**1. Introduction**

Fault diagnosis in power distribution system is an initial action in preventing power breakdown that will affect electrical consumers. Power utilities need to take proactive plan to ensure customer satisfaction and continuous power supply. Power breakdown is a problem to utilities as well as energy users and there are a lot of factors that can cause interruption to the power system. Power distribution system is exposed to approximately 80% of overall faults that come from a wide range of phenomena including equipment failure, animals, trees, severe weather and human factors (Marusic & Gruhonjic-Ferhatbegovic, 2006). Whenever any of these factors befall the power system, costumers will experience power failure which will disturb their daily transactions. Since customers need smooth and reliable power supply, utilities have to develop an electrical power that has quality, reliability and continuous availability; they are responsible for the planning of power restoration properly in order to maintain high market place. Most engineers in power distribution system have decided that power breakdown is related to system reliability issues (Richard, 2009).

One problem when breakdown occurs is the long time taken to provide reenergized power after fault. To quote some examples are the power breakdown that occurred in Keningau, Sabah, East Malaysia on July 5, 2009 in which about 2 to 3 hours were taken for repairing. A power failure also happened in Lembah Klang, West Malaysia on January 13, 2005 for 5 hours that affected many industries (Fauziah, 2005). In Cameron, Middle of Africa, the engineers had taken 2 hours to detect the fault location in AES-SONEL Ngousso substation on April 2006 (Thomas & Joseph, 2009). This phenomenon has to be considered seriously by power utilities so as to overcome frequent breakdowns and provide power restoration plan effectively. If they are unable to solve the problem effectively, they will lose consumers' confidence and power system maintenance will highly increase. In addition, power system that has low reliability encourages repeated significant faults. The faults require time for restoration. There is an index to control the duration within power interruption, which is called the customer average interruption duration index (CAIDI). Therefore, power utilities are urged to aim for low index value so that the system reliability can be maintained (Richard, 2009).

Fault Diagnosis in Power Distribution Network

exact fault points are still not known.

inference system (FIS).

**3.1 ANFIS's learning processes** 

following equation:

**3. The concept of adaptive neuro-fuzzy inference system** 

Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 317

cables (Sadeh & Afradi, 2009). It uses fundamental frequency of three-phase current and neutral current as inputs while fault location is calculated in terms of distance in kilometer. Although it gives a good performance, there are some imperfections in the fault location due to the wide range in distance. An ANN based fault diagnosis method has been implemented in an unbalanced underground distribution system (Oliveira, 2007). The method uses fundamental voltage and current phasors as inputs to the ANN for locating faults in the line sections. Another ANN based approach which combines the ant colony optimization algorithm has been developed for fault section diagnosis in the distribution systems (Zhisheng & Yarning, 2007). The method locates faults in terms of the line sections but the

Adaptive neural fuzzy inference system (ANFIS) is based on fuzzy logic modeling and uses artificial neural network as the learning algorithm. The system can teach, change the data environment or respond to the remote stimulus for adapting to the change of data environment (Michael, 2005). ANFIS produces constant and linear target by using respective zero and first-order polynomial equations and is also known as a Sugeno-type of fuzzy

ANFIS approach targets only one output from several given inputs. The target is manipulated through the performance of the membership function curve according to a particular data input. The curve parameters are identified based on the respective weighted values via the product in between the created learning rules. A ratio between the individual and overall weighted values is calculated. The ratio is gained by using the parameters of output membership function then, finally ANFIS predicts the target by producing an overall gained value as an output. Membership function parameters in input and output sides are adjusted through a learning process to get the targeted values. ANFIS uses hybrid algorithm that consists of a combination between back-propagation and least-square estimation techniques (Jang, 1993). The techniques are implemented in artificial neural network as a learning algorithm that gives very fast convergence and more accurate in ANFIS target.

The ANFIS model exhibits a predicted target whenever it is trained by using at least two columns of data. The last column is the target data and also as an output of the trained ANFIS, while the rest of the columns are the input data. Thus, an ANFIS structure has a single output with at least one column of input data. For the best prediction and high reliability of its performance, the model needs more elements in the column of the input data. However, this situation will also cause the processing time for learning to be slow. For that reason, the ANFIS has to be configured in a high speed processor. Every element in each row of the input data is called data variable in which the linguistic values of the relationship between them is by the rule of 'IF-THEN'. A total of the rule is proportional to the membership function value and the number of column data is linked by the

FD = P (1)

The first step for implementing power restoration plan is by developing a precise and an accurate fault diagnosis in power distribution system. Usually, fault diagnosis involves several tasks such as fault types classification, fault location determination and power restoration plan. Firstly, the types of fault must be classified. Then, the fault location can be determined accordingly. The fault location in power distribution system is very important in order to plan power restoration through power system reconfiguration by using operational states of circuit breakers (CB) and line isolators (LI). With such plan, it can fully help power operators to make a decision immediately for further action in power restoration.

The remaining parts of the chapter are organized as follows. Section 1 explains the introduction of the chapter followed by the literature review in Section 2. In Section 3, the concept of adaptive neuro-fuzzy inference system is addressed clearly. Next the ANFIS design for fault types classification and fault location determination is described in Section 4. The results of fault diagnosis are presented in Section 5. Finally conclusions are given in Section 6.
