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Transformer Monitoring



Smart Transformers: Enhancing Grid Efficiency and Reliability

The evolving demands of modern power grids necessitate the adoption of advanced technologies that can provide enhanced efficiency, reliability, and flexibility. Smart transformers are at the forefront of this technological revolution, offering a range of capabilities that significantly improve the...

Transformer Monitoring

hydrocarbon gas emission

Advancements in Dissolved Gas Analysis: Risk Assessment

In general, the purpose of periodic screening with DGA for power transformers is risk assessment. Is any transformer likely to fail in service? If so, how severe is the problem? Previous articles in this series have described ways to improve DGA interpretation. In this article we provide a glimpse of what modern statistics can say about risk assessment, after the previous steps are performed.
Conventional practice with IEEE or IEC guidelines is to compare gas...

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Sweep Frequency Response Analysis

Sweep frequency response analysis: Reliable demagnetization of transformer cores BY MARKUS PÜTTER, MICHAEL RÄDLER, BORIS UNTERER, OMICRON electronics GmbH Whenever a power or distribution transformer is isolated from the power system, it is very probable that residual magnetism remains in the...

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Dielectric Voltage Testing – Standard Methods

There are two standard methods from ASTM International: D877, Standard Test Method for Dielectric Breakdown Voltage of Insulating Liquids Using Disk Electrodes, and D1816, Standard Test Method for Dielectric Breakdown Voltage of Insulating Oils of Petroleum Origin Using VDE Electrodes. VDE stands...

Digital Twins for Substations: Bridging the Physical and Digital Worlds

In the rapidly evolving landscape of power grid management, digital twin technology is emerging as a game-changer for substations. By creating virtual replicas of physical assets, digital twins bridge the gap between the physical and digital worlds, enabling enhanced operational efficiency and...


The Role of AI and Machine Learning in Predicting Transformer Faults

AI and Machine Learning can predict transformer faults by analyzing dissolved gas data, thermal patterns, and vibration trends to identify insulation degradation, detect anomalies, and prevent costly power transformer failures before they occur. Why AI Integration into Transformer Diagnostics...

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