و کنترل سیستم های قذرتحفاظت تخصصی کنفرانس نهمین

9313 دی ماه 42-42، صنعتی امیرکبیر، دانشگاه برق دانشكذه مهنذسی

A novel fault location for distribution systems with

high penetration of distributed generation

Farzad Dehghani

Lorestan Electric Power Distribution Company, IRAN

Farzaddehghani81@yahoo.com

Hamid Nezami

Lorestan Electric Power Distribution Company, IRAN

Hamid13451037@gmail.com

Masoud Dehghani

Lorestan Electric Power Distribution Company, IRAN

Fd.dehghani@gmail.com

Ebrahim Sharifipour

Lorestan Electric Power Distribution Company, IRAN

Sharifipour-322@yahoo.Com

Abstract— With the recent trend of adopting and integrating

renewable resources and microgrids with distribution systems, it

is probable that distribution systems will have significant and

arbitrary penetration of distributed generation (DG) in the near

future. The fault location in this type of system will be a

challenge. This paper describes a general method to locate faults

in this type of system. The method uses synchronized voltage

measurements at the interconnection of DG units where the

injected current at a fault point can be calculated by using the

voltage change and its relevant transfer impedance on any bus. A

two-stage fault-location optimization model is proposed, along

with defining a matching degree index. The first stage is the fault

region identification stage, which uses the matching degree index

to determine the suspicious fault region in order to reduce the

search area. The second stage is used to identify the exact fault

section and fault distance. The method has been extensively

tested on a 60-bus distribution system for all types of faults with

various fault resistances on all sections of the system, with very

encouraging results. Results showed that proposed method is

more effective in distribution system with more DG connection

nodes.

Keywords-component; fault location; distribution networks;

distributed generation; voltage measurments

I. INTRODUCTION

Fault locating in power systems has been a major subject for power and protection engineers in recent years[1]. In distribution systems, due to large variations of fault impedance, non-homogeneity of line and load uncertainly fault location problem has more importance and is more difficult to solve than transmission and generation systems [2]. On the other hand, the reliability of these networks can be greatly increased if the exact location of fault is determined using modified protection systems. Presence of DGs in distribution systems has changed their simple and conventional radial configuration and results in more complexity of their operation, control and protection. Consequently, determining the accurate location of

probable faults will be more important in distribution systems including DG. Due to low fault impedance, it is not so intricate to find the fault location in high voltage transmission lines and is simply done by distance relays. On the contrary, we encounter various and relatively large impedances for faults in distribution systems which are extend ed in residential, urban and rural regions. With high amount of impedance and its extensive variations in distribution system, classic methods will not be appropriate to specify the fault location [3], [4].

Recently, some optimization algorithms and artificial intelligence such as genetic algorithm, graph theory, fuzzy logic and Petri Nets have been widely used to solve optimization problems in engineering because of their simplicity and high speed in finding the solutions. Calderaro et al. [5], [6] presents a method to build a Petri Net (PN), where an input is required in the form of current direction in different sections. A large number of sensors are required to localize the fault to a reasonably small zone and also the exact fault location is not attempted. Chao et al. [6], [7] presents a matrix algorithm for fault section location, but do not perform the exact fault location. A methodology using multilayer perceptrons (MLP) proposed by Javadian et al. [5], [8] recommends the system to be broken into radial zones, where each zone is being protected by a circuit breaker. This scheme needs extensive modification to the system topology. Also as ANN techniques are based on an empirical risk minimization principle, it suffers from major drawbacks such as local optimal solution, low convergence rate, over-fitting and especially poor generalization when the number of fault samples is limited. It also requires a considerable amount of training effort for good performance.

This paper describes a general method to locate faults in distribution system with penetration of DG. The proposed scheme assumes that digital fault recorders (DFRs) are located at the source buses (the substation source and the connection point of each DG units) and they record voltages of all three

Paper Code: PSPC_2015_09

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9313 دی ماه 42-42، صنعتی امیرکبیر، دانشگاه برق دانشكذه مهنذسی

phases. Moreover, the scheme assumes that the waveforms obtained from all DFRs are available as synchronized phasors. A two-stage fault-location optimization model is proposed, along with defining a matching degree index. The first stage is the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage is used to identify the exact fault section and fault distance. The proposed method has been extensively tested on a 60-bus distribution system for all types of faults with various fault resistances on all sections of the system, with very encouraging results.

II. BASIC PRINCIPLE OF THE FAULT-LOCATION

METHOD

In this paper a two-stage fault-location optimization model is proposed, along with defining a matching degree index. The first stage is the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage is used to identify the exact fault section and fault distance. Since the positive-sequence network is the only network existing for all types of faults, it is used for the analysis in this method. All quantities in this paper, if not specifically labeled, refer to positive-sequence quantities. In a n-bus distribution system with presence DGs, assume a fault occurs in line i-j between node i and node j. The fault distance between the fault node and node i is xLij, where Lij is the length of line i-j, and 0 ≤ x ≤ 1. The positive-sequence network being energized by the fault current is shown in Fig. 1.

In the during-fault period, the fault node n +1 can be treated as an additional current injection bus. From Fig. 1, the following nodal equation can be derived:

[

] [

] [

] (1)

Here, the voltage change is given by:

(2)

Where is the pre-fault positive-sequence voltage at bus i,

is the during-fault positive-sequence voltage at bus i, and

is the during-fault positive-sequence injected current at the

fault point. Using (1) the voltage change due to the fault current can be obtained. The current injection is then derived as follows:

(3)

From (3), the fault current injection can be calculated by

using the voltage change on any bus and its relevant transfer impedance. Suppose two buses k and j are source buses (the substation source and the connection point of each DG units). The fault current injection calculated from these two buses should be equal, i.e.:

(4)

Assuming there are m source buses in the distribution network, the following equations can be established on the fault bus, similar to (4):

(5)

Where is defined as:

| | (6)

It can be seen from (6) that is only dependent on the measured voltage change and transfer impedance, and the latter is the function of the fault-location variable. In other words, is independent of fault resistance, fault type, and the pre-fault loading condition. In this paper, is defined as the fault-location factor, which provides an efficient index for locating the fault position in the distribution network. In order to overcome the aforementioned difficulties, we define a matching degree µ, which is a function of fault distance, x:

√

∑ [ ]

(7)

Where ∑ and 0 ≤ x ≤ 1.

Theoretically, the matching degree µ is equal to zero only at the exact fault point. However, this may not occur because of errors in measurements and computational processing. Therefore, the fault-location problem is modeled as optimization problem where:

√

∑ [ ]

(8)

The optimal solution x* of (8) can thus be obtained by searching all of the lines in the network.

A. Fault region identification

In order to identify the fault region, first the matching degree µ at each node in the distribution network are computed, and then these matching degrees are sorted. Several nodes whose matching degrees are very small can be selected as suspicious candidates for the fault node, and the lines connecting those suspicious nodes are then considered as the fault region. To ensure the accuracy of the proposed algorithm, five possible nodes are used for a large distribution system.

Fig. 1: Positive-sequence network during the fault.

Remaining distribution network with penetration of DG

units (n buses)

Node i Node j

Node n+1

If

Fault point xLij (1- x)Lij

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9313 دی ماه 42-42، صنعتی امیرکبیر، دانشگاه برق دانشكذه مهنذسی

B. Exact fault location

Assume the suspicious fault region identified by Stage 1 includes n lines (L1, L2,..., Ln). Stage 2 is used to search those n lines to find the fault line and exact fault distance. The procedure of Stage 2 is as follows:

1) Let i =1.

2) Select line Li, search line Li by a small step Δx, and calculate the matching degree µk(x) by using (7), where

x=k Δx, k=1,2,…, 1/ Δx.

3) Estimate the possible fault point

by minimizing all of the calculated µi(x) of line Li.

4) Let i = i +1, go to Step 2) until i = n +1.

5) Estimate the fault point by minimizing all of the

calculated µi( ), where

(9)

Here, [ ] .

6) The resulting and are the fault line and fault distance.

III. SIMULATION RESULTS

The proposed fault-location scheme was tested on an actual 12.47-kV distribution system from a utility located in the southeastern region in the U.S. The simulations were performed using DIgSILENT Power Factory 13.2 software. The system topology is shown in Fig. 3 and load details can be found in [9]. The total load on the system is 2.5 MVA. The capacities of the DG units are shown in Fig. 3. The total capacity of all units forms 49% of the total load on the system. The proposed fault location algorithm shown in Fig. 2 was implemented in MATLAB software by using voltage phasors obtained from DIgSILENT software during fault condition.

A large number of tests were made to ensure the validity of the proposed fault-location algorithm. A three-phase short-circuit fault at line 32 with 70% line length to node 32 is used as an example. According to Section 2.1, the first step was to use the fault region identification stage. In this stage, the matching degrees of all buses were calculated to estimate the fault buses, and the computational results are shown in Fig. 4. Note that the matching degree values are regarded as 50 when they are more than 50 in Fig. 4. From Fig. 4, the matching degree at node 33 is the minimal value, so the lines 32, 33 and 34 were selected as the most likely fault lines. Moreover, to ensure accuracy of the proposed algorithm, we selected four possible fault nodes, including nodes 32, 33, 34 and 35. The lines connecting those suspicious buses are then considered as the fault region. After that, the exact fault-location stage was performed. All of the possible fault lines were searched. The four most suspicious fault locations are listed in Table I. Some test results are shown in Table II, and the results of the tests demonstrate the validity of the algorithm. Results in table II are achieved in case that all six DGs are connected to distribution system and all of them are equipped with digital fault recorders. In this case in equation 8, number of source nodes (m) is equal 7 (6 DGs + 1 substation bus). In another case only DG2, DG3, DG4 and DG6 are connected to system and number of source buses is equal 5. Table III shows some test results in this case. Error of estimated fault location in second case is more than first case because it has less source nodes. Results of table III show that the proposed method is more effective in distribution system with more DG connection nodes.

Yes

Stage 1

Fault region

identification

Stage 2

Exact fault

location

Establish Thevenin

equivalent of DGs

Establish the pre-fault

impedance matrix Zbus

Fault occurs?

No

Calculate the matching

degree µ at all buses

Estimate the possible fault

buses ki*(i=1,2,…,10)

Accept the lines connected with ki*

as the possible fault lines

Select one fault line in

suspicious fault region

Set fault location variable x=0

Update the during-fault impedance matrix,

calculate µ

x = 1?

All the possible fault lines

are searched?

Estimate the fault line and location by

minimizing µ

x= x+ x

Next possible line

No

No

Yes

Yes

Voltage data of

substation source

and DGs

Fig. 2: Flowchart of the proposed fault-location method.

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9313 دی ماه 42-42، صنعتی امیرکبیر، دانشگاه برق دانشكذه مهنذسی

Fig. 3: 60-bus distribution system simulated for testing.

Fig. 4: the matching degrees of all buses in case a three-phase short-circuit

fault at line 32 with 70% line length to node 32.

TABLE I FOUR MOST SUSPICIOUS LOCATIONS FOR STAGE 2

Suspicious fault location The matching

degree Error

Line Distance

31 0% from node 32 2.5931 Wrong*

32 69.9% from node 32 0.0024 0.1%

33 0% from node 33 1.9820 wrong

34 5.6% from node 33 1.9538 wrong * wrong in the error list denotes the estimated line is not the fault line.

TABLE II SIMULATION RESULTS UNDER DIFFERENT FAULT

CONDITIONS FOR THE 60-BUS DISTRIBUTION SYSTEM (ALL SIX

DGS ARE CONNECTED TO SYSTEM)

Fault

line Fault location (%)

Fault

type

Fault

resistance (Ω)

Estimated

location (%)

Error

(%)

2 30% from node 3 ABC 20 29.75 0.25

4 50% from node 4 BCG 5 50.42 0.42

7 25% from node 8 AG 35 25.57 0.57

12 10% from node 12 AB 15 9.99 0.01

17 35% from node 18 BG 2 35.41 0.41

24 5% from node 24 ABC 25 5.1 0.1

31 50% from node 27 CG 40 49.86 0.14

34 15% from node 33 BC 20 15.24 0.24

37 5% from node 37 ABC 10 5.06 0.06

42 30% from node 42 ABG 1 29.5 0.5

46 10% from node 46 AG 25 9.75 0.25

51 50% from node 51 AB 10 49.22 0.78

53 20% from node 54 BG 15 20.4 0.4

58 35% from node 56 ABC 10 35.61 0.61

TABLE III SIMULATION RESULTS UNDER DIFFERENT FAULT

CONDITIONS FOR THE 60-BUS DISTRIBUTION SYSTEM (ONLY DG2, DG3, DG4 AND DG6 ARE CONNECTED TO SYSTEM)

Fault line

Fault location (%) Fault type

Fault

resistance

(Ω)

Estimated

location

(%)

Error (%)

2 30% from node 3 ABC 20 26.25 3.75

7 25% from node 8 AG 35 21.88 3.12

12 10% from node 12 AB 15 8.75 1.25

24 5% from node 24 ABC 25 4.38 0.62

34 15% from node 33 BC 20 13.13 1.87

42 30% from node 42 ABG 1 26.25 3.75

46 10% from node 46 AG 25 8.75 1.25

53 20% from node 54 BG 15 17.5 2.5

58 35% from node 56 ABC 10 30.63 4.37

IV. CONCLUSIONS

This paper was described a general method to locate faults in distribution system with penetration of distributed generation (DG). A two-stage fault-location optimization model was proposed, along with defining a matching degree index. The first stage was the fault region identification stage, which uses the matching degree index to determine the suspicious fault region in order to reduce the search area. The second stage was used to identify the exact fault line and fault distance. The method has been extensively tested on a 60-bus distribution system for all types of faults with various fault resistances on all sections of the system, with very encouraging results. Results were showed that proposed method is more effective in distribution system with more DG connection nodes.

REFERENCES

[1] Mohammad Mehdi Kamali Faz , Javad Sadeh , yaser damchi, "Differential Equation Based Fault Location Method for HVDC Transmission Line", The 8th Power Systems Protection and Control Conference , 2014-01-15.

0 10 20 30 40 50 600

20

40

60

Node number

Mat

chin

g d

egre

e

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9313 دی ماه 42-42، صنعتی امیرکبیر، دانشگاه برق دانشكذه مهنذسی

[2] Dehghani, F, Nezami, H., „A new fault location technique on radial distribution systems using artificial neural network‟. 22nd Int. Conf. on Electricity Distribution (CIRED), Stockholm, pp. 1-4, 2013.

[3] Jiang, J.A; Liu, Y.H; Liu, C.W; Yang, J.Z; Too, T.M: An adaptive fault locator system for transmission lines, IEEE PES summer meeting, vol. 2, 1999. pp. 930–936.

[4] Meshal,A.; Al-shaher,A.; Manar, M.; Sabry,B.; Ahmad, S.; S. Saleh: Fault location in multi-ring distribution network using artificial neural network, Electric Power Systems Research, vol. 64, 2003 pp. 87–92.

[5] S.M. Brahma, “Fault Location in Power Distribution System With Penetration of Distributed Generation,” IEEE Trans. Power Delivery, no. 99, pp.1, Feb. 2011.

[6] V. Calderaro, A. Piccolo, V. Galdi, and P. Siano, “Identifying fault locationin distribution systems with high distributed generation penetration,” Proc. of AFRICON, Nairobi, Kenya, Sep. 2009, pp. 16.

[7] Y. Chao, Z. Xiangjun, and X. Yunfeng, “Improved algorithm for fault location in distribution network with distributed generations” Proc. Of Int. Conf. Intelligent Computation Technology and Automation, Hunan, Oct. 2008, vol. 2, pp. 893-896.

[8] S. Javadian, M. Haghifam, and N. Rezaei, “A fault location and protection scheme for distribution systems in presence of dg using MLP neural networks,” Proc. of IEEE Power Energy Soc. Gen. Meeting, Calgary, Alberta, Canada, Jul. 2009, pp. 18.

[9] S. M. Brahma and A. A. Girgis, “Development of adaptive protection scheme for distribution systems with high penetration of distributed generation,” IEEE Trans. Power Del., vol. 19, no. 1, pp. 56–63, Jan. 2004.