Series DC Arc Fault Detection Using Machine Learning Algorithms

Abstract

The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current-average value, median value, variance value, RMS value, and distance of the maximum and minimum values-were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

“…Data-driven FDS is primarily carried out using classification algorithms [4], [5], [6]. Having a set of sampled records corresponding to different system states, one can build a statistical model that maps incoming signal patterns to certain conditions such as different events of faults in the systems [7].…”

Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

“…The adoption of a neural network for arc failure detection was presented in [16]. In [17,18], several AI algorithms were adopted to detect DC series arc fault. In addition, five features in the time domain were utilized as inputs of learning algorithms.…”

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

“…In Refs. [19][20][21][22], numerous AI models were employed to diagnose series arcing events using different characteristics as inputs. The adoption of AI algorithms for parallel arc diagnosis was proposed in [23].…”