Time-Frequency Analysis of Power Quality Signals using Compact Kernel Distribution Technique

Detection of power quality disturbances (PQD) has become an important concern due to the increasing number of disturbing loads connected to the power line and to the susceptibility of some loads to the presence of these disturbances. Nowadays, this detection becomes more complicated since several disturbances can appear simultaneously. In this paper, a new time-frequency analysis technique called compact kernel distribution (CKD) is presented to detect different PQD. To test the proposal, synthetic and real signals provided by real electrical loads are analyzed. The viability of CKD technique is studied by analyzing their power spectrum. For comparison purposes, the Hilbert Huang transform, another recent time-frequency technique, is also used. The results indicate that CKD technique is a powerful tool for disturbances detection even when they appear simultaneously.