingcurve. In other words, the reactive power, required by the ma-chine to maintain self-excitation, and the load must be providedsolely by the excitation capacitor. Consequently, as the load isincreased, there is a decrease in magnitude of the terminal volt-age and frequency. Various techniques for improving voltageregulation (VR) such as a switched capacitor scheme, electronicload controllers, variable reactive power controllers, and othersolid-state controllers are reported in the literature [12]–[21],

which improve the performance of an SEIG significantly butinvolve complex and expensive control hardware.

Another challenge in DWPG systems using SEIGs is faultdetection (FD) in the system. Faults across the high-voltageterminals of the generator lead to economic losses and poweroutages. Forced outages are the primary concern of the remote-area consumer for causing economic duress. The SEIG isattractive for DWPG as the terminal voltages of the systemcollapse during short-circuit faults, and hence, the excitationof the machine is cut off driving the machine to just runfreely at the wind turbine rotor speed. This makes SEIG moreattractive for such power generation as the machine is faulttolerant and the general consumer does not have to worry aboutreplacing or repairing the machine. However, it is necessary forthe fault to be detected and communicated to the operator inorder to resume operation after fault inspection and clearance.Fault here cannot be detected using conventional schemes usingovervoltage sensors or overcurrent sensors as the voltage andcurrent collapse within a few cycles during a fault. Fast andaccurate FD will not only render immediate corrective actionbut also protect sensitive equipment along the line and preventthe domino effect. Thus, it is of vital importance to rapidlydetect and identify power system faults, assist the task of repairand maintenance, and reduce the economic effects of powerinterruption.

Understanding the significance of FD and VR in distributedwind DG system using an SEIG, this paper proposes an ex-clusive dual-purpose wavelet-based FD and VR schemes. Acomprehensive investigation of the developed algorithm hasbeen performed through a developed computer program andan experimental setup consisting of a low-end embedded sys-tem. This economic solution can be implemented to achievepreanalysis from the field before deploying an outage team, ifnecessary.

Section II of this paper elucidates the following: 1) thedeveloped analytical model of SEIG used in the investigations;2) a quantitative performance analysis on both the conventionalaluminum-rotor machine and the niche copper-rotor machine tostudy the machine behavior during load application and FD; and3) hence the need for the developed exclusive twofold wavelet-based module for FD and VR.

Sections III explains in length the step-by-step process in-volved in designing the wavelet-based module along with thecalculated and experimental results captured at every step of thedesign. The module designed in this section is implemented ona low-cost embedded system and tested for FD and VR acrossthe stator terminals of the SEIG. Finally, experimental resultsare investigated.

II. QUANTITATIVE PERFORMANCE ANALYSIS OF ALSEIGAND CUSEIG UNDER LOAD APPLICATION

AND FAULT INITIATION

A. Modeling of the SEIGs

The two-axis model of aluminum-rotor SEIG (ALSEIG) andcopper-rotor SEIG (CUSEIG) is developed using conventionalmachine equations based on the dq stator reference frame


theory (ω = 0) in order to bring out the performance of theSEIG under various loading conditions such as R and RLloads. The dq-axis stator and rotor voltage–current equationsat no-load conditions can be expressed as shown in (1) at thebottom of the page, and the excitation capacitor bank can bewritten as in (2), where Rs and Ls are the stator resistance andinductance; Rr and Lr are the rotor resistance and inductance;Lm is the magnetizing inductance; vqs, vds and iqs, ids arethe q- and d-axis components of the stator voltage and current,respectively; iqr and idr are the q- and d-axis components of therotor current; p is the differential operator; icq and icd are thecapacitor currents along the direct and quadrature axes; ωr isthe electrical rotor speed; ω is the speed of the reference frame;and C is the value of capacitance

[icq

icd

]=C

[p ω−ω p

] [vqs

vds

](2)

p

[ilqild

]=L−1

l

[vqs

vds

]− L−1

l R

[ilqild

]. (3)

The voltage and current equations of the machine under R andRL loads incorporate (3), where ilq and ild are the load currentsin q- and d-axis representations and R and Ll are the resistanceand inductance of the load.

Saturation characteristics of the machines were measured attheir rated frequency and incorporated in the aforementionedmodeling by fitting it with an arctangent continuous function asgiven in [22] and shown in

Xm = α (arctan(βim − γ) + δ) /im. (4)

The coefficient α is employed to make the estimated mag-netizing reactance Xm to match the measured reactance withrespect to the measured maximum magnetizing current Im.The coefficients β and γ determine the maximum valueand the initial value of the magnetization reactance, respec-tively. The coefficient δ makes the flux linkages to be zero whenIm is equal to zero.

B. Validation of the Developed SEIG Model andIllustration of the Need for VR

In order to validate the conventional two-axis model ofSEIGs developed in the previous part of this section and justifythe need for VR and FD in SEIGs, the performance of niche7.5-hp CUSEIG and conventional 7.5-hp ALSEIG was elicitedthrough a developed computer program and an experimentalsetup. The quantitative details obtained from these investi-gations also facilitated in designing the developed wavelet-based module. The machine equivalent circuit parameters,

TABLE IINDUCTION GENERATOR DATA

Fig. 1. Experimental setup of the dc motor coupled SEIG system used in theinvestigations.

resistances, and inductances determined from the standard no-load, dc, and blocked-rotor tests are presented in Table I. Theoutput parameters of both the machines were calculated by em-ploying the developed two-axis model and were experimentallyverified, for varying power factor loads. The measurementswere taken using a Tektronix 2024 digital storage oscilloscopeand a Fluke 434 power quality analyzer, after the machinesreached their rated speeds of 1 p.u., and the experimental setupis as shown in Fig. 1.

⎡⎢⎣

vqs

vds

00

⎤⎥⎦ =

⎡⎢⎣

Rs + pLs ωLs pLm ωLm

−ωLs Rs + pLs −ωLm pLm

pLm (ω − ωr)Lm Rr + pLr (ω − ωr)Lr

−(ω − ωr)Lm pLm −(ω − ωr)Lr Rr + pLr

⎤⎥⎦ ·

⎡⎢⎣

iqs

ids

iqr

idr

⎤⎥⎦ (1)


Fig. 2. Calculated and measured results of ALSEIG under an RL load of200 Ω and 0.26 H after the machine reached a rated speed of 1 p.u. atan excitation capacitance of 65 μF. (a) Calculated phase voltage profile.(b) Calculated stator current profile. (c) Measured phase voltage and statorcurrent profiles. (d) Calculated and measured reactive power profiles.

The calculated and measured terminal voltage, reactivepower, and stator current profiles of both ALSEIG and CUSEIGare shown in Figs. 2 and 3, respectively. The results shownin Figs. 2 and 3 were elicited under a static RL load appliedacross the stator terminals at around 62 and 82 s for ALSEIGand CUSEIG, respectively, after their voltages had reachedsteady state. From these figures, it can be observed that thereis a minimal variation in the measured and calculated resultsobtained using the conventional two-axis model of SEIG. Thisvariation in reactive power waveforms can be explained aselucidated in [23]–[25]. Results obtained using the developedtwo-axis model can be improved to match the experimentalresults by incorporating various ac conduction effects in therotors of both the machines. Also, a discrepancy in the vicinityof 2.5% between the calculated and measured voltages can beconsidered acceptable.

The voltage, current, and reactive power drop as seen fromFigs. 2 and 3 can be attributed to the distribution of reactive

Fig. 3. Calculated and measured results of CUSEIG under an RL load of340 Ω and 0. 44 H after the machine reached a rated speed of 1 p.u. atan excitation capacitance of 39.6 μF. (a) Calculated phase voltage profile.(b) Calculated stator current profile. (c) Measured phase voltage and statorcurrent profiles. (d) Calculated and measured reactive power profiles.

power by the capacitors between the machine and the load.Consequently, the reactive power available to the machine isless than the open-circuit conditions. The terminal voltage dropincreases as the loading increases for both the machines. If theconnected load R is resistive, then the reactance Xc of theexcitation capacitance C will dominate the parallel combina-tion of the load R and Xc as long as R is high. However,as R approaches zero, the machine is loaded heavily and willbe driven to cut off. In case of an RL load, if the value ofthe excitation capacitance C is small, then the load impedanceR + jXl will dominate the parallel combination of the load andXc. The effect of Xc will be diminished, driving the machineto cut off. On the other hand, if R + jXl is very large, then Xc

dominates the parallel combination of the load and R + jXl.If C is large, a further increase in C will result in reducingthe capacitive component in the circuit, driving the machineto cut off.

The value of the magnetizing reactance plays an importantrole for safe operation of the SEIG. Higher loading condition


pushes the magnetizing reactance to the unsaturated region,and hence, the voltage drop occurs. This is the reason whythe induction machine has the ability to protect itself fromoverloading current. The study performed earlier calls the needfor VR in SEIGs.

C. Background Literature on VR Schemes in SEIGs

VR schemes can be largely classified into shunt com-pensation and series compensation schemes. Each of theaforementioned schemes can be further classified into clas-sical, switching-device-based, and converter-based schemes.Quispe et al. [26] and Haque [27] propose VR schemes employ-ing switched controlled inductor and switched shunt capacitors,respectively. Research studies performed in [28] and [29] usethe static reactive power compensator for VR. Converter-basedshunt compensation VR schemes like static synchronous com-pensator based on voltage source converter and current-source-converter-based shunt compensation are presented in [30]–[32].Various converter-based, switching-device-based, and constant-voltage-transformer-based series compensation schemes arestudied in [33]–[39].

As stated in [37], an electronic load controller is used as a VRscheme in microhydro application; however, this method is noteconomically viable for other applications involving inductiveloads and needing VR [34]–[37]. The static reactive powercompensators reported in [40] is understood to be a popularVR scheme, but it pulls down its merits due to its expensivesized capacitors and inductors and injection of harmonics intothe system. VR scheme based on a decoupled load controller isdesigned for independent voltage and frequency regulation butconsists of a proportional integral (PI) controller whose designtends to complicate the scheme. Moreover, the performance ofthe PI controller greatly depends on the modeling of the systemand choice of proportional and integral gains; consequently,variations such as resistance changes due to the temperaturemay cause the controller to become inaccurate or unstable. Inaddition, a step-down transformer is required to measure theline voltage [31]. Understanding the need for VR from theaforementioned investigations and keeping in mind the existingexpensive and complex controllers tried out, a simpler cost-effective low-end dual-purpose wavelet-based VR scheme isproposed in this paper.

A wavelet, which has energy concentrated in time, is wellsuited to analyze the transient and nonstationary or time-varying phenomena. Wavelet transform (WT) can be dividedinto continuous WTs (CWTs) and discrete WTs (DWTs). ADWT-based methodology is chosen here as it has excellentsignal compaction properties for a variety of real-world signalswhile being computationally very efficient. The transient isrecognized within 1 ms, which allows the system to estimateand respond to the changing load current, stator current, andvoltage instantly. In a DG, the central controller regulatesvarious power-generating devices simultaneously; therefore, itinvolves multiple task management and complex calculations.The developed wavelet-based transient detection unit is capableof detecting load application independently, which significantlydecreases the complexity of the controller.

Fig. 4. Short-circuit voltage and current profiles of ALSEIG after faultinitiation at the stator terminals. (a) Measured stator voltage. (b) Measured loadcurrent. (c) Measured stator current.

Since the proposed module is based on detection of changingload and stator current transients, the VR scheme remains effec-tive, regardless of changes in system parameters. Additionally,any VR scheme previously discussed in this section can beimplemented using the same module, thus making it morerobust and flexible than the PI controller. Current-acquisitiondevices are generally integrated in a DWPG for monitoring andcontrol purposes; therefore, no additional sensors are neededfor the operation of this module, and hence, hardware cost isdecreased [41].

D. Exclusive Fault Analysis Across theStator Terminals of the SEIGs

Although short circuiting of an SEIG appears to instan-taneously de-excite the stator winding, thus causing voltagecollapse across the stator terminals, there is a transient statepreceding the complete decay of voltage. Hence, a three-phaseshort-circuit fault has been initiated across the stator terminalsof both the aluminum- and copper-rotor machines. Figs. 4 and 5show the measured results that represent the transient behaviorof aluminum- and copper-rotor machines, respectively. In bothcases, the negative x-axis shows the prefault conditions. Thetransients are observed on a Tektronix 2024 oscilloscope whichhas a sampling rate of 2 Gsamples. Analyzing the aforemen-tioned figures, the most observable phenomenon is the instant


Fig. 5. Short-circuit voltage and current profiles of CUSEIG after faultinitiation at the stator terminals. (a) Measured stator voltage. (b) Measured loadcurrent. (c) Measured stator current.

decay of voltage across the stator terminals on application ofthe three-phase fault. Since the fault is symmetrical, we knowthat it is sufficient to observe any one of the phasors duringthe fault. Owing to the inductive nature of the machine, themachine opposes the sudden change in current in the circuit,but due to instant collapse of voltage, the current decays almostcompletely in 0.07 s after the fault initiation. However, thecurrent does not completely decay until 0.1 s; hence, if the faultis cleared within 0.1 s after its initiation, the prefault conditionscan be regained instantly. In case the fault is not cleared withinthe stipulated time, the SEIG has to re-excite as it has lost itsresidual magnetism. The re-excitation time does not equal theprevious no-load excitation time of the SEIG because of theeffects of fault on the residual magnetism of the SEIG whichplay a pivotal role in the excitation time [42], [43].

The nature of the transients depends on factors such assaturation level, excitation capacitances, discharge times, rotordamping, instant of fault initiation, etc., in the system. As seenin Figs. 4 and 5, although the decay time of the current in bothALSEIG and CUSEIG appears to be more or less the same, thenature of transients is observed to be different. Since the copper-rotor machine has a steeper saturation curve than its aluminumcounterpart, decay of current takes relatively lesser time in thecopper-rotor machine.

E. Background Literature on FD Schemes in SEIGs

Conventional bus-bar fortification through relays is basedon phasor calculation methods and comparison of the phasoragainst encoded settings. The accuracy of such methods abatesconsiderably when the following occurs: 1) The fault conditionsand system operating conditions differ from the predeterminedsettings [44]–[48], and 2) the quantity of elements on the powerline increases, leading to complex power calculations. Fuzzy-knowledge and artificial-neural-network-based techniques forFD reported in the literature [49]–[52] require the past systemdata and expert knowledge of the field engineers and, hence,are system dependent. Traveling-wave-theory-based fault clas-sification schemes were also employed [53] but are rathercomputationally intensive. Since a fault is always associatedwith high-frequency transients, WT has recently emerged asa powerful tool for extracting vital information from voltageand current signals and has been successfully applied to variouspower system problems [54]–[57].

Selecting the filter and threshold wisely, it is possible tointegrate the VR function and FD function in the same unitwhich is applicable for different operating conditions and sys-tem parameters. FD unit is separate from the main controllerand other components of the entire system; hence, it will neitherbe influenced by malfunction of any other units nor increasethe computation time of the main controller. It improves thesafety and reliability while maintaining fast response. Theintroduced DWT-based FD method cannot only detect fault butalso collect postfault data, therefore allowing engineers to havecertain understanding on what happens to the system beforethey actually do tests. Fig. 6 shows the overall block diagramof the developed twofold wavelet-based module integration tothe SEIG system used in this paper.

III. DESIGN AND TESTING OF WAVELET-BASED

MODULE FOR FD AND VR

A. Filtering and Sampling Unit

Current transformers connected to the three-phase power lineare used to acquire instantaneous current amplitude which islater fed to the transient detection unit as a voltage signal.Fig. 7 shows the waveforms captured through a Tektronixdigital oscilloscope during load application and fault initiation.Extensive studies on the current pattern of the system showthat transients at the range of 4–10 kHz are dominant duringload perturbation, stator current variation, and fault inception[56], [58], [59]. Hence, this range of frequency contains a lot ofinformation required to capture the transients. Thus, a second-order multiple feedback (MFB) bandpass filter, as shown inFig. 8, is designed to remove unnecessary information from thesignal and retain the high-frequency information. The transferfunction of the MFB bandpass circuit is shown in

A(s) =− R2R3

R1+R3Cωms

R1R2R3R1+R3

C2ω2ms2 + 2R2R3

R1+R3Cωms + 1

(5)

where ωm is the midangular frequency of the filter.


Fig. 6. Overall block diagram of the developed twofold wavelet-based module integration to the SEIG system used in this paper.

Fig. 7. Load application and short-circuit profiles obtained through 1000 : 1turn ratio current transformer. (a) Stator current and load current profiles.(b) Three-phase short-circuit profile.

Fig. 8. Second-order MFB bandpass filter.

The filtered signal is now sampled according to Shannon’stheorem, which states that an Ω-band-limited function can bereconstructed completely from its values

(f(kT )|k ∈ Z) , T :=π

Ω(6)

sampled at the discrete points kT . All the harmonic componentsoccurring in f have a period length ≥ 2π/Ω. Thus, by requiring

Fig. 9. Measured high-frequency waveforms obtained as output voltages ofthe bandpass filter stage during load application and fault initiation. (a) Loadapplication. (b) Three-phase short-circuit fault initiation.

T := π/Ω, one makes sure that any pure oscillation possiblepresent in f would be sampled at least twice per period.

Fig. 9 shows the sampled data obtained out of the MFBfilter as high-frequency waveforms during load application andfault initiation. As seen from Fig. 9(a), the blue spike showsthe measured high-frequency transient waveform during loadapplication. It can also be observed that the changes in the loadand stator currents are indecipherable due to the high samplingrate used in the measurement. Fig. 9(b) shows the short-circuithigh-frequency transients which change in a smaller time inter-val than the waveforms in Fig. 7(b). The sampling frequency,therefore, is chosen as 20 kHz for this paper [60].

B. Wavelet Decomposition Unit

The filtered and sampled data are passed through a discretewavelet decomposition block. The input signal is refiltered todifferentiate various other high-frequency details in the system


and capture the information just required for VR and FDthrough conditional threshold selection. This difference in theinput and output of this block is illustrated in Section IIIthrough measured and calculated results.

1) Introduction to Wavelet Analysis: A wavelet is a mathe-matical function used to divide a given function or continuous-time signal into different scale components, assigning afrequency range to each scale component. Wavelets not onlydissect signals into their component frequencies but also varythe scale at which the component frequencies are analyzed.A WT is the representation of a function by wavelets. Thewavelets are scaled and translated as copies (known as “daugh-ter wavelets”) of a finite-length or fast-decaying oscillatingwaveform (known as the “mother wavelet”) [61]–[64]. Un-like the Fourier transform, WT does not need a single set ofbasis function [63], [64]. Instead, WTs have an infinite setof possible basis functions. Thus, wavelet analysis providesimmediate access to information that can be obscured byother time–frequency methods such as the Fourier analysis.The proposed logic detects the transients at high speed withrelatively good accuracy which fits the purpose. The waveletdoes not depend on in-depth history like artificial intelligencetechniques or expert knowledge which require large amount ofsystem data history for neural-network training or fuzzy rulebase formulation.

2) Implementation of the Wavelet Decomposition Unit: InCWTs, we consider the family

ψa,b(t) =1√aψ

(t − b

a

)(7)

where “a” is the scale factor, “b” denotes the shift factor, andΨa,b(t) is defined as the mother wavelet [62], [63]. To restrict

a and b to discrete values only, we choose a = a−(m/2)0 and

b = nb0am0 [61], where m and n range over Z and a0 > 1 and

b0 > 0 are fixed; this corresponds to

ψm,n(t) = a−m

20 ψ

(t − nb0a

m

am0

)= a−m

2 ψ(a−m0 t − nb0

)(8)

x(t) =∑∑

α〈x, ψm,n〉, ψm,n(t). (9)

The discrete subset of the half-plane consists of all thepoints (am, namb) with integers m and n belonging to a setof integers. The corresponding mother wavelet is given in (8)

DWT (m,n) = 2−m2

∑∑x(n)ψ

(t − n2m

2m

). (10)

A sufficient condition for the reconstruction of any signal xof finite energy is given by (9). Ψm,n is the discrete motherwavelet, respectively. The DWT is defined in (10). The discretewavelet is an orthonormal transform; the nth wavelet coefficientWn is associated with a particular scale and with a particularset of times. The DWT of a signal x[n] is defined as its innerproduct with a family of functions ϕj,k(t) and ψj,k(t) [61] asshown hereinafter

ϕj,k(t) = 2j/2ϕ(2jt − k)ψj,k(t) = 2j/2ψ(2jt − k)

}. (11)

Fig. 10. n-level asymmetric filter bank analysis.

The functions ϕ(t) and ψ(t) are scaling and wavelet functions,respectively, which are discretized at level j (j = 1, 2 . . . , n)and at translation k (k = 1, 2 . . . , t). For the implementationof the DWT as shown in Fig. 10, only the coefficients of twohalf-band filter, i.e., a low-pass h(k) and a high-pass g(k) =(−1)kh(1 − k) filter, are required, which satisfy the con-ditions in

ϕj+1,0(t) =∑k

h[k] · ϕj,k

ψj+1,0(t) =∑k

g[k] · ψj,k

⎫⎬⎭ . (12)

Hence, the corresponding DWT is as shown in

Aj+1,n =∑k

Aj,k · hj [k − 2n]

Dj+1,n =∑k

Aj,k · gj [k − 2n]

⎫⎬⎭ . (13)

A wavelet filter {hl : 0, 1, . . . , L − 1} for an infinite se-quence with at most L nonzero values must satisfy the follow-ing three basic properties [63]:

L−1∑l=0

hl = 0;

L−1∑l=0

h2l = 1;

L−1∑l=0

hlhl+2n =∞∑

l=−∞hlhl+2n = 0;

⎫⎪⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎪⎭

(14)

for all the nonzero value n.By imposing an appealing set of regularity conditions,

Daubechies (Db) came up with a useful class of wavelet filters;all of which yield a DWT in accordance with the notion of dif-ference of adjacent averages. Db wavelet is deemed most usedmother wavelet in power system studies due to its orthogonalproperty, which is potent for localization and classification ofdisturbances. In general, the Db wavelets are chosen to have thehighest number “A” of vanishing moments (this does not implythe best smoothness), for a given support width N = 2A, andamong the 2A − 1 possible solutions, the one is chosen whosescaling filter has external phase [66].

In comparison to other orthogonal wavelets like Haar andsymlet, Db gives higher yield in terms of computation complex-ity and filter response [67]. Haar wavelet is memory efficient


Fig. 11. Scaling and wavelet functions of Db6, respectively. (a) Scalingfunction. (b) Wavelet function.

and exactly reversible without edge effects [68]. However, asthe Haar window is only two element wide, any big changebetween an even and an odd value is not reflected in thehigh-frequency coefficients. Thus, high-frequency change is notdetected by such filter. Symlet wavelet is more symmetricalthan the Db but is still not exactly symmetric.

Study shows that symlet wavelet takes roughly five timesthe computational time when compared with the computationaltime of Db [69], thus rendering it useless for discrete dataflow with high frequency. Constraints such as computationtime, localization efficiency, and classification are consideredduring the selection of appropriate Db wavelet. Db orthogonalwavelets Db2–Db20 (even index number only) are commonlyused. The differences in filters mainly arise due to the lengthof the filters that define the wavelet and scaling functions.A wavelet becomes smoother with the increase in coefficientnumbers. The beginning of the event, however, presented moresevere transition than the end of the event. This fact was onlyobserved in Db2 and Db6 wavelets that selected the beginningand the end of the disturbance according to the value of thewavelet coefficients. The wavelet coefficients relating to thebeginning of the event (less rapid transition) presented absolutevalue greater than the wavelet coefficients of the end of theevent. Therefore, Db2 and Db6 wavelets were more selec-tive [70], where, in comparison, Db6 showed proportionateresponse at the beginning and end of the event. Thus, for thepurpose of all-round performance, Db6 was selected for thisstudy. Also, the use of DB6 wavelets allows less hardwarerequirement as the finite-impulse-response (FIR) filter buffersize is directly proportional to the order. The scaling andwavelet functions of Db6 are as shown in Fig. 11.

The scaling function ϕ(t) and wavelet functions ψ(t) arediscretized at level j. Depending on the level of details thatthe system is tracking, the level of processing varies. Here,considering both the computation complexity for real-timeprocessing and effectiveness of the filter for VR and FD, level 1

Fig. 12. FIR filter for wavelet decomposition.

TABLE IIHIGH-PASS FIR COEFFICIENTS

filter was used to obtain high-frequency details for the analysis.Fig. 12 shows the implementation of Db 6 decomposition 11th-order FIR high-pass filter; the high-frequency finite-impulsecoefficients of which are shown in Table II. High-frequencydetails were checked for a specified threshold which produceda high offset value when detected and digital low otherwise.Digital normalized signal is obtained for the high-frequencycomponents to isolate the dominant details in order to analyzethe signal. This empirical threshold and offset method removesundesirable transients caused by noise before moving the fil-tered data to the algorithm. High-frequency components werethen passed through a threshold tracker to identify the transientzone. A digital output is then produced to provide a signal tothe wavelet decomposition unit.

C. Comparator and Decider Unit

Extensive theoretical and experimental investigations wereperformed to obtain the thresholds for FD and VR under differ-ent loading conditions, excitation capacitances, and speeds ofthe rotor, for both ALSEIG and CUSEIG. According to [71],the window of usable data is limited to 3 ms due to the currenttransformer (CT) saturation during subtransient and transientcurrents. The data obtained during this span of 3 ms werecollected for posttransient analysis along with the peak valuesfor each phase.

The high-frequency details of all three phases collected dur-ing VR and FD, namely, HFa, HFb, and HFc, obtained afterwavelet decomposition, are continuously checked for signalsabove threshold values (V RTh and FDTh) for each phase,which is set according to the prior knowledge of system be-havior and the empirical values. Hereafter, this block wouldactivate the respective VR block or FD block on conditionalthreshold comparison through a control signal to perform VRor FD. Shown in Fig. 13 is the descriptive illustration of thecomparator and decider block.

D. Hardware Development and Testing VR and FD

The VR block can have any type of control schemesas stated in Section II. However, in order to validate this


Fig. 13. Comparator and decider unit.

wavelet-based transient detection scheme, a switched capacitorscheme for VR has been illustrated in this paper. The proposedVR scheme is applicable to almost any system as it is basedon the universal principle of change of capacitance to achieveVR. The authors would like to reiterate that the system isproposed only as a validation scheme for the module’s transientdetection capabilities. Hence, a simple power switch has beenused to enable switching of the capacitors. This scheme can beextended to static reactive power compensators, hybrid reactivepower compensators, thyristor switched capacitor and thyristorcontrolled reactor, and other flexible AC transmission systems.controllers that enable continuous change of capacitance in-stead of switching them in steps. However, this can be doneat the cost of control mechanisms for monitoring firing angledelay.

The scheme proposed involves a low-power gate drive signalfrom the 8-b master controller of the wavelet module whichtriggers the power switch. The master unit of the wavelet iscapable of sending digital/analog output used for selectingdifferent values of capacitances to achieve different levels ofVR at different loads. The compensation capacitance valuedetermines the bit pattern of the master’s output. The 5-Vgate trigger is enough to drive a power switch. In case ofhard-driven power switches, an amplifier may be employed tothe system after a V –I converter or an operational amplifier(op-amp) based on the requirement of a drive voltage or current.The principle behind estimating the capacitance is the change inreactive power requirement of the system calculated via currentand voltage monitoring done in the module as a part of transientdetection. The system can also be extended into a closed loopby getting an error voltage which is used by comparing it witha reference voltage.

The FD block receives the FD signal from the C&D block.After FD, the digital signal Peripheral Interface Controller(dsPIC) store the postfault data for 3 ms to ensure the oc-currence of a permanent fault. The three dsPICs used fortransient detection are controlled by the master microcontrollerto regulate the overall system. At the same instant, a communi-cation signal will be transmitted to the operator on successfuldetection of the fault and collection of data. This is replicatedin this research through lighting a LED in each phase by asignal emitted by the master controller. Also, the control signalwill be triggered to disconnect the system from the load, thusprotecting the entire system and ensuring safe operation.

The aforementioned scheme is implemented and testedon a low-end embedded system. This system consists ofthree dsPIC33F 16-b digital signal controllers (DSCs), with40-MIPS capability and up to 80-MHz speed with a phase-

Fig. 14. Experimental results for VR. (a) Second-order MFB bandpass filteroutput for load applied at negative half cycle of stator current signal. (b) Waveletdecomposition output corresponding to (a). (c) Second-order MFB bandpassfilter output for load applied at positive half cycle of stator current signal.(d) Wavelet decomposition output corresponding to (c).

locked loop. These chips have their own digital signal proces-sor engine capable of conducting single-cycle multiplicationand accumulation. In order to ensure a stable system, thetransient detection unit traces stability for 250 consecutivesamples. The prototype was tested for VR where a static loadwas applied across the stator terminals of the ALSEIG atperiodic intervals in order to test the robustness of moduleunder different conditions, initial excitation capacitances, anddifferent phase angles of stator current. Fig. 14 shows thatthe trigger was applied at different phase angles, i.e., negativeand positive half cycles, and the module was efficient enoughto identify the transient and capture high-frequency transientinformation at all instances. Although inherent system noiseis always present in any circuit, culminated by CT saturation


Fig. 15. Calculated results for VR. (a) Second-order MFB bandpass fil-ter output for load applied at positive half cycle of stator current signal.(b) Corresponding wavelet decomposition output.

and low-voltage processing circuit, the circuit employs NE5532dual low-noise op-amps, which feature very low noise, high-output-drive capability, high unity gain, and low distortion, andthe proposed scheme employs empirical threshold and offsetmethod to remove undesirable transients caused by noise beforemoving the filtered data to the algorithm. Also, the dsPICchosen has a signal-to-noise-and-distortion ratio of 69.5 dB,which means that the ratio between the amplitude of the originalsignal and noise is more than 1000. Meanwhile, the 12-b analogto digital converter capability of the microcontroller allowsaccurate low-voltage data collection [72]. Fig. 14(a) and (c)shows the experimental output data of the bandpass filter whichare sampled and stored in the dsPIC33F DSC. Correspondinghigh-frequency details obtained from experiments are shown inFig. 14(b) and (d).

The entire VR test process explained earlier was also simu-lated using a developed computer program for the ALSEIG, andthe corresponding calculated results are shown in Fig. 15. Thetransient or the high-frequency spike at 0.03 s is remarkablydistinguishable which enables to choose a specific threshold forVR. Going a step further to show the VR achieved through thismodule, ALSEIG was loaded at around 29 s after it reachedits steady state. The module detected the transient during loadapplication and enabled the power switch through the controlsignal to switch the compensating capacitances and sustain theoriginal voltage.

The amount of capacitance needed to be switched was alsoshown on the liquid crystal display used in the experimentalsetup. Fig. 16 shows the measured results obtained from aFluke power quality analyzer, and the corresponding calculatedresults are shown in Fig. 17. We can see from the experimentalresults that a small transient is seen around 30 s during theapplication of load, and then, the voltage stabilizes back toits original value. Load was switched off at around 44 s, andwe see that the voltage increases for a few milliseconds anddecreases back to its pretransient level due to the switching ofnew capacitance.

Fig. 16. Measured results for VR using switched capacitor scheme. (a) Statorvoltage and current profiles on load application and removal. (b) Correspondingload current profile.

Fig. 17. Calculated results for VR using switched capacitor scheme. (a) Statorvoltage and current profiles on load application and removal. (b) Correspondingload current profile.

Similarly, a three-phase short circuit was initiated at 0.05 sacross the stator terminals of the aluminum rotor machine withan excitation capacitance of 65 μF and a rotor speed of 1 p.u.,and the transients obtained on all three phases from the storeddata in the master controller are shown in Fig. 18. The transientswere detected within 3 ms after the initiation.


Fig. 18. Experimental results for short-circuit fault initiation. (a) Second-order MFB bandpass filter output for three-phase fault initiation. (b) Corre-sponding wavelet decomposition unit output.

IV. CONCLUSION

This paper has elucidated issues such as VR and FD inDWPG and confirmed the need for a robust and efficientscheme to regulate the voltage and detect faults across thepower line. The research findings presented in this paper canbe summarized as follows.

1) They illustrate the need for FD and VR in SEIGs throughinvestigations performed on two industrial 7.5-hp induc-tion machines.

2) A dual-purpose wavelet-based transient detection moduleis developed to perform FD and VR through conditionalthreshold selection.

3) Robustness of the module was illustrated through calcu-lated and experimental results during load application andfault initiation.

Future work could be performed toward the development ofa novel VR scheme incorporating the wavelet-based moduleintegrated to a static synchronous compensator.

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K. Lakshmi Varaha Iyer (S’10) received theB.Tech. degree in electronics and communicationengineering from Shanmugha Arts Science Technol-ogy and Research Academy University, Thanjavur,India, in 2009 and the M.A.Sc. degree in electricaland computer engineering from the University ofWindsor, Windsor, ON, Canada, in 2011.

He is currently a Research Associate with the Cen-tre for Hybrid Automotive Research and Green En-ergy, University of Windsor. His research presentlyfocuses on the design and control of electric ma-

chines, condition monitoring, and grid integration of renewable energy systems.

Xiaomin Lu (S’11) received the B.S. degree in en-gineering from Sun Yat-Sen University, Guangzhou,China, in July 2010. She is currently working towardthe M.A.Sc. degree at the University of Windsor,Windsor, ON, Canada.

Her research areas include development ofpermanent-magnet synchronous machine drive andcondition monitoring for electric vehicle drivetrainsystem and power system applications.


Yasir Usama received the B.S. degree in appliedsciences from the University of Windsor, Windsor,ON, Canada, in September 2009.

He was with Hydro One, Toronto, ON; BrightonBeach Power Plant; and Canadian Salt OjibwayMine, Windsor. He is currently with SNC–Lavalin,Toronto. He has been conducting research on thedevelopment of embedded-technology-based powersystem fault analysis. His research interest includesartificial intelligence implementation in wirelesscommunications and power systems.

Vamsi Ramakrishnan received the B.Tech. de-gree in electrical and electronics engineering fromShanmugha Arts Science Technology and ResearchAcademy University, Thanjavur, India in 2011.

He is currently working as a hybrid drivetrainengineer at Robert Bosch Engineering, Coimbatore,India. His research interests include modeling andanalysis of electric machines for wind power andhybrid electric vehicle applications.

Narayan C. Kar (S’95–M’00–SM’07) received theB.Sc. degree in electrical engineering from theBangladesh University of Engineering and Technol-ogy, Dhaka, Bangladesh, in 1992 and the M.Sc. andPh.D. degrees in electrical engineering from KitamiInstitute of Technology, Kitami, Japan, in 1997 and2000, respectively.

He is an Associate Professor with the Electricaland Computer Engineering Department, Universityof Windsor, Windsor, ON, Canada, where he holdsthe Canada Research Chair position in hybrid drive-

train systems. His research presently focuses on the analysis, design, andcontrol of permanent-magnet synchronous, induction, and switched reluctancemachines for hybrid electric vehicle and wind power applications, testing andperformance analysis of batteries, and development of optimization techniquesfor hybrid energy management system.

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