Phase Identification for Power Supply Network in Commercial Buildings

Xu, Dakang; Jiang, Wenqian; Feng, Li; Liu, Bo; Luan, Wenpeng

doi:10.1109/acpee56931.2023.10135965

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…In our prior work Ref. [26], we used a sliding window model to extract segments from the voltage time series with significant fluctuation difference across phases for the commercial substation with three-phase power supply. These segments were then combined to form a voltage correlation detection sequence, which ultimately achieved the correct associations between all customer metres and three-phase feeders.…”

Section: Detection Sequence Generation For Improved Phase Differentia...mentioning

confidence: 99%

“…According to the test in Ref. [26] and analysis of Section 2.2, the fluctuations in the voltage profile are more apparent during high-load periods, where it is easier to obtain voltage curve segments with more significant fluctuation differences among phases.…”

Section: Detection Sequence Generation For Improved Phase Differentia...mentioning

confidence: 99%

“…Based on sliding window model in Ref. [26], this paper improves it by utilising dynamic window length. Set the window length option set at each update of the window as w = {4,6,8,10}.…”

Section: Customer Phase Identificationmentioning

confidence: 99%

See 2 more Smart Citations

Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

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Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

show abstract

Section: Detection Sequence Generation For Improved Phase Differentia...mentioning

confidence: 99%

Section: Detection Sequence Generation For Improved Phase Differentia...mentioning

confidence: 99%

See 1 more Smart Citation

Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

View full text Add to dashboard Cite

show abstract

Phase Identification for Power Supply Network in Commercial Buildings

Cited by 1 publication

References 11 publications

Improved topology identification for distribution network with relatively balanced power supplies

Improved topology identification for distribution network with relatively balanced power supplies

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