Series AC Arc Fault Detection Using Decision Tree-Based Machine Learning Algorithm and Raw Current

Series AC arc fault is dangerous because it may lead to electrical fire hazards. It is very challenging to detect it accurately. This paper proposes a decision tree-based machine learning algorithm, random forest (RF), to detect series AC arc faults. The proposed model is simpler and lighter than traditional artificial neural network (ANN) or deep neural network (DNN) based algorithms. The model is evaluated with load currents of 8 different types of loads collected following the IEC62606 standard. The arcs in the database are generated by simulating loose cable connections and insulation faults, corresponding to the two most common types of arc faults. A grid search algorithm is used for hyperparameter tuning and a precision-recall trade-off analysis is performed for an optimal classification threshold of the proposed algorithm. The experimental evaluation of the model, using raw current as input, demonstrated an arc fault detection accuracy of 99.78% at 10 kHz sampling rate of the data. The proposed model is further optimized using the Gradient Boosting method to make it lightweight so that it can be implemented in a resource-limited edge computing device at ease with lower latency. The optimized model has an accuracy of 99.65% and an average runtime of 10.22 ms per sample (1 cycle) when implemented in Raspberry Pi 3B. This proves the feasibility of the model for practical deployment in real-world arc fault detection applications.