Voltage Correlations in Smart Meter Data

Mitra, Rajendu; Kota, Ramachandra; Bandyopadhyay, Sambaran; Arya, Vijay; Sullivan, Brian; Mueller, Rick; Storey, Heather; Labut, Gerard

doi:10.1145/2783258.2788594

Cited by 34 publications

(19 citation statements)

References 9 publications

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“…In the literature, it has been proposed to use energy measurements [6] or voltage measurements [7], [8] to (re)-discover the network topology, including the phase identification of the smart meters. Paper [6] proposes a method using graph theory based on a principal component analysis (PCA), whereas [7], [8] use a correlation-based approach exploiting the similarities between the voltage measurements of the same phase (k-means clustering).…”

Section: B Automatic Methodsmentioning

confidence: 99%

“…Paper [6] proposes a method using graph theory based on a principal component analysis (PCA), whereas [7], [8] use a correlation-based approach exploiting the similarities between the voltage measurements of the same phase (k-means clustering). Papers [1], [9], [10] explicitly identify the phases with either energy or voltage measurements.…”

Section: B Automatic Methodsmentioning

confidence: 99%

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Phase Identification of Smart Meters by Clustering Voltage Measurements

Frédéric

Sutera

Geurts

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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Abstract-When a smart meter, be it single-phase or threephase, is connected to a three-phase network, the phase(s) to which it is connected is (are) initially not known. This means that each of its measurements is not uniquely associated with a phase of the distribution network. This phase information is important because it can be used by Distribution System Operators to take actions in order to have a network that is more balanced.In this work, the correlation between the voltage measurements of the smart meters is used to identify the phases. To do so, the constrained k-means clustering method is first introduced as a reference, as it has been previously used for phase identification. A novel, automatic and effective method is then proposed to overcome the main drawback of the constrained k-means clustering, and improve the quality of the clustering. Indeed, it takes into account the underlying structure of the low-voltage distribution networks beneath the voltage measurements without a priori knowledge on the topology of the network. Both methods are analysed with real measurements from a distribution network in Belgium. The proposed algorithm shows superior performance in different settings, e.g. when the ratio of single-phase over threephase meters in the network is high, when the period over which the voltages are averaged is longer than one minute, etc.

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Section: B Automatic Methodsmentioning

confidence: 99%

Section: B Automatic Methodsmentioning

confidence: 99%

Phase Identification of Smart Meters by Clustering Voltage Measurements

Frédéric

Sutera

Geurts

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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“…The voltage measured by the SMs depends on the input voltage at the transformer, the length of the connecting cable connecting and local loads in the distribution network. SMs that are not broken and are connected to the same transformer are thus expected to display a high correlation between their voltages time series [17]. Figure 2 illustrates this by showing the voltages measured by two SMs that are physically close and connected to the same transformer.…”

Section: Amis and Voltage Monitoringmentioning

confidence: 97%

“…Detection of broken SMs is challenging since variations in consumption readings cannot be easily distinguished between customer-dependent variations and variations dependent on broken SMs. Because of the physical properties of electricity, a broken SM can be potentially identified by comparing the voltage readings of its lines with the ones of the lines of a working SM connected to the same transformer (as explained in Section 2 they are expected to have readings that follow the same temporal curves) [17].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…E.g. voltage time series correlations have been used in order to verify the documented grid topology, either by correlating smart meter data with transformers [17,18], or by correlating only smart meter data [16]. To the best of our knowledge, voltage data has never been used to identify broken smart meters nor have voltage correlations been done in a streaming and distributed fashion.…”

Section: Related Workmentioning

confidence: 99%

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LoCoVolt

Rooij

Gulisano

Papatriantafilou

2018

Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

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Smart Grids and Advanced Metering Infrastructures are rapidly replacing traditional energy grids. The cumulative computational power of their IT devices, which can be leveraged to continuously monitor the state of the grid, is nonetheless vastly underused. This paper provides evidence of the potential of streaming analysis run at smart grid devices. We propose a structural component, which we name LoCoVolt (Local Comparison of Voltages), that is able to detect in a distributed fashion malfunctioning smart meters, which report erroneous information about the power quality. This is achieved by comparing the voltage readings of meters that, because of their proximity in the network, are expected to report readings following similar trends. Having this information can allow utilities to react promptly and thus increase timeliness, quality and safety of their services to society and, implicitly, their business value. As we show, based on our implementation on Apache Flink and the evaluation conducted with resource-constrained hardware (i.e., with capacity similar to that of hardware in smart grids) and data from a real-world network, the streaming paradigm can deliver efficient and effective monitoring tools and thus achieve the desired goals with almost no additional computational cost.

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