Weighted entropy and modified MDL for compression and denoising data in smart grid

• Published in: International journal of electrical power & energy systems 2021-12, Vol.133 (C), p.107089, Article 107089
• Main Author: Khan, Jesmin
• Format: Article
• Language: English
• Subjects: Compression; Entropy; Minimum description length; Noise; Smart grid; Wavelet packet
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.107089

•The wavelet function and decomposition level are selected adaptively from offline analysis.•A weighted version of Shannon entropy is proposed to find the best basis of the signal.•A modified MDL algorithm is proposed to independently threshold at each node to denoise.•A different threshold criteria is implemented for compression. Denoising and compression of power system data from the measurement and monitoring instruments in smart grid is an important topic. Compression is essential for the transmission and storage of a big amount of smart grid data through the communication channels and denoising is essential as the noise produces erroneous results in further analysis of the power system data. This paper presents a technique for denoising and lossy compression of data in smart grid communication using wavelet packet transform (WPT). The paper proposes weighted entropy to calculate the best basis of a signal from the complete WPT. Then the paper presents the application of the proposed best basis algorithm for the denoising and compression of the signal. A modified minimum description length (MDL) algorithm is proposed that allows to adjust the threshold for the denoising that does not require the use of a noise estimation formula. A set of real power system data recorded by various measurement and monitoring instruments in smart grid is utilized to assess the effectiveness of the proposed technique. Both the denoising and compression performance on the simulation show promising results such that the proposed algorithm could be considered as a potential technique for the real time noise removal and compression application. Results from the comparison are presented with Shannon entropy - MDL based WPT, wavelet transform (WT) based method, fuzzy transform and Matlab’s state of art technique.