Abstract
Partial discharge (PD) monitoring is one of the most used tools for diagnosing the condition of electrical equipment and machines that operate normally at high voltage levels. Ideally, PD identification can be easily done if there is a single source acting over the electrical asset during the measurement. However, in industrial environments, it is common to find the presence of multiple sources acting simultaneously, which hinders the identification process, due to sources of greater amplitude hiding the presence of other types of sources of lesser amplitude that could eventually be much more harmful to the insulation system. In this sense, the separation of PD through the use of clustering techniques allows individual source recognition once they have been clearly separated. This article describes the main clustering techniques that have been used over time to separate PD sources and electrical noise. The results obtained by the different authors in the utilization of each technique demonstrates good performance in terms of separation.
Highlights
In the modern electrical industry, engineers and specialist technicians are responsible for maintaining and operating the electrical equipment, cables, and machines that integrate the electrical systems of substations or power plants [1]
Regarding high voltage electrical assets, it is well known that a big part of the failures usually occurs in the insulation system, due to the uncontrolled presence of multiple ageing mechanisms of electrical, mechanical, thermal, and environmental origin [2], [3], [5]–[7], which, over time, The associate editor coordinating the review of this manuscript and approving it for publication was Pavlos I
This work presents an exhaustive summary of the main clustering techniques used in the separation processes of partial discharges (PD) sources and electrical noise
Summary
In the modern electrical industry, engineers and specialist technicians are responsible for maintaining and operating the electrical equipment, cables, and machines that integrate the electrical systems of substations or power plants [1]. If the noise levels are very high, it would not be possible to identify other insipient sources of PD that were associated with some type of serious insulation failure For this reason, in order to avoid omitting important information associated with the condition of the equipment, it is recommended to carry out a separation process prior to any identification process [22], [28]–[42]. Source separation is carried out mainly through clustering techniques based on the mathematical analysis of the waveform of the acquired pulses, and on the assumption that each type of PD source has a specific behaviour (temporal or spectral) Under these premises, it is possible to extract characteristic parameters from the signals, whether in frequency, time, load, energy, or other variables that allow correct differentiation between sources. As described by the authors, with these new changes in the structure of the technique, there was an improvement in the speed and identification rate of the sources
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