Abstract
Phase imbalance in the U.K. and European low-voltage (415 V, LV) distribution networks causes additional energy losses. A key barrier against understanding the imbalance-induced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the clustering, classification, and range estimation (CCRE) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then, CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such a few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%.
Original language | English |
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Article number | 8606229 |
Pages (from-to) | 2825-2835 |
Number of pages | 11 |
Journal | IEEE Transactions on Power Systems |
Volume | 34 |
Issue number | 4 |
Early online date | 9 Jan 2019 |
DOIs | |
Publication status | Published - 31 Jul 2019 |
Keywords
- Energy loss
- low voltage
- phase imbalance
- power distribution
- three-phase power
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
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Profiles
-
Kang Ma
- Department of Electronic & Electrical Engineering - Lecturer
- Centre for Sustainable Power Distribution
Person: Research & Teaching