Smart asset management system for power transformers coupled with online and offline monitoring technologies

• Published in: Engineering failure analysis Vol. 154; p. 107674
• Main Authors: Biçen, Yunus, Aras, Faruk
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Asset management; Degradation; Electrical engineering; Insulation; LabVIEW; Material defect; Power transformer; Electrical engineering; Degradation; Insulation; Material defect; Asset management; Power transformer; LabVIEW
• ISSN: 1350-6307; 1873-1961
• DOI: 10.1016/j.engfailanal.2023.107674

•A concept for combining online and offline monitoring technologies was proposed.•With an easy-to-use user interface, less specialist knowledge is required.•More accurate diagnostics with the holistic intelligent asset management system.•Matrix-based parallel computation makes it a fast and easy-to-operate tool. Predictive maintenance strategies have gained popularity in recent years due to the advantages they provide over traditional maintenance strategies. Online monitoring technologies are critical for implementing predictive maintenance strategies. However, in some cases, the information generated by online systems may not be accurate and may need to be verified with offline monitoring technologies. In this study, a fault-sensitive matrix-based smart asset management system that is compatible with both online and offline technologies has been developed for power transformers. The developed system has the ability to assess multi-input parameters simultaneously and holistically. Because of the system's matrix structure, having a large number of input parameters or expanding them later is not an issue. Furthermore, the algorithms that will evaluate the input parameters are independent and distinct from one another. Because of its compatibility with the data acquisition card and its design options for the user interface, LabVIEW has been chosen for the system's development. The functionality of the system has been tested by deliberately generating faults in a test cell. While the high-resolution sensor data obtained and the calculated results are displayed on the interface, the failure probabilities are evaluated and displayed in a separate window. Intentionally generated faults have been diagnosed with high accuracy after going through the online monitoring and offline verification processes.