An Arc Grounding Fault Detection Method in Distribution Networks Using Chaotic DC Waveform Recognition

Single-phase grounding faults (SPGFs) in distribution networks are often accompanied by electric arcs, making detection challenging due to irregular zero-sequence voltage and current. This article proposes a new method for detecting arc grounding faults (AGFs) in distribution networks using Toeplitz inverse covariance-based clustering (TICC) and dynamic time warping (DTW) to recognize chaotic direct current (CDC) waveform features. Initially, the article examines the asymmetry in current during AGF occurrences and concludes that asymmetric components persist throughout the fault duration. The concept of CDC is then proposed and obtained through specially designed sensors. Subsequently, the TICC algorithm is utilized to segment real-time CDC waveforms, extract the abnormal CDC waveform, and identify faulty lines. Finally, real-time AGF diagnosis of abnormal CDC waveforms is conducted using the DTW algorithm. By collecting data from a real substation in Jiangsu, China, and taking experiment verification in China ultrahigh voltage (UHV) Test Base, the results demonstrate that the classification accuracies for the training and test sets reached 96% and 100%, respectively, with the errors in diagnosing AGFs not exceeding 0.3 s. As a result, it enhances the safety and stability of power grid operations.