## 2 Predictive Model
### Classification-Based Methods
Furthermore, modular NNs are used to diagnose transmission lines from the voltage and current signals of their elements (busses, transmission lines, and transformers). Given its modular nature, the diagnosis can be carried out by element, by area, or for the entire context of the electrical system [36]. In [37], adaptive linear neural networks and feed forward neural networks are combined to classify electrical disturbances that affect the electric equipment. The best classification results are obtained when only a single disturbance appears; when more disturbances are combined, the effectiveness is reduced, but it is worth noticing that the effectiveness percentage obtained exceeds 90% for a noiseless condition and exceeds 77% for a noisy condition in the presence of six combined disturbances. In addition, the overall methodology takes 46.5 milliseconds per half cycle analyzed. [PERSON] et al. [25] present a survey for fault diagnostics in smart micro grids, in which they discuss the faults within various components of a micro network, e.g., photovoltaic panels, wind turbines, conventional generation systems, as well as cables and transmission lines, etc., where several classification algorithms such as NNs, decision trees, and FLSs, among others, are presented. Regarding the transformers, [PERSON] and [PERSON] [38] propose the neural-fuzzy network modeling and the local statistical approach for the detection of incipient faults in power transformers. Another technique commonly used for diagnosis of transformers is the decision tree [39; 40; 41]. [PERSON] et al. [39] and [PERSON] et al. [40] used experimental data from a dissolved gas analysis (DGA) to illustrate the performance of their decision tree-based models. In [39], they present a comparison between the method based on the algorithm C4.5 and other methods used in DGA. They use only 162 samples for the analysis, obtaining the following accuracy: 99.38% for the proposed method, 98.15% for the rules extracted, 88.03% for Duval Triangle, 63.25% for Dorrenburg IEC C57.104, and 56.41% for Rogers IEC C57.104. In [40], a decision-tree C4.5 algorithm obtained an effectiveness of 86% for a thermoelectric fault in oil-immersed transformer. [PERSON] and [PERSON] [41] analyze the current of a power transformer to discriminate between the current signals generated by the irnush effect and the ones generated by its internal faults. The processing time of the proposed approach is 0.12s and provides an accuracy greater than 96%, exceeding the accuracy of the support vector machine (93.33%). As can be noted, the type of variable to be analyzed by the decision tree-based methods is not restricted; in fact, the use of vibration signals for the diagnosis of faults in monoblock centrifugal pumps [42] and motors of internal combustion [43] are also presented. The latter also compares the classification accuracy obtained by the J48 algorithm, random forest tree algorithm, linear model tree algorithm, best first tree algorithm, and functional tree algorithm, where the linear model tree algorithm provides the best results, offering classification accuracy of 100% using statistical features. In general, it can note that the decision tree algorithms are a practical, economical, and very effective approach. In addition to these different types of decision trees, the fault tree is another alternative used for the diagnosis of systems. For example, [PERSON] et al. [44] evaluate the reliability of a power system for energy delivery by constructing a fault tree structure, which represents the system configuration and includes all the possible flow routes of interruption of the power supply from the generators to the loads, including energy transfer limitations, common cause failure of power lines, energy flows and the capacity of generators, and loads in the power system.