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DG CONNECTED DISTRIBUTION SYSTEM PROTECTION
USING CORRELATION TECHNIQUE
Y. FATHIMA PRIYADARSINI 1 & B. DURGA PRASAD2 1Student M. Tech 2nd Year, Department of EEE, GITAM University Visakhapatnam, Andhra Pradesh, India
2Assistant Professor, Department of EEE, GITAM University Visakhapatnam, Andhra Pradesh, India
ABSTRACT
In the operation of any power system protective relaying plays a critical role. To isolate the power system faulted
sections the protection schemes must guarantee fast, selective and reliable relay operation. Due to the increasing
penetration of distributed generation (DG) and the smart grids Distribution systems are transforming from the commonly
radial nature toward a Meshed and looped structure. For the protection of interconnected sub-transmission systems over
current relaying scheme is the best choice regarding to technical and economical point of view
KEYWORDS: Distributed Generation (DG) Directional over Current Relays, Correlation
Received: Mar 26, 2016; Accepted: Apr 08, 2016; Published: Apr 21, 2016; Paper Id.: TJPRC:JPSMJUN2016010
INTRODUCTION
In the operation of any power system protective relaying plays a critical role. To isolate the power system
faulted sections the protection schemes must guarantee fast, selective and reliable relay operation. Due to the
increasing penetration of distributed generation (DG) and the smart grids Distribution systems are transforming
from the commonly radial nature toward a Meshed and looped structure. For the protection of interconnected sub-
transmission systems over current relaying scheme is the best choice regarding to technical and economical point of
view
Distributed systems have different impacts due to integration of DG and one of the major impacts is on the
power system [1]. The type of the distributed system and type of DG are the factors, the impact of DG integration
and protection scheme depends. It has been shown that in [2]
inverter -based DG (IBDG) generate lower fault current levels than that of synchronous –based DG
(SBDG) which results in more impact on the protection systems. IBDG fault currents typically range from 1 to 2
per unit .So its impact on distributed system is less. IBDG have almost negligible impacts on protection
coordination of Radial distribution systems which uses reclosers, fuses, and over-current relays [3, 4]. In case of
SBDG it affects the fuse saving strategy because it operates before the recloser’s first operation. Fuse replacement
or retuning the settings of the recloser or over-current relay is used for mitigating such problems [5, 6, 7, 8, and 9].
With growing penetration of DG there is a need for modifications and changes to the present distribution protection
philosophies [10].To enhance the DG fault ride and maintaining the low voltage period remaining short protective
devices which provide quick fault isolation are necessary[11].
Elaborate protection schemes have been developed to detect various conditions using current and voltage
measurements through current transformers and potential transformers. Microprocessor – based relays offer many
Original A
rticle TJPRC: Journal of Power Systems & Microelectronics (TJPRC: JPSM) Vol. 2, Issue 1, Jun 2016, 87-98 © TJPRC Pvt. Ltd.
88 Y. Fathima Priyadarsini & B. Durga Prasad
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advantages over conventional schemes. Fault locating has become a standard feature in all microprocessor – based relays
.This thesis represents a descriptive overview of Pearson’s correlation technique.
Correlation between sets of data is a measure of how well they are related. It shows the linear relation between
two sets of data. In most power system relaying algorithms, the first step always involves fault detection and classification.
The present relaying algorithm uses the values of three phase currents to identify the fault location and finding which type
of fault it was.
Currents typically range from 1 to 2 per unit .So its impact on distributed system is less. IBDG have almost
negligible nature of the problem.
The focus of the thesis is towards distributed system protection in a power system. It mainly concerns with the
protection against short circuit faults such as line to ground fault, double line to ground fault, line to line fault and three
phase faults. For a fault being occurred in a line the operator or the equipment associated with protection should quickly
detect the fault and send a trip decision to the circuit breaker to open the corresponding phase ,for this we need to identify
the type of disturbance i.e., either fault or transients ,once identifying the disturbance is a fault then go for the fault
classification .Now for restoration process fault clearing should be done manually in case of permanent faults which
require fault location. The protection relaying includes fault detection, classification and location.
PEARSON’S CORRELATION TECHNIQUE
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard
deviations. Two letters are used to represent the Pearson correlation: Greek letter rho (ρ) for a population and the letter “r”
for a sample.
• For a Population
Pearson's correlation coefficient when applied to a population is commonly represented by the Greek letter ρ (rho)
and may be referred to as the population correlation coefficient or the population Pearson correlation coefficient. The
formula for ρ is:
Where:
is the covariance
is the mean of
is the expectation.
is the standard deviation of
The formula for ρ can be expressed in terms of mean and
expectation. Since
DG Connected Distribution System Protection using Correlation Technique 89
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Then the formula for ρ can also be written as
Where: is the mean of
• Is the Expectation
For a Sample
Pearson's correlation coefficient when applied to a sample is commonly represented by the letter r and may be
referred to as the sample correlation coefficient or the sample Pearson correlation coefficient. We can obtain a formula for
r by substituting estimates of the covariances and variances based on a sample into the formula above. So if we have one
dataset {x1,...,xn} containing n values and another dataset {y1,...,yn} containing n values then that formula for r is
Where
are defined as above
(the sample mean); and
Analogously for
Rearranging gives us this formula for r:
Where
are defined as above
This formula suggests a convenient single-pass algorithm
for calculating sample correlations, but, depending on the
numbers involved, it can sometimes be numerically
unstable.
90 Y. Fathima Priyadarsini & B. Durga Prasad
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• Mathematical Properties
The absolute values of both the sample and population
Pearson correlation coefficients are less than or equal to 1.
Correlations equal to 1 or −1 correspond to data points lying
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2nd International Conference On Power System Analysis Control And Optimization (ICPSACO-2015)
Exactly on a line (in the case of the sample correlation), or toa bivariate distribution entirely supported on a line
(in thecase of the population correlation). The Pearson correlationcoefficient is symmetric: corr(X, Y) = corr(Y, X).
A key mathematical property of the Pearson correlationcoefficient is that it is invariant to separate changes
inlocation and scale in the two variables. That is, we maytransform X to a + bX and transform Y to c + dY, where a, b, c,
and d are constants with b, d ≠ 0, withoutchanging the correlation coefficient. (This fact holds forBoth the population and
sample Pearson correlation coefficients.)
• Interpretation
Relationship between X and Y perfectly, with all datapoints lying on a line for which Y increases as X increases.A
value of −1 implies that all data points lie on a line forwhich Y decreases as X increases. A value of 0 implies that there is
no linear correlation between the variables.
More generally, note that (The correlation coefficientranges from −1 to 1. A value of 1 implies that a
linearequation describes the Xi − X)(Yi − Y) is positive if andonly if Xi and Yi lie on the same side of their respective
means. Thus the correlation coefficient is positiveif X i and Yi tend to be simultaneously greater than, orsimultaneously less
than, their respective means. The correlation coefficient is negative if Xi and Yi tend to lie on opposite sides of their
respective means. Moreover, the stronger is either tendency, the larger is the absolute
Figure 1
Figure 2
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So According to Correlation Formula
• Consider the first half cycle data as ‘x’ and second half cycle as ‘y’.
• Calculate the Pearson’s coefficient ‘r’ by using equation 2 for ‘n’ of samples.
• Now the data named as ‘y’ becomes as ‘x’ and the following cycle as ‘y’ and the process repeats for given number
of cycles.
• If the coefficient ‘r’ is equal to 1 then there is no fault in the particular cycle and if any variation exists then we
can say fault exists in the particular cycle.
• So by comparing each half cycle data we can estimate the fault.
OVER CURRENT RELAYING SCHEME
Value of the correlation coefficient.
Example
In the below waveform if we convert the entire negative half cycles into positive half cycles we get the waveform
as shown in Figure2.
For this we had taken a three –bus meshed system with three generators and three transmission lines as shown in
below Figure 3
Figure 3
2nd International Conference On Power System Analysis Control And Optimization (ICPSACO-2015)
For example if a fault occurs at point A between buses 1 and 2 the resultant RMS values of currents and voltages
for each half cycle and for each phase is shown in below Table1. All this indicates results are taken when fault inception
angle is zero.
92 Y. Fathima Priyadarsini & B. Durga Prasad
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Table 1: Fault between Buses 1 and 2
Table 2: Healthy System Data is Shown in Below
Phase RMS Current RMS Current VALUES FOR HALF VALUES FOR EACH
CYCLE(KA) PHASE(K
A)
I12 I23 I13 I12 I23 I13
A-A1 2.9 1.307 2.92 3.081 1.261 2.866 3
B-B1 3.1 1.224 2.729 3.081 1.261 2.866 C-C1 3.1 1.183 2.934 3.081 1.261 2.866
7
Similarly fault is applied between the buses 1and 3 and 2 and 3. According to the results the thresh hold value for
current is taken as 3.2 kA.
When fault inception angle is zero, then we are getting exact classification of faults. But if the fault inception
angle is changed i.e.; 18 degrees then this scheme fails to give
correct classification. The results are shown in the following Table-3
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Table 3: At Fault Inception Angle =18o
Fault AG BG CG AB BC G G
Outp √ √ √ √ √ ut
Fault CA AB BC ABC ABC G G
Outp √ × × √ √ ut
√ - indicates correct identification of fault
×- indicates fault identification of fault
From the above table we observe that when the fault is at phases B and C , it appears as LG- fault at phase B. So
over current relaying scheme fails in this situation.
So in order to have correct fault detection then we are going for Pearson’s Correlation Technique.
SYSTEM AND SIMULATION SET UP
Here the test case represents the 3-bus system. Assume the base voltage for the bus as 11 kV and system
frequency as 50 Hz.
Table 4: Impedances and Capacitances of the System
Table 5: Generation, Loads and Bus Voltages for the System
2nd International Conference On Power System Analysis Control And Optimization (ICPSACO-2015)
94 Y. Fathima Priyadarsini & B. Durga Prasad
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Phase-B
Figure 4
In this paper we constructed 3- bus meshed system in simulink with the above parameters. Matlab coding is done
for over current relaying and Pearson’s technique for classification of faults.
RESULTS
When the fault is applied between the buses 1-2 .
[1] When the fault is L-G(phase A- G) , then the resultant waveform for current is as shown below
Figure 5
[2] When the fault is L-L-G (phase A, B ) , then the resultant waveform of phase A and phase B current’s are as
shown below.
Phase-A
Figure 6
[3] When the fault is L-L(phase B, C ) , then the resultant waveform of phase A and phase B current’s are as
shown below.
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Phase-B
Figure 7
Phase-C
Figure 8
When Correlation technique is used the resultant waveforms are:
[1a] when the fault is L-G (phase A- G) , then the resultant waveform for current is as shown below.
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96 Y. Fathima Priyadarsini & B. Durga Prasad
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Figure 9
[2a] when the fault is L-L (phase B, C), then the resultant waveform of phase B and phase C current’s are as
shown below.
Figure 10
CONCLUSIONS
In general for distributed systems over current relaying scheme is used. But this scheme failed to give accurate
results if the fault inception angle changes. By adopting Pearson’s Correlation technique the discrimination of the faults is
more accurate than over current relaying scheme.
So from the results we can say Pearson’s correlation algorithm gives 100 percent accuracy as it compares the data
cycle by cycle which shows even a small variation.
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