HVDC Fault detection and Identification in monopolar topology using deep learning

Abstract

Fault detection, identification(classification), and isolation are very important to ensure continuous power transmission in a power grid system. Fast and accurate methods are thus very critical for fault diagnosis in power systems. Many research papers attempted different learning methods in this area, and the focus was either on a subset of the aspects or only on the critical asset faults. The key results from some of the relevant ones are taken up for comparison. This paper describes the novel technical results in detecting and identifying all types of AC and DC faults in the HVDC station by using a fully convolutional neural network (FCNN) deep learning algorithm. The performance is evaluated with an experiment on symmetrical monopolar HVDC station simulated in Power Systems Computer-Aided Design (PSCAD). The novel significance of the results includes applying the learned knowledge from one station to validate on the other station data, the quick time to detect and identify faults, the confusion matrix, classification reports with probability of 99.24% for detection and 97.73% for identification, False alarm rate of 1.35%, and zero percent missed faults. The adaptability of the trained model from the learned knowledge to schematically related HVDC stations is discussed.