Deep Machine Learning-based Asset Management Approach for Oil-Immersed Power transformers using Dissolved Gas Analysis

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Jin, Lan, Kim, Dowon, Chan, Kit Yan, Abu-Siada, Ahmed
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Asset management; Condition monitoring; Cost analysis; Data acquisition; Discharges (electric); Dissolved gas analysis; Dissolved gases; Fault detection; Fault diagnosis; Feature extraction; Gas analysis; Life estimation; Machine learning; Mineral oils; Oil insulation; Power transformer insulation; Power transformers; Real time; Remnant life estimation; Training; Transformers; Voltage transformers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3366905

Reliable operation of oil-immersed power transformers is crucial for electrical transmission and distribution networks. However, the aging of high voltage assets including power transformers along with the increasing of load demand have heightened the importance of adopting cost-effective asset management strategies. Dissolved gas analysis (DGA) has been recognized as a valuable diagnostic tool for detecting potential faults and monitoring the condition of oil-immersed power transformers. Traditional offline DGA method involves periodic sampling and laboratory analysis, which often results in delayed detection and response to emerging faults. To address these limitations, online DGA approach has been emerged to provide real-time monitoring and continuous data acquisition. This paper presents a new asset management approach for mineral oil-immersed power transformers by analysing the online DGA data using convolutional neural networks. The proposed approach provides real time solutions to classify emerging fault type and predict transformer health deterioration level with high accuracy. Results show that the accuracy of fault diagnostics of the proposed approach is approximately 87%.