Researches On Short-circuit Identification And Prediction In Low-voltage System

In case that short circuit fault occurs in low-voltage system,the circuit,equipment,and even personal safety will be seriously damaged unless the short-circuit current be limited effectively.So the characteristics of short-circuit fault need to be extracted early and effectively,so as to identify short-circuit fault early and isolate the faulty line accurately before the short-circuit current develops.However,there are some noises in the sampling signal that affect the accuracy of fault feature extraction.In view of the limitations of filtering algorithm and short-circuit identification,this thesis finds out an effective filtering method under a laboratory low-voltage power distribution system,analyzes and realizes the early detection of fault relevant branches.On this basis,with deeply study on short-circuit current prediction,this thesis mainly contains the following aspects:(1)On the basis of the existing experiment system,the experimental scheme is designed and the experimental platform is set up.Furthermore,the corresponding simulation model is established based on single phase short-circuit fault,and the characteristics of short-circuit current are analyzed.Moreover,through comparing short-circuit current waveforms of fault relevant branches,this thesis also expounds the necessity of short-circuit early identification.(2)Considering the different loads,working conditions and signal interference sources,four filtering methods including mean filtering,median filtering,morphological filtering and wavelet filtering are compared and analyzed.And then this thesis finds out a filtering method which is more effective,more suitable for low-voltage system and more easily realized in PC.(3)The early identification method of short-circuit fault based on wavelet-packet detail decomposition algorithm is proposed in this thesis.Current data that acquired through simulation model is used to analyze the validity and rapidity of short-circuit early identification.This thesis also realizes this method by FPGA module of NI Compact-RIO system,and the practical experimental results show that this method is effective,fast and real-time.(4)Combining with the short-circuit characteristics of low-voltage distribution system,the grey prediction model GM(1,1)is introduced to research on short-circuit current peak value prediction,and the short-circuit current peak value prediction model based on GM(1,1)is built.This thesis takes the phase angle of short-circuit fault voltage and short-circuit current as the input parameters,the first and second peak of short-circuit current as the output parameters,then tests the prediction model.The test results verify that the prediction method can realize the prediction of short-circuit current peak values in real-time and online.In conclusion,considering the different loads,working conditions and signal interference sources,the filtering effects of different filtering methods are researched in this thesis.On this basis,through the wave-packet detail decomposition algorithm realizes early identification of short-circuit fault.Then the short-circuit current prediction method is studied,which provides the technological for the full range selective and coordinated protection in low-voltage distribution system.