Development of Disturbance Type Detection Using Convolution Neural Network for Fault Signature Analysis - Putra, Kharisma Darmawan; Garniwa, Iwa; Jufri, Fauzan Hanif; Oh, Seongmun (Universitas Indonesia, 2025)
The development of technology in electrical systems is growing rapidly, increasing power system complexity, which causes the operation and maintenance of the power system networks to become more complicated, especially when a disturbance occurs in the networks. To overcome the issue, there is a need to utilize the tools available as much as possible to manage the power system networks. Nowadays, the power system network is equipped with protection relays and controls that provide various data about the systems, such as the Disturbance Fault Recorder (DFR), which monitors and records the system’s characteristics during network disturbance events. DFR holds information on the system’s parameters during a fault, but it cannot recognize the type or cause of the disturbance. Hence, this paper proposes a method based on the Convolution Neural Network (CNN) model to analyze the DFR’s data and determine the type/cause of disturbance so it can be used to manage the follow-up actions properly. Based on the research results, CNN, with six types of disturbance classification, has an accuracy of 93,87%. Based on the results obtained, the accuracy of CNN using the VGG19 type in handling disturbance analysis in graphical patterns is satisfactory.