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

Ivan N. da Silva, Carlos G. Gonzales, Rogério A. Flauzino, Paulo G. da Silva Junior, Ricardo A. S. Fernandes, Erasmo S. Neto, Danilo H. Spatti and José A. C. Ulson

Additional information is available at the end of the chapter

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

**1. Introduction**

This chapter presents an approach based on expert systems, which is intended to identify and to locate internal faults in power transformers, as well as to provide an accurate diag‐ nosis (predictive, preventive and corrective), so that proper maintenance can be per‐ formed. In fact, the main difficulty in using conventional methods, based on analysis of acoustic emissions or dissolved gases, lies in how to relate the measured variables when there is an internal fault in a transformer. This kind of situation makes it difficult to de‐ sign optimized systems, because it prevents the efficient location and identification of pos‐ sible defects with sufficient rapidity. In addition, there are many cases where the equipment must be turned off for such tests to be carried out. Thus, this chapter proposes an architec‐ ture for an intelligent expert system for efficient fault detection in power transformers us‐ ing different diagnosis tools, based on techniques of artificial neural networks and fuzzy inference systems. Based on acoustic emission signals and the concentration of gases present in insulating mineral oil and electrical measurements, intelligent expert systems are able to provide, as a final result, the identification, characterization and location of any electrical fault occurring in transformers.

With the changes occurring in the electricity sector, there is a special interest on the part of power transmission companies in improving and defining strategies for the maintenance of power transformers. However, when a fault occurs in a transformer, it is generally removed from the system and sent to a maintenance sector to be repaired. With this in mind, some

© 2012 da Silva et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 da Silva et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

However, studies [1] and [2] present a gap with regard to internal fault diagnosis for pow‐ er transformers, because they only identify the type of failure and do not locate the parti‐

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

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

5

In order to provide a better fault diagnosis for power transformers, some studies have used acoustic emissions to locate faults due to partial discharges. Among these investigations, in [8], the authors propose a geometric analysis of the arrival times of acoustic emission signals in order to properly locate the sources of partial discharges. In the proposed methodology, they use both time measurements from sensors and pseudo-measurements, which provide

In the context of these studies, this chapter aims to determine the necessary procedures for the development of a methodology based on information from sensors for both dissolved gases and acoustic emissions. The purpose of this methodology is achieve satisfactory re‐ sults for identifying internal faults, and, in the case of faults due to partial discharges, to lo‐ cate them accurately to help in the process of decision-making related to the maintenance of

The tasks of identifying and locating internal faults in power transformers are extremely im‐ portant, since they have a very high aggregate cost for purchase and for maintenance. Dis‐ solved gas analysis and the analysis of partial discharges by means of acoustic emission sensors are essential for maintaining the equipment, and can bring many benefits, such as reducing the risk of unexpected failures, extending the useful life of a transformer, decreas‐ ing maintenance costs and reducing maintenance time (due to the precise location of the failure). Furthermore, with the processing of these data by means of intelligent expert sys‐ tems, it becomes possible to provide answers to help in the decision-making process about

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

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

greater precision in the tracking system of partial discharges.

al discharges.

transmission transformers.

the power transformer analyzed.

**2. Internal Faults in Transformers**

be conducted on the equipment during normal operation [12].

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‐ nal faults [1-2, 5, 7].

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‐ tional methods.

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‐ trolled failure scenarios.

However, studies [1] and [2] present a gap with regard to internal fault diagnosis for pow‐ er transformers, because they only identify the type of failure and do not locate the parti‐ al discharges.

In order to provide a better fault diagnosis for power transformers, some studies have used acoustic emissions to locate faults due to partial discharges. Among these investigations, in [8], the authors propose a geometric analysis of the arrival times of acoustic emission signals in order to properly locate the sources of partial discharges. In the proposed methodology, they use both time measurements from sensors and pseudo-measurements, which provide greater precision in the tracking system of partial discharges.

In the context of these studies, this chapter aims to determine the necessary procedures for the development of a methodology based on information from sensors for both dissolved gases and acoustic emissions. The purpose of this methodology is achieve satisfactory re‐ sults for identifying internal faults, and, in the case of faults due to partial discharges, to lo‐ cate them accurately to help in the process of decision-making related to the maintenance of transmission transformers.

The tasks of identifying and locating internal faults in power transformers are extremely im‐ portant, since they have a very high aggregate cost for purchase and for maintenance. Dis‐ solved gas analysis and the analysis of partial discharges by means of acoustic emission sensors are essential for maintaining the equipment, and can bring many benefits, such as reducing the risk of unexpected failures, extending the useful life of a transformer, decreas‐ ing maintenance costs and reducing maintenance time (due to the precise location of the failure). Furthermore, with the processing of these data by means of intelligent expert sys‐ tems, it becomes possible to provide answers to help in the decision-making process about the power transformer analyzed.
