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 enhanced convolutional neural network(CNN)is proposed.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 the fault current.Secondly,variational mode decomposition(VMD)is applied to decompose the line-mode components into multiple intrinsic mode function(IMF)components,with the most informative IMF component being chosen as input for the VMD-CNN model.Then,an efficient classification model,support vector machine(SVM),is employed to classify the fault occurrence region by training on the extracted IMF components as inputs for SVM,ensuring 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 the sparrow search algorithm to achieve accurate fault location in hybrid three-terminal HVDC transmission lines.The simulation results reveal that with a transition resistance of 100 Ω,the 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 Ω,the relative error remains below 0.3％for different fault types.The proposed method enhances fault location accuracy under diverse fault locations,transition resistances,and fault types.
NETL
NSTL
万方数据
