Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries

Abstract

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non-deterministic components using the OHD matrix, including discrete impulse dictionary (I), cosine dictionary (C), sine dictionary (S) and the ℓ 1 -norm optimisation algorithm. Then, the hard-thresholding, uniform threshold dead-zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries Ψ = [I C S] and Ψ = [I C], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE-1159-PQE and GIM-PQE databases and generated using the Microgrid model. The results show that the method with dictionary Ψ = [I C S] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

An Algorithm Based on Sparse Decomposition for Estimating Harmonic and Interharmonic Components of Stationary Signals in Power Systems

“…The dictionary must be composed of analytical functions or parameterized waveforms containing the same characteristics of the signals. This dictionary supports the SSD in returning an appropriate sparse representation [23]. The stationary signals can have adequate sparse representations using frequency dictionaries, which can be represented by the Fourier dictionary.…”

“…In the last decade, numerous applications in signal processing have used the Sparse Signal Decomposition (SSD) technique with Overcomplete Hybrid Dictionary (OHD) for detection and classification of modulated signals [16], analysis of ultrasound signals [17], [18], automatic target recognition in radar images [19], denoising and analysis of biomedical signals as electrocardiogram (ECG) [20], [21] and electroencephalogram (EEG) [22], among others. In the analysis of signals in power systems, [23] presents a study for compression and denoising of signals with power quality disturbances, while [24] does the detection and classification of these disturbances. Both use an overcomplete dictionary formed by three matrices: one of unitary impulses delayed in time, one generated by Discrete Cosine Transform (DCT) and another generated by Discrete Sine Transform (DST).…”

“…The method has presented better performance in the tests carried out when compared to the Wavelet Transform (WT) and DCT methods. However, [23] and [24] do not justify the choice of DCT-II and DST-II to compose the OHD. Moreover, coefficients are not compensated, which leads to inaccurate results once samples are time-shifted by a half sampling time.…”

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

“…is the kth DCT coefficient [26]. In this work, we implement the DCT filtering approach for simultaneous removal of BW and PLI noises with the BW frequency range of 0-1 Hz and the grid power-line frequency range of 48-52 Hz.…”

Vegetation High-Impedance Faults’ High-Frequency Signatures via Sparse Coding

“…Sparse representation and neural network approach were proposed in [9] to identify arc faults in distribution systems, where the learned bases resulted in a high accuracy classifier for the relevant types of arcs. In [10], sparse representations are used to denoise and compress power systems disturbances, that despite originated from a pre-defined set of basis, overperforms other techniques. In the field of machine fault diagnosis, sparse representations were also used to accurately classify roller bearing faults in [11] with support vector machines.…”