Fault section location method for distribution network based on distributed sensing and random forest

To address the challenges of weak electrical characteristics, complex fault types, and transient diversity caused by grid-connected distributed power sources in low-current grounded distribution networks, a method for locating distribution network fault sections based on distributed sensing and random forests was proposed. An improved IEEE33 node model was constructed on PECAD/EMTDC. Distributed sensing nodes and multi-type and multi-location faults were configured. Effective value and sequence component features were extracted to construct a section-level fault fingerprint library. A random forest multi-classifier was used to output the probabilities of each section and determine the section where the fault is located. Noise was added to verify the robustness of the method. Simulation results demonstrate that the method exhibits good robustness and interpretability, with a fault section location accuracy of up to 99.4%, enabling highly reliable section location under complex operating conditions.