As an indispensable part of people's daily life, it is crucial to ensure the stability and safety of power equipment. In order to diagnose the equipment failure in time, this paper uses the YOLOv3 model-based infrared monitoring system to monitor the power equipment in real time and derive the infrared monitoring images and various status data of the power equipment. The obtained data is integrated with the structured data of power equipment, classified according to the type of equipment, and imported into the data analysis main central station. The improved KPCA method and artificial neural network algorithm were used to normalize and denoise the data. The neural network algorithm is applied to the data processing of power grid system, and the corresponding probabilistic neural network is constructed to analyze and diagnose the fault situation of power equipment, so that the fault warning can be issued in a timely and hierarchical manner.
Research on the fault diagnosis technology of power equipment based on artificial intelligence technology
12.10.2022
1611149 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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