This paper presents an intelligent fault classification approach to power transformer
dissolved gas analysis (DGA). Support vector machine (SVM) is powerful for the problem …

This paper focuses on a Smart Fault Diagnostic Approach (SFDA) based on the integration
among the output results of recognized dissolved gas analysis (DGA) techniques. These …

Abstract Dissolved Gas Analysis (DGA) is one of the most common techniques to detect the
incipient faults in the oil-filled power transformers. In this paper, a new approach of DGA …

Finding out the transformer incipient faults and their development trend has always been a
central issue for electric power companies. In this paper, a novel approach combing wavelet …

This paper presents a machine learning-based approach to power transformer fault
diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the …

Most presented dissolved gas analysis (DGA) techniques were interested in determining the
fault types (FTs), but few articles discussed the corresponding severity of these faults. Here …

The development of a new dissolved gas analysis (DGA) method often requires a
comparative study to assess the accuracy of the proposed technique. This is faced with the …

This paper presents an intelligent fault classification approach to power transformer
dissolved gas analysis (DGA). Artificial neural network (ANN) is powerful for the problem …

E-Nose finds its use in a wide range of applications such as quality assessment in food
processing to toxic gas identification in chemical industry either in the offline or online mode …

Transformer is the main important equipment in electrical power system. Early stage
detection of the transformer faults has great economic significance because it considered …