Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Karimi, Hazhar Sufi; Natarajan, Balasubramaniam

doi:10.1109/tsg.2021.3102179

Cited by 44 publications

(7 citation statements)

References 46 publications

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“…However, the limited sensors are placed optimally such that the network state can be derived from the sparse signals. This principle of compressed or sparse sensing is used in [18], however, with a mixed integer non-linear programming approach which is computationally intensive, given the NP-hard nature of the state estimation problem. In contrast, some works use the principle of compressed or sparse sensing as in [18], however, with a mixed integer non-linear programming approach which is computationally intensive, given the NP-hard nature of the state estimation problem.…”

Section: Related Workmentioning

confidence: 99%

Learning Power Grid Outages With Higher-Order Topological Neural Networks

Chen

Jacob

Gel

et al. 2024

IEEE Trans. Power Syst.

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Section: Related Workmentioning

confidence: 99%

Learning Power Grid Outages With Higher-Order Topological Neural Networks

Chen

Jacob

Gel

et al. 2024

IEEE Trans. Power Syst.

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“…Also, the AMI measurements are loosely time-synchronized with possible delays of hours [6]. Hence, real-time imputation of the slow-rate measurements is necessary for a reliable DSSE Finally, it is likely that network topology information available to the utility is incorrect or completely unknown [7], [8]. Hence, one of the critical challenges in distribution system state estimation is properly aggregating and reconciling noisy, corrupted, heterogeneous, and incomplete time-series data and network topology information for a reliable DSSE.…”

Section: A Problem Statementmentioning

confidence: 99%

“…More details about the matrix completion based DSSE can be found in [18], [21]. Additionally, the impact of uncertain topology on matrix completion based DSSE is considered in [8], [21]. Additionally, a more comprehensive integrated robustness analysis of graph filter and matrix completion based DSSE will be considered in our future work.…”

Section: Matrix Completion Based Dssementioning

confidence: 99%

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

Dahale

Natarajan

2022

IEEE Trans. Power Syst.

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The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi timescale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. The proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.

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“…With the development of smart grid, the development of electric power and the continuous improvement of people's living standards, the people's demand for electricity grows year by year [1]. It strongly promotes the development of China's electric power industry has developed from purely focusing on the construction of power supply and transmission network to the construction of power supply and transmission network [2]. However, due to the limitation of the traditional development mode, the development level of China's distribution network is still far below the level of power supply construction and transmission network construction [3].…”

Section: Introductionmentioning

confidence: 99%

Novel topology identification method for distribution network based on graph database

Zhu,

Wang,

Cui

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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In the power system, the distribution network is an important infrastructure for safeguarding people's livelihood and the last link for serving residential users. Its safe and stable operation greatly affects the quality and reliability of users' electricity consumption. With the continuous promotion of the smart grid strategy, the power system measurement technology is constantly improved, which provides new ideas for distribution network topology identification, and also makes it possible to accurately estimate the impedance parameters of distribution lines. In this paper, a topology identification method is proposed based on distribution network data analysis. Firstly, the voltage fluctuation principle of load nodes in the distribution network is introduced, which provides a theoretical basis for the extraction of feature data. Secondly, for the three-phase unbalance phenomenon existing in the medium voltage distribution network, an imputation method for the outlet voltage of distribution transformers is proposed. The distribution network is equivalently modeled according to the degree of similarity between the characteristic data of each distribution transformer. Thirdly, the equivalent network is cut by using the standardized cutting method in the spectral clustering analysis method to get the correspondence between distribution transformers and feeders. And the topology reconstruction method of distribution network based on mutual information and maximum spanning tree algorithm is tested.

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Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Cited by 44 publications

References 46 publications

Learning Power Grid Outages With Higher-Order Topological Neural Networks

Learning Power Grid Outages With Higher-Order Topological Neural Networks

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

Novel topology identification method for distribution network based on graph database

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