The use of partial discharge and temperature rise characteristics to achieve online operation condition assessment of switchgear cabinet is of great value to the management and maintenance of the power transmission and distribution system. Based on the theory of current field and heat conduction in this paper

the electric-thermal coupling model of switchgear cabinet insulators is set up

the temperature and electric field intensity distribution of the fault generation period of insulator from surface contamination to fault is analyzed. Using ultraviolet and infrared photoelectric sensors

a switchgear fault detection device based on ZigBee wireless communication is developed

which can achieve real-time monitoring of such two characteristic quantities as partial discharge and temperature rise. In addition

a large amount of partial discharge and temperature rise data are collected by the device

and the relationship among ambient temperature and relative humidity

temperature rise and apparent discharge are studied. The correctness of the simulation analysis model is verified and the accuracy of the system is tested. Finally

based on the adaptive fuzzy neural network

a fault diagnosis model of heterogeneous feature information fusion is set up

which realizes the accurate assessment of the condition of the switchgear cabinet.