Tensor Completion based State Estimation in Distribution Systems

Madbhavi, Rahul; Karimi, Hazhar Sufi; Natarajan, Balasubramaniam; Srinivasan, Babji

doi:10.1109/isgt45199.2020.9087747

Cited by 16 publications

(11 citation statements)

References 13 publications

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“…On the power system measurement completion problem, researchers also proposed methods based on the matrix completion [28] and tensor completion [29], [30]. These two methods both consider the low-rank property of measurements.…”

Section: Sr Compared With Tensor Completionmentioning

confidence: 99%

“…As far as we know, there are no published works on temporal SR on multi-sensors on a graph. On the power system measurement completion problem, researchers also proposed methods based on the matrix completion [28] and tensor completion [29], [30]. These methods generally perform the tensor/matrix decomposition followed by tensor/matrix reconstruction, exploiting the lowrank property of measurements.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Graph Super Resolution on Power Distribution Network Measurements

et al. 2021

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The applications of super-resolution (SR) technology in the field of image completion are successful. Nevertheless, industry applications demand not only image completion but also the topology and time-series completion. In this paper, the SR technology on a topology graph is studied in the scenario of recovering measurements in power distribution systems for cost saving and security & stability improvement. The power flow and voltage magnitude measurements on feeders are reported at different frequencies. In this paper, a new data completion method considering distribution system topology is proposed. Firstly, the graph convolutional neural network (GCN) is used for spatial-temporal convolution on a graph, and then the power system state estimation (SE) is used introducing the physical constraints. This method realizes the super-resolution of distribution system measurements, improves the state awareness of distribution systems. Hence, it helps to improve the efficiency of distribution network operation and to reduce equipment failures.

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Section: Sr Compared With Tensor Completionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Graph Super Resolution on Power Distribution Network Measurements

et al. 2021

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“…An efficient dynamic solution for online smart grid topology identification using CS is presented in [11]. Recently, [12] and [5] introduced the idea of implementing DSSE based on matrix and tensor completion algorithms, respectively. Both these methods utilize the sparsity or smoothness of raw measurements.…”

Section: A Related Workmentioning

confidence: 99%

“…Apart from exploiting spatial correlation, the existence of inherent spatio-temporal correlation in states and measurements can be leveraged using tensor completion algorithms. Tensor completion based approaches for DSSE estimation is proposed in [5] and is demonstrated to provide accurate state estimation in lowobservable systems. Using low-rank canonical polyadic decomposition, [17] presents a model-free state estimation and energy forecasting framework for distribution systems.…”

Section: A Related Workmentioning

confidence: 99%

“…Alternatively, an increase in the number of grid edge sensors and smart meters lead to congestion issues in the underlying communication network [3]. Recently, there has been a plethora of efforts to develop sparsity-based DSSE techniques that can simultaneously deal with the issues of unobservability and minimal data access [4], [5]. This paper not only presents a comprehensive comparison of these potential DSSE solutions but also introduces new robust formulations to deal with bad data and parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study

et al. 2020

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The power distribution grid is typically unobservable due to a lack of measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Therefore, developing state estimation methods that enhance situational awareness at the grid edge with compressed measurements is critical. For this purpose, a suite of sparsity-based approaches that exploit the correlation among states/measurements in spatial as well as temporal domains have been proposed recently. This paper presents a systematic comparison and evaluation of these approaches. Specifically, the performance and complexity of spatial methods (1-D compressive sensing and matrix completion) and spatio-temporal methods (2-D compressive sensing and tensor completion) are compared using the IEEE 37 and IEEE 123 bus test systems. Additionally, new robust formulations of these sparsity-based methods are derived and shown to be robust to bad data and network parameter uncertainties. Among the sparsity-based approaches, compressive sensing methods tend to outperform matrix completion and tensor completion methods in terms of error performance.

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Dynamic signal recovery in distribution grids using compressive lossy measurements

Karimi

Natarajan

2022

Sustainable Energy, Grids and Networks

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Tensor Completion based State Estimation in Distribution Systems

Cited by 16 publications

References 13 publications

Temporal Graph Super Resolution on Power Distribution Network Measurements

Temporal Graph Super Resolution on Power Distribution Network Measurements

Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study

Dynamic signal recovery in distribution grids using compressive lossy measurements

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