Research On The Health Monitoring And Fault Diagnosis Of Low Voltage Circuit Breaker

A circuit breaker is one of the important switching equipment for control and operation in the power system.It is a key part to ensure the safe and reliable operation of the power system.It plays the role of opening and closing normal lines and breaking fault lines in the power supply and distribution system.Therefore,the operating performance of low-voltage circuit breakers is critical to the safety,stability,and economic operation of the power grid.As the low-voltage circuit breakers perform various actions,they will have various effects,so the failures caused by the operation of the circuit breakers are Diagnostic research is an indispensable task.While diagnosing its fault,the low-voltage circuit breaker and various controllers cooperate to control,protect and monitor the power system,so that when a fault occurs in the system,the low-voltage can be made in time.The circuit breaker operates,quickly removes the faulty part of the system,or cuts off the entire power supply,thereby preventing the fault from expanding and avoiding huge economic losses and human casualties.This paper diagnoses and monitors the mechanical faults of low-voltage circuit breakers based on the current characteristic signals of the switching coils.Firstly,the research status of the mechanical fault diagnosis of low-voltage circuit breakers is introduced.And a detailed analysis of the relevant theory of the closing coil current and the corresponding fault type,according to the closing coil current signal to determine the health status of the circuit breaker.Firstly,the current signal is pre-processed by wavelet transform to accurately identify the singular points in the fault signal,so that the current waveform of the closing and closing coil after the denoising and smoothing process can be obtained,the waveform characteristic value is extracted and then the fault is identified On this basis,the artificial intelligence method is added,and the method of wavelet analysis and fuzzy neural network is used to diagnose the low voltage circuit breaker.This improved method first uses wavelet analysis as the basis to extract time and current characteristic parameters t₁、t₂、t₃、t₄、i₁、i₂、i₃,then add a fuzzy layer in the fuzzy neural network,perform a relative fuzzy operation on the feature parameters,and finally input the blurred feature parameters to the neural network for fault recognition and classification,and finally,Through experimental and simulation research,the combination of wavelet transform and fuzzy neural network adopted in this paper is effective.The experimental results show that the fault diagnosis model of the low voltage circuit breaker of a fuzzy neural network can accurately diagnose the type of fault and has a good Practicality.Finally,a low-voltage circuit breaker online monitoring and fault diagnosis system was built.The system is composed of a host computer and a lower computer.It can realize various functions such as historical query of low-voltage circuit breaker data and fault diagnosis.A good human-machine interface is provided to provide simple and easy Understand the interactive operation.