Research On Intelligent Diagnosis And Early Warning Technology For Thermal Faults Of Electrical Equipment

Most electrical equipment has structural tightness,poor heat dissipation,high current,high voltage,strong magnetic field,etc.It not only destroys the stable operation of electrical equipment,but also leads to the failure of electrical equipment,which seriously shortens the service life of the equipment.In order to effectively ensure the economic,safe and stable normal operation of electrical equipment,it is of great theoretical value and engineering practical significance to study the continuous online intelligent thermal fault diagnosis and early warning technology of electrical equipment.In this paper,the switchgear is taken as the research object.Based on the detailed analysis of the research status of temperature monitoring system and related technologies for switchgear at home and abroad,a real-time monitoring and intelligent fault diagnosis system for switchgear is proposed,and the early warning system for thermal failure is realized.In view of the high voltage operation environment,a wireless data monitoring platform is designed and built.It collects the temperature of buses,contacts and environment as the basic data of thermal fault diagnosis and early warning,and collects the current and voltage of buses as the auxiliary data of thermal fault diagnosis and early warning at the same time.At the same time,the CT power supply is designed based on electromagnetic induction technology.The core structure is optimized and improved to solve the problem of oversaturation.It ensures the continuous operation of the system and realizes electrical isolation.Considering the influence of ambient temperature and load current on temperature rise,a thermal fault diagnosis and early warning algorithm based on dynamic warning threshold is proposed.A temperature prediction model based on BP neural network is established,and the model is trained and verified bythe measured data of normal operation.The model can accurately predict the normal operating temperature under different ambient temperatures and load currents.Using the relative temperature difference method,the current dynamic early warning threshold is determined by the thermal fault defect standard correction,which effectively improves the accuracy of the early warning.Finally,the actual test of the built system hardware platform is carried out.The experimental data show that the modules of the system work normally,and the temperature prediction model based on BP neural network can accurately diagnose and diagnose the thermal fault.This paper presents an intelligent diagnosis and early warning system for thermal faults of switchgear,and a dynamic early warning threshold algorithm based on BP neural network temperature prediction.It is not only suitable for switchgear,but also can be used in other kinds of electrical equipment.It not only effectively solves the problem of false alarm and missed alarm of traditional threshold algorithm,improves the accuracy of temperature monitoring and fault early warning of electrical equipment,but also lays a foundation for intelligent thermal fault diagnosis and early warning,and guarantees the safe and stable operation of electrical equipment.