Characteristic study and time-domain discrete-wavelet-transform based hybrid detection of series DC arc faults

DC arc fault introduces major safety concerns in a wide variety of components in dc networks. However, the randomness and instability of dc arc makes it difficult to be detected. In this paper, an experimental system was designed and tested at different load current, dc source voltage, and gap length to evaluate the impact of each parameter to the dc arc. Based on the experimental results, characteristics in the electrical behavior were studied and fault detection oriented analysis was conducted. A detection algorithm utilizing both time and time-frequency domain characteristics was proposed to differentiate between dc arc fault and normal condition. The detection algorithm was then realized on a digital signal processing board and tested to verify the effectiveness. Experimental results show that the proposed algorithm can detect arc fault in a timely manner and is free of nuisance trip from normal circuit operations such as load change condition.