Diagnosis of series DC arc faults - a machine learning approach

Increasing prevalence of DC sources and loads has resulted in DC distribution being re-considered at a micro-grid level. However, in comparison to AC systems, the lack of a natural zero crossing has traditionally meant that protecting DC systems is inherently more difficult – this protection issue is compounded when attempting to diagnose and isolate fault conditions. One such condition is the series arc fault, which poses significant protection issues as their presence negates the logic of overcurrent protection philosophies. This paper proposes the IntelArc system to accurately diagnose series arc faults in DC systems. IntelArc combines time-frequency and time domain extracted features with hidden Markov models to discriminate between nominal transient behavior and arc fault behavior across a variety of operating conditions. Preliminary testing of the system is outlined with results showing that the system has the potential for accurate, generalized, diagnosis of series arc faults in DC systems.