Modes preserving wavelet based multi-scale PCA algorithm for compression of smart grid data

In this paper, wavelet based multiscale PCA algorithm is proposed for effective compression of smart grid data under normal as well as fault conditions. The signal decomposition is done using wavelet transform, followed by the de-correlation using PCA, to achieve maximum compression, while simultaneously preserving the dominant modes of the signal and bad data rejection. The optimum decomposition scale of wavelet transform is selected based on the energy of wavelet coefficients in each scale. The proposed algorithm employing multiscale PCA computes the principal components of the wavelet coefficients at each scale, followed by combining the results at relevant scales. The wavelet coefficients of a particular scale corresponding to the dominant eigen values are retained for signal compression. The dominant modes in the signal are sorted based on their energy content. This overcomes the drawback of false detection of modes and lower accuracy of estimation which arises when systems of higher order are used. Prony analysis is performed to check ability of the compression strategy to preserve the modal information. The phasor data are simulated under fault conditions in the IEEE 30-bus system. The results from Prony analysis indicate that multiscale PCA effectively compresses the disturbance signals while preserving the model information of the signal.