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Transformer Monitoring




The Truth About Cast-Resin Transformers

Debunking the myths surrounding aluminum usage BY SCOTT MAY, VIJAI NARAYANAN, ANDREW LAWLESS, Siemens Much has been written about the use of aluminum versus copper for transformer conductors within the electrical industry. Aluminum conductors have been used successfully in the electrical industry...

Transformer Monitoring

Zensol new instrument for OLTC testing

According to CIGRE A2.34, the dynamic resistance measurement or DRM (OFFLINE) is a test that offers diagnostics for several diverter or selector switch malfunctions such as: contact problems, broken springs, broken transition resistors, poor contact pressure, inadequate transition time, momentary open circuit, and synchronism motion issues.

Zensol’s DRM principle

Tap windings are powered with a DC voltage source. The current fluctuations are recorded during the switching process. The schematic below shows the principle of the...

Related Articles


The Truth About Cast-Resin Transformers

Debunking the myths surrounding aluminum usage BY SCOTT MAY, VIJAI NARAYANAN, ANDREW LAWLESS, Siemens Much has been written about the use of aluminum versus copper for transformer conductors within the electrical industry. Aluminum conductors have been used successfully in the electrical industry...

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...

Advantages of Headspace Hydrogen Monitoring for Network Transformers

INTRODUCTION The utilization of online dissolved gas analysis monitoring has proven to be one of the most effective predictors of overall transformer health and condition. Monitoring can vary greatly from nine gas to single gas systems to best suit the customers application when considering...


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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