Power Transformer

Power transformers are critical components of electricity grids, and their reliable operation is paramount for grid stability and efficient energy delivery. Current research focuses on improving transformer health monitoring and fault prediction, employing advanced techniques like physics-informed neural networks (PINNs), knowledge graphs combined with gradient boosting, and various machine learning algorithms (e.g., random forests, support vector machines, and ensemble methods) to analyze diverse data sources, including dissolved gas analysis and phase-resolved partial discharge signals. These advancements aim to enhance predictive maintenance, improve grid resilience, and address emerging cybersecurity threats associated with increasingly connected smart transformers.

Papers

May 10, 2024

Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
Ibai Ramirez, Joel Pino, David Pardo, Mikel Sanz, Luis del Rio, Alvaro Ortiz, Kateryna Morozovska, Jose I. Aizpurua
Transformer Megatron Decepticons Transformer Based Decision Transformer Life Prediction Hotspot Detection Power Transformer

February 11, 2024

Power Transformer Fault Prediction Based on Knowledge Graphs
Chao Wang, Zhuo Chen, Ziyan Zhang, Chiyi Li, Kai Song
Knowledge Graph Limited Fault Power Transformer Transformer Fault

February 25, 2023

Cybersecurity Challenges of Power Transformers
Hossein Rahimpour, Joe Tusek, Alsharif Abuadbba, Aruna Seneviratne, Toan Phung, Ahmed Musleh, Boyu Liu
Power Grid Cyber Attack Threat Landscape Security Challenge High Performance Transformer Power Transformer

November 20, 2022

Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers
Iurii Katser, Dmitriy Raspopov, Vyacheslav Kozitsin, Maxim Mezhov
Machine Learning Anomaly Detection Transformer Based Gas Concentration Power Transformer

October 21, 2022

Feature Engineering and Classification Models for Partial Discharge in Power Transformers
Jonathan Wang, Kesheng Wu, Alex Sim, Seongwook Hwangbo
Classification Model Feature Engineering Feature Set Power Transformer Partial Discharge

March 14, 2022

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Machine Learning New Framework Power System Trustworthy Machine Learning Decarbonization Study Wind Farm Power Transformer