首页|Studies from Curtin University Add New Findings in the Area of Machine Learning (Deep Machine Learning-Based Asset Management Approach for Oil- Immersed Power T ransformers Using Dissolved Gas Analysis)
Studies from Curtin University Add New Findings in the Area of Machine Learning (Deep Machine Learning-Based Asset Management Approach for Oil- Immersed Power T ransformers Using Dissolved Gas Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Perth, Australia, by NewsRx correspondents, research stated, "Reliable operation of oil -immersed power transformers is crucial for electrical transmission and distribu tion networks." Our news correspondents obtained a quote from the research from Curtin Universit y: "However, the aging of high voltage assets including power transformers along with the increasing of load demand have heightened the importance of adopting c ost-effective asset management strategies. Dissolved gas analysis (DGA) has been recognized as a valuable diagnostic tool for detecting potential faults and mon itoring the condition of oil-immersed power transformers. Traditional offline DG A method involves periodic sampling and laboratory analysis, which often results in delayed detection and response to emerging faults. To address these limitati ons, online DGA approach has been emerged to provide real-time monitoring and co ntinuous data acquisition. This paper presents a new asset management approach f or mineral oilimmersed power transformers by analysing the online DGA data usin g convolutional neural networks. The proposed approach provides real time soluti ons to classify emerging fault type and predict transformer health deterioration level with high accuracy."
Curtin UniversityPerthAustraliaAus tralia and New ZealandCyborgsEmerging TechnologiesFinanceMachine Learnin g
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