The relatively low cost of distribution transformers dictates that the cost of any condition monitoring system for such transformers should be as low as possible. Premature failure of any transformer in an electrical grid does invariably lead to customer inconvenience and costs. Partial discharges are a key indicator of the state of health of a transformer's insulation and can be non-intrusively monitored by means of transient earth voltage (TEV) sensors, thereby minimizing installation costs of the condition monitoring system. The high bandwidth of partial discharge pulses traditionally requires very high speed (and very expensive) digitizers. Sub-Nyquist sampling can be employed to reduce the required sampling rate to monitor insulation degradation. In this paper, partial discharge pulses measured with TEV sensors with a bandwidth of 40 MHz are sampled at 12.8 kHz with the aim of constructing phase-resolved partial discharge (PRPD) patterns as cheaply as possible. The resulting PRPD patterns are then sent to a support vector machine based classifier to detect various insulation defects timeously. Simulation results show that this approach is able to detect different types of partial discharge events.
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