Abstract
In order to realize smart fire electric power alarm monitoring, this paper designs a smart fire electric power monitoring system based on the cyber-physical model. In the physical space, the system receives the information, including the voltage, temperature, current, instantaneous power and other characteristic data from the SDF400 fault electric arc detection sensor. In the information space, the system builds a feature anomaly judgment model based on the kmeans++ clustering algorithm and the mathematical criterion including bessel formula and raida criterion. Experimental test data show that the anomaly judgment model has an average 92.4% correct alarm rate and 2% false alarm rate. The precise results of the model provide a basis for smart fire electric power safety monitoring and support smart power control application scenarios such as smart circuit closing.
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