Compressed empirical mode decomposition methodology for power system disturbance signals and its classification using bio inspired algorithm

Abstract

The usage of non-linear devices, power electronic equipment and the occurrence of faults in the electrical equipments results in deviation of the normal sinusoidal waveform of voltage and current. The power system disturbance signals are voltage sag, voltage swell, voltage transients, flicker, voltage interruption, and harmonics. For the power system disturbance signal analysis, it is the vital task to detect the time interval from which the voltage and current variations occur. The power system disturbance signals occupy the large frequency spectrum because of its large sampling rate. So it produces the megabytes of data which leads to the requirement of high storage space. Therefore, the compression methods are essential for reducing the storage space of the disturbance signals. The extraction of features from the power system disturbance signals is very important for the disturbance signal classification. The wavelet transform (WT) and empirical mode decomposition (EMD) is used for the detection of the power system disturbance signals. The storage space is reduced by the wavelet transform, fcm thresholding with Huffman coding, principal component analysis with the wavelet transform and the proposed compressed empirical mode decomposition. These techniques characterize and compress the disturbance signals through decomposition, thresholding and reconstruction. The performance parameters such as compression ratio and root mean square error is evaluated. The disturbance signals are classified with the help of SVM classifier, KNN classifier and the proposed PNN-BAT classification methods. The detection of power system abnormalities using Hilbert Huang transform (HHT). HHT can be applied to both non-stationary as well as nonlinear signals. HHT is a time-frequency analysis method having a low order of complexity and does not include the frequency resolution and time resolution fundamentals newline