一种适用于混合三端直流输电线路的故障定位方法

To address the challenges posed by complex structures in fault location within hybrid three-terminal high voltage direct current (HVDC) transmission lines, a fault location method based on enhanccd convolutional neural network (CNN) is proposcd. Firstly, the fault current data of the hybrid three-terminal HVDC transmission System is acquired by modeling the System using PSCAD/EMTDC Software, with the fault current being decoupled using the Clarke transform to obtain the line-mode components of thc fault currcnt. Sccondly, variational modc decomposition (VMD) is applied to decompose the line-modc components into multiple intrinsic mode function (IMF) components, with thc most informative IMF component being chosen as input for the VMD-CNN modcl. Thcn, an efficient Classification model, support vector machinc (SVM), is employed to classify the fault occurrcnce region by training on the extracted IMF components as inputs for SVM, cnsuring precise identification of the fault region. Finally, a VMD-CNN model is developed for fault location, extracting fault Information from traveling wave signals and optimizing CNN hyperparameters using thc sparrow search algorithm to achieve aecurate fault location in hybrid three-terminal HVDC transmission lines. The Simulation results reveal that with a transition resistance of 100 n, thc relative error in fault location is below 0. 17% for various fault locations; at a fault position of 460 km, the relative error is under 0. 25% for different transition resistance scenarios; and with a transition resistance of 50 fl, the relative error remains below 0. 3% for different fault types. The proposed method enhanecs fault location aecuraey under diverse fault locations, transition resistances, and fault types.