Incipient Fault Diagnosis in Power Transformers by DGA using a Machine Learning ANN - Mean Shift Approach

• Published in: IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (Online) pp. 1 - 6
• Main Authors: Soto, Alex R. E., Lima, Shigeaki L., Saavedra, Osvaldo R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• Subjects: Artificial neural networks; DGA; IEC Standards; Machine learning; Machine learning algorithms; Mean Shift; Network Neural Artificial; Power transformers; Training
• ISSN: 2573-0770
• DOI: 10.1109/ROPEC48299.2019.9057143

The power transformer is a valuable asset of the electrical system. A damage causes the interruption of electrical service and high repair costs for companies. Therefore, the detection of faults in incipient conditions is essential. In the recent literature Dissolved Gas Analysis (DGA) is the best accepted technique to the diagnosis of incipient faults in power transformers. This paper presents an approach to diagnosis fault by DGA using deep neural network, the drawbacks of the number of training patterns (amount of data) is satisfactory solved with using the Mean Shift algorithm. Likewise, the input and output parameters are conveniently selected, the input parameters being the gas relations established in the IEC 60599 standard acting in parallel with a new ratio of proposed gas (Rnew=C2H2 / C2H6) and a binary output. The proposed approach achieved an accuracy of 100%, both in the training and validation process as well.