Abstract
Series dc arc fault generates arcing noise cross-talk to adjacent electrical loads, making it challenging to identify the arc fault location, which creates a fire hazard that endangers the system’s safety. This study proposes a series arc fault identification method in a dc zonal electrical distribution using Random Forest based local detectors to monitor constant power loads, and output predicted nominal and arc fault probabilities. The predicted probabilities are then sent to a centralized master detector to obtain the final decision. With full communication capability among all local detectors, the master detector makes the final decision using Random Forest algorithms. If there is any disconnection in the communication links, the local detector can operate independently by using the predicted arc fault probability to output the flag signal autonomously, while the master detector continues to operate with other local detectors. The proposed fault identification method is experimentally verified with a zonal electrical distribution testbed that comprises three constant power loads.
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