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Transformer Monitoring


Transient Analysis of Power Transformers Using Finite Element Method

A transformer relies on electromagnetic induction through coils to transfer electric energy between two or more circuits. Varying current in the transformer’s primary winding creates varying magnetic flux in the core, which produces varying electromotive force (EMF) or voltage in the...

Power Transformer Failures

Electric utilities maximize utilization of their assets, while maintaining reliability. Power transformers are typically reliable, but have been known to fail suddenly and/or prematurely. Transformer failures are both costly and can leave customers dissatisfied, harming the reputation of a utility....

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...

Transformer Monitoring

Hydrogen Gas in Transformer Oil

Why event-based fault type identification is better than a sample-by-sample approach

Introduction

Fault type identification is an important step in dissolved gas analysis (DGA). When a significant amount fault gas production is detected in a transformer, we want to know what kind of physical condition could be responsible for the gassing. Knowing the type of fault can help to identify the nature and location of the problem in the transformer. That in turn can also suggest what physical inspections or tests may be warranted in order...

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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...


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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