Title of Invention

A NOVEL SYSTEM FOR DETECTION AND CLASSIFICATION OF POWER SYSTEM TRANSMISSION LINE FAULTS

Abstract

Conventional electromechanical or solid state relays suffered from inflexibility, duplication efforts, complexity and cost. The present invention relates to a novel system for detection and classification of transmission line faults characterized in that the said system (i) a 3-phase variac (1) is connected to the main supply; (ii) output of (i) is connected to a 3-phase isolation transformer (2) of output whereof is connected to 100 km line model (3) which in turn is connected to star connected load of 10W per phase, the said model (3) having a mechanical fault creation arrangement with jumper cable to create type as well as location of faults at every 10 km ; (iii) switching circuit (4) connected to (3) to create a desired fault inception angle; (iv) data acquision card is connected between (3) and high speed computer (6); (v) computer monitor (6) displays the type and location of fault with the help of (5), employing LABVIEW software.

Full Text

The present invention relates to a novel system for detection and classification of power/transmission line faults. More particularly this invention pertains to a digital relay based on frequency domain approach for detection and classification of power system transmission line faults employing wavelet multi resolution analysis (hereinafter referred to as *MRA/ for the sake of brevity) to overcome the difficulties associated with conventional voltage and current-based measurements due to the effect of factors like, inter alia, fault inception angle, fault impedance and fault distance. The wavelet transform devised and effectively applied in the novel system of the subject invention captures the dynamic characteristics of the non-stationary transient fault signals using wavelet MRA coefficients, thereby enabling a transmission line of 100 km in length representing the EHV transmission, wherein by application of software developed for this purpose in accordance with this invention it has been possible to detect and classify accurately the different types of power system faults. Earlier electromechanical relays were used for transmission line protection. These had several drawbacks such as high burden on instrument transformers, longer operating time, contact problems, etc. Solid-state relays which avoid most of these difficulties were gradually replacing electromagneting relays. Though successfully used, these suffer from a number of disadvantages like inflexibility, duplication of specification efforts, and inadaptability to changing system conditions, complexity and cost. Software (digital) scheme avoid most of these difficulties. Programmable equipment can respond faster and may be used to implement complex threshold characteristics at low cost. They can also be self-checking in nature thereby requiring less maintenance and providing greater reliability. Conventional relaying scheme usually work based on the magnitudes of current signals where the fundamental component of fault currents in time-domain is compared with the preset nominal values. Hence these relays fail to operate during fault currents with low magnitudes. On the other hand, frequency domain analysis of fault currents checks for the presence of harmonics in current waveforms which appear during fault conditions due to abrupt change of current magnitude. Thus, by applying frequency domain analysis, faults can be easily detected and classified even in cases of low fault current in case of arcing faults, where conventional relays fail to operate. Many of the fault classification algorithms are mostly developed for shunt faults having low impedance (non-arcing faults). But distance relays fail to operate during high impedance faults due to under reach. As the transient disturbances are quite low during high impedance faults, relays based on current measurements do not distinguish the fault conditions from normal conditions as the fault current will not be considerably high. The present invention aims at overcoming the difficulties envisaged in the conventional methods. The principal object of this invention is to provide a novel system for detection and classification of power transmission line faults. A further object of this invention is to develop an algorithm for detection and classification of transmission line faults using wavelet MRA with Daubechies-four (referred to herein as Db-4) wavelet based on measurements and comparison of sharp variation in the values of the currents for the three phases in the sixth stage MRA detail signals extracted from the original line faults and read on the host computer with monitor. A still further object of this invention is to provide a system using an algorithm in conjunction with components/equipments which takes into account the effects of factors like fault inception angle, fault impedance and fault distance while detecting and classifying the nature of transmission line fault(s). Another object of this invention is to provide a system using an algorithm based on wavelet transform capable of capturing dynamic characteristics of non-stationary signal using short-data window, efficiently analyzing the signals having transients or discontinuities, e.g. the post-fault voltage/current waveform. The foregoing objects are achieved by the present invention which relates to a novel system for detection and classification of a novel system for detection and classification of power system transmission line faults having the undernoted principal components - (a) power supply source, (b) a transmission line model for 100 km length including 10 watts per phase load, (c) switching circuit for incorporating fault inception angle (FIS), (d) data acquisition system and digital cathode ray oscilloscope (CRO) and (e) computer, character- ized in that - (i) a three phase variac [ rating: 440V / 0 - 470V, 50 Hz ] is connected to the three phase main supply; (ii) output of three phase variac is connected to 3-phase isolation transformer [ rating: 440V / 20V (line to line voltage) and 180 VA ]; (iii) output of the isolation transformer is connected to the 100 km transmission line model; (v) presence of star connected 1 Ow bulb load (per phase) causes the current to pass from 0 km to 100 km of the transmission line model; and (vi) the DAQ card and digital CRO monitor the three phase currents at the Oth km of the transmission line model. Brief theoretical background of the invention : The continuous WT (CWT) of a signal x (t) is defined as - I where l//(t) is the mother wavelet and other wavelets are its dilated and translated versions, a and b are the dilation parameter and the translation parameters respectively. In general, the mother wavelet is compactly supported in both time- domain and frequency-domain, which, together with the dilation and translation operations, provides variable time-frequency resolution ability for different frequencies. The discrete WT (DWT), instead of CWT, is always used in practice. For a signal x (t), its DWT coefficient with respect to a discrete weavelet y/mn can be represented by where is defined by For some special choices of If/, aQ and bQ, {lf/ma, m,n eZ} constitute an orthonormal basis of L2 (i?) (the space of finite-energy functions). The most frequently used selections are a0 =2 and b0 = 1. A signal x (t) can then be represented by its DWT coefficients as The function, Iff (t) itself is called the mother wavelet which in this case is the Db-4 wavelet. The output of the DWT consists of the remaining "approximation" components, and all of the accumulated "detail" components. The actual implementation of DWT is done by multi-resolution analysis (MRA). By using MRA a signal can be analyzed at different frequency bands with different resolutions. The signal to be analyzed is decomposed into a smooth approximation and a detail. The approximation is further decomposed into an approximation and detail and the process is repeated. The decomposition of the signal is obtained by successive high pass and low pass filtering of the time domain signal. The successive stages of decomposition are known as levels and the above procedure is known as sub-band coding. The approximation and details at level-1 and level-2 have been represented using suffixes al and a2. The MRA details at various levels contain the features for detection and classification of faults. Owing to the unique feature of providing multi-resolution both in time and frequency by wavelets, the sub-band information can be extracted from the original signal. Thus for transmission line fault analysis in a power system, applied to faults, these sub-band informations are seen to provide useful clues regarding the faults for their classification in an elegant way. The generated time domain signal for each case is analyzed using Wavelet transform. Amongst different decomposed levels, only 6th level detail coefficients of current signals is considered for the analysis, because the absolute values of the summation of 6th level detail coefficients of current signals for all inception angles considered for the analysis are found to be higher as compared to those from other level output. It indicates that total area under the characteristics of 6th level outputs is more than that of other level outputs. Another reason for the 6th level output to be selected as the parameter for fault classification is that the summation of 6th level detail coefficients of current signals satisfies the characteristic relationships for all types of faults for classification purposes. Thus based on sixth level detail coefficients of current signals, an efficient generalized algorithm for fault classification based on the Wavelet analysis is depicted below. The transmission line faults in a power system are usually classified as single line to ground (L-G), double line to ground (L-L-G), double line (L-L), and 3 phase symmetrical (L-L-L) and (L-L-L-G) faults. The fault inception angle has a considerable effect on the phase current samples and therefore also on wavelet transforms output of post-fault signals as confirmed from several simulation studies. As the waves are periodic, it is sufficient to study the effect of inception angle in the range of 0° to 180°. The algorithm proceeds as follows : Let S , S. and S be the summation of sixth level detail a a c coefficients values for current in phases a, b and c, respectively. When the summation of S , Sw and S is equal to zero, then the a' b c ^ fault can be either L-L-L or L-L fault. The discrimination between these two types of faults is based on the fact that the magnitudes of Sa, Sb and S are comparable to each other. In case of L-L fault, the addition of any two phases summations namely, either S +S. or SK+S„ or S +S tends to nearly equal to zero and remaining phase summation coefficient is very small and almost negligible compared to other two summations having equal values with opposite signs. If the above condition is not satisfied, then it is considered as L-L-L fault. When the summation of Sa, Sb and Sc is not equal to zero, then it can be either L-G or L-L-G fault- If the absolute value of any two coefficients is equal and always much smaller than the absolute value of the remaining coefficients, then it is a L-G fault. If the absolute value of any two coefficients is not equal to zero and is always much higher than the absolute value of remaining coefficients, then it is a L-L-G fault. Sequence of steps for calculation of Sa, S^ and Sc : The following steps are incorporated to obtain the wavele|t summation coefficients S , S. and S„ : a* o c 1. The fault currents of each phase are measured continuously. 2. These fault currents are then sampled with a sampling frequency of 12.5 kHz. 3. These samples are then convolved with the low-pass filter coefficients of sixth level Db-4 wavelet to obtain the detail coefficients. 4. The obtained coefficients from the above step are summed up to get the wavelet summation coefficients Sa, Sb and Sc for the three phase currents la, lb and Ic, respectively. The invention will now be described in more details with the help of the illustrative drawings accompanying this specification wherein - Fig.lA shows one level decomposition diagram with real signals; Fig.IB depicts the 'fault classification algorithm' in frequency domain; Fig.2 (a) shows laboratory model of a 100 km 3 phase transmission line; Fig.2(b) illustrates the basic block for every 10km of 3 phase transmission line; Fig.3 gives a block diagram of switching circuit; Fig.4 is the block diagram representation for fault classification in LABVIEW using wavelets; Fig.5 (a) is the sub(VI) diagram for wavelet transform; Fig.5(b) is the sub(VI) diagram for wavelet coefficient calculation; Fig.5(c) shows sub(VI) for fault classification for L-L-L and L-L faults; Fig.5(d) is sub(VI) for fault classification for L-G and L-L-G faults; Fig.6 shows the set up for real time fault analysis of the transmission lines; With regard Fig.lA, this is a schematic diagram of 'Approximation' and 'detailed' components on the basis of the output of the DWT. As shown therein, the input current signal is split into its high and low frequency components. Hence at the sixth level, since the high frequency components fall in the range of 97.65 Hz to 195.31 Hz within which the 2nd and 3rd order harmonics lie, these co-efficients are considered for further analysis. As would be discussed in detail hereafter, the wavelet block in LABVIEW software including classification algorithm shown Fig.IB computes discrete wavelet transform and supports the waveform data, taking into account the most used wavelets. The output of this signal is made to pass another virtual instrument (also referred to herein as VI for the sake of brevity) block which in turn produces the wavelet coefficient. Fig.5(a) of the drawings shows the LABVIEW implementation for computation of S , S. and S„ of discrete wavelet detail coefficient for 6th level a b c for each of the three phase currents of transmission line. Then the output is passed into another sub-program virtual instrumentation [abbreciated herein as Sub-(VI)], as shown in Fig.5(b) of the drawings for calculation of wavelet co-efficients for one phase and it is same for remaining two phases for generating wavelet co-efficients. IMPLEMENTATION OF CLASSIFICATION ALGORITHM USING LABVIEW SOFTWARE OF THIS INVENTION (i) Modelling of Transmission Lines A transmission line was modelled keeping in view the actual existing lines of 400KV. The main concept involved in modeling was to keep the p.u. (Per Unit) values similar and expressing with respect to a common base voltage and base KVA. At first the p.u. values of model transmission lines was determined and with help of those values and base impedance of actual system, L and C were determined for actual system. The values of L and C were checked with that of recorded values of actual system. Model line parameters : Base Voltage - 20V Base VA = 180VA Base Current = 5Amp Length designed for = lOOKm ■*" Positive and Negative sequence parameters (per lOKm) : Resistance = 0.17 Q Inductance = 0.13195 mH Capacitance » 8.936 |iF w Negative sequence parameters (per lOKm) : Resistance = 0.08 Q Inductance = 0.4510 mH Capacitance = 6.09 jiF (ii) Three Phase Transmission Line Model - Please see Figs. 2(a) and 2(b) of the drawings. The basic scheme employs a lOOKm transmission line as shown in Figure 2(a) which consists of ten basic blocks shown in Figure 2(b). It is connected through isolating transformer which steps down to 440/20 volts supply, 50 Hz at one end meant to serve as sending end. The model line is rated at 5 Amps. At receiving end of line resistive load of around 10W (per phase) is connected. Faults are applied at the terminals of any module by means of switching circuit. The switching circuit shown in Figure 3 creates fault at particular angle known as fault inception angle. (iii) SWITCHING CIRCUIT Faults transient studies of electrical systems require simulation of system voltage which starts its waveform from a fixed value of phase angle. Thus real time study of such system becomes difficult in mini models, network analysers and without the help of a switching unit which can control the starting point of the voltage waveform. A signal generator or frequency multiplier is used to permit one degree resolution of switching angle selector. This produces a frequency of 360f, where f is the base frequency of signal waveform that needs switching. One complete cycle = 360 degrees Frequency - 50Hz Thus a frequency of 360 X 50 = 18KHz permits one degree resolution of switching angle selector. Thus every successive zero crossing of high frequency wave corresponds to one degree angle interval of the base signal. Thus the angle sensing becomes independent of reference signal frequency. So a high frequency digital counter counts the number of zero crossing of high frequency wave and with the help of decoder circuit and decade switches, only a single pulse count is selected at a time for thyristor or the IGBT firing instant- This totally eliminates the possibility of wrong triggering for if the decoded pulse count fails to trigger the IGBT's, no more triggering signals are available for the control circuitry. A low frequency counter counts pulses/sec and controls the duration of the on condition of the equipment. The unit is put off by a separate relay contact which is activated from the low frequency counter. Both phase angle and duration control are performed by digital counters which are reliable and independent of supply frequency and voltage variations. (iv) SOFTWARE IMPLEMENTATION USING LABVIEW - See Fig.4 of the drawings. (v) Wavelet Block This computes discrete wavelet Transform and supports the waveform data and includes the most used wavelets. This output of this signal is made to pass into another VI block which is turn produces the wavelet coefficient. The subVI(l) block diagrm is as shown in Fig.5 (a) of the drawings and is also same for remaining two phase. The Figure 5(a) shows the LABVTEW implementation (sub VI diagram) for computation of discrete waveform to be used in wavelets. Then the output is passed into another Sub VI for calculation of wavelet coefficients. The second Sub VI block diagram of a phase is shown in Figure 5(b) and is same for remaining two phases and it generates the wavelet coefficients. The generated coefficients are checked whether their sum is zero or not. If true, then the adder checks whether the sum of two inputs is zero or not i.e. (Sa+Sb=0) or (Sb+Sc=0) or (Sc+Sa=0). Then tolerance check is done to the adder output. If again true, it will show L-L fault and if not then output is made to pass through HOT blocks which computes the negation of the input. The output signals are passed through merge block which merges two or more signals into single output which shows the L-L-L fault, Figure 5{c). But if the sum of coefficients is not zero then absolute value of input is determined and compared with another absolute value of other input i.e. |Sal = |Sbl and so on is done. If the values are equal tolerance check is done and the output is given as L-G fault or else it is L-L-G fault fFigure 5(d)]. Correspondingly, across each faults bulbs are placed and glowing of bulb represents the type of fault that has occurred,, REAL TIME IMPLEMENTATION OF FAULT DETECTION AND CLASSIFICATION ALGORITHM Fig. 6 of the drawings show the set up for real time fault analysis of the transmission lines, wherein there is shown - w Isolation transformer w Transmission line block set «•" Switching angle circuits w* Data acquisition system Experimental procedure : The three isolating transformers were fed from three phase variac and voltage was stepped down to 20V by isolating transformer which is the input to the tranmission line block. A switching circuit was used to create fault in the line. The fault inception angle has a considerable effect on the phase current samples and therefore several simulation studies were perform to obtain post fault signals. The transmission line was connected to a balanced load of 10W per phase and fault inception angle was selected by setting the single pole ten way switch and at that particular angle, a fault was created. The real time data acquisition was done through data acquisition card which is a high performance multifunctional analog to digital Input/Output board for Personal Computer. After the three current signals are acquired using data acquisition card, they are passed through LABVIEW program as shown in the Fig.4. After acquisition of the current signals, the Sa, Sb and Sc are first calculated using LABVIEW software as shown in the Fig.4 and then these values are used for classification using the same LABVIEW program. Wavelets provide the unique feature of providing multiple resolutions both in time and frequency and hence the sub-band information can be extracted from the original signal. Thus for the transmission line fault analysis in power system, applied to fault, these sub-band information has seen to provide useful clues regarding the fault for their classification in an elegant way. The generated time domain signals for each case is analyzed using wavelet transform. Among the several decomposed levels only the sixth level detail coefficients of current signals is considered for analysis. This is because summation of sixth level output satisfies the characteristic relationship of all types of faults for classification purposes. The wavelet algorithm was made to recognize the type of fault occuring in the system. Corresponding to every type of fault, there is one LED (light emitting diode) in the front panel of LABVTEW software. When a particular fault occurs in the system, then the signals are processed through the data acquisition system to the computer and the algorithm analysis was done to the inputted signal. Correspondingly one LED glows showing the type of fault that has occurred. Simultaneously current and voltage waveform of faulted phase and healthy phase was traced from the computer. Then in the hardware model a fault was created at about 50Km with a fault inception angle of 15°. The sigle pole 10 way switch was selected at 15° manually for fault inception angle. The currents were stepped down individually and then made to pass through data acquisition card which converts analog signal to digital signal. Then applying discrete wavelet transform to the three acquired signals, it generates the Sa, Sb, Sc coefficients which were displayed on the monitor. The coefficients were checked accordingly to determine which type of fault was occurred. For example if |sJ = |Scl & «Sa, it is an L-G fault involving phase A. So when the condition was satisfied the corresponding LED started to glow indicating the type of fault that has occurred in the system. LABVTEW interpretation shows the type(s) of fault occurred in the system. The algorithm for fault classification reponded properly when different types of fault were given to the transmission line block. Advantages 1. The system of this invention enables early detection of line fault with consequent speedy restoration of power supply in industrial as well as domestic sectors, particularly in the former, thereby ensuring avoidance of financial loss. 2. Speedy recording of the events for the purpose of adopting remedial measures for specific user industries by ascertaining the nature of interruption. As the present invention may be embodied in several forms without departing from the spirit or essential characteristics thereof, it should also be understood that the above described examples and illustrations are not limited by any of the details of the foregoing description, unless otherwise specified, but rather should be construed broadly within its spirit and scope as defined in the appended claims and, therefore, all changes and modifications that fall within the meets and bounds of the claims, or equivalences of such meets and bounds are intended to be embraced by the appended claims. We claim: 1. A novel system for detection and classification of power system transmission line faults in which the principal components comprising; (a)power supply source, (b)a transmission line model for 100 km length including lOwatts per phase load, (c)switching circuit by which faults are applied at the terminals of any module, (d)data acquisition system and digital cathode ray oscilloscope (CRO) and (e) computer, wherein three phase variac (1) is connected to three-phase isolation transformer (2) of which output is connected to the 100 km transmission line model (3) and the said model (3) having a mechanical fault creation arrangement to create type as well as location of faults at every 10 km is connected to star connected load of 10 w (3)per phase and switching circuit (4) as well as a three channel digital CRO are connected to the transformer line model (3)to create a desired fault inception , angle (FIA) and between the transmission line model (3) and High speed computer (6) data acquisition card (DAQ)(5) is connected and jumper cable for creating faults, Characterised in that wevelet captures the dynamic characteristics of the non- stationary transient fault signals using wavelet MRA coefficients, thereby enabling a transmission line of 100 km in length representing the EHV transmission to detect and classify accurately the different types of power system faults wherein data acquisition is carried out through data acquisition card serving as a high performance multifunctional analogue to digital input/output board for the said host computer. 2. A system as claimed in Claim 1, where in the sequence of operations is conducted under fault conditions comprising. (i) the switching circuit (4)is set at a particular angle, fault inception angle (FIA)varying between 0 and 360 degrees; (ii) main three phase supply is switched on; (iii) three phase variac (1) is set at 440V, which is the input for the isolation transformer (2); (iv) an output voltage of 20V (line to line voltage) given by the isolation transformer (2) is fed to the transmission line model (3); (v) presence of star connected 10 w bulb load (per phase) causes the current to pass from 0 km to 100 km of the transmission line model (3); and (vi) the DAQ card (5)and digital CRO (5)monitor the three phase currents at the 0 th km of the transmission line model (3). 3. A system as claimed in Claims 1 and 2, in which shock hazard is eliminated in the isolation transformer by grounding the secondary side neutral point and the sub-band information can be extracted from the original signal due to multi- resolution in time as well as in frequency by wavelet. 4. A system as claimed in Claims land 2, in which values per unit (p.u) of 400 KV transmission lines of 100 km length are kept similar and expressed with respect to a common base voltage and base KVA, and with the p.u. values and base inpedance of actual system, inductance L and capacitance are determined for the actual system, employing (i)model line parameters, ii)positive and negative sequence parameters (per 10Km)and (iii)zero sequence parameters as under: Model line parameters: Base Voltage = 20V BaseVA=180VA Base Current = 5 Amp Length designed for = 100Km Positive and Negative sequence parameters (per 10 Km) Resistance(R1) = 0.17 Q Inductance(L1) = 0.13195 mH . Capacitance(C1) = 8.936 µF Zero sequence parameters (per 10 Km) Resistance(Ro) = 0.08 Ω Inductance(Lo)= 0.4510 mH. Capacitance(Co) = 6.09 µF and the said transmission line consisting often basic blocks are connected through isolation transformer (2)which steps down the line voltage from 440 . volts to 20 volts at 50Hz ait one end serving as the sending end, current rating of the line being 5 ampere, and at the receiving end of line, there is connected a resistive load of 10 W per phase. 5. A novel system for detection and classification of power system transmission line faults, substantially as hereinbefore described with particular reference to the accompanying drawings. Abstract Title - "A novel system for detection and classification of power system transmission line faults." Conventional electromechanical or solid state relays suffered from inflexibility, duplication efforts, complexity and cost. The present invention relates to a novel system for detection and classification of transmission line faults characterized in that the said system (i) a 3-phase variac (1) is connected to the main supply; (ii) output of (i) is connected to a 3-phase isolation transformer (2) of output whereof is connected to 100 km line model (3) which in turn is connected to star connected load of 10W per phase, the said model (3) having a mechanical fault creation arrangement with jumper cable to create type as well as location of faults at every 10 km ; (iii) switching circuit (4) connected to (3) to create a desired fault inception angle; (iv) data acquision card is connected between (3) and high speed computer (6); (v) computer monitor (6) displays the type and location of fault with the help of (5), employing LABVIEW software.

Documents:

00611-kol-2008-abstract.pdf

00611-kol-2008-claims.pdf

00611-kol-2008-correspondence others.pdf

00611-kol-2008-description complete.pdf

00611-kol-2008-drawings.pdf

00611-kol-2008-form 1.pdf

00611-kol-2008-form 2.pdf

00611-kol-2008-form 3.pdf

00611-kol-2008-pa.pdf

611-KOL-2008-(06-11-2012)-CLAIMS.pdf

611-KOL-2008-(06-11-2012)-CORRESPONDENCE.pdf

611-KOL-2008-(06-11-2012)-FORM-2.pdf

611-KOL-2008-(06-11-2012)-OTHERS.pdf

611-KOL-2008-(09-12-2011)-ABSTRACT.pdf

611-KOL-2008-(09-12-2011)-AMANDED CLAIMS.pdf

611-KOL-2008-(09-12-2011)-AMANDED PAGES OF SPECIFICATION.pdf

611-KOL-2008-(09-12-2011)-DESCRIPTION (COMPLETE).pdf

611-KOL-2008-(09-12-2011)-DRAWINGS.pdf

611-KOL-2008-(09-12-2011)-EXAMINATION REPORT REPLY RECEIVED.pdf

611-KOL-2008-(09-12-2011)-FORM-1.pdf

611-KOL-2008-(09-12-2011)-FORM-2.pdf

611-KOL-2008-(09-12-2011)-OTHERS.pdf

611-KOL-2008-(30-10-2012)-PA.pdf

611-KOL-2008-ARGUMENTS.pdf

611-KOL-2008-CANCELLED PAGES.pdf

611-KOL-2008-CORRESPONDENCE.pdf

611-KOL-2008-EXAMINATION REPORT.pdf

611-KOL-2008-GRANTED-ABSTRACT.pdf

611-KOL-2008-GRANTED-CLAIMS.pdf

611-KOL-2008-GRANTED-DESCRIPTION (COMPLETE).pdf

611-KOL-2008-GRANTED-DRAWINGS.pdf

611-KOL-2008-GRANTED-FORM 1.pdf

611-KOL-2008-GRANTED-FORM 2.pdf

611-KOL-2008-GRANTED-FORM 3.pdf

611-KOL-2008-GRANTED-SPECIFICATION-COMPLETE.pdf

611-KOL-2008-PA.pdf

611-KOL-2008-PETITION UNDER RULE 137.pdf

611-KOL-2008-REPLY TO EXAMINATION REPORT.pdf

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