### Abstract

Original language | English |
---|---|

Pages (from-to) | 1-1 |

Number of pages | 1 |

Journal | IEEE Transactions on Power Systems |

Early online date | 30 Jul 2019 |

DOIs | |

Publication status | E-pub ahead of print - 30 Jul 2019 |

### Cite this

**A Statistical Approach to Guide Phase Swapping for Data-Scarce Low Voltage Networks.** / Fang, Lurui; Ma, Kang; Zhang, Xinsong.

Research output: Contribution to journal › Article

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TY - JOUR

T1 - A Statistical Approach to Guide Phase Swapping for Data-Scarce Low Voltage Networks

AU - Fang, Lurui

AU - Ma, Kang

AU - Zhang, Xinsong

PY - 2019/7/30

Y1 - 2019/7/30

N2 - Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415V) networks, improves network efficiency by reducing capacity waste and energy losses. A key challenge against phase swapping is that the majority of LV networks are data-scarce, i.e. there is a general lack of data in LV networks. In light of this, this paper proposes a new statistical approach to develop phase swapping guidance for data-scarce LV networks with neither time-series network measurements nor customer metering data. Firstly, given a set of data-rich LV networks (with time-series phase currents data collected at LV substations throughout a year), typical load profiles and their weights in each of the three phases are extracted by applying a non-negative matrix factorization method. Then, phase swapping guidance are developed for data-rich LV networks along with their rebalancing potentials (rebalancing potentials refer to the reduction of phase imbalance degree). Secondly, a rapid screening model is developed to identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are developed for these networks. Case studies reveal that the statistical approach produces effective phase swapping guidance, which reduce the phase imbalance degrees for 99% of the LV networks and the maximum reduction is 35%.

AB - Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415V) networks, improves network efficiency by reducing capacity waste and energy losses. A key challenge against phase swapping is that the majority of LV networks are data-scarce, i.e. there is a general lack of data in LV networks. In light of this, this paper proposes a new statistical approach to develop phase swapping guidance for data-scarce LV networks with neither time-series network measurements nor customer metering data. Firstly, given a set of data-rich LV networks (with time-series phase currents data collected at LV substations throughout a year), typical load profiles and their weights in each of the three phases are extracted by applying a non-negative matrix factorization method. Then, phase swapping guidance are developed for data-rich LV networks along with their rebalancing potentials (rebalancing potentials refer to the reduction of phase imbalance degree). Secondly, a rapid screening model is developed to identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are developed for these networks. Case studies reveal that the statistical approach produces effective phase swapping guidance, which reduce the phase imbalance degrees for 99% of the LV networks and the maximum reduction is 35%.

U2 - 10.1109/TPWRS.2019.2931981

DO - 10.1109/TPWRS.2019.2931981

M3 - Article

SP - 1

EP - 1

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

ER -