scholarly journals Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

2021 ◽  
Author(s):  
Nicholas Zaragoza ◽  
Vittal Rao
Keyword(s):  
Singular Value Decomposition ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Time Series Data ◽  
Singular Value ◽  
Identification Accuracy ◽  
Series Data ◽  
Phase Identification ◽  
Value Decomposition

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

scholarly journals Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

2021 ◽  
Author(s):  
Nicholas Zaragoza ◽  
Vittal Rao
Keyword(s):  
Singular Value Decomposition ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Time Series Data ◽  
Singular Value ◽  
Identification Accuracy ◽  
Series Data ◽  
Phase Identification ◽  
Value Decomposition

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

scholarly journals Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

2021 ◽  
Author(s):  
Nicholas Zaragoza ◽  
Vittal Rao
Keyword(s):  
Singular Value Decomposition ◽  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Singular Value ◽  
Identification Accuracy ◽  
Series Data ◽  
Phase Identification ◽  
Power Distribution System ◽  
Value Decomposition

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of time series data. K- medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br><br>

Acquisition of Embodied Knowledge on Gesture Motion by Singular Value Decomposition

2011 ◽  
Vol 15 (8) ◽  
pp. 1011-1018 ◽  
Author(s):  
Isao Hayashi ◽  
◽  
Yinlai Jiang ◽  
Shuoyu Wang ◽  
Keyword(s):  
Time Series ◽  
Singular Value Decomposition ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Singular Value ◽  
Series Data ◽  
Embodied Knowledge ◽  
Interval Time Series ◽  
Nonverbal Information ◽  
Value Decomposition

Communication is classified in terms of verbal and nonverbal information. We discuss an acquisition method of knowledge from nonverbal information. In particular, a gesture is an efficient form of nonverbal communication as well as in verbal ways, and we formulate here a method that measures similarity and estimation between gestures. A gesture includes human embodied knowledge, and therefore the visible bodily actions can communicate particular messages. However, we have infinite patterns for gesture, determined by personality. Recently, the singular spectrum analysis method is utilized as an attractive method. In this paper, we propose a new method for acquiring embodied knowledge from time-series data on gestures using singular value decomposition. The motion behavior is categorized into several clusters with similarity and estimation between interval time-series data. We discuss the usefulness of the proposed method using an example of gesture motion.

scholarly journals Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification

2019 ◽  
Vol 22 (12) ◽  
pp. 2687-2698 ◽  
Author(s):  
Zhen Chen ◽  
Lifeng Qin ◽  
Shunbo Zhao ◽  
Tommy HT Chan ◽  
Andy Nguyen
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Identification Accuracy ◽  
Piecewise Polynomial ◽  
Truncated Singular Value Decomposition ◽  
Force Identification ◽  
Moving Force ◽  
Value Decomposition

This article introduces and evaluates the piecewise polynomial truncated singular value decomposition algorithm toward an effective use for moving force identification. Suffering from numerical non-uniqueness and noise disturbance, the moving force identification is known to be associated with ill-posedness. An important method for solving this problem is the truncated singular value decomposition algorithm, but the truncated small singular values removed by truncated singular value decomposition may contain some useful information. The piecewise polynomial truncated singular value decomposition algorithm extracts the useful responses from truncated small singular values and superposes it into the solution of truncated singular value decomposition, which can be useful in moving force identification. In this article, a comprehensive numerical simulation is set up to evaluate piecewise polynomial truncated singular value decomposition, and compare this technique against truncated singular value decomposition and singular value decomposition. Numerically simulated data are processed to validate the novel method, which show that regularization matrix [Formula: see text] and truncating point [Formula: see text] are the two most important governing factors affecting identification accuracy and ill-posedness immunity of piecewise polynomial truncated singular value decomposition.

Comparison of Matrix Dimensionality Reduction Methods in Uncovering Latent Structures in the Data

2010 ◽  
Vol 09 (01) ◽  
pp. 81-92 ◽  
Author(s):  
Ch. Aswani Kumar ◽  
Ramaraj Palanisamy
Keyword(s):  
Dimensionality Reduction ◽  
Time Series Data ◽  
Matrix Decomposition ◽  
Decomposition Methods ◽  
Singular Value ◽  
Series Data ◽  
High Dimensional ◽  
Reduction Methods ◽  
Latent Structures ◽  
Value Decomposition

Matrix decomposition methods: Singular Value Decomposition (SVD) and Semi Discrete Decomposition (SDD) are proved to be successful in dimensionality reduction. However, to the best of our knowledge, no empirical results are presented and no comparison between these methods is done to uncover latent structures in the data. In this paper, we present how these methods can be used to identify and visualise latent structures in the time series data. Results on a high dimensional dataset demonstrate that SVD is more successful in uncovering the latent structures.

Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation

2017 ◽  
Vol 21 (7) ◽  
pp. 1240-1250
Author(s):  
Yinlai Jiang ◽  
Isao Hayashi ◽  
Shuoyu Wang ◽  
Kenji Ishida ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Sliding Window ◽  
Singular Values ◽  
Singular Value ◽  
Series Data ◽  
Singular Vectors ◽  
Walking Difficulty ◽  
The Right ◽  
Value Decomposition

A method based on singular value decomposition (SVD) is proposed for extracting features from motion time-series data observed with various sensing systems. Matrices consisting of the sliding window (SW) subsets of time-series data are decomposed, yielding singular vectors as the patterns of the motion, and the singular values as a scalar, by which the corresponding singular vectors describe the matrices.The sliding window based singular value decomposition was applied to analyze acceleration during walking. Three levels of walking difficulty were simulated by restricting the right knee joint in the measurement. The accelerations of the middles of the shanks and the back of the waist were measured and normalized before the SW-SVD was performed.The results showed that the first singular values inferred from the acceleration data of the restricted side (the right shank) significantly related to the increase of the restriction among all the subjects while there were no common trends in the singular values of the left shank and the waist. The SW-SVD was suggested to be a reliable method to evaluate walking disability. Furthermore, a 2D visualization tool is proposed to provide intuitive information about walking difficulty which can be used in walking rehabilitation to monitor recovery.

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