Phase Identification of Single-Phase Customers and PV Panels via Smart Meter Data

Heidari-Akhijahani, Adel; Safdarian, Amir; Aminifar, Farrokh

doi:10.1109/tsg.2021.3074663

Cited by 21 publications

(20 citation statements)

References 24 publications

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“…To the authors' knowledge, the first paper that includes full power flow equations for MILP-PI is [29] (and its follow-up [30]), and it is the state-of-the-art method which is most similar to the one proposed in this paper. The use of power flow equations for PI exposes it to errors in the cable impedance data, which may negatively affect the PI accuracy.…”

Section: Contributions and Comparison With Other Methodsmentioning

confidence: 99%

“…Heidari et al [29] propose a MILP technique with linearized power flow equations, which consists of minimizing the least absolute difference between measured values and system variables. This is solved in their follow-up work [30] with an accelerated Benders decomposition. Contrary to most (if not all) statistical methods, MILP techniques are accurate in the presence of power injection from PV, which alters voltage patterns making correlation harder [30].…”

mentioning

confidence: 99%

“…This is solved in their follow-up work [30] with an accelerated Benders decomposition. Contrary to most (if not all) statistical methods, MILP techniques are accurate in the presence of power injection from PV, which alters voltage patterns making correlation harder [30]. This seems an important advantage as the number of PV installations is increasing.…”

mentioning

confidence: 99%

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Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Vanin¹,

Acker²,

D’hulst³

et al. 2022

Preprint

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To address the challenges and exploit the opportunities that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these require accurate network models to work effectively, including user phase connectivity. However, such information is often not available. This paper proposes a novel method to identify the phase connectivity of single-and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Consequently, the duration of measurement efforts/campaigns aimed at obtaining this type of information reduces, accelerating the system knowledge acquisition process and potentially reducing the campaigns' costs. A technique is illustrated that allows to solve large networks in acceptable time despite the considerable number of integer variables. Extensive computational results are presented for publicly available low voltage feeders.

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Section: Contributions and Comparison With Other Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Vanin¹,

Acker²,

D’hulst³

et al. 2022

Preprint

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“…Phase identification methods that use SM data can be categorized into 3 groups based on whether they use: a) mixed-integer programming (MIP) approaches typically using power data, b) machine learning, with voltage data (MLV), or c) machine learning, with power data (MLP). In [9]- [13], a MIP approach is used to solve the phase identification problem. These optimization-based implementations need measurements of the consumers' demand and the distribution transformer's supply, and use the principle of conservation of power to determine which consumer is connected to which phase.…”

Section: Related Workmentioning

confidence: 99%

“…However, this requires knowledge of line impedance values, which are typically not known in sufficient detail in low voltage networks. While [9], [12] do not rigorously model the network physics, in [13], a MIP formulation is proposed which explicitly adds linearized power flow equations as constraints.…”

Section: Related Workmentioning

confidence: 99%

Low Voltage Customer Phase Identification Methods Based on Smart Meter Data

Hoogsteyn¹,

Vanin²,

Koirala³

et al. 2022

Preprint

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The increased deployment of distributed energy generation and the integration of new, large electric loads such as electric vehicles and heat pumps challenge the correct and reliable operation of low voltage distribution systems. To tackle potential problems, active management solutions are proposed in the literature, which require distribution system models that include the phase connectivity of all the consumers in the network. However, information on the phase connectivity is in practice often unavailable. In this work, several voltage and power measurement-based phase identification methods from the literature are implemented. A consistent comparison of the methods is made across different smart meter accuracy classes and smart meter penetration levels using publicly available data. Furthermore, a novel method is proposed that makes use of ensemble learning and that can combine data from different measurement campaigns. The results indicate that generally better results are obtained with voltage data compared to power data from smart meters of the same accuracy class. If power data is available too, the novel ensemble method can improve the accuracy of the phase identification obtained from voltage data alone.

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A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives

Liu

Guo

Chen

et al. 2023

Energy

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Phase Identification of Single-Phase Customers and PV Panels via Smart Meter Data

Cited by 21 publications

References 24 publications

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Low Voltage Customer Phase Identification Methods Based on Smart Meter Data

A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives

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