Electrical insulation faults produce partial discharges (PD)

which can be analysed to identify specific types of defects. PD clustering is a widely used method to identify PD sources

although its success depends largely on the feature maps used. In this paper

three widely used feature maps

or separation maps

are compared: chromatic

energy wavelet with principal component analysis (EW-PCA)

and time–frequency (TF). To compare and evaluate

five scenarios with multi-PD environments with noise were developed. The clustering ability of the maps was evaluated using two performance indicators: intercluster distance and intracluster distance. The results indicate that the EW-PCA map performed the best in all scenarios

correctly identifying the largest number of data points and producing the clearest and most distinct clusters. The TF map created distinct clusters in several scenarios

but not all. The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps. Given the results

it is important in fieldwork to use a wide range of PD clustering

accompanied by performance metrics that support a less biased decision tailored to the test object.