The utility provider has a network of about ~4200 distribution transformers located across the service areas. Maintaining these number of DTs is an operational challenge for them due to breakdowns, high scheduled maintenance cost and public inconvenience. They wanted to build a more robust, scientific and predictive solution - to forecast distribution transformer failure with high degree of confidence - therefore also improving customer satisfaction with their services.
Risk of failure
The utility provider was look at a machine learning driven solution that could predict the chances of failure with a high degree of reliability.
A failure prediction accuracy of 75% over validation dataset
Pre-emptive scheduling of maintenance, thereby reducing downtime
Better planning & scheduling of maintenance
A web based interface with automated reporting was provided to the utility services provider at every 24 hour intervals.
Random breakdowns. Difficult to predict.
The utility services provider had spread across teams. The operations team held some data, the maintenance team had few other data sets and so on. They did not have a centralized database to dig into. The distribution transformer (DT) to meter mapping had a lot of data quality and consistency issues. And since DTs have a life of more than 10 years on an average, the data was not available for the lifetime for most of the DTs.
In addition to the above, scheduled maintenance data was not properly maintained, with a high percentage of missing values. The biggest challenge however was the low percentage of failure cases - creating a highly imbalanced class with ~.025% failures. Because of this failure rate, standard Classification algorithms would not work, and we proposed a boosting approach to create more balanced classes.
Automated, accurate prediction of failure risk
The predictive maintenance (PdM) solution deployed for the client had an accuracy of 75% on the hold-out sample.
With the web based interface having integrated reporting, the client now gets reports at desired intervals. This enables the client to:
a. Identify DTs that are at higher risk of failure and proactively address them before a breakdown
b. Improved and planned maintenance scheduling, reducing inconvenience and revenue losses
c. Improved customer satisfaction levels with better, uninterrupted supply of power
Predictive Maintenance (PdM) solution enabled the client to serve customers with the desired efficiency.