Abstract
On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs.
Highlights
Partial discharge (PD) activity within a rotating machine is an indication of an insulation defect and a causative mechanism of further insulation deterioration
The fractal theory is presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source partial discharge (PD) patterns related to motors, as outlined in IEC 60034
Since the risk associated with PD 2 and PD 4 is at a high level [34], it is highly recommended that a more detailed inspection of the motor is carried out once a PD fault with these fractal features is detected
Summary
Partial discharge (PD) activity within a rotating machine is an indication of an insulation defect and a causative mechanism of further insulation deterioration. Most of the techniques introduced above provide promising fault recognition results when only one PD type exists but perform less well when multiple sources are considered Most of these approaches, which required a massive amount of data for training and testing, were tested and validated on one specific motor under laboratory conditions. In a practical on-line situation, the three challenges in this paper when attempting autonomous PD pattern classification in motor condition assessment could be summarised as follows: (i) processing PD pattern without voltage phase information [19]; (ii) establishing a general pattern classification reference suitable for practical applications; (iii) recognising multiple PD sources, which may occur in a medium-voltage (MV) motor simultaneously. Stage 1 is the establishment of PD pattern recognition reference At this stage, firstly, six standard types of motor PDs from IEC 60034-27 are converted from 3D PRPD patterns to 3D PPD patterns. In a PPD image of a resolution of 128 × 128, c equals to 128 for the convenience of observation that the polar origin is in the centre of the image; the z-axis represents the PD count n
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