In order to overcome defects of traditional ultra high frequency（UHF） detection such as high cost

strict requirements on system sampling rate and data storage

we developed an economical UHF on-line monitoring system for HV switchgear by incorporating down-mixing with power detection technology. We used the proposed system to monitor the partial discharge（PD） signals of four typical defect models

namely surface discharge

air gap discharge

needle-plate discharge and free particle discharge

and obtained their energy spectrum in different frequency bands. Then

taking the sub-band energy of PD signals as eigenvectors

we proposed a PD pattern recognition method for HV switchgear by using Mahalanobis distance as clustering analysis tool.The results show that

the energy distributions of different PD models are more significantly different than the same PD models

and that different types of PD apparently present a feature of regional clustering in energy triangle plot. Moreover

by using the proposed PD recognition method

the accurate rate of PD recognition for the four typical PD defects is 99.125%.