![]() ![]() The training and testing datasets were generated for each DGA method, and trained to each ML algorithm. Three DGA fault identification methods for mineral oil insulated transformer were studied, namely DTM1, DTM4, and DTM5. This study examines six commonly used machine learning algorithms to support DGA fault identification of power transformer: decision tree, support vector machine, random forest (RF), neural network, Naïve Bayes, and AdaBoost. However, no study has done thorough investigation on the use of suitable machine learning algorithm for the ML-based implementation of this matter. The use of computer-based technology has been implemented in recent years to support transformer fault identification. The method consists of several triangles, which still requires expertise for fault identification. Several interpretation methods have been proposed, one of the most reliable of which is the graphical Duval triangle method (DTM). Dissolved gas analysis (DGA) is a powerful tool to monitor the condition of a power transformer. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |