Watson Addressing Grid Failures

 Important domain to cover.  

MIT-IBM Watson AI Lab Tackles Power Grid Failures with AI  in Datanami   By Jaime Hampton

Next time your power stays on during a severe weather event, you may have a machine learning model to thank.

Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid.

The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages.

Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce. There can be difficulty with quickly extracting data for anomaly detection, or the “task of identifying unusual samples that significantly deviate from the majority of the data instances,” as defined in the researchers’ paper.

The ML model can be trained without annotated data on power grid anomalies, which is advantageous because much of the data collected by the sensors is unstructured.

“In the case of a power grid, people have tried to capture the data using statistics and then define detection rules with domain knowledge to say that, for example, if the voltage surges by a certain percentage, then the grid operator should be alerted. Such rule-based systems, even empowered by statistical data analysis, require a lot of labor and expertise. We show that we can automate this process and also learn patterns from the data using advanced machine-learning techniques,” said Chen in an MIT News article. .... '

Simulating the PowerGrid

We are much in need of better power grid simulations.  PNNL is working on it. Note the broader look at multiple task modeling and improvement.  Worth looking at for other industrial multiple task and goal operations.  Full and more detailed article at the link. 

PNNL Researchers Speed Power Grid Simulations Using AI

New artificial intelligence approach holds promise to deliver fast, accurate results; could benefit other national priorities

Lynne Roeder, PNNL

Conceptual artwork for multitask learning in power gird simulations

Most modern phones and cars are programmed to “learn” from their environment—sounds, facial features, and even common driving routes. Patterns of recognition allow these systems to accurately predict and suggest preferred options in the blink of an eye.

Now imagine a system that could provide the same precision and responsiveness for critical national challenges, such as disease diagnoses, weather forecasting, and power grid reliability.  

A new software application called the Smart Power Grid Simulator (Smart-PGSim) uses neural networks, a type of artificial intelligence (AI), to efficiently solve power grid simulations crucial for planning and optimizing electricity delivery. Initial test results showed Smart-PGSim solved power flow calculations about three times faster than a traditional numerical model, without a loss in precision.

Developed by Pacific Northwest National Laboratory (PNNL) computer scientist Gokcen Kestor and collaborators from the University of California, Merced, the methodology behind Smart-PGSim uses a novel neural network approach called multi-task learning modeling. The researchers believe it is the first such application of AI for the power grid. ... "