A Flexible Scheme for Transmission Line Fault Identification Using Image Processing For a Secure

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

A Flexible Scheme for Transmission Line Fault Identification Using Image Processing For a Secured Smart Network 1

D.Vijayakumar,2V.Malathi

1

Assistant Professor, Department of Electronics and Communication Engineering, LathaMathavan Engineering College, Madurai, India, 2Professor ,Department of Electrical and Electronics Engineering, Anna University Regional Office, Madurai, Tamil Nadu, India 1

vkkumarin@gmail.com, 2vmeee@autmdu.ac.in

Abstract:-This paper describes a methodology that aims to find and diagnosing faults in transmission lines exploitation image process technique. The image processing techniques have been widely used to solve problem in process of all areas. In this paper, the methodology conjointly uses a digital image process Wavelet Shrinkage function to fault identification and diagnosis. In other words, the purpose is to extract the faulty image from the source with the separation and the co-ordinates of the transmission lines.

The segmentation objective is the image division its set of parts and objects, which distinguishes it among others in the scene, are the key to have an improved result in identification of faults.The experimental results indicate that the proposed method provides promising results and is advantageous both in terms of PSNR and in visual quality.

Index Terms—Image processing, Fault detection ,Fault diagnosis, Transmission line.

I. INTRODUCTION Image is an important way of access to information for people. But noises largely reduce the perceptual quality of images and may result in fatal errors. Image denoising has been a fundamental problem in image processing. The wavelet transform is one of the popular tools in image denoising due to its promising properties for singularity analysis and efficient computational complexity. The noise is occurred in images during the acquisition process, since the intrinsic and thermal fluctuations of acquisition devices. The other reason is only low count photon unruffled by the sensors while comparing others, the signal dependent noise is imperative. It should be unease.

Image processing takes part in medical field of the essence. During the disease diagnosis, the consequences of many types of equipment in the medical field are in digital format. There are many prehistoric methods are used for denoisingwhich have its own annoyances. The fundamental undertaking in every sort of picture transforming is discovering an effective picture representation that portrays the noteworthy picture emphasizes in a minimized structure. The main step in order to achieve fault detection and diagnosis is to select a set of inputs whose information is capable to allow the fault identification. This paper uses digital image processing techniques to extract some variables from the tested image. Once all data is collected, it´s necessary to apply digital image processing techniques. These variables are 74

used by the diagnosis tool developed. This strategy is known as bagging and is applied here to improve the power of generalization of the fault detection system . A heuristic is used to determine the optimal number of neurofuzzy networks in the thermovisiondiagnosis.H.m, and MBiswas,[1] stated a generalized picture denoising strategy utilizing neighboring wavelet coef.in Signal,Image and Video Processing techniques. The image processing techniques have been widely used to solve problems in process of all areas [2, 3]. Digital Image Processing consists of a set of techniques used to make transformations in one or more images with the objective to enhance the visual information or scenes analysis to get an automatic perception or recognition from machines [4].Many methods related to image transmission using filtering techniques of multimedia applications over wireless sensor network have been proposed by researchers. Pinar SarisarayBoluk et al. [5] presented two techniques for robust image transmission over wireless sensor networks. The first technique uses watermarking whereas the second technique is based on the Reed Solomon (RS) coding which considers the distortion rate on the image while transmission for wireless sensor networks. Renu Singh et al. [6] proposed wavelet based image compression using BPNN and Lifting based variant wherein optimized compression percentage is arrived using these two adaptive techniques.

Pinar SarisarayBoluk, studied image quality distortions occurred due to packet losses using two scenarios, considering watermarked and raw images to improve the Peak-Signal-to-Noise-Ratio (PSNR) rate. In digitalimage processing Zhang Xiao-hong and Liu Gang [7] proposed SPIHT method to reduce the distortion in images. In [8] the authors Wenbing Fan and Jing Chen, Jina Zhen proposed an improved SPIHT algorithm to gain high compression ratio.K. Vishwanath et al. [9] presented image filtering techniques on larger DCT block which speed ups the operation by eliminating certain elements.James R. Carr [10] applied spatial filter theory to kriging for remotely sensed digital images. The method proposed improved image clarity.Buades et al[11] states the Neighborhood filters used for image process and PDE’s. Yu,H.[12] et al mentioned the Image denoising using shrinkage of filter in the wavelet process and joint consensual filter in the dimensional Area.

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Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

II. IMAGE PROCESSING TECHNIQUES The Digital Image Processing may be divided in the following steps: Image acquisition Image segmentation.

Image matching using edge detection NeighShrinkSURETransformation Method.

1. Image acquisition A tested image is used for the process of fault analysis and diagnosis, for image acquisition. The parameters of the image are shown in table.

Spatial Resolution(IFOV) Digital Image Enhancement

Detector Type

Spectral range Focus Electronic zoom function Thermal sensitivity@50/60Hz

1.3 mrad Normal and enhanced Focal plane array (FPA)Uncooled microbolometer 7.5 to 13 µm Automatic or Manual 2,4,8 interpolating

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Aptive Threshold, Region Growing and Watershed Transformation. This paper uses gradient operator for achieving better edge preservation. After segmentation the gradient operator is used for achieving better edge preservation.

4. Image Edge detection The obtained image will be processed and compares the image with the input values. Among the key features of an image i.e. edges, lines, and points, that edge can be detected from the abrupt change in the gray level.

An edge is the border between two different regions. Edge identification strategies spot the pixels in the picture that compare to the edges of the articles seen in the picture.

The result is a binary image with the detected edge pixels.

Common algorithms used are Sobel, Canny, Prewitt and Laplacian operators of MATLAB. The gradient is calculated and the table shows the compared values of the output image. Here the gradient based Edge Detection that detects the sides by probing for the most and minimum within the differential of the image.Theimagefunctions can be identified and it is compared with various PSNR values as shown in the table.

5. Image Denoising Despite being more suitable to low bit rate environments, such as mobile and wireless channels

0.08 c at 30 c

Table.1. Imaging Performance

Input Image with electrical parameters

Fault Image

Image segmentation

Fig.1.Process of Identification of faults

2.Wireless network A WSN (wireless sensor network) is a wireless network which consists of sensors. Sensors are used to monitor physical and environmental conditions. The development of such networks was originally motivated by surveillance. The wireless sensor networks are for electrical systems monitoring.The image will be transmitted from the transmitting end to the receiving end terminal for the analysis of the image.

3. Segmentation Segmentation is the most complex step of image processing system based and it can be made by many ways, depending on the problemcharacteristic and the purposes to be reached.The segmentation objective is the image division its set of parts and objects. It’s necessary to use the whole information available related to the problem in order to have a successful segmentation. If the objective is the segmentation of a specific object, its most meant characteristics, which distinguishes it among others in the scene, are the key to have a good result.The segmentation objective is the image division its set of parts and objects. It’s necessary to use the whole information that voltage, current, temperature of the symmetrical transmission line is given to the input parameter is set in the image for transmission. It’s evident that the acquired image fits the image center to identify it after the acquisition. The most common tools used at the segmentation are: Point or Line detection, Edge detection, Gradient operators, Laplacian, Houghlight Simple or 75

ImageD etection

Original Image Recovery

As a result, it is desirable to remove the noise if possible.

It shows that if any fault occurs during the transmission line the voltage, current and temperature level will get as faulty values. And the faulty images will get retrieved to the original values by thresholding the values. The principle behind this is that when noise occurs as fault during transmission it will be notified the other node .The retrieving of the fault can be done by Wavelet Shrinkage function.

6. Edge Reconstruction for Images The image will be reconstructed by using the image masking and diagnosis method to retrieve the input values. The input voltage, current and temperature change in values can be monitored by this image processing techniques. The wavelet-based image compression is advantageous over the earlier block-based compression techniques. Distortion around these edges is perceptually objectionable and cannot be easily avoided if images are required to betransmitted at low bit rates. This type of edge distortion is easily seen in the tested images.

7. Wavelet Shrinkage function NeighshrinkSURE transformation is one of the methods in wavelet shrinkage function which is used to diagnosis the faults in transmission line. This wavelet thresholding work is reason astute and relies on upon the coefficients of same area inside the diverse channels, still as on their oldsters inside the coarser wavelet subband. A non-excess, orthonormal, wavelet change is beginning connected to the blunder data ,took after by the (subbandsubordinate) vector-esteemed thresholding of individual

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Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

multichannel wavelet coefficient that square measure at long last conveyed back to the picture area by converse wavelet change. The NeighShrink are efficient image denoising algorithms that are based on universal threshold and discrete wavelet transform. In order to determine the NeighShrink, the optimal threshold and neighbouring window size are calculated as, (λs,Ls)=argλ,Lmin SURE(ws,λ,L)………..(1) where λ is optimal threshold, L is neighboring window size , s is subbandandsure (ws,λ,L)= Ns + Σ ||gn(wn)||22 + 2 Σ …...(2) In equation it is an unbiased estimate of the risk on subband s and L is an odd number and greater than1,forexample,3,5,7,9,etc.Ns noisy wavelet coefficients from subband s, =

: , ∈

,

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

method using the quality measure PSNR which is calculated as follows.

PSNR = 10log ………… (4) Here the performance of proposed method is compared with different denoising scheme. Mean Square Error (MSE), which requires two m x n grey-scale, images iand k. Where one of the images is considered as a noisy approximation of the other and it is defined as: The MSE is defined as: ∑ ∑ ‖I(i, j) − k(i, j)‖ …(5) MSE = The comparison of PSNR obtained with these five different images can be seen in table 2. Table 2 comparison isbased on theNeighshrinkSURE transformation method. As shown in table 2, the PSNR of image denoised by the proposed method is obviously outperforms as compared to existing methods. It can be seen that PSNR obtained with the proposed method isenhanced are compared to existing methods

into the 1-D vector = : = 1, … ( < ) − gn(wn= …….…(3) ( ℎ ) − In the equation (3) gn(wn) is thenth Wavelet coefficient.

Input Image

Fault Value level

1 5 10

PSNR Value using Neighsh rink 48.5480 36.9973 32.8917

MSE value

ELAPSED TIME

0.9084 12.9822 33.4125

10.916196 11.701278 10.960715

20

29.3423

1

48.5480

0.9084

5

36.9973

12.9822

10

32.8917

33.4125

10.833803

20

29.3423

75.6567

10.144114

1 5 10

48.3809 36.6428 32.6126

0.9440 14.0864 35.6301

16.546169 16.360380 15.879918

20

29.0225

81.4386

17.639207

Fig.2.Input

75.6567

Fig.3.Faulty image

11.884871

12.634479 11.751183

Fig.4.Binary gradient mask Fig.5.Dilated gradient mask

Table.2.calculation of PSNR value with elapsed time Fig.6.Diagnosis Image

III. SIMULATION RESULTS In order to evaluate the performance of the proposed method, the experiment is performed on a representative set of standard 8 bit gray scale CVG-UGRdatabase, such as House, Lena, Barbara, Pepper, Boat each of size 512x512,256x256 corrupted by simulated additive white Gaussian noise with a standard deviation equal to10,15,20. Several methods were used to filter the noisy image. The paper evaluated the performance of proposed NITTTR, Chandigarh

IV. CONCLUSION This paper presents a method for detection and diagnostics of failures in transmission line. The input values of the transmission line are injected in the image and it is transmitted in a network. The obtained image values are processed by the Neigh Shrink SURE function.

And if an fault is observed or any noise is occurred in the image it tends to change the characteristics of the image.

Thus the changes can be proceeding by the Neigh EDIT -2015 76

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

ShrinkSURE function and the original values of the input are obtained. Thus it ensures that the fault can be detected and it is diagnosed.The diagnostics tool implemented showed itself as a powerful tool to identify the fault.

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Using neighbr. waveletcoef.,” Signal,Image and Video Processing Springer -SViP March, 2013 [2] P. Sollich and A. Krogh, “Learning with ensembles: how over-fitting can be useful”, In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo eds., Advances in Neural Information Processing Systems 8, Denver, CO, MIT Press, Cambridge, MA,pp.190-196, 1996.

[3] L. K. Hansen and P. Salamon, “Neural network ensm. , IEEE Trans.

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[5] Pinar SarisarayBoluk, SebnemBaydere, A. EmreHarmanci, “Robust Image Transmission Over Wireless Sensor Networks “, Mobile Networks and Applications, ACM , Vol. 16 .Apr 2011

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[6] Renu Singh, SwanirbharMajumder, U. Bhattacharjee, A. Dinamani Singh,” BPNN and Lifting Wavelet Based Image Compression”, Information and Communication Technologies, Vol. 101.,Springer 2010 [7] Zhang Xiao-hong, Liu Gg.,” Res. of the SPIHT Comp.Based on Wavelet Int.pol.Matg. Image”, Communications in Computer and Information Science, Vol. 225,Springer 2011 .

[8] Wenbing Fan, Jing Chen, Jina Zhen,” SPIHT Algorithm Based on Fast Lifting Wavelet Transform in Image Compression “, Computational Intelligence and Security,Vol. 3802, Springer 2005.

[9] K. Viswanath, Jayanta Mukherjee, P.K. Biswas,” Image filtering in the block DCT domain using symmetric convolution”, ACM Vol. 22 ,Feb 2011 [10] James R. Carr, “Application of spatial filter theory to kriging”, Mathematical Geology, Springer Nov 1990 Vol. 22 .

[11] A. Buades, B. Coll, and J. Morel, “Neighborhood filters and PDE’s,” Numer. Math., vol. 105, pp. 1–34, 2006.

[12] Yu,H., Zhao, L.,Wang, H., “ Image denoising using TS filter in thewavelet domain and jt.bil.filter in the sptlDmin”. IEEE Tr. Im.Procs.Vol.19(10),pp. 2364–2369,2009.

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