Analysis of electricity stealing based on user electricity characteristics of electricity information collection system

Wu, Qiutong; Zhang, Muxin; Liao, Liying

doi:10.1016/j.egyr.2022.01.143

Cited by 12 publications

(1 citation statement)

References 4 publications

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“…Admittedly, the current measurements themselves are subject to fluctuating variations and are not conducive to using them to calibrate the breaker remote confidence quantities. However, within a substation, the substation automation system can collect the rms value of the current for each cycle in real time with the help of relay protection [9][10]. The threshold for residual detection needs to be adjusted manually during operation, with the threshold adjusted higher at the beginning of the state estimation input, otherwise the original signal will lose too much information and may make subsequent work unrecognisable.…”

Section: Design Of Bad Data Identification Methodsmentioning

confidence: 99%

A deep learning-based approach for identifying bad data in power systems

Dong

Chen

et al. 2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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In the actual operation process, some of the power system bad data identification methods have the problem of low accuracy, for this reason, a deep learning-based power system bad data identification method is designed to improve this defect. The data is collected from power system users, the phase deviation caused by non-integer sampling is reduced by high sampling rate, the measurement signal period is obtained, the operational state of the distribution network is evaluated based on deep learning, the state vector is calculated, the maximum standard residual value is found, the location of the bad data is obtained, and the bad data identification method is designed. Experimental results: The mean accuracy of the power system bad data identification method in the paper is: 78.26%, which indicates that the designed power system bad data identification method performs better after fully integrating the deep learning.

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Section: Design Of Bad Data Identification Methodsmentioning

confidence: 99%