Research On The Detection Method Of Arc Fault Under Non-linear Loads

With the development of smart grid,the non-linear load in low-voltage distribution lines leads to fault arc current waveform and the normal load current waveform very similar.And the main drawback of fault arc detection method based on arc current characteristics is that there is only one information source,and it can lead to false action and refused action of arc fault circuit breaker.In order to solve this problem,this paper utilizes the electric field signal and magnetic field signal of arc radiation and arc current as the input signals,and proposes a fault arc detection method based on neural network and D-S evidence theory.Firstly,we obtained current signal,electric field signal and magnetic field signal of the arc fault using the arc fault test platform after analyzing the electromagnetic radiation theory.The experiment of arc fault under different experimental conditions was carried out;Secondly,waveform similarity method is employed to get the characteristics of arc current signal.The DFT method is used to analyze the characteristics of the electric and magnetic signals in the frequency domain,the modulus maximum method and the fuzzy c-means clustering method is used to analyze the characteristics of arc radiation signal under various load current in time domain.The results show that the analysis method can be used as the basis for fault detection;Finally,the BP neural network is used to establish the mapping relationship between arc fault and each characteristic,and the fault arc identification rate of each characteristic variable is obtained.all of the identification rates are taken as the input of D-S evidence theory method to realize multi characteristic information fusion.The experimental results under typical load prove that the proposed method can effectively improve the detection accuracy of fault arc.