Dynamic Encryption of Power Internet of Things Data Based on National Secret Algorithm

Zhou, Jing; Wu, Qian; Li, Jin; Li, Jiaxin

doi:10.1007/978-3-031-06791-4_39

Cited by 1 publication

(1 citation statement)

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“…Generators, transformers, power lines, circuit breakers and other electrical equipment are important components of ubiquitous power IoT [1][2][3][4][5][6][7]. With the improvement of information level at present, the power IoT uses wireless sensors as sensing units to build a sensing system for electrical equipment state, which is used for network fault handling or other advanced applications [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Research on State Detection Method of Electrical Equipment Based on Wireless Sensor Network Signal Processing

Liang¹,

Ding²,

Jin-fa³

et al. 2022

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False alarms and omissions of key measurement signals may even affect the reliability of decision-making in power IoT system, lead to safety accidents and bring huge economic losses. In order to improve the reliability of system operation and the information management level of electrical equipment, it is necessary to identify and extract suddenly changed signals from massive measurement signals collected by wireless sensor networks, and to detect the working state of electrical equipment by judging the source of signals. Therefore, this article studies the state detection method of electrical equipment based on wireless sensor network signal processing. In the second chapter, a cluster splitting and merging method is designed to solve the problem that the existing detection methods tend to ignore the imbalance of cluster size. In the third chapter, according to the data characteristics of measurement signals collected by wireless sensor networks, a similarity measurement criterion for composite time series of measurement signals is proposed, and the corresponding distance matrix is generated based on this criterion. In the fourth chapter, wavelet decomposition is used to decompose the initial measurement signals collected by wireless sensor networks, and then the signals are compressed twice based on compressed sensing. Then the abnormal signal data information is imported into support vector machine for training to realize the real-time detection of abnormal signals of electrical equipment state. Experimental results verify the effectiveness of the proposed algorithm.

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