It proposes a fault diagnosis method for high-voltage direct current(HVDC) systems based on artificial neural networks(ANNs).Firstly

the main fault types of direct current transmission system converter stations are analyzed based on the measured fault data of a direct current transmission system converter station in the past 3 years

and they are classified into 4 types: AC fault

DC fault

converter valve fault

and commutation failure.Then

based on the measured data from the site

a fault dataset is established

collecting and organizing 11 channel fault features that are representative and significant within 15 cycles for the 4 types of faults.On this basis

the fault samples are randomly divided into training and testing sets

and the model network parameters are updated to complete model training.Finally

it sets up a calculation example

and testes the diagnosis model using the testing set.Support vector machine(SVM) and Naive Bayes are set up as comparisons.The results show that the proposed method can achieve an accuracy rate of over 91% for fault diagnosis of HVDC systems

with high accuracy and strong robustness

and is suitable for fault identification and diagnosis of HVDC systems.