Phase identification and substation detection using data analysis on limited electricity consumption measurements

Jimenez, Victor Adrian; Will, Adrián; Rodríguez, Sebastián

doi:10.1016/j.epsr.2020.106450

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…This major drawback makes these approaches unideal. Nevertheless, as shown in [19,26,27] and validated by this study's results, the correlation between variations in consumer consumption and supply in phase feeders can solve the drawback of the energy-balance approach. Thus, the proposed method does not use the energy conservation law (Eq.…”

Section: Problem Formulationsupporting

confidence: 61%

“…Jimenez et al [26] improved the algorithm of Xu et al by introducing a change in the customer's processing sequence and some statistical metrics to evaluate if the number of variations found and the correlation results were significant enough to validate the identification. These changes result in a significant improvement in accuracy with fewer samples and a lower percentage of missing measurements than the initial algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Phase topology identification in low-voltage distribution networks: A Bayesian approach

García

Mora-Merchán

Larios

et al. 2023

International Journal of Electrical Power & Energy Systems

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Section: Problem Formulationsupporting

confidence: 61%

Section: Literature Reviewmentioning

confidence: 99%

Phase topology identification in low-voltage distribution networks: A Bayesian approach

García

Mora-Merchán

Larios

et al. 2023

International Journal of Electrical Power & Energy Systems

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“…Yi et al [35] propose a grouped correlation-based method for phase identification with varying degrees of unmetered loads. Jimenez et al [36] use the correlations of power measurements between loads and each phase of a substation's transformer. The phase with the largest correlation is assigned to the load.…”

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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“…p P v is the absolute value of the active power variation of node p at the th v ; s 0 H  is an adjustment parameter value [9]. Specifically, to enhance the performance of the following correlation analysis methods, the extreme point features of the series are subsequently extracted, as follows: (5) Applying the above described method to select feature points.…”

Section: ( )|mentioning

confidence: 99%

Identifying Topology of Low Voltage Systems via Incorporating Measurement Correlations

Zhang

2022

J. Phys.: Conf. Ser.

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The management of intelligent power distribution systems is limited due to the lack of observability in low voltage (LV) systems. Ambiguous logical relations among branch units, topological unavailability of consumers increase the difficulty of topology identification (TI). To solve this problem, a three-step framework is proposed via measurement correlations. The first step applies correlations of active power features to identify the up-down and parallel, and phase connection of branch units; the second step specially calculates active power correlations between segments and non-vacant consumers, determines the phase connections, and identifies the connected lines of non-vacant consumers; the third step associates vacant consumers with non-vacant consumers by voltage profiles. Finally, robustness against errors and sample sizes is explored, and the results demonstrate that the proposed algorithm can accurately identify the LV topology based on limited samples of voltage magnitude and active power.

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Phase identification and substation detection using data analysis on limited electricity consumption measurements

Cited by 21 publications

References 13 publications

Phase topology identification in low-voltage distribution networks: A Bayesian approach

Phase topology identification in low-voltage distribution networks: A Bayesian approach

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

Identifying Topology of Low Voltage Systems via Incorporating Measurement Correlations

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