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 |
|---|---|
| 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 - 1 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
Cite this
A Statistical Approach to Estimate Imbalance-Induced Energy Losses for Data-Scarce Low Voltage Networks. / Fang, Lurui; Ma, Kang; Li, Ran; Wang, Zhaoyu; Shi, Heng.
In: IEEE Transactions on Power Systems, Vol. 34, No. 4, 8606229, 01.07.2019, p. 2825-2835.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Statistical Approach to Estimate Imbalance-Induced Energy Losses for Data-Scarce Low Voltage Networks
AU - Fang, Lurui
AU - Ma, Kang
AU - Li, Ran
AU - Wang, Zhaoyu
AU - Shi, Heng
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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%.
AB - 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%.
KW - Energy loss
KW - low voltage
KW - phase imbalance
KW - power distribution
KW - three-phase power
UR - http://www.scopus.com/inward/record.url?scp=85067598738&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2019.2891963
DO - 10.1109/TPWRS.2019.2891963
M3 - Article
VL - 34
SP - 2825
EP - 2835
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 4
M1 - 8606229
ER -