General classification

Nowadays there are a lot of algorithms to detect a fault in a transmission or in a distribution network although there is the possibility to classify them. As explained before these algorithms use the collected magnitudes from field devices or other generated information by themselves in the moment of the fault and the historical information collected by high system in other faults or issues.

The magnitudes such as current, voltage, frequency, etc.; from several devices and their indications are collected by a centre control. In this centre there will be an analysis in order to determinate where is the fault in the network. It is important to highlight that these data will be historical information for next faults so the operator of the network will be need to establish an Advanced Distributed Management System (ADMS) to manage this data and integrate the field devices. In following chapters, the concept of ADMS will be exposed.

Other publications such as [84] classifies these methods in centralized or decentralized methods. According to this classification it is possible to indicate two important differentiations about the data collected by the ADMS. If the high system uses the magnitude from AEDs and FPIs in order to locate the fault will establish a centralized algorithm but if use the partial information generated by the AEDs and

FPIs such as the result of 21FL or the status of several ANSI protection and other indications will establish a decentralized algorithm. This classification is according to implementation.

After this first classification, it highlights that there is another classification about the algorithms: methods focus in a network model and methods focus in acquire knowledge according to [85]. In fact, the centralized model could be separated between these kinds of methods due to the historical information is an acquire knowledge, nevertheless the decentralized algorithm is classified inside methods based in network model.

On the other hand, it is interesting to think in acquired knowledge by the system as an artificial intelligent system [86]. Thus, it is possible to use statistical tools comparing with other previous faults of which its kind and location are known in order to identify the current fault. The application of artificial intelligent can provide benefits due to the knowledge is growing or validating in every new event. According to [85] and [86] it is possible to determinate a global classification; there are methods based on impedance measurement, methods based on analysis of travelling waves and advanced systems based on the application neural networks. It is important to remark that there are technical reports in IEC such as [87] where the proposal is to find a mix between travelling waves and communications by IEC 61850.

Another classification is in function of network architecture. As mentioned previously in chapter 3, the architecture of the network is an important skill and then the algorithms are developed in base of the kind of the network.

Other important classification for fault location in electrical distribution is focusing in the way to analyse the network. As mentioned before the methodology can be based according to the model or through the acquired knowledge but this methodology could be applied for analyse the fault section location or to locate the substation outgoing where the fault is.

The algorithms based on to locate the fault can be direct, indirect or even practical. The direct methodology uses a matrix in order to describe the network structure and also uses the information from the network devices. Nevertheless, the indirect methods use an intelligent algorithm with the historical information applying an Artificial Neural Network (ANN). Finally, the practical methods are focusing in to use the direct report only from the field devices. This last method does not work properly in networks with DG and in meshed structure, it is necessary to implement and additional analysis in order to discriminate the information from field devices. This is important point in order to select an algorithm for current networks due to the DG is experimenting an important increase as mentioned before, then this selection is relevant.

information and to determinate in which outgoing from the substation is the fault. Besides, inside this fault line selection methods, there are other active selecting methods which act on the network in order to detect the fault.

In last decades a lot of algorithms have been developed in order to improve the electrical distribution network. As it has been commented before there are algorithms based on the model such [88], where there is a comparative between impedance and voltage. This kind of method based in the model is a good solution for transmission lines of distribution network, although is difficult to use in common lines for two reasons, there is not a good knowledge about the network and there is loads distributed along the line. Therefore, with these partial indications according to different skills of the networks it is possible to establish a general classification about these methodologies. Following, in the table 5.4 there is a classification about these methodologies. It is important to highlight that an algorithm can be classified in more than one skill regarding its definition.

Although other authors establish this classification according to the methodology. Regarding to [89] there is another classification which considers that there are 5 kinds: Integrated methods and other similar methods such as [90], learning-based methods such as [91] and [92], travelling waves-based methods such as [58] and [93], impedance-based methods such as [94] and sparse measurement-based methods such as [95]. Therefore, the classification of this thesis from table 5.4 tries to identify the several skills and application fields from different fault location algorithms.