**2. Literature review**

Many research works on fault diagnosis incorporate artificial intelligent approaches which process the information from alarms and protection relays in power distribution and transmission systems (Zhiwei et al., 2008; Souza et al., 2004; Mohamed & Mazumder, 1999; Binh & Tuyen, 2006). An expert system has been implemented in cooperation with SCADA and EMS to develop a more efficient and precise centralized fault diagnosis system in transmission networks (Sekine et al., 1992). The approach registers information such as fault location, causes of fault and identifies unwanted operation of protection devices. Voltage and current sensors are installed on transmission lines for real time implementation and this involves a high cost. Artificial neural network (ANN) based fault diagnosis method in the distribution system is then developed to locate the fault, identify the faulty protection devices and isolate the faulty sections. Fault location and fault states of lines and bus sections are obtained using the information from alarm relays (Mohamed & Mazumder, 1999). This technique provides effective information to the operator for decision making but most distribution systems are not completely equipped with alarm relays.

A combination of ANN and fuzzy logic has been used to process the information from alarms and protection relays (Souza et al., 2004) for the purpose of identifying the faulty components and line sections. A wavelet based ANN approach is developed for fault detection and classification (Silva et al., 2006). The approach uses oscillographic data from fault recorders and therefore requires communication networks between remote power system and digital fault recorders. A substation fault diagnosis system has been developed using the Petri net theory (Jingbo & Longhua, 2006). In this method, the information from circuit breakers and faulty protection devices are configured based on mathematical formulations to calculate the precise fault section. Two Petri net concepts, namely, neural Petri net and fuzzy neural Petri net are used for locating faults at the lines or sections (Binh & Tuyen, 2006). However, these methods are not suitable for fault diagnosis in distribution systems due to lack of information of alarm and protective relays.

A new and accurate fault location algorithm using adaptive neuro-fuzzy inference system (ANFIS) has been developed for a network with both transmission lines and under-ground

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

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

Many research works on fault diagnosis incorporate artificial intelligent approaches which process the information from alarms and protection relays in power distribution and transmission systems (Zhiwei et al., 2008; Souza et al., 2004; Mohamed & Mazumder, 1999; Binh & Tuyen, 2006). An expert system has been implemented in cooperation with SCADA and EMS to develop a more efficient and precise centralized fault diagnosis system in transmission networks (Sekine et al., 1992). The approach registers information such as fault location, causes of fault and identifies unwanted operation of protection devices. Voltage and current sensors are installed on transmission lines for real time implementation and this involves a high cost. Artificial neural network (ANN) based fault diagnosis method in the distribution system is then developed to locate the fault, identify the faulty protection devices and isolate the faulty sections. Fault location and fault states of lines and bus sections are obtained using the information from alarm relays (Mohamed & Mazumder, 1999). This technique provides effective information to the operator for decision making but

A combination of ANN and fuzzy logic has been used to process the information from alarms and protection relays (Souza et al., 2004) for the purpose of identifying the faulty components and line sections. A wavelet based ANN approach is developed for fault detection and classification (Silva et al., 2006). The approach uses oscillographic data from fault recorders and therefore requires communication networks between remote power system and digital fault recorders. A substation fault diagnosis system has been developed using the Petri net theory (Jingbo & Longhua, 2006). In this method, the information from circuit breakers and faulty protection devices are configured based on mathematical formulations to calculate the precise fault section. Two Petri net concepts, namely, neural Petri net and fuzzy neural Petri net are used for locating faults at the lines or sections (Binh & Tuyen, 2006). However, these methods are not suitable for fault diagnosis in distribution

A new and accurate fault location algorithm using adaptive neuro-fuzzy inference system (ANFIS) has been developed for a network with both transmission lines and under-ground

most distribution systems are not completely equipped with alarm relays.

systems due to lack of information of alarm and protective relays.

operators to make a decision immediately for further action in power restoration.

given in Section 6.

**2. Literature review** 

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 exact fault points are still not known.
