A security technology of power relay using edge computing

Sang, Zhongqing; Yang, Kai; Zhang, Rencheng

doi:10.1371/journal.pone.0253428

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…There are some attempts to apply edge computing in power system protection. Sang et al (2021) applied optimized simulated annealing algorithm (OSAA)-based edge computing for extraction of the forward and reverse traveling waves for relay protection of intelligent substations. Potential applications of machine learning techniques in protection and dynamic security assurance of distribution systems are also reviewed by Aminifar et al (2021).…”

Section: Cognitive Edge Computing For Digital Relaymentioning

confidence: 99%

Cognitive Edge Computing–Based Fault Detection and Location Strategy for Active Distribution Networks

et al. 2022

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This article proposes a fault detection and location strategy based on cognitive edge computing to harvest the benefits of cognitive edge computing and address the special needs of active distribution networks (ADNs). In the proposed strategy, an ADN smart gateway is used to compile data in a central repository where it will be processed and analyzed. The intermediary smart gateway includes a protection unit where the fault detection, location, and isolation are accomplished through a combination of virtual mode decomposition (VMD), support vector machine (SVM,) and long short-term memory (LSTM)–type deep machine learning tools. The local measurements of branch currents and bus voltages are processed through VMD, and the informative decomposed components are provided as inputs to the SVM-based fault detection unit and LSTM-based fault location unit. The smart digital relay passes trip commands to the respective circuit breaker/s and submits compiled data regarding the history of faults and protection actions to the upper-level units. The findings from simulation results demonstrate the effectiveness of the proposed strategy to provide fast and accurate fault detection and protection against all types of faults and locations in the ADN.

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Section: Cognitive Edge Computing For Digital Relaymentioning

confidence: 99%

Cognitive Edge Computing–Based Fault Detection and Location Strategy for Active Distribution Networks

et al. 2022

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“…The PLOS ONE Editors retract this article [ 1 ] because it was identified as one of a series of submissions for which we have concerns about peer review integrity and similarities across articles. These concerns call into question the validity and provenance of the reported results.…”

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confidence: 99%