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文章摘要
基于SSHM模型的智能电能表操纵行为检测算法研究
Research on the detection algorithm of rigged behaviors in smart meters based on SSHM model
Received:March 11, 2020  Revised:May 16, 2022
DOI:10.19753/j.issn1001-1390.2023.09.028
中文关键词: 隐马尔可夫模型  智能电能表  状态检测  非技术损失
英文关键词: hidden Markov model, smart meter, state detection, non-technical loss
基金项目:
Author NameAffiliationE-mail
Li Rui* Measurement Center of State Grid Beijing Electric Power Research Institute, Beijing 102600, China lirui_epri@163.com 
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中文摘要:
      由于智能电能表操纵行为产生的异常状态不仅会造成严重经济损失,而且会导致电网运行状态误判从而影响电网安全性。文章提出了一种基于超态隐马尔可夫(SSHM)模型的智能电能表操纵行为检测和定位方法。该方法在隐马尔可夫模型中引入超态量,并基于该模型转移矩阵和观测矩阵的稀疏性特点,给出了改进Viterbi算法,降低了数据存储和计量难度;通过包含典型操纵行为的数据集对模型的关键性能参数进行了评估和优化,从而建立起了高性能的SSHM模型;通过操纵行为算例分析对比了该方法与其他方法的检测性能,证明了该方法具有极高的准确性,是解决智能电能表操纵行为异常状态检测与定位问题的有效手段。
英文摘要:
      The abnormal state caused by the manipulation of smart meters will not only lead to serious economic losses, but also lead to the misjudgment of operation state of power grid, which affects the security of power grid. Based on super state hidden Markov (SSHM) model, a method for detecting and locating the operation behaviors of smart meters is proposed in this paper. Firstly, the super state is introduced into the hidden Markov model, and an improved Viterbi algorithm is given based on the sparsity of the transfer matrix and observation matrix in SSHM model, which can reduce the difficulty of data storage and measurement. Secondly, key performance parameters of model are evaluated and optimized by the data set containing typical manipulation behavior of smart meters, thus a high-performance SSHM model is established. Finally, the performance of the proposed method is compared with other methods through an example of manipulation behavior analysis, the case study proves that the proposed method is an effective means to detect the abnormal state and locate the rigged behaviors of smart meters with high accuracy.
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