AI based failure analysis for one type of transformers in power systems

• Main Author: Xiong, Yifu
• Other Authors: Hu Guoqiang
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/169772
• Institution: Nanyang Technological University

Electricity is now required for human productivity and daily existence. Electricity is vital to social, scientific, and technical advancement, among other things. The power system, which transmits and distributes the power produced by the power plant to each consumer, is a crucial component of the power system's transmission, distribution, and consumption. Electricity reliability is crucial for the country as well as the household. The transformer is a crucial node in the transmission of electric energy since it is the main piece of equipment used for power grade transfer and is in charge of raising and reducing the voltage in the power grid. Transformer failure will have an impact on the delivery and distribution of power energy, leading to a widespread blackout and the forced shutdown of industry and life, resulting in significant losses for society as a whole and the nation. Additionally, the transformer anticipating failures may identify and address a variety of possible problems and concealed threats early on, allowing the transformer to continue operating for a long period. AI-based failure analysis has shown to be a successful method for identifying and anticipating equipment breakdowns in power systems. For one type of transformer used in power systems by processing the data of dissolved gas(DGA), we describe an AI-based failure analysis system in this study. To recognize possible failures and their causes, the suggested approach incorporates machine learning algorithms, data analysis methods, and domain expertise. The technology trains a machine learning model using past data from transformer monitoring sensors and maintenance logs. The model is then used to forecast upcoming failures and offer useful information for maintenance and repair activities. By decreasing the frequency of transformer failures and limiting downtime, the suggested solution seeks to boost the consistency and efficiency of power systems. The results show that the AI-based failure analysis system can achieve high accuracy in predicting transformer failures and provide valuable insights for maintenance and repair operations.