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Predicting Power Transformer Failure in a Manufacturing Plant
This project focuses on helping learners apply various Data Mining and Machine Learning (Regression) techniques to predict power transformer failure.
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Difficulty: IntermediateFoundational knowledge or experience in statistics or analytics is recommended.
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Duration: Approximately 3 hours
Case Overview
- A transformer is a device that is used to transfer electricity between two or more circuits through electromagnetic induction.
- A transformer is a device that is used to transfer electricity between two or more circuits through electromagnetic induction. They are inherently prone to abuse, which can result in catastrophic breakdowns and accidents.
- For this prediction model can be prepared to alleviate the transformer fails to operate properly, power will be cut off, resulting in damage to the transformer and the power system, which may result in more serious harm.
- Learn to apply various Regression techniques and root cause analysis methods can be deployed to minimize sources of error.
What’s included
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills You Will Learn
Associated Learning Tasks
- Transformers act as a very critical link between power generation & supply. Mineral oils present in the transformers keep them cool, but when it is overcharged, it can create a lot of heat & sparks.
- This massive overpressure may eventually cause the transformer to rupture with a loud boom, flash, and emit smoke to the air.
- In this case we will predict the life expectancy of a transformer by analyzing its internal condition.
- Data preprocessing techniques are carried out to ensure we feed the model with clean data.
- Variance inflation factor is applied to check for the presence of multicollinearity.
- Linear Regression model is applied to the data and their assumptions are checked.
- A transformer can fail for various reasons, but commonly they fail due to overloading, wear & tear, power surges, and lightning strikes.
- They always are prone to overuse which can result in terrible failures & accidents.
- Hence, finding their life expectancy much before can prevent disasters.
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