A Planar Location Method for DC Arc Faults Using Dual Radiation Detection Points and DANN
Kui Li, Shuangle Zhao, Yao Wang
Abstract
Arc faults in dc microgrids or photovoltaic (PV) systems are difficult to detect and isolate. Especially in location research, there are few related results, and arc fault detection is immature. This article proposes a planar location method that requires only two detection points. Because the height of many dc systems is much smaller than their horizontal dimension, an antenna array and a fault source form a horizontal triangle to locate the dc arc faults. Then, the signal pulses are distinguished and extracted using a cross correlation technique. A neural network (NN) and received signal strength indicators (RSSIs) are used to estimate the arc distance. However, the data from the dual detection points are insufficient for meeting the demands of NN training and may lead to a large location error. Therefore, a data-augmented NN (DANN) technique was introduced into the algorithm to enhance the accuracy and robustness of distance estimation. The experimental verification results show that the method can effectively locate faults under a variety of arcing conditions. Moreover, the proposed method has intrinsic immunity to normal current fluctuations (NCFs). It can be used in PV plants or smart buildings with solar cells to avoid nuisance trips and improve the effectiveness of fault protection.