**6. Intelligent Systems Used for the Identification and Location of Internal Faults in Power Transformers**

As already mentioned in Section 1, a wide range of papers may be found in the literature, which are concerned with the identification and location of internal faults in transformers. However, there are very few papers which use intelligent systems applied to the same pur‐ pose, also taking into account experiments with acoustic emission sensors, electrical meas‐ urements and dissolved gases.

Among the most prominent papers found in the literature, we can highlight a few that use fuzzy inference systems and artificial neural networks for the analysis of dissolved gases [2, 17-19] and, for decision making, data from acoustic emission sensors [13].

As may be observed in papers [2, 17-18], which have fuzzy systems applied to the analysis of dissolved gases, the only notable difference lies in the fact that each one proposes differ‐ ent input variables to solve the problem and also different classes of faults. Thus, each paper has different settings of rules and of discourse universes for each input variable.

Therefore, a task of great importance is analyzing dissolved gases is the data preprocessing step, where the most relevant variables are obtained to characterize internal faults in power transformers.

As for those papers that analyze acoustic emission data, they typically employ conventional techniques [6-10]. However, the authors in [13] perform a series of experiments with partial discharges in insulating oil. However, these tests are not performed in order to apply the methodology to power transformers, but rather to identify partial discharges in any envi‐ ronment where oil is the insulator. Therefore, in order to identify the partial discharges, the authors use a MLP artificial neural network with backpropagation training, where the accu‐ racy rates were above 97%.

Following the above context, it appears that the development of a method for identifying and locating internal faults in power transformers requires a number of steps, which are set out below:


It is worth mentioning that, out of the 6 steps mentioned above, most attention should be given to the allocation and acquisition of data, because bad data acquisition can affect the whole process of identifying and locating faults. It is also important to emphasize that the calculations made during the preprocessing of the signals was devised in order to extract the characteristics that best represent the positioning of the partial discharge in relation to the acoustic emission sensor. However, for this first stage of testing the expert system and the hardware used in the acquisition of the signals, we used the experimental tank.

In order to better represent the embedded software, a block diagram detailing the calcula‐ tions to be performed by the software is set out below (Figure 24).

On the other hand, the validation stage has the purpose of verifying the integrity of previ‐ ously conducted training, so that the learning ability (generalization) of neural networks can

As already mentioned in Section 1, a wide range of papers may be found in the literature, which are concerned with the identification and location of internal faults in transformers. However, there are very few papers which use intelligent systems applied to the same pur‐ pose, also taking into account experiments with acoustic emission sensors, electrical meas‐

Among the most prominent papers found in the literature, we can highlight a few that use fuzzy inference systems and artificial neural networks for the analysis of dissolved gases [2,

As may be observed in papers [2, 17-18], which have fuzzy systems applied to the analysis of dissolved gases, the only notable difference lies in the fact that each one proposes differ‐ ent input variables to solve the problem and also different classes of faults. Thus, each paper

Therefore, a task of great importance is analyzing dissolved gases is the data preprocessing step, where the most relevant variables are obtained to characterize internal faults in power

As for those papers that analyze acoustic emission data, they typically employ conventional techniques [6-10]. However, the authors in [13] perform a series of experiments with partial discharges in insulating oil. However, these tests are not performed in order to apply the methodology to power transformers, but rather to identify partial discharges in any envi‐ ronment where oil is the insulator. Therefore, in order to identify the partial discharges, the authors use a MLP artificial neural network with backpropagation training, where the accu‐

Following the above context, it appears that the development of a method for identifying and locating internal faults in power transformers requires a number of steps, which are set

**•** Acquisition of data from sensors in accordance with the requirements commented upon

**•** Data preprocessing stage (definition of the most relevant variables and application of oth‐

**•** Allocation of sensors (acoustic emission and dissolved gases);

17-19] and, for decision making, data from acoustic emission sensors [13].

has different settings of rules and of discourse universes for each input variable.

**6. Intelligent Systems Used for the Identification and Location of**

**Internal Faults in Power Transformers**

urements and dissolved gases.

be analyzed.

22 Advances in Expert Systems

transformers.

out below:

in Section 3;

er necessary tools);

**•** Training or tuning of intelligent systems;

racy rates were above 97%.

As can be seen in Figure 24, it may be noted that the embedded software, after obtaining the acoustic signal, applies some computations in order to extract the characteristics that may represent the signal appropriately. Through these features, the expert system is able to dis‐ tinguish these signals and to locate the source of partial discharges.

In this context, during the preprocessing step of the signs, the following calculations are performed: RMS, Energy, Length, Amplitude, Rise Time and Threshold. Finally, after ob‐ taining the signal characteristics, they are sent to the computer through a USB (Univer‐ sal Serial Bus).

Upon receipt of these data, the expert system is then responsible for providing information regarding the location of any partial discharge in the transformer. In order to better repre‐ sent the overview of expert system, a block diagram is shown in Figure 25. In this figure, it may be noted that, after the received data concerning the characteristics commented upon previously, these are provided as input to the expert system.

**Author details**

**References**

39(6), 597-610.

Ricardo A. S. Fernandes1

Ivan N. da Silva1\*, Carlos G. Gonzales2\*, Rogério A. Flauzino1

\*Address all correspondence to: insilva@sc.usp.br

1 University of São Paulo (USP), Brazil

3 São Paulo State University (UNESP), Brazil

*ISEI*, 06-09June ,San Diego, USA.

, Erasmo S. Neto2

2 São Paulo State Electric Power Transmission Company (CTEEP), Brazil

*ence EIC2009*, 31 May 2009-03June, Montreal, Canada.

*neering, SIBIRCON*, 11-15 July, Listvyanka, Poland.

*INTELEC*, 18-22 October, Incheon, Korea.

*tion and Computing Science, ISIC*, 21-22 May, Manchester, UK.

, Danilo H. Spatti1

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

[1] Yang, Z., Tang, W. H., Shintemirov, A., & Wu, Q. H. (2009). Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers. *IEEE Transactions on Systems*, Man, and Cybernetics- Part C: Applications and Reviews,

[2] Németh, B., Laboncz, S., & Kiss, I. (2009). Condition Monitoring of Power Transform‐ ers Using DGA and Fuzzy Logic. *Proceedings of the IEEE Electrical Insulation Confer‐*

[3] Snow, T., & Mc Larnon, M. (2010). The Implementation of Continuous Online Dis‐ solved Gas Analysis (DGA) Monitoring for All Transmission and Distribution Sub‐ stations. *In: Proceedings of the IEEE International Symposium on Electrical Insulation,*

[4] Szczepaniak, P. S., & Klosinski, M. D. G. (2010). DGA-based Diagnosis of Power Transformers- IEC Standard Versus k-Nearest Neighbors. *In: Proceedings of the IEEE International Conference on Computational Technologies in Electrical and Electronics Engi‐*

[5] Peng, Z., & Song, B. (2009). Research on Transformer Fault Diagnosis Expert System Based on DGA Database. *In: Proceedings of the 2nd International Conference on Informa‐*

[6] Mohammadi, E., Niroomand, M., Rezaeian, M., & Amini, Z. (2009). Partial Discharge Localization and Classification Using Acoustic Emission Analysis in Power Trans‐ former. *In: Proceedings of the 31st International Telecommunications Energy Conference,*

[7] Veloso, G. F. C., Silva, L. E. B., Lambert-Torres, G., & Pinto, J. O. P. (2006). Localiza‐ tion of Partial Discharges in Transformers by the Analysis of the Acoustic Emission.

, Paulo G. da Silva Junior2

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

and José A. C. Ulson3

,

25

**Figure 25.** General diagram of the expert system.

In Figure 25 we can also observe that the expert system is composed of intelligent tools, such as artificial neural networks and fuzzy inference systems, which aim to locate partial dis‐ charges. Upon locating a partial discharge in transformer transmission, the operator may submit the equipment for maintenance (if necessary). Thus, the intelligent system here has the function of assisting the decision-making of the electric utility.
