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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


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


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.



• 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

118

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



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


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.



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



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




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.



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.

121




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.

REFERENCES

1 C. J. Mozina, “Impact of smart grids and green power generation on distribution systems,” IEEE Trans. Ind. Appl., vol. 49,

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3 T. K. Abdel-Galil et al., Protection Coordination Planning With Distributed Generation, CETC 2007-149/2007-09-14, Sep.

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Documents

10 . TJPRCJPSM - DG CONNECTED DISTRIBUTION . TJPRCJPSM - DG... · DG CONNECTED DISTRIBUTION SYSTEM PROTECTION ... In most power system relaying algorithms, the first step always involves