Sparse and Orthogonal Method for Fast Bad Data Processing in Distribution System State Estimation

Hebling, Gustavo M.; Massignan, Julio A. D.; London, João Bosco Augusto; Oliveira, Renato de

doi:10.1109/powertech46648.2021.9494749

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Erroneous measurements are processed, identified, and eliminated here, those which are erroneous due to faults in the communication or to the package of false metrics/data [64] from the calculated state. This area belongs to the 2,87% of the works, where the methodologies are based on statistical analysis, such as the chi-square test, normalized residual test [45,87], and orthogonal formulation [88]. Besides, techniques with approaches to neural networks and geometrics are also included.…”

Section: Detection and Identification Of Bad Datamentioning

confidence: 99%

Bibliometric study of distribution system state estimation: advances and challenges

Lara-Sánchez,

Samper,

Colomé

2024

DYNA

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An active distribution network with a large amount of distributed energy resources (DER) requires knowledge of the operational status of the network. In this sense, state estimation is one of the most widely used techniques and a well-developed concept in transmission systems. DERs have some monitoring, protection, and control devices. But due to the large size of the network and the number of users, the massive installation of meters is not yet economically feasible. Therefore, it is necessary to generate artificial measurements to perform all stages of distribution system state estimation (DSSE). DSSE is currently the subject of active research, so this article performs a descriptive bibliometric study, which qualitatively and quantitatively analyzes the topics found in the specialized literature in the period from 2000 to 2022 and part of the 2023. It also identifies the advances, challenges, and proposals for future lines of research in DSSE.

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