Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis

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1 Detection of Faults in Power Syste Using Wavelet Transfor and Independent Coponent Analysis 1 Prakash K. Ray, 2 B. K. Panigrahi, 2 P. K. Rout 1 Dept. of Electrical and Electronics Engineering, IIIT, Bhubaneswar, India. 2 Dept. of Electrical Engineering, SOA University, Bhubaneswar, India. 3 Asit Mohanty, 4 Harishchandra Dubey 3 Dept. of Electrical Engineering, CET, Bhubaneswar, India. 4 Dept. of ECE, The University of Texas at Dallas, USA. ABSTRACT: Uninterruptible power supply is the ain otive of power utility copanies that otivate the for identifying and locating the different types of faults as quickly as possible to protect the power syste prevent coplete power black outs using intelligent techniques. Thus, the present research work presents a novel ethod for detection of fault disturbances based on Wavelet Transfor (WT) and Independent Coponent Analysis (ICA). The voltage signal is taken offline under fault conditions and is being processed through wavelet and ICA for detection. The tie-frequency resolution fro WT transfor detects the fault initiation instant in the signal. Again, a perforance index is calculated fro independent coponent analysis under fault condition which is used to detect the fault disturbance in the voltage signal. The proposed approach is tested to be robust enough under various operating scenarios like without noise, with 20-dB noise and variation in frequency. Further, the detection study is carried out using a perforance index, energy content, by applying the existing Fourier transfor (FT), short tie Fourier transfor (STFT) and the proposed wavelet transfor. Fault disturbances are detected if the energy calculated in each scenario is greater than the corresponding threshold value. The fault detection study is siulated in MATLAB/Siulink for a typical power syste. 1 INTRODUCTION Modern power utilities require a efficient protection schee to protect the syste itself as well as the connected equipents for iproving better perforance under noral as well as abnoral/faulty operating scenarios. Now-a-days the electroechanical relays are replaced by digital relays because of their characteristics like faster operation, accuracy and reliability. The fault detection through digital relays and fault detector (FD) is very vital for ipleenting any real-tie solutions (Phadke 1988, Dash 2000). In the literature, so ay work are for onitoring and identification of faults in power syste. Fault situation can be identified by coparing the difference between the value between two consecutive cycles when higher than a threshold value based on Phasor (Sidhu et al. 2002, Sachdev et al., 1991). But, its deerit being the odeling of fault resistance. Kalan filter is used for fault detection based on estiation ethod (Chowdhury et al. 1991, Zadeh et al. 2010, & Girgis 1982). Wavelet transfor (WT) is being used for the detection of fault disturbances in power syste (Ukil & R. Živanović 2007) which detects the changes in the signal. Then, adaptive filters and wavelet transfor in cobination was used for fault identification in power syste (Ray et al. 2010). But, these techniques affected by variation in frequency, noise etc. This paper proposes an algorith for fault detection using wavelet transfor and independent coponent analysis. The sudden changes are to be detected by the detection techniques so that suitable solutions can be adapted to protect the power syste fro any type of disturbances. The proposed ethod perforances are being analyzed under different operating scenarios like in presence of noise, haronics and frequency variations. Wavelet transfor (WT) and independent coponent analysis (ICA) are suitable candidates for detection of fault disturbances because of efficient tiefrequency resolutions and their reliability to extract the suitable features for identification purpose. Again, the techniques are considered under different operating conditions in order to assess the robustness and accuracies (Pradhan et al. 2005). This aterial is presented to ensure tiely disseination of scholarly and technical work. Copyright and all rights therein are retained by the authors or by the respective copyright holders. The original citation of this paper is: P. K. Ray, B. K. Panigrahi, P. K. Rout, A. Mohanty, H. Dubey, "Detection of Faults in Power Syste Using Wavelet Transfor and Independent Coponent Analysis", First International Conference on Advanceent of Coputer Counication & Electrical Technology, October 2016, Murshidabad, India, DOI: /RG

2 The techniques used for detection analysis is presented in Section 2 followed by the algorith of ipleentation in Section 3. Then, Section 4 explains the result analysis both qualitatively and quantitatively, followed by the conclusions in Section 5. 2 TECHNIQUES FOR FAULT DETECTION This section describes the techniques used for identification of different types of faults in wind syste connected to grid power syste. The WT and ICA as detection techniques are presented briefly with their atheatical odeling. Different operating scenarios are taken as case studies to test these ethods for fault identifications which help in assessing both noral as well as faulty operating conditions. The details of these ethods are as follows. 2.1 Wavelet transfors (WT) The wavelet transfor is a signal processing algorith which is useful in detection of abnoral operating conditions based on decoposition of the power signals into different ranges of frequencies by the help of a series of low-pass and high-pass filters. This usually provides us a tie-frequency ulti-resolution analysis that greatly useful for identifying any short of abrupt variations in the electrical paraeters such as voltage, phase, current, frequency etc. Here, Daubechies4 (db4) is being used as the other wavelet basis function for the fault detection analysis (Ukil & R. Živanović 2007). Usually, the signal is divided into a set of approxiate (a) and detail (d) co-efficient representing the low-frequency and high frequency bands respectively. The decoposition is as presented below in Figure 1. Figure 1: Wavelet decoposition tree Considering a voltage signal of the power syste as V(t), the continuous wavelet transfor (CWT) is expressed as: 1 * t N CWT ( V, M, N) V ( t) dt a M (1) Where M and N are called as dilation and translation paraeters and Ψ is known as the wavelet basis function. Now WT in discrete for as: 1 * n km 0 DWT( V, M, N) V ( k) M 0 k M 0 (2) Where M and N are replaced by M and km, and k, are integers. Scaling function in one stage is expressed as su of that of next stage and can be given by: ( t) h ( n) 2 (2 t n) n (3) Using the above equation, the original voltage signal can be written as [8]: o/2 o /2 ( ) o( )2 (2 ) ( )2 (2 ) k k o V t a k t k d k t k (4) Where J0 is the coarse adustent paraeter in the scaling function. The detail and approxiate coefficients are written: a ( k) a ( ) h( 2 k) 1 d ( k) c ( ) h ( 2 k) Independent Coponent Analysis (ICA) (5) (6) Independent Coponent Analysis (ICA) is an advanced or odified version of principal coponent analysis (PCA). It uses high order statistical analysis based de-correlation for the source input signal and provides iportant inforation regarding the irregularities presence. In ICA, the input data is odeled as linear coefficients that are utually independent to each other. Based on the blind source separation algorith, it can efficiently and accurately transfor the input voltage signal into utually and statistically independent coponents, thereby helps the detection process (Hyvärinen et al. 2001, Pöyhönen et al. 2003, Lopez et al. 2011, & Dubey et al. 2011). The utual inforation nothing but the individual independency easureent of the input signal and its entropy is Gaussian in nature and can be written as: J ( y) h( vgaussian ) h( v) (7) Differential entropy H of an input signal y with density p (η) is given by: h( v) p log p d (8) h v Here, the negentropy is estiated based on the estiation of probability functions of input signals Then, rather the negentropy can be expressed as:

3 T T 2 i i i gaussian J v J e w x e g w x e g v (9) Here, e is the statistical expectation and G is a nonquadratic factor. For n linear ixtures taken as x, x,. x for the n independent coponents, we can express : x a1s1 a2s2... ansn For all (10) Here, x is a rando ixture and s is a rando independent variable. Assuing x as the rando vector with eleents x, x,. x and rando vector with eleents s, s,. s, if A is the atrix having eleents a, we can take colun vectors; x,as the transpose of x. Then, we can write in atrix for: x As (11) Where, A is a colun atrix of eleents a and then odel is expressed as: n x aisi (12) i1 The ICA atheatical odel is an iterative one where the independent coponents are called latent variables. Here the input ix atrix is considered to be unknown and the coponents s are statistically independent. Finally, once the atrix A is estiated, we can deterine the inverse, W, then can evaluate the independent coponent by the following expression: s Wx (13) 3 FAULT DETECTION METHODOLOGY The fault detection ethodology is explained in this section for the grid-connected wind power syste as follows: (i) A grid-connected wind power syste is siulated in MATLAB where different types of fault are created. The voltage signal is extracted at PCC under faulty conditions. The signal is then passed through wavelet transfor to get the tiefrequency analysis for identification of faulty situations. (ii) The voltage signal is processed through to find the ean and de-correlation. This is the 1st level of processing.. (iii) Then, X data size is reduced and filtered for better data redundancy. (iv) Then, the principal coponents (PC) of the input data are deterined. (v) In the current study, the independent coponents are calculated based on fixed point iteration [12]-[15]. (vi) Then, de-correlating atrix, W and the independent coponent, S of voltage signal is deterined fro which a atrix, x is generated. (vii) And finally, the pre-fault signal is considered for constructing the atrix, x which can be used to calculate the perforance index and fault is detected when the index is ore than a set threshold value. The perforance index is given by PI ( k) ( noral ( absolute ( W ( k)* x ( k) s ( k))) ) (14) 4 SIMULATED RESULTS f n f This section explains the detection results and corresponding discussions in a wind syste connected to grid. The power syste is siulated in MATLAB/Siulink environent and the grid is of 230 kv, 50 Hz rating consisting of theral based power plant. The considered grid-connected power syste is shown in Figure 2 and here, the sapling frequency is taken as 2 khz. Figure 2: Grid-connected wind power Syste. 4.1 Fault detection using wavelet transfor The voltage signal at point of coon coupling (PCC) is taken offline under AG fault. The signal is processed through WT which detects the fault instant based on filtering through a set of low-pass and high-pass filters. The voltage signal with its detection result is shown in Figure 3. Siilarly, the detection result using AB and ABCG fault is shown in Figure 4 and Figure 5 respectively. These results clearly show that WT nicely detect the fault initiation instant nicely. But, as noticed when the fault is cleared, as shown in Figs., the recovery to 2

4 noral wavefor is un-noticed by wavelet transfor. Figure 3: Detection of phase-to-ground fault in wind syste connected to grid (red- voltage signal; green-wt output) Figure 4: Detection of phase-to-phase fault in wind syste connected to grid (red- voltage signal; green-wt output) 4.3 Phase-to-phase fault detection using Independent Coponent Analysis Next, a phase-to-phase fault is near to the point of coon coupling and the corresponding voltage signal at the PCC. Then, the index as explained in ICA section is calculated under different operating conditions like without noise, with 20-dB noise and under variation in frequency condition. The siulated of the perforance index obtained fro ICA is shown in Figure 6 (b). It is observed that, the fault initiated at near about s detected through sudden increase in the index value. Before the fault, the index value is observed to be alost zero. Once, the fault occurs, the value increases suddenly and if we set a threshold value, then the can be detected. But in this case, we have not set the threshold value. Rather, we are assessing the faulty condition based on sudden increase. (a) Figure 5: Detection of three-phase-to-ground fault in wind syste connected to grid (red- voltage signal; green-wt output) 4.2 Phase-to-ground fault detection using Independent Coponent Analysis A phase-to-ground fault is near to the point of coon coupling and the corresponding voltage signal at the PCC is taken offline. Then, the index as explained in ICA section is calculated under different operating conditions like without noise, with 20-dB noise and under variation in frequency condition. The siulated of the perforance index obtained fro ICA is shown in Figure 6 (a). It is observed that, the fault initiated at near about s detected through sudden increase in the index value. Before the fault, the index value is observed to be alost zero. Once, the fault occurs, the value increases suddenly and if we set a threshold value, then the can be detected. But in this case, we have not set the threshold value. Rather, we are assessing the faulty condition based on sudden increase. (b) Figure 6: Detection of (a) phase-to-ground fault (b) phase-tophase fault in wind syste connected to grid 4.4 Detection of fault disturbances using the perforance index (PI) This sub-section describes the detection technique using a perforance index called energy content of the processed faulty signal. The voltage signal at the point of coon coupling is extracted and processed through different signal processing transfors like Fourier transfor (FT), Short tie Fourier transfor (STFT) and wavelet transfor. Then the output wavefor after processing is used to calculate energy which is copared with a threshold value to know the faulty or noral operating conditions in the power syste. It is

5 observed that proposed WT provides larger energy value so that a suitable threshold can be chosen to accurately detect the faulty conditions. The index in different cases is shown in Table 1. Table 1 Energy content using FT, STFT,WT Scenario Energy content FT STFT WT AG Fault BG Fault CG Fault AB Fault BC Fault ABC Fault CONCLUSIONS This paper presented detection of fault disturbances using Wavelet Transfor and Independent Coponent Analysis. Voltage signal extracted at PCC is being processed through the above techniques under different fault and operating scenarios. It is observed that WT transfor detected the fault instant very accurately. But, in soe cases, it could not discriinate the disturbance condition ay be because of noise or frequency variation. Therefore, the faults are accurately detected using ICA under all operating scenarios REFERENCES Phadke, G. & J. S. Thorp (1988). Coputer Relaying for Power Systes, New York, John Wiley. Dash, P. K., A. K. Pradhan, & G. Panda (2000). A novel fuzzy neural network based distance relaying schee. IEEE Trans. on Power Delivery. 15, Sidhu, T. S., D. S. Ghotra, & M. S. Sachdev (2002). An adaptive distance relay and its perforance coparison with a fixed data window distance relay. IEEE Trans. on Power Delivery. 17, Sachdev, M. S. & M. A.Nagpal (1991). Recursive least square algorith for power syste relaying and easureent applications. IEEE Trans. on Power Delivery. 6, Chowdhury, F. N., J. P. Christensen, & J. L.Aravena (1991). Power syste fault detection and state estiation using Kalan filter with hypothesis testing. IEEE Trans. on Power Delivery. 6, Zadeh, R.A., A. Ghosh, & G. Ledwich (2010). Cobination of Kalan Filter and Least-Error Square Techniques IEEE Trans. on Power Syste 25 (4), Girgis A. (1982). A new Kalan filtering based digital distance relaying IEEE Trans. on Power Apparatus and Systes 101, Ukil, A. & R. Živanović (2007). Abrupt change detection in power syste fault analysis using wavelet transfor. International Conference on Power Systes Transients, Montreal, Canada.June Ukil, A. and R. Živanović, (2007). Application of Abrupt Change Detection in Power Systes Disturbance Analysis and Relay Perforance Monitoring. IEEE Trans. on Power Delivery 22 (1), Ray, P. K., H. C. Dubey, S. R.Mohanty, N. Kishor, & K. Ganesh (2010). Power quality disturbance detection in grid-connected wind energy syste using wavelet and S- transfor, IEEE ICPCES,1-4. Pradhan, A. K., A.Routray, & S. R.Mohanty (2005). A Moving Su Approach for Fault Detection of Power Systes Electric Power Coponents and Systes 34 (4), Hyvärinen, A., J. Karhunen, & E. Oa (2001). Independent Coponent Analysis. A Wiley Interscience Publication, John Wiley & Sons, Inc. Sanna, Pöyhönen., P. Jover, & H. Hyötyniei (2003). Independent coponent analysis of vibrations for fault diagnosis of an induction otor Proceedings of IASTED International Conference Circuits, Signals, and Systes, Lopez, M. G., H. M.Lozano, L. P. Sanchez, &L. N. O. Moreno (2011). Blind Source Separation of Audio Signals Using Independent Coponent Analysis and Wavelets 21st International Conference on Electrical Counications and Coputers (CONIELECOMP) Mexico, Dubey, H.C., S. R. Mohanty, & N. Kishore (2011). Abrupt change detection of fault in power syste using independent coponent analysis International Conference on Signal Processing, Counication, Coputing and Networking Technologies (ICSCCN),

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