### Abstract

Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415 V) 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. First, 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 nonnegative 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). Second, a rapid screening model is developed to efficiently identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are then developed for these data-scarce networks with high rebalancing potentials. 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%. Validation results show that the average reduction of the phase imbalance degree for data-scarce networks is only 14.3% less than that for data-rich networks.

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

Article number | 8781915 |

Pages (from-to) | 751-761 |

Journal | IEEE Transactions on Power Systems |

Volume | 35 |

Issue number | 1 |

Early online date | 30 Jul 2019 |

DOIs | |

Publication status | Published - 31 Jan 2020 |

### Keywords

- Low voltage
- phase balancing
- phase imbalance
- phase swapping
- power distribution
- statistical approach
- three-phase power

### ASJC Scopus subject areas

- Energy Engineering and Power Technology
- Electrical and Electronic Engineering

### Cite this

*IEEE Transactions on Power Systems*,

*35*(1), 751-761. [8781915]. https://doi.org/10.1109/TPWRS.2019.2931981

**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

*IEEE Transactions on Power Systems*, vol. 35, no. 1, 8781915, pp. 751-761. https://doi.org/10.1109/TPWRS.2019.2931981

}

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 - 2020/1/31

Y1 - 2020/1/31

N2 - Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415 V) 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. First, 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 nonnegative 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). Second, a rapid screening model is developed to efficiently identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are then developed for these data-scarce networks with high rebalancing potentials. 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%. Validation results show that the average reduction of the phase imbalance degree for data-scarce networks is only 14.3% less than that for data-rich networks.

AB - Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415 V) 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. First, 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 nonnegative 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). Second, a rapid screening model is developed to efficiently identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are then developed for these data-scarce networks with high rebalancing potentials. 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%. Validation results show that the average reduction of the phase imbalance degree for data-scarce networks is only 14.3% less than that for data-rich networks.

KW - Low voltage

KW - phase balancing

KW - phase imbalance

KW - phase swapping

KW - power distribution

KW - statistical approach

KW - three-phase power

UR - http://www.scopus.com/inward/record.url?scp=85078366034&partnerID=8YFLogxK

U2 - 10.1109/TPWRS.2019.2931981

DO - 10.1109/TPWRS.2019.2931981

M3 - Article

VL - 35

SP - 751

EP - 761

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 1

M1 - 8781915

ER -