Consumers’ Phase Identification in Low Voltage Station Area Based on Wavelet Analysis of Consumption Data

Qingning, Peng; Liu, Xutao; Hu, Feihu; Ma, Jian-wei; Sun, Kaiqiong

doi:10.1109/icpics52425.2021.9524193

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…There have been many approaches to phase identification over the past decade, from hardware based, to data-driven topology and phase identification using a greedy algorithm with voltage measurements. Qingning et al [34] perform a wavelet decomposition of the measurements. The measurements are reconstructed using their meaningful time-frequency characteristics, as quantified by an influencing factor.…”

Section: B Discussionmentioning

confidence: 99%

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

Zaragoza¹,

Rao²

2022

Preprint

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<div>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 present a feature engineering pipeline that combines Singular Value Decomposition (SVD) with an Interquartile Range (IQR)-based nonlinear filter to denoise matrices of voltage magnitude measurements. We empirically show that this pipeline is effective in denoising data that contains either additive Gaussian, Laplacian, Cauchy, or mixed Gaussian/Cauchy noise. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of non-stationary time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.</div>

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Section: B Discussionmentioning

confidence: 99%

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

Zaragoza¹,

Rao²

2022

Preprint

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Customer-Phase Identification in Low-Voltage Distribution Networks Prone to Data Losses Using Machine Learning-based Algorithms

Mugerwa,

Elsabrouty,

Megahed

et al. 2023

2023 24th International Middle East Power System Conference (MEPCON)

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Research on user phase identification algorithm based on improved cloud model and adaptive segmented voltage

Guo,

Zhang,

Dong

et al. 2023

IFS

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To solve the problem of inaccurate user phase identification, the paper proposes a new algorithm based on improved cloud model and adaptive segmented voltage algorithm. Firstly, the new algorithm uses improved cloud model to calculate the digital features of station area and users’ voltage sequences quickly. Secondly, the paper uses the adaptive segmentation voltage algorithm to divide the full voltage sequences into three parts automatically to add local features into phase identification. Finally, the paper calculates cosine similarity between each segmented voltage cloud model to identify users’ voltage phase. The analysis based on station data and field verification shows that the new algorithm has not only improved the calculation efficiency by 41% compared with traditional user phase identification algorithm, but also increased the difference in identification results between different phases by 1000 times. In the final result, the accuracy of the new algorithm is 95%. The new algorithm has more obvious differentiation and higher accuracy. The analysis results based on the actual engineering data also prove the feasibility and effectiveness of the new user phase identification algorithm.

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Consumers’ Phase Identification in Low Voltage Station Area Based on Wavelet Analysis of Consumption Data

Cited by 4 publications

References 2 publications

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

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

Customer-Phase Identification in Low-Voltage Distribution Networks Prone to Data Losses Using Machine Learning-based Algorithms

Research on user phase identification algorithm based on improved cloud model and adaptive segmented voltage

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