Big Data – Resolving the Power Transformer Dilemma

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North America relies on an aging electrical grid, some of which originated in the 1880s. This old and complex patchwork system of power generating plants, power lines, and substations operate cohesively to power homes and businesses.

Age is an important indicator of remaining life and structural strength. As equipment gets older, it breaks down causing an increasing number of power outages. A recent project investigated 2,300 “problem” transformers out of the total US installation base of 115,000 large power transformers. Of these 2,300, a total of 750 failed – for a failure rate of 32%! The industry cost of power interruptions caused by transformer failure can be considerable.

Transformers are the most important (and costly) equipment in an electrical power network. These aging pieces of the system put a difficult choice in front of the world’s electric utility companies: replace the critical transformers with new units or try to extend the working life of the existing fleet of older units.

In a recent presentation at TechCon Euro, LumaSense expert Jeff Golarz, Director of Business Development for Transmission and Distribution Solutions, discussed the implementation of “Intelligent Sensing at the Edge” throughout the entire electrical supply network to enhance the performance and information flow from existing assets. This option is much faster, more economical, and can provide what’s needed in short order…but does come with a catch! Once you start applying intelligent sensing to the electrical grid, you have just stepped into the bear trap of “Big Data.”

Why use Intelligent Sensors? 

It is apparent that there is a tremendous need for investment in the grid and corresponding grid structure. But, in reality, we are not going to be able to meet these investment numbers in time to prevent unnecessary outages and failures. This is where Intelligent Sensors come in.

Intelligent sensors can vary in their form, fit, and function. The most well-known Intelligent Sensors include on-line dissolved gas analysis (DGA), bushing monitoring, winding hot spot monitoring, top oil temperature monitoring, partial discharge monitoring, and load tap changer (LTC) monitoring.

Older substations are ideal locations for Intelligent Sensor upgrades in order to gain valuable information for asset and substation optimization. The value of continuous online Intelligent Sensor monitoring on transmission and distribution systems has been well documented. Benefits of this monitoring include such things as asset optimization, enabling condition based maintenance, detecting component failure before it actually occurs and enabling safe dynamic loading.

What about all of the data?

With all of these sensors, there can be up to 5 Gigabits per second of data streaming out of the substation. Due to the large amount of data, this is where Big Data can rear it’s ugly head and cause disruptions and confusion.

The latest trend in Intelligent Sensing is to enable maximum amount of sensing capability at the Intelligent Sensor head itself, which is referred to as sensing at the edge. The asset Intelligent Sensor has the capability for data storage, data processing, control functionality, and data processing. The data being processed is data that is reported “by exception,” which means it has been analyzed and meets certain predetermined criteria to be sent back to the processing center for alert and notification. This sensing philosophy can reduce the overall data flow by 80% and puts only data “of interest” in the hands of experts.

What can we do now?

Implementing smart Intelligent Sensors with intelligent sensing at the edge provides a modernization scheme that can occur quickly and cost effectively. The drive and new force of asset monitoring in the generation, transmission, and distribution industries is through intelligent sensing at the edge, which:

  • Makes data (especially Big Data) manageable
  • Minimizes failure mechanisms and security risks
  • Creates the first stem to resolve the transformer dilemma

Allowing for 24/7 online dissolved gas analysis (DGA) monitoring of either all or the most critical transformers provides an immediate indication of pending faults that could cause catastrophic loss of these assets. DGA uses “intelligent sensing at the edge” philosophy because the key objective in DGA analysis of fault gases is to correctly diagnose the fault that is potentially generated.

One thought on “Big Data – Resolving the Power Transformer Dilemma

  1. It is really good that the electrical companies are working on having better transformers so that there aren’t as many power outages. It sounds like these intelligent sensors are helpful in monitoring various possible problems. I am sure this will make it easier to identify issues before they cause too many problems. Thanks for sharing more about these!

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