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...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.展开更多
基金support of the Secretaría Nacional de Ciencia,Tecnología e Innovación(SENACYT)under Grant IDDSE19-007the Agencia Nacional de Investigación y Desarrollo(ANID)under Grants Fondecyt 1230135 and Fondef TA24I10002the Sistema Nacional de Investigación(SNI)of Panama under Grant 16-2021.
文摘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.
