In 2003, a power outage caused by out-of-phase system conditions impacted over 50 million people in the northeastern United States. Overgrown trees impacting power lines began a chain reaction of failures that led to the huge outage. Some customers in areas including upstate New York took up to three months to have power returned to their homes. Better monitoring of connected assets could be key to avoiding situations like this in the future.
How can monitoring connected assets on the grid improve uptime?
One strong concern for electrical utilities is the condition and longevity of transformers. Other than overhead-line failures, transformers have the highest rate of major failure out of all grid components. During peak loads, it’s very common for transformers to be overloaded.
The Onitsha Electricity Distribution Network was studied to determine transformer failure rates. It was found that following insulation failures, overloading was the second-highest cause of failure. Overloads caused 22.5% of all transformer failures in its grid.
Overloads can cause the transformer to fail earlier than would be otherwise expected. When the transformer fails unexpectedly, it can create serious issues for your utility. You could simply replace your transformers prior to failure. Unfortunately, that’s very expensive. Being able to figure out exactly when the transformer is most likely to fail allows you to get the longest use out of it without leaving customers without power. But how do you predict transformer failures?
The level of analytics needed to predict transformer failures was virtually impossible when the northeast blackout occurred. Today’s digitization technology makes it possible right now. But what can it really do for your power utility, production, or distribution organization? Let’s take a look at one scenario.
A study of metropolitan transformer overloads and failures
A major metropolitan area is having significant issues with transformer failures. The budget does not allow it to replace transformers without solid evidence that the transformer will fail shortly. Predicting end-of-life issues with a transformer is very difficult. With digitization on the horizon, the utility decided to see what solutions might be available to solve the problem.
The utility installed a solid digital core and analytics system. Internet of Things (IoT) technology allows load levels to be measured once a minute on over 100 transformers scattered across the metropolitan region. This enables the utility to collect over 200 million data points over the course of the year. A few years ago, analyzing that sheer volume of data would have been virtually impossible, especially for a utility.
With that level of detail, the utility can observe the conditions of the monitored transformers. They can see which transformers had what level of load over particular time periods – a single day, a week, a month, or the entire year. Represented by pinpoints, the transformers show different shades to represent their highest level of load. This gives the utility a good overall view of the condition of its connected assets.
The insights don’t stop there. The utility can limit the results to show only the transformers that remain in excess of 100% of their engineered capacity. The search can be completed in a quarter of a second. That’s a fraction of the time that was required for that level of analysis just a few years ago. They can also limit results to show transformers with an average load over 100%, which puts the transformer in priority for replacement.
Beyond that, the analytics available from the upgrade allow the utility to discern additional information on the transformers. Statistics are available on the transformer manufacturer and model to anticipate longevity and failure rates. With this data, the analytics program can calculate how much lifespan the transformer loses due to being overloaded. By having this information available, the utility can determine the expected remaining useful lifespan of each transformer.
With a few clicks of a button, it can change the view of each transformer to heat maps. This allows the utility to see where it has problem areas. Having this information allows the utility to plan for maintenance and replacements. It can stay ahead of unexpected failures and related outages. This allows it to maximize their budget to areas where it is most needed.
Having this capability drastically changed how the utility operates. Its customer service department sees significantly fewer complaints because of this change. Its crews can act in a proactive manner, staying ahead of many repair or replacement issues with the transformers. The utility can better spend its budget because there is a better expectation of what is most likely to happen in the future.
This scenario is not only completely possible with today’s technology, it’s already happened. SAP created a proof of concept test to help a utility in the greater New York City metropolitan area get a grip on its transformer loads and failure potential. SAP’s S/4HANA is a leading digital core technology that makes it possible. We can help your organization reduce your downtime through better analytics. Please feel free to contact us for more information.
Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading Accelerating Digital Transformation in Utilities.