**2. Internal Faults in Transformers**

feasibility studies have been conducted, aimed at supporting the electrical system in order to maintain the supply of energy, reducing operation costs and maintenance. Among these investigations, researches have been accomplished into the identification of internal faults in power transformers. In this case, the analysis of dissolved gases [1]-[5] and/or of acoustic emissions [6]-[10] can be highlighted. Within the context of economic viability, it is worth noting the increasing difficulty of removing an operating power transformer and placing it under maintenance. Thus, the above techniques, which evaluate parameters or quantities that indicate the current state of the transformer, have emerged as a more attractive alternative.

Although some papers deal with the development of tools for monitoring sensors [3], very few papers can be found on the efficient use of both sensor types (dissolved gases and acoustic emissions) in the same study. This is probably due to the fact that the cost asso‐ ciated with the acquisition of these sensors is very high. Another factor that should be highlighted is the growing use of intelligent tools for identifying and locating of inter‐

The increasing use of intelligent tools is due to the fact that conventional techniques are not always able to achieve high accuracy rates of fault identification. In one of the most out‐ standing studies in the area [1], which makes a comparison between conventional and intel‐ ligent tools, the authors propose a method based on obtaining association rules that perform the best analysis of dissolved gases and satisfactorily ensure reliable identification of fail‐ ures. The authors compared the proposed technique with other conventional methods (Rog‐ ers and Dornenburg) and intelligent techniques (Neural Networks, Support Vector Machines and *k*-Nearest Neighbors). A total of 1193 samples from dissolved gas sensors were acquired, which were divided into two sets of data in order to evaluate each technique used, i.e., one for training (1016 samples) and the other for validation (177 samples). After all training and validation processes had been conducted, the following accuracy rates were ob‐ tained: Artificial Neural Networks (62.43%), Support Vector Machines (82.10%), k-Nearest Neighbors (65.85 %), Rogers (27.19%), Dornenburg (46.89%) and Association Rules (91.53%). According to the results, it can be clearly seen that intelligent systems outperform conven‐

In addition to this paper, in [2], the authors make a more detailed analysis of gases. In this analysis, a total of 10 kinds of fault were considered, namely: partial discharge, thermal fail‐ ures lower than 150°; thermal failures greater than 150° and lower than 200°; thermal fail‐ ures greater than 200° and lower than 300°; cable overheating; current in the tank or iron core, overheating of contacts; low energy discharges, high energy discharges, continuous sparkling (a luminous phenomenon that results in the breakdown of the dielectric by dis‐ charge through the insulating oil), and partial discharge in solid insulation. It is worth men‐ tioning that the method applied in this study was based on a fuzzy inference system, which was tested under controlled fault conditions. Other tests were also realized in Hungarian substation transmission transformers, where the method performed well against the uncon‐

nal faults [1-2, 5, 7].

4 Advances in Expert Systems

tional methods.

trolled failure scenarios.

The diagnosis of the status and operating conditions of transformers is of fundamental impor‐ tance in the reliable and economic operation of electric power systems. The aging and wear and tear of transformers determine the end of their useful life; thus, the occurrence of faults can affect the reliability or availability of the power transformer. Understanding the mecha‐ nisms of deterioration and having technically feasible and economically viable repair strat‐ egies enables us to correlate faults with the operating evolution of the equipment in service [11].

Many techniques have been proposed to ensure the integrity, reliability and functionality of power transformers, all of which seek trinomial low cost, efficiency and rapid diagnosis. Among several techniques available for detecting internal faults in power transformers, acoustic emission analysis can be highlighted because it is not invasive, allowing analysis to be conducted on the equipment during normal operation [12].

A power transformer can be affected by a variety of internal faults, such as partial discharge, electrical arcs, sparks, corona effects, and overheating. Of these, Partial Discharge (PD) can be highlighted, since it is directly related to the insulation conditions of a power transform‐ er, which in turn trigger the occurrence of more severe faults. PD in high voltage systems occurs when the electric field and localized areas suffer significant changes which enable an electric current to appear [6].

**Transformer**

Electric signals

**Figure 1.** Laboratorial setup diagram.

tests to be accomplished.

**3.1. Electrical measurements**

the electrical system.

**3.2. Acoustic measurements**

several characteristics that require a correct specification:

Acoustic signals

**Data Acquisition**

Dissolved gas

analysis

The structures highlighted (inside the black boxes) are those that present the greatest chal‐ lenges for configuration and parameterization, which are entirely dependent on the type of

The most complete and detailed tests are (given their wide coverage of internal faults) more complex and expensive due to the various devices necessary used for the fault detection and location process, because more sensors and also data acquisition hardware are necessary.

Electrical parameters are also necessary for a correct characterization of internal transformer faults, especially when dealing with systems that require databases for normal operating conditions and with situations when a system has to be restored following a disturbance. This is the case of artificial neural networks, which require quantitative data for the learning process. It is necessary to measure voltages and three-phase primary and secondary cur‐ rents, totaling 12 electrical parameters. The acquisition frequency in this case must not be high, because the purpose is to investigate the most predominant harmonic components in

The acoustic signals are captured by acoustic emission sensors distributed evenly through‐ out the tank, which are externally connected to the power transformer. Such sensors have

**Final Diagnosis**

**Data Processing**

http://dx.doi.org/10.5772/51417

7

Intelligent Systems for the Detection of Internal Faults in Power Transmission Transformers

According to [13], PD can be grouped into 8 classes:

