**4.2.2 A procedure for identifying fault location**

The procedure for locating fault in a power distribution network by the implementation of ANFIS2 and ANFIS3 modules is clearly shown in Fig.12. The first stage is a selection of power network for testing. Then the network layout is drawn in XY plane for locating the selected fault points along the feeder and radial lines. Fig.13 presents an example of the layout. The detail specification of the network is in the next sub-section. This network layout is embedded in fault analysis simulation software such as PSS-ADEPT to collect the fault current data at each fault point.

Next, the three-phase RMS post-fault current is collected at the main substation through a simulation of fault analysis to the selected power distribution network. The fault analysis is applied to every point of the fixed coordinates while considering 10 types of fault and several fault resistors (Rf). For example, by using three fault resistors and 163 fault points, there are 1335 simulations for single fault to ground and about 486 simulations for double fault to ground. Meanwhile, about 643 simulations are required for phase to phase and three-phase faults. Therefore, the total simulation is about 2464 for power distribution network in the 47 buses practical system. The data collected is arranged in such a way that it has three columns of input parameters and one column of target values. The target is either X or Y coordinates in which they are used to train ANFIS2 and ANFIS3 respectively.

Fault Diagnosis in Power Distribution Network

Fig. 13. A single line diagram of 47 buses practical system

Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 329

By identifying the fault location in terms of 'XY' coordinates, more precise and accurate location not only in terms of distance from the feeding substation can be yielded. The structure of ANFIS2 and ANFIS3 are quite simple so they undergo a very fast process in the training stage. However, the simulation process should be done repeatedly due to too much fixed fault points in selected power distribution network. If any network has more feeder and radial lines with long line distance, the fault point should also be more. Thus, the number of ANFIS models will also increase.

Fig. 12. A procedure for developing ANFIS2 and ANFIS3 in fault location identification

#### **4.2.3 The 47 buses practical system**

A single line diagram of the 47 buses practical system is illustrated in Fig.13. The system has seven 11 kV feeders and four 33 kV feeders including 87 CBs and 9 LIs in 11 kV feeder. In this chapter, only 11 kV feeders are used for simulating and testing in order to observe the performance of the developed fault diagnosis system. There are about 2464 line data of three-phase RMS post-fault current that is recorded during the simulation stages. Bus B1 is a power source bus in which it is located on coordinate (1, 2.2) while a monitoring bus B2 is coordinated on (1.8, 2.7). B2 is used for recording the three-phase RMS post-fault current during fault.

By identifying the fault location in terms of 'XY' coordinates, more precise and accurate location not only in terms of distance from the feeding substation can be yielded. The structure of ANFIS2 and ANFIS3 are quite simple so they undergo a very fast process in the training stage. However, the simulation process should be done repeatedly due to too much fixed fault points in selected power distribution network. If any network has more feeder and radial lines with long line distance, the fault point should also be more. Thus, the

Select the power distribution network

SIMULATION PROCESS Consider 10 types of fault with fault resistors of 30Ω and 40Ω (AG, BG, CG, 3P, AB, BC, CA, ABG, BCG and CAG)

> Record post-fault 3-phase RMS current according to fault points

Fig. 12. A procedure for developing ANFIS2 and ANFIS3 in fault location identification

A single line diagram of the 47 buses practical system is illustrated in Fig.13. The system has seven 11 kV feeders and four 33 kV feeders including 87 CBs and 9 LIs in 11 kV feeder. In this chapter, only 11 kV feeders are used for simulating and testing in order to observe the performance of the developed fault diagnosis system. There are about 2464 line data of three-phase RMS post-fault current that is recorded during the simulation stages. Bus B1 is a power source bus in which it is located on coordinate (1, 2.2) while a monitoring bus B2 is coordinated on (1.8, 2.7). B2 is used for recording the three-phase RMS post-fault current

X Y 1.7 9.1 0.3 5.2

Train the post-current as an input and the output in X and Y coordinates for developing ANFIS2 and ANFIS3 modules

number of ANFIS models will also increase.

**4.2.3 The 47 buses practical system** 

during fault.

Fig. 13. A single line diagram of 47 buses practical system

Fault Diagnosis in Power Distribution Network

**4.3.1 ANFIS4 design for planning power restoration** 

Fig. 15. ANFIS4 design for power restoration plan

XY coordinate

load total (TSL).

"

**4.3.2 A procedure to train ANFIS4 for power restoration plan** 

the purpose of power restoration plan. The steps are as follows: i. Isolate the fault feeder or radial with the minimized load. ii. Identify the non-service loads (NSL) in volt-ampere (VA).

v. Calculate the difference between CSF and TSL.

restoration plan cannot proceed.

procedure as shown in Fig.16.

Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 331

Fig.15 shows an ANFIS4 design in a power restoration plan in a 47 buses practical system. The design has 93 modules namely ANFIS4-1 to ANFIS4-93 according to the total of CB and LI in the power network. The input and output data to the ANFIS4 are XY coordinates and binary number respectively. The binary represents the operational states of CB and LI as

**ANFIS module Input Output**  ANFIS4-1 Post-fault 3-phase RMS current 1 or 0

ANFIS4-1 ANFIS4-63 ANFIS4-64 ANFIS4-93

CB1 CB63 LI1 LI30

XY coordinate

ANFIS4-93 " 1 or 0

There are several steps in developing ANFIS4 as well as collecting and training the data for

iii. Identify support feeder or radial that is available to the fault feeder and determine its

vi. If NSL is greater than the difference, identify another support feeder or radial. If the feeder is available, repeat steps (iii) to (vi). But if the feeder is not available, the power

vii. If the NSL is smaller than the difference, the plan shall be implemented with a

In this case, a 47 buses practical system has been selected for developing and testing the ANFIS4 module. There are 59 fixed fault points in the selected network, so the collected

iv. Identify the support feeder capacity (CSF) in the power distribution network.

Table 4. Input and output parameters for ANFIS4 module in power restoration plan

XY coordinate

listed in Table 4. Digit '1' indicates 'close' position while '0' is for 'open' position.

XY coordinate

#### **4.3 Power restoration plan**

Power restoration is a very important consideration in the development of fault diagnosis system especially in a distribution network. Hence, easy and fast action must be taken seriously to plan a power restoration procedure so that the power can be reenergized immediately in a safe and proper manner. This problem can be solved using the ANFIS approach by applying the operational states of CB and LI as shown in Fig.14. This simple technique uses only fault points in XY coordinates and the target is in operational states of CB and LI. These parameters are trained to develop the ANFIS4 model. The power restoration plan considers some requisite processes before developing the ANFIS4 module which are as follows: make sure the power network has a support feeder or a radial that is the nearest to fault feeders. No service loads due to line isolation (NSL) must be calculated in volt-ampere (VA). In addition, the total of loads in the supported feeder (TSL) and actual capacity of the feeder (CSF) should be defined clearly. If NSL is smaller than the differentiation between CSF and TSL, the power restoration plan will be carried to the next action.

Fig. 14. A procedure for planning power restoration

Power restoration is a very important consideration in the development of fault diagnosis system especially in a distribution network. Hence, easy and fast action must be taken seriously to plan a power restoration procedure so that the power can be reenergized immediately in a safe and proper manner. This problem can be solved using the ANFIS approach by applying the operational states of CB and LI as shown in Fig.14. This simple technique uses only fault points in XY coordinates and the target is in operational states of CB and LI. These parameters are trained to develop the ANFIS4 model. The power restoration plan considers some requisite processes before developing the ANFIS4 module which are as follows: make sure the power network has a support feeder or a radial that is the nearest to fault feeders. No service loads due to line isolation (NSL) must be calculated in volt-ampere (VA). In addition, the total of loads in the supported feeder (TSL) and actual capacity of the feeder (CSF) should be defined clearly. If NSL is smaller than the differentiation between CSF and TSL, the power restoration plan will be carried

Identify fault location in XY coordinate

Isolate the line feeder or radial fault with minimizing involved loads

> Develop ANFIS4 module to identify operational states of CB and LI for power restoration plan

> > End

NO YES

CSF - TSL = Q

Is NSL < Q ?

NO

No restoration plan

**4.3 Power restoration plan** 

to the next action.

YES

Fig. 14. A procedure for planning power restoration

Is there another support feeders ?

Determine the following items: 1 – No service loads in VA (NSL)

2 – Total of loads in supported feeder (TSL) 3 – Actual capacity of the supported feeder (CSF)

## **4.3.1 ANFIS4 design for planning power restoration**

Fig.15 shows an ANFIS4 design in a power restoration plan in a 47 buses practical system. The design has 93 modules namely ANFIS4-1 to ANFIS4-93 according to the total of CB and LI in the power network. The input and output data to the ANFIS4 are XY coordinates and binary number respectively. The binary represents the operational states of CB and LI as listed in Table 4. Digit '1' indicates 'close' position while '0' is for 'open' position.

Fig. 15. ANFIS4 design for power restoration plan


Table 4. Input and output parameters for ANFIS4 module in power restoration plan

#### **4.3.2 A procedure to train ANFIS4 for power restoration plan**

There are several steps in developing ANFIS4 as well as collecting and training the data for the purpose of power restoration plan. The steps are as follows:


In this case, a 47 buses practical system has been selected for developing and testing the ANFIS4 module. There are 59 fixed fault points in the selected network, so the collected

Fault Diagnosis in Power Distribution Network

**5. The result of fault diagnosis** 

overall data training.

prediction.

practical system.

**Actual target**

1

113

225

337

449

561

673

785

897

1009

1121

1233

**Number of data**

1345

1457

Actual target Percentage error

1569

1681

1793

1905

2017

2129

2241

2353

with the same data.

Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 333

Fault diagnosis performance is measured through a precision and accuracy of ANFIS prediction. The measurement is in percentage error for ANFIS1, ANFIS2 and ANFIS3 while in absolute error for ANFIS4. The 47 buses practical system is used to test the ANFISs. The prediction results from ANFIS1, ANFIS2 and ANFIS3 are presented for practical systems that consist of 1232 test data sets. Meanwhile, ANFIS4 predicts about 2743 test data sets for the same system. The number of test data set is taken from 50% of

Fig.17 shows the curve of a percentage error and a real target value for ANFIS1 prediction in classifying fault types in 47 buses practical system. The average percentage error for such power systems is 2E-5%. Meanwhile, the maximum percentage error for the same power system is 0.52%. From the result on ANFIS1 in classifying the types of fault in terms of integer 1 to 10, it can be said that the ANFIS module is able to show precise

Fig. 17. The result on ANFIS1 prediction for classifying types of fault in the 47 buses

**5.2 The result of ANFIS2 for identifying fault location in terms of X coordinate** 

The average percentage error of 1.2E-5% is the result of ANFIS2 prediction for identifying the fault location in terms of X coordinate as shown in Fig.18. From the figure, the maximum percentage error is 1.8% in the 47 buses practical system. As a conclusion based on the result, it can be seen that the developed ANFIS2 module is more precise in predicting the fault location than the developed ANN (artificial neural network) module


**Percentage error**

**5.1 The result on ANFIS1 prediction for classifying types of fault** 

data has about 5487 lines which includes 63 CBs and 30 LIs in the practical system. Each line consists of three rows including X, Y coordinates and the integer '1' or '0' represents an operational state of CB and LI. The point coordinates are the input signal to the ANFIS4 whereas the integer is the output. Due to the 93 devices for all CBs and LIs, the ANFIS4 modules should be developed regarding to the numbers of the device. Thus, the modules are labeled as ANFIS4-1 to ANFIS4-63 for all CBs and followed by ANFIS4-64 to ANFIS4-93 for all LIs. Table 5 shows a distribution of the data set for each module to train them.

Fig. 16. A procedure for developing ANFSI4 module in power restoration plan


Table 5. A distribution of the data set for power restoration plan
