Electric Power Systems Research 127 (2015) 109–117 Contents lists available at ScienceDirect Electric Power Systems Research j o ur na l ho mepage: www.elsevier.com/locate/epsr A cognitive system for fault prognosis in power transformers Fernando Cortez Sica a,b , Frederico Gadelha Guimarães c,∗ , Ricardo de Oliveira Duarte d , Agnaldo J.R. Reis e a Graduate Program in Electrical Engineering – Federal University of Minas Gerais – Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil b Department of Computer Science, Federal University of Ouro Preto (UFOP), Ouro Preto, Brazil c Department of Electrical Engineering, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil d Department of Electronics, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil e Department of Control Engineering and Automation, Federal University of Ouro Preto (UFOP), Ouro Preto, Brazil a r t i c l e i n f o Article history: Received 6 February 2015 Received in revised form 30 April 2015 Accepted 23 May 2015 Keywords: Power transformers Knowledge-based systems Cognitive systems Fault prognosis Fault diagnosis Dissolved Gas Analysis a b s t r a c t The power transformer is one of the most critical and expensive equipments in an electric power system. If it is out of service in an unexpected way, the damage for both society and electric utilities is very significant. Over the last decades, many computational tools have been developed to monitor the ‘health’ of such an important equipment. The classification of incipient faults in power transformers via Dissolved Gas Analysis (DGA) is, for instance, a very well known technique for this purpose. In this paper we present an intelligent system based on cognitive systems for fault prognosis in power transformers. The proposed system combines both evolutionary and connectionist mechanisms into a hybrid model that can be an essential tool in the development of a predictive maintenance technology, to anticipate when any equipment fault might occur and to prevent or reduce unplanned reactive maintenance. The proposed procedure has been applied to real databases derived from chromatographic tests of power transformers found in the literature. The obtained results are fully described showing the feasibility and validity of the new methodology. The proposed system can help Transformer Predictive Maintenance programmes offering a low cost and highly flexible solution for fault prognosis. © 2015 Elsevier B.V. All rights reserved. 1. Introduction Power transformers are considered key-elements for the Elec- tric Utilities (EU). When those equipments fail, households, industries, and hospitals, to name a few, are prone to suffer some- how. Besides, an unplanned interruption in the power supply can be translated into heavy fines for the EU. Hence, tools for diagnosis, fault detection and fault prognosis are required. In the context of power transformers, several studies are noteworthy regarding the aspects of protection, monitoring and diagnostics, see for instance [1–5]. For many years, preventive maintenance programmes in power transformers consisted of inspections, tests and actions in peri- odic time intervals usually suggested by the manufacturers or determined through practical experience. It was also common the application of routine tests and procedures such as: measurement ∗ Corresponding author. Tel.: +55 3134093419. E-mail addresses: sica@iceb.ufop.br (F.C. Sica), frederico.g.guimaraes@gmail.com (F.G. Guimarães), agnreis@gmail.com (A.J.R. Reis). of dielectric losses, insulation resistance and winding resistance; physical–chemical analysis and chromatographic oil analysis; man- ual or automatic monitoring of temperature [6]. Such analyses allowed the operators to verify if a given transformer was operat- ing normally or if there were evidences of thermal and/or electrical failures, for instance. These kind of failures stem from natural wear, environmental actions and overloads, among other causes. Refer- ence work in this area can be found in [6–8]. Among several fault detection methods, many faults that occur in power transformers can be detected if one measures the gases concentrations in their insulating oil. This procedure is known as fault detection via Dissolved Gas Analysis (DGA) [6]. Usually, DGA can be carried out in two modes: off-line and on-line modes. In the off-line mode, the power transformer has to be disconnected from the power system and an oil sample is collected and taken to a laboratory where it will be analysed via a gas chromatography technique. Yet in the on-line mode, the power transform is kept connected to the power system and the DGA is performed in loco with a determined time interval (e.g. every 2 h) using, e.g., a com- pact closed-loop gas chromatograph unit, which is mounted on or near the monitored transformer. Techniques such as optical and chromatography [9–11], electrical–chemical systems [12,13] and http://dx.doi.org/10.1016/j.epsr.2015.05.014 0378-7796/© 2015 Elsevier B.V. All rights reserved.