Non-contact Measurement Of Mechanical Characteristics Of High Voltage Circuit Breaker And Its Defect Diagnosis

At present,the mechanical characteristic measurement and defect diagnosis of high voltage circuit breakers are mostly realized by using sensors to obtain mechanical characteristic parameters after power outage.In this paper,a non-contact measurement and defect diagnosis method for mechanical characteristics of high voltage circuit breakers with spring actuator is proposed.The main research contents and results are as follows:Firstly,this paper proposes a non-contact measurement method for the solenoid stroke of high voltage circuit breaker.Based on the principle of active visual calibration,the measurement system under safe electrical distance is designed.The pixel value of the solenoid stroke is calculated by using K-Means image clustering algorithm and CLSD linear detection algorithm.Combined with the precise positioning function of laser sensor,the vernier caliper is used to calibrate the pixel equivalent at a fixed distance,and the actual physical value of the electromagnet stroke is calculated.The calculation results are compared with the field measurement results,and the relative error is small,which verifies the effectiveness of the method.Secondly,a simulation experiment platform for mechanical characteristics of high voltage circuit breakers,including the stroke defect of electromagnet,is built.Considering that the dynamic contact is sealed in the arc extinguishing chamber and the target is prone to local missing when the light is insufficient,this paper applies the deep learning method to the measurement of the marker stroke curve rigidly connected with the dynamic contact.Firstly,the YOLOv5 framework is used to detect the markers in the image.Secondly,the DeepSort algorithm is used to track its trajectory to obtain the marker stroke curve.According to the rigid connection relationship between the marker and the dynamic contact,the dynamic contact stroke curve is finally obtained.At the same time,the breaking current waveform is extracted by using the advantages of wavelet transform in the detection of mutation points.After low-pass filtering,it is combined with the stroke curve to obtain the curve variation law of each defect state of high voltage circuit breaker,and its influencing factors are analyzed.The characteristic values of stroke and current curves of each state are calculated by the feature definition method and the derivative definition method,respectively,which provides a sample data set for the defect diagnosis of high voltage circuit breakers.Finally,a grey wolf optimization support vector machine defect diagnosis method for high voltage circuit breaker based on random forest feature optimization is proposed.Firstly,the random forest algorithm is combined with the support vector machine model to determine the optimal feature subset database,and then the parameters of support vector machine are optimized based on the grey wolf algorithm to realize the defect diagnosis of high voltage circuit breaker.The calculation results show that after the feature optimization of the random forest algorithm,the diagnostic accuracy of the model can be improved to a certain extent,and the diagnostic time can be reduced.Secondly,the parameter optimization of the support vector machine combined with the grey wolf optimization algorithm can further improve the diagnostic accuracy.