Abstract
Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.
Highlights
Transformers represent one of the most expensive and significant elements of a power network [1].their correct operation is fundamental for both utilities and consumers
Mathematics 2020, 8, 575 to different power quality problems and harsh operating conditions that can lead to different faults such as winding and core deformations, broken clamping structures, and short-circuited turns, among others [2,3]
Winding faults represent the most severe problem in a transformer; it is fundamental to develop methodologies that allow their detection on early stages and, implement proper solutions
Summary
Transformers represent one of the most expensive and significant elements of a power network [1].their correct operation is fundamental for both utilities and consumers. Vibration analysis has demonstrated, on one hand, to overcome some drawbacks of the aforementioned methods [5] and, on the other hand, to improve the diagnosis results through its combination with other methods [9], becoming a very attractive topic for many researchers around the world Another advantage is that the vibration signals maintain a direct relation with the mechanical performance of the winding [4,5,10]; from this point of view, changes or alterations in the windings will modify the vibration patterns. The problem becomes more difficult when faults of low severity are considered
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