A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

Vanin, Marta; Acker, Tom Van; D’hulst, Reinhilde; Hertem, Dirk Van

doi:10.1109/pmaps53380.2022.9810582

Cited by 5 publications

(28 citation statements)

References 43 publications

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“…most popular residual definition is the quadratic weighted least squares [34]. In this paper, to avoid non-linearities, weighted least absolute values (WLAV) are used instead:…”

Section: A Single-phase Usersmentioning

confidence: 99%

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Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Vanin¹,

Acker²,

D’hulst³

et al. 2022

Preprint

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To address the challenges and exploit the opportunities that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these require accurate network models to work effectively, including user phase connectivity. However, such information is often not available. This paper proposes a novel method to identify the phase connectivity of single-and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Consequently, the duration of measurement efforts/campaigns aimed at obtaining this type of information reduces, accelerating the system knowledge acquisition process and potentially reducing the campaigns' costs. A technique is illustrated that allows to solve large networks in acceptable time despite the considerable number of integer variables. Extensive computational results are presented for publicly available low voltage feeders.

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“…most popular residual definition is the quadratic weighted least squares [34]. In this paper, to avoid non-linearities, weighted least absolute values (WLAV) are used instead:…”

Section: A Single-phase Usersmentioning

confidence: 99%

“…The MILP-SE-PI is implemented in Julia/JuMP [35], and relies on the PowerModelsDistribution.jl [36] and Power-ModelsDistributionStateEstimation.jl [34] libraries. These are flexible toolboxes which make it relatively easy to choose a different power flow formulation or measurement types, like phasors.…”

Section: Notes On Implementation and Measurement Choicementioning

confidence: 99%

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Vanin¹,

Acker²,

D’hulst³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, these parameters are typically either unknown or inaccurate for a given DG [15], [16]. Therefore, estimating the line parameters is crucial to the success of state estimation approaches [10], [17] and maintaining grid stability [18].…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation of Line Parameters for a Nonlinear Model of Distribution Grids

Kapoor,

Wills,

Hendriks

et al. 2024

Preprint

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“…One important expect of the energy transition is the implementation of a smart grid. The smart grid requires many measurement devices and points to have an observable system [2]. Smart meters are the enabler of such a smart grid.…”

Section: Introductionmentioning

confidence: 99%

“…The state estimation technique used in the transmission network tries to estimate the missing points during the process. On the other hand, the existing low voltage state estimation technique still relies on complete data of smart-meter [2]. Hence, using the statistical imputation technique to fill the missing data seems a more reasonable choice in handling the missing data of the SM.…”

Section: Introductionmentioning

confidence: 99%

Review of statistics based coping mechanisms for Smart Meter Missing Data in Distribution Systems

Koirala

Hertem

2022

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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The large-scale deployment of smart meters means accumulating massive data on consumers' electricity consumption. The data collected from smart meters contains a lot of information that is useful to support the operation and management of low voltage distribution systems. However, data quality is key to the performance and accuracy of such support methods. For instance, missing data is unavoidable due to hardware or software failures in the meter, communication network, and end server. Incomplete data from smart meters ranging from a few samples to a few weeks block makes these data difficult to use. This paper deals with such missing data points in the smart meter data. Missing data can be solved by neglecting data with missing points, enhancing communication infrastructure, or using statistical imputation methods. Statistical imputation based methods in literature were evaluated in smart-meter data made public by the Belgian distribution system operator in this paper. Concretely, eight different imputation methods commonly used in the literature are implemented to estimate the missing points in the data set from actual smart-meter data. Linear imputation was suitable for shorter missing periods, while bilinear imputation performed better in larger blocks of missing data points.

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A Framework for Constrained Static State Estimation in Unbalanced Distribution Networks

Cited by 5 publications

References 43 publications

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Maximum Likelihood Estimation of Line Parameters for a Nonlinear Model of Distribution Grids

Review of statistics based coping mechanisms for Smart Meter Missing Data in Distribution Systems

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