Abstract
| Language | English |
|---|---|
| Pages | 1-1 |
| Number of pages | 1 |
| Journal | IEEE Transactions on Power Systems |
| Early online date | 9 Jan 2019 |
| DOIs | |
| Status | E-pub ahead of print - 9 Jan 2019 |
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, 09.01.2019, p. 1-1.Research output: Contribution to journal › Article
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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 - 2019/1/9
Y1 - 2019/1/9
N2 - Phase imbalance in the UK and European low voltage (415V, 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 CCRE (Clustering, Classification, and Range Estimation) 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. The 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 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 UK and European low voltage (415V, 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 CCRE (Clustering, Classification, and Range Estimation) 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. The 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 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%.
U2 - 10.1109/TPWRS.2019.2891963
DO - 10.1109/TPWRS.2019.2891963
M3 - Article
SP - 1
EP - 1
JO - IEEE Transactions on Power Systems
T2 - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
ER -