Voltage-based clustering to identify connectivity relationships in distribution networks

Arya, Vijay; Mitra, Rajendu

doi:10.1109/smartgridcomm.2013.6687925

Cited by 29 publications

(30 citation statements)

References 6 publications

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“…Consequently, there is interest in lower cost ways of determining the phase each meter is on. In some countries communications to the smart meter use the power line, and this can allow the smart meter's phase to be determined [6], but this is not the case in many countries, e.g. the United Kingdom communicates the readings via the mobile phone network.…”

Section: Background On Phasingmentioning

confidence: 99%

“…The second class involves comparing voltage time series at a smart meter with those at other smart meters and with the voltage time series of the substation phases [6,8,9,10]. Differences between the approaches in this class are whether step changes in the substation voltages are the feature to be matched to or whether the smart meter time series are clustered together first and then matched to a substation phase.…”

Section: Background On Phasingmentioning

confidence: 99%

“…This clustering of smart meters first can also allow connectivity problems to be identified, i.e. being connected to [6]. Although using voltage time series has been the preferred direction recently, it is more dependent on the network model in that the voltages will vary down a circuit in line with the loads along it.…”

Section: Background On Phasingmentioning

confidence: 99%

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Using grouped smart meter data in phase identification

Brint

Poursharif

Black

et al. 2018

Computers & Operations Research

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Access to smart meter data will enable electricity distribution companies to have a far clearer picture of the operation of their low voltage networks. This in turn will assist in the more active management of these networks. An important current knowledge gap is knowing for certain which phase each customer is connected to. Matching the loads from the smart meter with the loads measured on different phases at the substation has the capability to fill this gap. However, in the United Kingdom at the half hourly level only the loads from groups of meters will be available to the network operators. Therefore, a method is described for using this grouped data to assist with determining each customer's phase when the phase of most meters is correctly known. The method is analysed using the load readings from a data set of 96 smart meters. It successfully ranks the mixed phase groups very highly compared with the single phase groups.

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Section: Background On Phasingmentioning

confidence: 99%

Section: Background On Phasingmentioning

confidence: 99%

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Using grouped smart meter data in phase identification

Brint

Poursharif

Black

et al. 2018

Computers & Operations Research

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“…For example, phase of low-voltage meters can be identified on the basis of voltage relationships. The presented methods can be divided into two categories: correlation analysis [2]- [4] and cluster analysis [5]. According to [2] and [4], each meter location is plotted against each of the three primary voltages and the connection phase is then determined by the most strongly correlated pair.…”

Section: Introductionmentioning

confidence: 99%

“…On contrary, instead of comparing with primary voltages, voltage phase of each meter is identified by comparing each meter location with voltage phases of a reference house [4]. Cluster analysis (kmeans method) have also been applied for phase identification of small microgrid system with 33 measurement points [5]. Based on a correlation matrix and relative hourly voltage magnitude levels, smart meter data is used to detect wrongly connected customers in a transformer neighborhood [6].…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis for primary feeder identification using metering data

Khumchoo

Kongprawechnon

2015

2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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This paper presents a methodology to identify the connected feeder of high-usage customers in a primary distribution network. The proposed methodology considers voltage characteristics of radial distribution and actual measurements. Based on 15-minute intervals metering, cluster analysis is applied to categorize customer patterns on the basis of voltage correlation. Afterwards, support vector classification is also introduced for outlier assigning and cluster separation. The feasibility of this method is demonstrated on a practical distribution network of industrial estate area. The result indicates that all of the customers is correctly identified, and its correctness percentage is also better than the existent network representation. Additionally, wavelet reduction offers the same performance as the original time-domain feature but more efficient use of time.

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Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

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Voltage-based clustering to identify connectivity relationships in distribution networks

Cited by 29 publications

References 6 publications

Using grouped smart meter data in phase identification

Using grouped smart meter data in phase identification

Cluster analysis for primary feeder identification using metering data

Applications of optimization models for electricity distribution networks

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