Researchers in the US have built a new smart tool that has the ability to instantly spot abnormal power grid conditions that result in wildfires, equipment damage and blackouts.

The platform was developed by a research team at the Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) located in Tennessee. It integrates artificial intelligence (AI) to rapidly analyze grid data.

According to the scientists, the technology uses advanced signal processing and machine learning to identify subtle grid disturbances that often go unnoticed by conventional monitoring systems. It can therefore automatically alert a utility to dangerous grid behaviors that require immediate response.

It is being validated using five years of field data collected by Southern California Edison (SCE), one of the US’ largest electric utilities. “The faster we realize what’s happening, the faster we can respond,” Ali Ekti, PhD, ORNL project leader, said.

Detecting grid threats

The tool can detect seven types of electrical faults, which create abnormal current or voltage in the grid. First of all, it can identify arcing faults, which happen when electricity jumps through an air gap between a power line and another object, like the ground.

Because these faults often generate only small increases in electrical current, they can evade traditional sensors and fail to trigger circuit breakers. This means that dangerous electrical arcs may persist for extended periods, and therefore increase the risk of wildfires.

ORNL’s new analytics system continuously monitors grid signals. It automatically alerts utilities once it recognizes abnormal conditions. “This tool is designed to provide utilities with a continuous pathway from signals to analytics to decisions,” Ekti elaborated.

As per the scientists, the tool relies on advanced analysis of waveform data, which captures changes in voltage, current, and frequency across the grid. Since arcing faults are often too subtle to be visible in raw waveform recordings, they created AI-assisted algorithms that amplify weak signals and highlight previously hidden disturbances.

Proving the technology

During testing with real utility data, the team increased waveform signal visibility from just six percent to 72 percent, using the ORNL algorithms. This allowed the tool to uncover faults that would otherwise remain undetected.

The platform was trained with data from ORNL’s Grid Event Signature Library. This web-based repository contains more than 5,700 waveform signatures collected from power grid events.

Apart from arcing faults, the system can also spot and classify six other categories of grid disturbances. These include overcurrent faults, recloser operations, blown fuses, short-lived faults, capacitor switching events, motor starts, as well as line-switching operations.

“Having more insight into the specific meaning of these signals will allow us to approach issues like arcing with a sense of urgency, so we know when we need to get a crew of first responders on the scene as soon as possible,” Michael Balestrieri, SCE senior engineer, concluded in a press release.

The next phase of the project will involve training an upgraded version of the tool using utility-specific data and assessing its performance on an SCE demonstration circuit. The ultimate goal is to integrate the detection algorithms into the utility’s internal analytics platform.

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