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

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Abstract
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%.
Funding Information
  • National Natural Science Foundation of China (51877112)
  • Natural Science Foundation of the Jiangsu Higher Education Institutions of China (17KJA470006)

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