荆臻, 王莉, 杨梅, 王者龙, 王晓泳. 基于超状态隐马尔可夫模型的智能电能表非侵入式故障远程检定[J]. 电测与仪表, 2023, 60(2): 196-200. DOI: 10.19753/j.issn1001-1390.2023.02.028
引用本文: 荆臻, 王莉, 杨梅, 王者龙, 王晓泳. 基于超状态隐马尔可夫模型的智能电能表非侵入式故障远程检定[J]. 电测与仪表, 2023, 60(2): 196-200. DOI: 10.19753/j.issn1001-1390.2023.02.028
JING Zhen, WANG Li, YANG Mei, WANG Zhe-long, WANG Xiao-yong. Non-intrusive remote error detection and localization for smart meters based on super-state hidden Markov model[J]. Electrical Measurement & Instrumentation, 2023, 60(2): 196-200. DOI: 10.19753/j.issn1001-1390.2023.02.028
Citation: JING Zhen, WANG Li, YANG Mei, WANG Zhe-long, WANG Xiao-yong. Non-intrusive remote error detection and localization for smart meters based on super-state hidden Markov model[J]. Electrical Measurement & Instrumentation, 2023, 60(2): 196-200. DOI: 10.19753/j.issn1001-1390.2023.02.028

基于超状态隐马尔可夫模型的智能电能表非侵入式故障远程检定

Non-intrusive remote error detection and localization for smart meters based on super-state hidden Markov model

  • 摘要: 存在故障或误差的智能电能表不仅给电网企业带来经济损失,而且其中的安全隐患容易影响电网的稳定运行,尤其是对成分复杂的智能电网体系。针对这一问题,提出一种基于超状态隐马尔可夫模型(Super-State Hidden Markov Model, SSHMM)对故障电能表进行非侵入式远程检测与定位。该方法不仅能发现已经出现故障的电能表,还可以对最有可能出现故障的电能表进行估计,为电网企业的运营管理提供参考,在真实数据集上的实验结果验证了该方法的有效性与稳定性。

     

    Abstract: Smart meters with faults or errors can cause economic losses to power grid companies, and the hidden safety hazards can also easily affect the stable operation of the power grid, especially for smart grid systems with complex components. Aiming at this problem, non-intrusive remote detection and location of faulted electricity meters based on a super-state hidden Markov model(SSHMM) is proposed in this paper. This method can not only find the meter which has failed but also estimate the meter which is most likely to fail and provide a reference for the operation and management of the power grid enterprises. Experimental results on real data sets verify the efficiency and stability of the proposed method.

     

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