Analysis of PCA based compression and denoising of smart grid data under normal and fault conditions

Mehra, Rachit; Bhatt, Nirav; Kazi, Faruk; Singh, Navpreet

doi:10.1109/conecct.2013.6469311

Cited by 33 publications

(14 citation statements)

References 7 publications

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“…where CV k is the percentage cumulative variance (CV) of the k-th PC, λ m is an eigenvalue of the m-th PC, and γ is CV bound, respectively [8,9]. The number of PCs k needed to satisfy Equation (5) are decided from the distribution in each detail matrix.…”

Section: Data Compression Via Mspcamentioning

confidence: 99%

“…As can be seen, the EC of the voltage signal is higher than that of the frequency signal. This is because voltage is a local variable, whereas frequency is a global variable [8]. Voltage is subject to global operations as well as local operations, which leads to large variations in features.…”

Section: Mspca Parameter Settingmentioning

confidence: 99%

“…An application of PCA for PMU data in steady state is presented in Reference [7]. Two kinds of power-system conditions, ambient and event state, are considered when compressing PMU data by using PCA [8]. An application of PCA to detect a power-system event and to reduce dimensionality is proposed in Reference [9].…”

Section: Introductionmentioning

confidence: 99%

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Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Lee

Kim

et al. 2019

Energies

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This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

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Section: Data Compression Via Mspcamentioning

confidence: 99%

Section: Mspca Parameter Settingmentioning

confidence: 99%

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Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Lee

Kim

et al. 2019

Energies

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“…But these works dealt with the compression of simulated data obtained from steady state operation. For simulated data under fault conditions, PCA based compression schemes are applied in [7]. Power quality disturbance data are compressed using WT in [8], [9] and [10].…”

Section: Related Workmentioning

confidence: 99%

Embedded-Zerotree-Wavelet-Based Data Denoising and Compression for Smart Grid

Khan

Bhuiyan

Murphy

et al. 2015

IEEE Trans. on Ind. Applicat.

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In this paper, an embedded zerotree wavelet transform (EZWT) based technique is employed for the data denoising and compression in the smart grid (SG). The EZWT is a simple but effective data compression algorithm. This technique does not need any training, pre-stored tables or codebooks, and prior knowledge of the data source. In this paper, the analysis and subsequent compression and denoising properties of the EZWT are examined for power system signals in SG. The proposed approach is evaluated using phasor measurement unit (PMU) and power system data. Experimental results demonstrate that the presented method both compresses the signal and depresses the noise contained in the signal. Comparative compression and noise removal results are presented with the wavelet transform (WT).

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“…Lossy compression is more focused on similarity search and information extraction and cannot rebuild the original data, which plays an important role in power load curve analysis . The common lossy compression algorithms include discrete Fourier transform , discrete wavelet transform , principal component analysis , singular value decomposition (SVD) , and combinations of these methods. Focusing on smart meter data compression, a load data compression method based on load states and load events was proposed .…”

Section: Introductionmentioning

confidence: 99%

An efficient edge sparse coding approach to ultra‐short‐term household electricity demand estimation

Sun

Liu

Zhang

et al. 2018

IEEJ Transactions Elec Engng

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With the opening of electricity market, the interaction between grids and users is becoming more and more frequent. Household electricity demand estimation is a significant and indispensable process of the necessary precise demand response in the future. Large‐scale coverage of the Advanced Metering Infrastructure provides a large volume of user electricity data and brings opportunities for residential electricity consumption forecasting, but, on the other hand, it has brought tremendous pressure on the communication link and data computing center. This paper proposes an efficient edge sparse coding method based on the K‐singular value decomposition (K‐SVD) algorithm to extract hidden usage behavior patterns (UBPs) from load datasets and reduce the cost of communication, storage, and computation. The load of representative household appliances is introduced as the initial dictionary of the K‐SVD algorithm in order to make the UBPs more proximate to the residents' daily electricity consumption. Then, a linear support vector machine (SVM)‐based method with UBPs is used to predict the subsequent interval household electricity demand. The experimental result shows that the proposed algorithm can effectively follow the trend of the real load curve and realize accurate forecasting of the peak electricity demand. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Analysis of PCA based compression and denoising of smart grid data under normal and fault conditions

Cited by 33 publications

References 7 publications

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Embedded-Zerotree-Wavelet-Based Data Denoising and Compression for Smart Grid

An efficient edge sparse coding approach to ultra‐short‐term household electricity demand estimation

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