Site Loader

This paper
proposes an intelligent method to detect High Impedance Fault ( HIF) in
distribution network using Wavelet Transform ( WT ) and data mining based
Decision Tree ( DT ) model. The proposed method uses WT to decompose current
signal and extracts significant energy features of the signal. A data mining
based DT model reduces the features of the signal and also frame a rules with
limit to decide HIF or non-HIF cases. The current signal data for HIF and
non-HIF events have been acquired by an accurate model of an actual
distribution system using MATLAB / SIMULINK. The simulation results show that
the proposed method can provide a consistent and powerful protection for HIF.

keywords:
High impedance fault, Distribution network, Data mining,  Decision tree.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Introduction:

The electric power distribution
system is mostly an overhead lines. These are more vulnerable to breakdown of power supply
due to the fact that are exposed to dissimilar climatic circumstances. Some of
these failures may be detected and located easily. However, there are few
failures which cannot be detected by conventional protective means. For
example, When an energised broken or unbroken line connected to high impedance
objects or surfaces draw less amount of current 1 with no evidence of defect,
such kind of fault classified as High Impedance Fault (HIFs). If The
distribution system running with unidentified HIF for hours or days leads to
damaging the equipment connected to the supply. Moreover, from the
investigation it is observed that, followed by HIF, the electric arcs
turn out an arbitrary, erratic and asymmetric current 2. The distribution
systems are close to populated areas, where the electrical arcs  lead to fatal for public.

 

Detection of HIFs are matter of
interest from early1970, but the enlightening on detection process yet to be
completed.  A method based on lower order
harmonics ratio presented in 3. The drawback of such kind of methods need to
set one or few threshold value which is affecting security of detection method.
Time-frequency analysis based methods 4-5 exposed good performance in the
detection process. However,  percentage
of  false detection  is shown as major setback for practical applications.

 

Time domain approaches use to
detect the change in current or voltage signal from pre-fault conditions. Mathematical
morphology based time domain methods found in 6-8, which is effective
detection method in balanced system, there are few issues associated with
unbalanced network.

 

Wavelet Transform (WT) has been extensively used in signal
processing because of its capability to detect the frequency component and
their position in time. More than a decade such methods applied to power system
protection. Although WT based methods are providing good detection rate with
linear loads 9-13, no evidence of non linear loads inclusion with the systems
while detecting HIFs except  in 14. The existence of non-linear loads
(NLLs) in the system has continuously increasing right through the power
distribution grids. whereas a huge quantity of existing methods have failed to
consider NLLs  while modelling and
designing of practical HIF detection methods. The NLLs and HIFs characteristics
are closely resembles each other, which will makes the existing methods less
effective. Hence, In this paper, an enhanced method for detection of HIFs
including huge variation of NLLs is Proposed.

 

            The rest of the paper has been
organized as follows. Section 2 explains the test system and also the
characteristics of HIF model engaged for simulation. The process of proposed
methodology has been thoroughly discussed in Section 3, including decomposition
of signal, feature extraction, feature selection and classification. Section 4 has
results and discussion and the paper concluded with main highlights of the work
in section 5.

Post Author: admin

x

Hi!
I'm Erica!

Would you like to get a custom essay? How about receiving a customized one?

Check it out