This paper presents a methodology for identifying and diagnosing faults in transmission lines using digital image processing techniques, specifically wavelet shrinkage functions. The approach focuses on image acquisition, segmentation, and edge detection to enhance fault detection while addressing challenges like noise reduction and image clarity. Experimental results indicate that the proposed method significantly improves fault identification metrics, demonstrating its effectiveness in the context of smart networks.

1. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
74 NITTTR, Chandigarh EDIT-2015
A Flexible Scheme for Transmission Line Fault
Identification Using Image Processing For a
Secured Smart Network
1
D.Vijayakumar,2
V.Malathi
1
Assistant Professor, Department of Electronics and Communication Engineering, LathaMathavan Engineering College,
Madurai, India, 2
Professor ,Department of Electrical and Electronics Engineering, Anna University Regional Office,
Madurai, Tamil Nadu, India
1
vkkumarin@gmail.com, 2
vmeee@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
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.

2. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
75 NITTTR, Chandigarh EDIT-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)
1.3 mrad
Digital Image Enhancement
Normal and enhanced
Detector Type
Focal plane array
(FPA)Uncooled
microbolometer
Spectral range
7.5 to 13 µm
Focus
Automatic or Manual
Electronic zoom function
2,4,8 interpolating
Thermal sensitivity@50/60Hz
0.08 c at 30 c
Table.1. Imaging Performance
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
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
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 (subband-
subordinate) vector-esteemed thresholding of individual
Input Image with
electrical parameters
Fault
Image
Image
segmentation
ImageD
etection
Original
Image
Recovery

3. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 76
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)||2
2 + 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,
=
: , ∈
,
into the 1-D vector
= : = 1, …
gn(wn=
−
−
( < )
( ℎ )
…….…(3)
In the equation (3) gn(wn) is thenth
Wavelet coefficient.
Input
Image
Fault
Value
level
PSNR
Value
using
Neighsh
rink
MSE
value
ELAPSED
TIME
1 48.5480 0.9084 10.916196
5 36.9973 12.9822 11.701278
10 32.8917 33.4125 10.960715
20 29.3423
75.6567
11.884871
1 48.5480 0.9084 12.634479
5 36.9973 12.9822
11.751183
10 32.8917 33.4125 10.833803
20 29.3423 75.6567 10.144114
1 48.3809 0.9440 16.546169
5 36.6428 14.0864 16.360380
10 32.6126 35.6301 15.879918
20 29.0225 81.4386 17.639207
Table.2.calculation of PSNR value with elapsed time
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
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:
MSE = ∑ ∑ ‖I(i, j) − k(i, j)‖ …(5)
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
Fig.2.Input Fig.3.Faulty image
Fig.4.Binary gradient mask
Fig.5.Dilated gradient mask
Fig.6.Diagnosis Image
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
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 76
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)||2
2 + 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,
=
: , ∈
,
into the 1-D vector
= : = 1, …
gn(wn=
−
−
( < )
( ℎ )
…….…(3)
In the equation (3) gn(wn) is thenth
Wavelet coefficient.
Input
Image
Fault
Value
level
PSNR
Value
using
Neighsh
rink
MSE
value
ELAPSED
TIME
1 48.5480 0.9084 10.916196
5 36.9973 12.9822 11.701278
10 32.8917 33.4125 10.960715
20 29.3423
75.6567
11.884871
1 48.5480 0.9084 12.634479
5 36.9973 12.9822
11.751183
10 32.8917 33.4125 10.833803
20 29.3423 75.6567 10.144114
1 48.3809 0.9440 16.546169
5 36.6428 14.0864 16.360380
10 32.6126 35.6301 15.879918
20 29.0225 81.4386 17.639207
Table.2.calculation of PSNR value with elapsed time
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
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:
MSE = ∑ ∑ ‖I(i, j) − k(i, j)‖ …(5)
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
Fig.2.Input Fig.3.Faulty image
Fig.4.Binary gradient mask
Fig.5.Dilated gradient mask
Fig.6.Diagnosis Image
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
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 76
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)||2
2 + 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,
=
: , ∈
,
into the 1-D vector
= : = 1, …
gn(wn=
−
−
( < )
( ℎ )
…….…(3)
In the equation (3) gn(wn) is thenth
Wavelet coefficient.
Input
Image
Fault
Value
level
PSNR
Value
using
Neighsh
rink
MSE
value
ELAPSED
TIME
1 48.5480 0.9084 10.916196
5 36.9973 12.9822 11.701278
10 32.8917 33.4125 10.960715
20 29.3423
75.6567
11.884871
1 48.5480 0.9084 12.634479
5 36.9973 12.9822
11.751183
10 32.8917 33.4125 10.833803
20 29.3423 75.6567 10.144114
1 48.3809 0.9440 16.546169
5 36.6428 14.0864 16.360380
10 32.6126 35.6301 15.879918
20 29.0225 81.4386 17.639207
Table.2.calculation of PSNR value with elapsed time
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
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:
MSE = ∑ ∑ ‖I(i, j) − k(i, j)‖ …(5)
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
Fig.2.Input Fig.3.Faulty image
Fig.4.Binary gradient mask
Fig.5.Dilated gradient mask
Fig.6.Diagnosis Image
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

4. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
77 NITTTR, Chandigarh EDIT-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.
REFERENCES
[1] Hari Om, and MantoshBiswas, “A generlz. Image denoising mtd.
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.
Pattern Analys.and Mc. Int., vol.12(10):pp.993-1001, 1990.
[4] William K. Pratt. Digital Image Processing. John Wiley e Sons,
INC., 2 edition, 1991.
[5] Pinar SarisarayBoluk, SebnemBaydere, A. EmreHarmanci, “Robust
Image Transmission Over Wireless Sensor Networks “, Mobile
Networks and Applications, ACM , Vol. 16 .Apr 2011
[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.