Phase identification in smart grids

Arya, Vijay; Seetharam, Deva P.; Kalyanaraman, S.; Dontas, K.; Pavlovski, Christopher J.; Hoy, S.; Kalagnanam, Jayant

doi:10.1109/smartgridcomm.2011.6102329

Cited by 84 publications

(47 citation statements)

References 3 publications

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“…This method of phase identification is less computationally intensive as [33], where a mixed-integer program (MIP) is formed to identify the phase of connection of customers. [34] solves a combinatorial optimization problem by a Tabu search method.…”

Section: Simulation Resultsmentioning

confidence: 99%

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

Weckx

D’hulst

Driesen

2015

IEEE Trans. Smart Grid

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Section: Simulation Resultsmentioning

confidence: 99%

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

Weckx

D’hulst

Driesen

2015

IEEE Trans. Smart Grid

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“…Our prior [10,11] work describes an analytics approach to infer the phase of a customer for a variant of problem encountered in Australia, where a dt is 3-phase and its customers are single-phase. In this work, we show that a similar approach is applicable even to the north american market, although the problem instances may become large.…”

Section: Related Workmentioning

confidence: 99%

“…We essentially determine a lc solution X that optimally fits the measurements by minimizing either the L 1 or L 2 norm of the error vector e. As long as errors do not grow significantly with measurements, these approaches are expected to retrieve the true underlying leaf connectivity solution with increasing number of measurements [11].…”

Section: The Noisy Lc Problem and Solutionmentioning

confidence: 99%

Inferring connectivity model from meter measurements in distribution networks

Arya

Jayram

Pal

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

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We present a novel analytics approach to infer the underlying interconnection between various metered entities in a radial distribution network. Our approach uses a time series of power measurements collected from different meters in the distribution grid and infers the underlying network between these meters. The collected measurements are used to set up a system of linear equations based upon the principle of conservation of energy. The equations are analyzed to estimate a tree network that optimally fits the time series of meter measurements. We study experimentally the number of measurements needed to infer the true underlying connectivity with the help of both synthetic and real smart meter measurements in the noiseless setting.

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“…It can be seen that the phase of connection applied to this case was an alternating sequence of U,V,W,U,V,W,... This method of phase identification is less computational intensive as [13], where a mixed-integer program (MIP) is formed to identify the phase of connection of customers. [14] solves a combinatorial optimization problem by a Tabu search method.…”

Section: Phase and Grid Identificationmentioning

confidence: 99%

Parameter identification of unknown radial grids for theft detection

Weckx

González

Tant

et al. 2012

2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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This paper proposes an algorithm to detect the stealing of electricity by illegal connections in smart grids with unknown or uncertain cable lengths. Many new applications in the rising smart grid context will require information of the grid topology. We show that with measurement data of smart meters, the grid can be identified, as well as the phase of connection. One of the applications requiring grid information is the detection of electricity theft by double feeding. Electricity theft is a problem faced by all power utilities. Financial impacts are a reduced income for the system operator and the necessity to charge more to other customers. Stealing of electricity by double feeding can not be detected by the smart meter or by analysing the load profiles. Therefore it is suggested in this paper to use measurements of smart meters to identify the grid parameters and detect irregularities of specific customers.

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Phase identification in smart grids

Cited by 84 publications

References 3 publications

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

Inferring connectivity model from meter measurements in distribution networks

Parameter identification of unknown radial grids for theft detection

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