Development of Acoustic Emission Sensor Optimized for Partial Discharge Monitoring in Power Transformers

Abstract

The acoustic emission (AE) technique is one of the unconventional methods of partial discharges (PD) detection. It plays a particularly important role in oil-filled power transformers diagnostics because it enables the detection and online monitoring of PDs as well as localization of their sources. The performance of this technique highly depends on measurement system configuration but mostly on the type of applied AE sensor. The paper presents, in detail, the design and manufacturing stages of an ultrasensitive AE sensor optimized for partial discharge detection in power transformers. The design assumptions were formulated based on extensive laboratory research, which allowed for the identification of dominant acoustic frequencies emitted by partial discharges in oil–paper insulation. The Krimholtz–Leedom–Matthaei (KLM) model was used to iteratively find optimal material and geometric properties of the main structures of the prototype AE sensor. It has two sensing elements with opposite polarization direction and different heights. The fully differential design allowed to obtain the desired properties of the transducer, i.e., a two-resonant (68 kHz and 90 kHz) and wide (30‒100 kHz) frequency response curve, high peak sensitivity (−61.1 dB ref. V/µbar), and low noise. The laboratory tests confirmed that the prototype transducer is characterized by ultrahigh sensitivity of partial discharge detection. Compared to commonly used commercial AE sensors, the average amplitude of PD pulses registered with the prototype sensor was a minimum of 5.2 dB higher, and a maximum of 19.8 dB higher.

Fiber Optic Acoustic Sensor Based on SMS Structure With Thin Polymer Diaphragm for Partial Discharge Detection

“…Some recommended PZT are VS30 (20-80 kHz), VS75 (30-120 kHz, frequency resonant at 75 kHz), and type R15i (50 kHz to 400 kHz, resonant frequencies: 75 kHz and 150 kHz) [39].…”

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm

“…A frequently used method in the diagnosis of oil-filled power transformers is a partial discharge (PD) detection using an acoustic emission (AE) technique. Many cases of power transformer breakdowns are related to insulation system failures, which might have been caused by the high activity of partial discharges [14]. Kunicki et al [15] proposed a method for detecting defects of power transformers.…”

“…The classification is based on various machine learning algorithms (ML), which finds a pattern in data based on expert feedback. Therefore, in [12][13][14][15], the supervised learning [16] was used to train a model for various applications. However, in this research, the analyzed data were gathered from a significant distance, thus unsupervised learning was used [17] to find anomaly in the background noise, the source of which was unknown.…”

“…However, in this research, the analyzed data were gathered from a significant distance, thus unsupervised learning was used [17] to find anomaly in the background noise, the source of which was unknown. In the case of maintenance operation issues [14], reinforcement learning [18] was used to find optimum actions for a given operation status of a transformer. In this paper, the proposed method utilizes both supervised and unsupervised learning.…”

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