**6. Conclusion**

334 Fuzzy Inference System – Theory and Applications

Fig. 18. The result of ANFIS2 prediction for identifying fault location in terms of X

1134

**Number of data**

1237

1340

Actual target Percentage error

1443

1546

1649

1752

1855

1958

2061

2164

2267

2370

**5.3 The result of ANFIS3 for identifying fault location in terms of Y coordinate** 

on the average percentage error for 2464 numbers of data training in the system.

Fig. 19. The result on ANFIS2 prediction for identifying fault location in terms of Y

1046

1141

1236

**Number of data**

1331

Actual target Percentage error

1426

1521

1616

1711

1806

1901

1996

2091

2186

2281

2376

Fig.20 shows the result on ANFIS4 prediction in determining the operational states of CB and LI for power restoration plan. Both devices are provided in the 47 buses practical system as a test network for the task. The result shows that the average absolute and maximum absolute errors are 1.14E-7 and 0.028 respectively. Referring to the Fig.20, B1 up

Fig.19 shows the result of ANFIS3 for identifying the fault location in terms of Y coordinate in the 47 buses practical system. It is found that about 2.1E-2% is the average percentage error and 10.7% is the maximum percentage error of the ANFIS3 prediction. As a conclusion, ANFIS3 has a high precision for identifying the fault location in terms of Y coordinate based




**Percentage error**


0

5

**Percentage error**

10

15

0

1

2

coordinate in the 47 buses practical system.

413

516

619

722

825

928

1031

0

**Actual target**

1

96

191

286

381

476

571

666

761

856

951

1

104

207

310

2

4

6

**Actual target**

8

10

coordinate in the 47 buses practical system.

**5.4 The result on ANFIS4 for planning power restoration** 

In general, this chapter describes an accurate method for identifying various fault types as well as the location for the purpose of power restoration plan using the operational states of CB and LI in the power distribution system. For this purpose, the ANFIS approach has been developed by using the representative integers 1 to 10 in classifying ten types of fault. This adaptive approach is also implemented to identify the fault location in power distribution system in terms of geometrical coordinates. Since the developed ANFIS modules are a useful fault diagnosis tool in completing the task, this approach is continuously developed for power restoration plan through network reconfiguration by controlling the operational states of CB and LI. The performance of ANFIS is tested on a 47 buses practical system in which it shows more precision when predicting the target for the developed fault diagnosis system.
