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Detection and Monitoring of Supraharmonic Anomalies of an Electric Vehicle Charging Station

With the increasing number of electric vehicles (EV) connected to low voltage grids, concerns about the spread of higher frequency harmonics are growing. In order to estimate the impact and propagation of these waveform distortions, monitoring systems for a long term power quality assessment are required. A major challenge for such systems is the efficient processing of the large emerging datasets. The objective of the proposed method is to describe the Supraharmonic emission of and EV charging station by identifying irregular patterns in the high frequency harmonic spectrum and grouping detected anomalies with similar characteristics. The measurements are processed locally on the meter, only sending decisive results to the operator. The presented results show that the extent of Supraharmonic emission can still be described in detail while reducing the required data capacity of the communication infrastructure of the monitoring system.