conference cnc 2022.pptx

3rd International Conference on Communication, Networks and
Computing (CNC 2022)
Methodologies to Classify
Faults in Power Transmission
Lines
Paper ID-6694
Mr.V.Rajesh Kumar
Kalasalingam Academy of Research and
Education,Srivilliputhur.
12/25/2022 3rd International Conference on Communication,Networks and Computing (CNC 2022) 1

Abstract. The vast array of electrical power systems and its
applications necessitates the development of appropriate
fault classifications algorithms in an electrical power
transmission lines in order to improve the efficiency of the
system and avert catastrophic damage. A wide variety of
methods are proposed in the technical literature for this
goal. This survey paper examines different methodologies
used to classify faults in an electrical power transmission
lines as and summaries its key approach.
Keywords: SVM, Technical Literature, GSM, Pilot Scheme
.

1 Introduction
• The most important responsibility in
safeguarding the electric power lines is to
protect transmission lines from exposed
faults
• Previously, numerous scholars presented
various fault categorization systems.
• when a new user begins their investigation
in this field, he or she may be confused
about which method to apply to classify the
type of the error

1 Introduction (cont.…)
Many researchers have previously devised
several ways, each of which has its own set of
pros and cons. So, by selecting papers from
reputable publications, this review article will
provide a clear overview of all the existing fault
classification algorithms

2.Review Classification:
2.1 (SVM) Support Vector Machine
Support Vector Machine is an unique approach to learn
about (Pattern recognition functions) separately to classify
the tasks or to perform functioning estimation in regression
issues (SVM). It's a statistical learning theory-based computer
learning technique. In this approach the input vectors are
unsystematically mapped onto a topological feature space. It
is used to solve a variety of classification issues. The following
are the explanations for articles based on SVM.

2.2 Genetic Algorithm
In this the main distinctive between genetic
algorithms and standard optimizations approach is
nothing but the GA uses a populace of points at once,
whereas classic optimization methods use a single
point approach.
This implies GA works on multiple designs at the
same time.

2.3 DWT-ELM Approach
The suggested fault classifier is compared to the
DWT-ANN fault classifier, and has been found that
this type of classifier has higher accuracy as well as it
takes a less duration to learn than any other method
(RAY ET AL- 2012)

2.4 FPGA Method
The use of Field Programmable Gate Array (FPGA) in
the branch of electric power systems is increasing due
to new advancements in the FPGA leading edge
technology mostly in the hardware and software.And
due to reduction in cost. Valsan and Shanti Swarup
(2009) provided a fault analysis in electric
transmission lines by FPGA method .(VALSAN AND
SHANTI SWARUP-2009)

2.5 GSM Method
(SUJATHA AND VIJAYAKUMAR-2011) created a
massive GSM approach that can be used to improve
the reliability of previously built special protection
systems during network outages. Here, a significant
GSM is used to send information from one network to
another and also to identify any changes in
transmission parameters to secure the entire
transmission and distribution process (SUJATHA AND
VIJAYAKUMAR-2011)

2.6 PMU Method
(JIANG ET AL.-2002) proposed a PMU-based protection
mechanism (2002, 2003). It demonstrated an adaptive
transmission system protection strategy based on
synchronised phasor readings. The protection scheme for the
phasor measuring unit (PMU) includes detecting and
classifying the faults in transmission lines. To reach the whole
line protection, this technique used synchronised phasor
quantity to construct a multi-functional protecting relays
(JIANG ET AL.,-2002).

2.7 Decision tree based method
This is certainly the ultimate advanced approach to split the
sample information into a set of decision protocols. Tree
classification are also called as decision tree which is used to
solve classification problems. The below publications shows
the decision tree method process aids to classify the faults.
A decision tree method by Shahrtash and Jamehbozorg
(2008). It is used in the power transmission system to classify
faults. Using travelling waves generated by the fault detector
and it calculates the exact fault inception time.

2.8 Motley-information measurements
(Ling et al-2009) developed an unique method to
classify fault based on motley-information
measurements of fault ephemeral, as well as
information entropy and complexity measurements.
This approach can be used with a variety of transitory
components (Ling and colleagues, 2009)

2.9 Fast Estimation Method using Phasor components
(Saha et al-2010a,b) suggested a new approach for identifying
problematic phases in transmission systems. This proposed approach
was based on phase current readings and quick phasor component
estimating in a limited information window. The key collection
technique makes use of the relationships between current magnitudes
for various fault loops. By the use of neutral and phase currents, this
approach can distinguish between grounded and ungrounded faults
(Saha et al., 2010a,b).

2.10 PCA Method
(Alsafasfeh et al. 2010) suggested a novel fault classification approach
.This research depends on phase currents in the initial (1/4)th of a cycle
in a consolidated system that utilizes the symmetrical components
method and principal component investigation to deliver unrivaled
discoveries (PCA). This algorithm has the advantage of being able to be
utilised at either extremity of a transmission lines and eliminating the
exigency for data communication devices

2.11 Pilot Method
MAHAMEDI (2011) proposed a new method of classification for faults
based on reactive power in both usual and fault conditions. The
indication of reactive power estimated by one relay should be
connected with the indication of reactive power estimated by one more
relay in a pilot technique. The main advantage of this technique is that
it does not require any kind of setup. To specify a threshold for any
parameter, the relay is not required.

2.13 Euclidean Distance Based Function Method
This method is based on Euclidean distance between consecutive
current samples is proposed for the protection of transmission line
(PRASAD AND PRASAD - 2014). This method is then used to detect
incorrect phases. Signals containing load alteration, noise, frequency
digression, spikes, and faults are used to evaluate the procedure's
relative performance under various power system scenarios (Prasad
and Prasad, 2014)

Conclusion
Several new fault classification methods, as well as
their key characteristics, were incorporated in this
paper. All of these approaches have unique
characteristics, and research is still ongoing to reduce
the operation time of relays at high speeds. As a
result, new algorithms based on sophisticated
optimization approaches and flexible alternating
current transmission strategies are more
computationally effective and suitable for real-time
applications are required.

References
1. Alsafasfeh, Qais, Abdel-Qader, Ikhlas, Harb, Ahmad, 2010. Symmetrical pattern and PCA based framework for fault detection
and classification in power systems. IEEE Conference Publications, 1–6.
2.de Souza Gomes, André, Azevedo Costa, Marcelo, de Faria, Thomaz Giovani Akar, Matos Caminhas, Walmir, 2013. Detection
and classification of faults in power transmission lines using functional analysis and computational intelligence. IEEE Trans.
Power Deliv. 28 (July (3)), 1402–1413.
3.Jamehbozorg, A., Shahrtash, S.M., 2010. A decision tree-based method for fault classification in double-circuit transmission
lines. IEEE Trans. Power Deliv. 25 (October (4)), 2184–2189.
4.Jiang,Joe-Air, Chen, Ching-Shan, Fan, Ping-Lin, Liu, Chih-Wen, Chang, Rong-Seng, 2002. A composite index to adaptively
perform fault detection, classification, and direction discrimination for transmission lines. IEEE Conference Publications vol. 2,
912–917.
5.Jiang, Joe-Air, Chen, Ching-Shan, Liu, Chih-Wen, 2003. A new protection scheme for fault detection, direction discrimination,
classification, and location in transmission lines. IEEE Trans. Power Deliv. 18 (January (1)), 34–42.
6.Ling, Fu,Zhengyou, He,Zhiqian,Bo, 2009. Novel approach to fault classification inEHV transmission line based onmulti-
informationmeasurements of fault transient. IEEE Conference Publications, 1–4.
7.Mahamedi, Behnam, 2011. A novel setting-free method for fault classification and faulty phase selection by using a pilot
scheme. IEEE Conference Publications, 1–6.
8.Mahanty, R.N., Dutta Gupta, P.B., 2004. Application of RBF neural network to fault classification and location in transmission
lines. IEE Proc. Gener. Transm. Distrib. 151 (March (2)), 201–212.
9.Malathi, V., Marimuthu, N.S., 2008. Multi-class support vector machine approach for fault classification in power
transmission. IEEE Conference Publications, 67–71. Prasad, Ch. Durga,
10.Prasad, D.J.V., 2014. Fault detection and phase selection using euclidean distance based function for transmission line
protection. IEEE Conference Publications, 1–4.
11.Rahideh, Abdolhamid, Gitizadeh, Mohsen, Mohammadi, Sirus, 2013. A fault location technique for transmission lines using
phasor measurements. Int. J. Eng. Adv. Technol. 3 (October (1)), 241–248.
12.Ray, Papia, Panigrahi, B.K., Senroy, N., 2012. Extreme learning machine based fault classification in a series compensated
transmission line. In: IEEE Conference Publications, December, pp. 1–6.

Thank You
12/25/2022
3rd International Conference on Communication,Networks and
Computing (CNC 2022)
21

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