Short-Term Stochastic Real-Time Schedule of Power System with Hybrid BESs

He, Yubin; Liang, Sheng; Gu, Huijie; Li, Bao; Li, Wenchao; Wang, Ruiqi; Xue, Xizhen; Ai, Xiaomeng

doi:10.1109/icpst56889.2023.10165430

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Linear Eigenvalue Statistics (Les)1

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2024

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“…The radius distribution of the eigenvalue will decrease with the occurrence of an event, deviating from the normal value, so the LES value can be used as a high-dimensional statistic to detect whether there is an event in the system. The minimum value can be calculated based on historical data for specific systems, that is, if it is lower than the threshold, abnormal events will occur in the system [8] .…”

Section: Linear Eigenvalue Statistics (Les)mentioning

confidence: 99%

Abnormal power consumption detection based on EEMD-RMS

Jiang,

Zhu,

Wang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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Based on EEMD and RMS, a method of abnormal power consumption detection by using a high-dimensional statistical index was proposed. Firstly, power users were divided into typical load types based on the density space clustering algorithm. For each load type, EEMD was used to transform the energy consumption curve into the high-dimensional empirical mode function group, and then Chebyshev polynomial index in the random matrix theory was used to monitor the abnormal behavior. The high-dimensional statistical index was constructed to judge abnormal power consumption. The algorithm in this paper has general applicability to multi-source load and a good discriminating effect.

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Section: Linear Eigenvalue Statistics (Les)mentioning

confidence: 99%