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Time-frequency analysis is a key requirement for the detection of power quality disturbances (PQDs). Due to non-stationary nature of the disturbances and resolution restriction posed by the uncertainty principle, most signal processing techniques fail to single-handedly detect different types of disturbances with desired precision. As a solution to this problem, a near-perfect time-frequency analysis technique is presented for PQDs. It utilizes the concept of instantaneous frequency (IF)-based time–frequency representations. A carefully chosen Gaussian window in the windowed Fourier transform followed by the slight post-processing gives a high spectral concentration on the IF trajectory by eliminating unwanted frequencies. Several comparative case studies are shown, which support the effective detection and classification capabilities offered by the proposed technique.

Near-Perfect Time–Frequency Analysis of Power Quality Disturbances