Dynamic Fault Classification and Location in Distribution Networks

This paper presents a method for detecting, classifying and localizing faults in MV distribution networks. This method is based on only two samples of current or voltage signals. The fault detection, faultclassi cation and fault localization are based on the maximum value of current and voltage as a function of time. A study is presented in this work to evaluate the proposed method.A comparative study between current and voltage method detection has been done to determine which is the fastest. In addition, the classi cation and localization of faults were made by the same method using two samples signal. Simulation with results have been obtained by using MATLAB / Simulink software. Results are reported and conclusions are drown.


INTRODUCTION
Electric power systems have developed rapidly in recent years and these systems have become important in all branches of the modern econ-omy.With the growth of world population, and development in all areas, the demand for electric power is growing rapidly.Medium-Voltage electrical power distribution lines are an essential part of an electrical power grid that must ensure the continuity of power supply to Medium Voltage (MV) and Low Voltage (LV) consumers.That is not always the case, These lines experience faults which are caused by storms, lightning, snow, freezing rain, insulation breakdown and, short circuits caused by birds and other external objects [1].These faults must be detected, classied and localized quickly and correctly so that our system remains stable.
When a fault occurs in a distribution networks, the fault current is always greater than the rated load current and the fault voltage will be smaller than the nominal network voltage.

USED METHOD
For the detection of electrical faults in any network there are several methods.Most methods use the maximum values of the voltage or current (V max , I max ) comparing them to a thresh- old value, in this paper we will use a new method based on two samples which is as follows: The equation of the voltage is as follows: We have the voltage at the moment k: The voltage at the moment k + 1: We know that: Therefore, Eq. ( 5) becomes: We replace Eq. (2) in Eq. ( 7) and we get: With (Eq.( 2) ) 2 +(Eq.(9) ) 2 , we give: We do the same thing for the current "i", the result is: The current can be written as: From Eq. ( 2) we have: From Eq. ( 7) we have: Using Eq. ( 13) and Eq. ( 16) we can obtaining the expression of θ k .Using Eq. ( 17) and Eq. ( 18), we obtain the nal value of θ k : where Using Eq. ( 2) and Eq. ( 12), the fault impedance Z k can be determined as: We note: To localize the fault, the fault impedance Z k can be determined by: With: where

Fault detection and classication
Fig. 1 presents the owchart of the method to detect and classify the fault.

Fault localization
The apparent positive-sequence fault impedance measured is proportional to the fault distance, which can be estimate for each fault type [14,15] as shown in Table 1.
Where a, b and c indicates faulty phases.
g indicates ground fault.
Z OL is the zero-sequence line impedance.
Z dL is the positive-sequence line impedance.
I R is the residual current (3I 0 ).
I 0 is the zero-sequence current.
The fault location (m) can be determined by using impedance Z k or the reactance X k .Using the reactance, the fault location (m) is: X d is the positive sequence line reactance (Ω/km).

3.
POWER SYSTEM MODEL Fig. 2 shows the block Simulink of our 25 kV, 50 Hz network under the software MATLAB.The      In Fig. 5 (a) the black dots represent the maximum voltage value calculated at each instant.
In Fig. 5 (b) we can see, the rst dot that is dierent from zero is at the instant 0.061 sec.
Therefore it is concluded that the detection

Fault classication
To classify the fault by the proposed method, the maximum voltage values are used and the same steps as the detection for each phase are followed.To classify the ground fault, we use the zero sequence voltage signal.
We programmed a fault classier algorithm and we created several types of faults.The results are as follows:   The fault classier indicates that phase "b" is always zero, which implies that it is a double-   The fault classier indicates that the phases "a", "b" and "c" vary from "0" to "1" so we can conclude that it is a three-phase fault.The fault is classied on the phase "b" at the instant 0.061 sec.and the phases "a" and "c" at the instants 0.062 sec.so the fault classication time is equal c 2017 Journal of Advanced Engineering and Computation (JAEC) According to the tests studied we note that faults without ground are classied faster than faults with ground.

Fault localization
The fault is supposed appears at the end of each section that is to say at 5 km, 10 km, 15 km and 20 km of the distribution line.From Fig. 10 we can see a stability in the response and the distance is detected rapidly, it is clear that the nal value of the fault locator is the same value of the supposed fault distance.
Figure 11 shows the fault location as a function of time using the reactance for double-phase fault with ground.Note: the three-phase fault gives us the same results as the double-phase fault with ground.
However, the fault location estimative is affected by many parameters, including fault resistance RF, which may be high for ground faults.
In this study we have noted that the maximum value of fault resistance that can be accepted by the proposed technique is 8 Ω for all fault type and at each section.

CONCLUSIONS
A method of two samples was presented in cator takes some time to the approximate the nal value, the response isunstable and the distance is not detected rapidly, but the distance is determined after 400 ms.
The detection and localization of faults in electrical networks plays an important role in the correct operation of protective relays.Fault detection and localization conventional methods for distribution lines are broadly classied as impedance based method which uses the steady state fundamental components of voltage and current values [2]-[6].Wavelet method which is based on low pass lters and high pass lters [7]-[9], and knowledge based method which uses articial neural network and/or pattern recognition techniques [10]-[12].Digital relays that use the wavelet method and methods based on articial neural networks for detecting and locating faults have a weakness because they have been designed for specic networks unlike the digital relay based on conventional algorithms that are designed on the basis of current or voltage amplitude measurements Increase of current magnitude or decrease of voltage magnitude could be considered as a measure to detect andclassifya system in fault.The measure of reactance or impedance of the line is considered to locate the fault.In [13] the authors use two methods to localise the fault in transmission line.The rst method is based on the rst and second derivative of the circuit equation and the second method is based on the integral of the circuit equation.In this paper, an algorithm is proposed to detect, classify and locate faults on distribution network as a function of time.The method is based only on two samples of signal current or voltage.

V
a , V b and V c indicate voltage phasors.I a , I b and I c indicate current phasors.

Fig. 3
Fig. 3 shows the steps performed by the digital relay for fault detection, classication and localization.

Fig. 3 :Fig. 4
Fig. 3: The steps performed by the digital relay for fault detection, classication and localization.

Fig. 5
Fig. 5 shows the voltage signal, the maximum voltage value and the output fault detector signal in function of time.In Fig.4(a) the black dots represent the maximum current value calculated at each instant.In Fig.4(b) we can see, the rst dot that is dif-

Fig. 4 :
Fig. 4: Fault detector output using the maximum current value.

Fig. 6
Fig. 6 represents the fault classier output as a function of time for a single-phase to ground fault (a-g).

Fig. 6 :
Fig. 6: Fault classier output for the single-phase to ground fault in the phase "a".

Fig. 8
Fig. 8 represents the fault classier output as a function of time for a double-phase fault withground (a-c-g).

Fig. 7 :
Fig. 7: Fault classier output for the single-phase to ground fault in the phase "a".

Fig. 8 :
Fig. 8: Fault classier output forthe double-phases fault with ground in the phases 'a', 'c' and the ground.

Fig. 9
Fig. 9 represents the fault classier output as a function of time for a three-phase fault (a-b-c).

Fig. 10 Fig. 10 :
Fig. 10 shows the fault location as a function of time using the reactance for single-phase to ground.

Fig. 11 :
Fig. 11: Fault location as function of time using the reactance for double-phase fault with ground.

Figure 12
Figure12shows the fault location as a function of time using the reactance for double-phase fault without ground.

Fig. 12 :
Fig. 12: Fault location as function of time using the reactance for double-phase fault without ground.
this paper to detect, classify and localize the fault in the distribution networkwith function of time.The method can be used by numerical relay.The fault detection by the voltage gives a faster response instead of the current.In addition, that faults without ground are classied faster than faults with ground.Concerning the fault locator, for single phase to ground fault, there is stability in the response and the distance is detected rapidly.The distance is determined after 100 ms.For multiphase fault, the fault lo-

Table 1 .
Single fault impedance equation for negligible fault resistance.