**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 with the same data.

Fault Diagnosis in Power Distribution Network

power restoration plan in 47 buses practical system.

(UKM) and the Universiti Teknologi Malaysia (UTM).

Dallas, Texas, USA, May 21-24, 2006

5, New Delhi, India, n.d.

power restoration plan.

**6. Conclusion** 

0 0.2 0.4 0.6 0.8 1 1.2

**Actual target**

**7. Acknowledgment** 

**8. References** 

Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 335

to B26 represent 26 buses in the 11 KV of practical system. According to the result, ANFIS4 module gives a high accuracy in the prediction of the operational states of CB and LI for

**B1 .................................................................................... B26**

Fig. 20. The result on ANFIS4 prediction for determining operational states of CB and LI for

**Number of data**

Actual target Absolute error


0 0.01 0.02 0.03

**Absolute error**

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

This work of research has been financially supported by the Univerti Kebangsaan Malaysia

Binh, P.T.T., & Tuyen, N.D. (2006). Fault Diagnosis of Power System Using Neural Petri Net

Butler-Purry, K.L. & Marotti, M. (2006). Impact of Distributed Generators on Protective

and Fuzzy Neural Petri Nets, *Power India Conference, IEEE*, pp. 5, ISBN 0-7803-9525-

Devices in Radial Distribution Systems, *Proceedings of the IEEE Power Engineering Society in Transmission and Distribution Conference,* pp. 87-88, ISBN 0-7803-9194-2,

more precision when predicting the target for the developed fault diagnosis system.

Fig. 18. The result of ANFIS2 prediction for identifying fault location in terms of X coordinate in the 47 buses practical system.

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

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 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 coordinate in the 47 buses practical system.

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

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 to B26 represent 26 buses in the 11 KV of practical system. According to the result, ANFIS4 module gives a high accuracy in the prediction of the operational states of CB and LI for power restoration plan.

Fig. 20. The result on ANFIS4 prediction for determining operational states of CB and LI for power restoration plan in 47 buses practical system.
