Development of Fault Detector for Series Arc Fault in Low Voltage DC Distribution System using Wavelet Singular Value Decomposition and State Diagram

Background noise interference is one of the most important factors for low-voltage power line communication’s reliability. By analyzing the background noise of low-voltage power line communication’s channel, the background noise’s measuring circuit is set up and the AR model of the measured background noise is established. Both of them are respectively using singular value decomposition and Levinson-Durbin (LD) recursive method to calculate the AR model’s parameters and a comparative analysis of the simulation is made. The results induct: parameters acquired from the methods of singular value decomposition and LD recursive method are feasible, the parameter model from singular value decomposition is relatively complex, but extremely accurate, which is suitable for the off-line calculation and analysis of the low-voltage power line’s background noise; the parameter model from LD recursive method is very simple, but has a greater loss of accuracy, fitting for online quickly generation of the low-voltage power line’s background noise.

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

10.36227/techrxiv.15102072 ◽

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 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.

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

10.36227/techrxiv.15102072.v2 ◽

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.

Download Full-text

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

10.36227/techrxiv.15102072.v1 ◽

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 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.

Download Full-text