Engineers use brain cells to power smart grid

Apr 17, 2013
G. Kumar Venayagamoorthy, Ph.D., director of the < a href="http://www.nsf.gov/cgi-bin/goodbye?http://www.rtpis.org">Real-Time Power and Intelligent Systems Laboratory at Clemson University leads a team of researchers using living brain cells to solve complex problems in a real-time computer-simulated power grid. Credit: Clemson University

(Phys.org) —The unmatched ability of the human brain to process and make sense of large amounts of complex data has caught the attention of engineers working in the field of control systems.

"The brain is one of the most robust computational platforms that exists," says Ganesh Kumar Venayagamoorthy, Ph.D., director of the Real-Time and Intelligent Systems Laboratory at Clemson University. "As power-systems control becomes more and more complex, it makes sense to look to the brain as a model for how to deal with all of the complexity and the uncertainty that exists."

Led by Venayagamoorthy, a team of and engineers is using neurons grown in a dish to control simulated . The researchers hope that studying how neural networks integrate and respond to complex information will inspire new methods for managing the country's ever-changing power supply and demand.

In other words, the behind our future might not be what you think.

Power to the people

America's strategy for providing power began in the late 1800s as a number of isolated generating plants serving regional customers. Over the next 50 years, the electric system was rapidly transformed into an inter-connected "grid" that ensured access to power when equipment failed or during periods of unexpected demand.

Today, with nearly 200,000 miles of high-voltage lines connecting over 6,000 power plants, America's power grid has been called the world's largest single machine.

Unfortunately, the grid's aging infrastructure wasn't built to handle today's ever-increasing demand. According to the U.S. Department of Energy, the average power generating station in the United States was built in the 1960s, using even older technology. Today, the average substation transformer is 42 years old, two years past its expected life span.

Another problem is that while the system has a great capacity to produce power, it doesn't actually have a way to store power.

This can spell trouble during periods of unexpected high demand, which can result in a massive loss (blackout) or reduction (brownout) in electricity. In 2003, 50 million people in eight states and one Canadian province were left without power when a single transmission line in Ohio was damaged by a tree limb.

Tomorrow's power grid will need to be able to anticipate usage and quickly compensate for unexpected need.

The "on-demand" power production strategy of our current system also makes it difficult to incorporate renewable sources of energy, such as wind and solar power, which can't be cranked up or down in response to peaks and lulls in power consumption.

"In order to get the most out of the different types of renewable energy sources, we need an intelligent grid that can perform real-time dispatch and manage optimally available energy storage systems," says Venayagamoorthy.

A smarter electric power grid

While technologies such as solar panels, wind turbines and hybrid electric vehicles will help reduce our non-renewable energy consumption, experts believe the development of a "smart" grid, capable of monitoring and controlling the flow of electricity from down to individual appliances, will have the largest impact.

According to the Department of Energy, if the current grid were just 5 percent more efficient, the energy savings would be equal to removing 53 million cars from the planet.

While a number of strategies have been proposed to optimize grid performance and incorporate intermittent energy sources, the ultimate goal is to create a distributed energy delivery network characterized by a two-way flow of electricity and information.

For Venayagamoorthy, looking to the brain for inspiration was a no-brainer.

"What we need is a system that can monitor, forecast, plan, learn, make decisions," says Venayagamoorthy. "Ultimately, what we need is a control system that is very brain-like."

What would the brain do?

Because the brain operates in a completely different way than traditional computing systems, the first step was to try to make sense of how the brain integrates and responds to data. To do so, Venayagamoorthy enlisted the expertise of neuroscientist Steve Potter, Ph.D., director of the Laboratory for NeuroEngineering at the Georgia Institute of Technology.

A leader in the field of learning and memory research, Potter recently pioneered a new method for understanding how the brain integrates and responds to information at the network level. The technique involves growing neurons in a dish containing a grid of electrodes that can both stimulate and record activity. The electrodes connect the neuronal network to a computer, allowing two-way communication between the living and the electronic components.

Potter's group has had success with this approach in the past, having shown that living neuronal networks can be made to control computer-simulated animals and simple robots.

In the current project, the network is trained to recognize and respond to voltage and speed signals from Venayagamoorthy's power grid simulation.

"The goal is to translate the physical and functional changes that occur as living neuronal network learns into mathematical equations, ultimately leading to a more brain-like intelligent control system," says Venayagamoorthy.

The purpose is to develop brain-inspired computer code, meaning living brain cells won't be part of the final equation.

What have we learned so far?

The collaboration has already yielded encouraging results.

The investigators have successfully "taught" a living neuronal network how to respond to complex data, and have incorporated these findings into simulated versions called bio-inspired artificial neural networks (BIANNS). They are currently using the new and improved BIANNS to control synchronous generators connected to a power system.

Venayagamoorthy and his team hope that this work will pave the way for smarter control of our future power grid.

This project was supported by NSF's Office of Emerging Frontiers in Research and Innovation (EFRI), which strives to keep the nation at the forefront of engineering research by investing in new and transformative projects.

For more information about this project, check out brain2grid.org.

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User comments : 11

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QuixoteJ
1 / 5 (5) Apr 17, 2013
[the article]"The brain is one of the most robust computational platforms that exists," says Ganesh Kumar Venayagamoorthy, Ph.D.

Isn't it THE most robust computational platform that exists? Arguably, at least?
Irukanji
5 / 5 (2) Apr 17, 2013
[the article]"The brain is one of the most robust computational platforms that exists," says Ganesh Kumar Venayagamoorthy, Ph.D.

Isn't it THE most robust computational platform that exists? Arguably, at least?


There are some CPUs on spacecraft leaving the solar system which were built to last a long time. They are well shielded, resistant to cold(or warmed by decaying plutonium), require almost no power to run and can withstand large blows without significant damage due to the nature of CPU production in the 70's.

The brains generally can't withstand environments too hot, too cold, radiation, require constant nutrients, etc or they will die. If these factors can be dealt with, then sure, it could possibly be one of the most robust platforms. However most animal brains require a body even if the normal conditions are maintained.
Requiem
2.3 / 5 (6) Apr 17, 2013
[the article]"The brain is one of the most robust computational platforms that exists," says Ganesh Kumar Venayagamoorthy, Ph.D.

Isn't it THE most robust computational platform that exists? Arguably, at least?


No, it is the most intuitive understanding platform that we know of existing. Computation is something entirely different, and computers are much better at it in general.

Some of the first, room-sized, computers were built specifically to make up for the deficiency that the human brain has to do ballistics calculations.
Birger
1 / 5 (1) Apr 17, 2013
"The researchers hope that studying how neural networks integrate and respond to complex information will inspire new methods for managing the country's ever-changing power supply and demand"
I thought current neural networks are incapable of being scaled up, requiring the use of sophisticated algorithms instead for problems beyond a certain complexity...
Let's hope the grid is not an intractable problem.
StarGazer2011
1 / 5 (4) Apr 17, 2013
I for one welcome our BIANN overlords :)
If we made a BIANN from biological neurons complex enough that it became intelligent/self aware, would it be artificial intelligence or not?
Skepticus
1 / 5 (3) Apr 18, 2013
Neural gel packs from Voyager.
gwrede
1 / 5 (2) Apr 18, 2013
All this hoopla about the human brain, and then the article never even says that they _actually_ are using human brain cells. Which should generate as much controversy as using human stem cells used to.

I bet they are using mouse cells. The big difference in inferential power between the mouse and human brain is not in the cells but in the overall structure, so no loss there.

As for the feasibility for the entire project, I think it would be easier using a good old fashioned AI program. After all, the problem domain is well specified.
antialias_physorg
2 / 5 (4) Apr 18, 2013
I thought current neural networks are incapable of being scaled up,

You can scale them up, but usually there is an optimum scale for the problem you want to solve (larger networks don't give better results).
Neural networks are 'simply' connected nodes with variable/adjustable weights. Some nodes are the input layer and some nodes are the output layer (and some - usually a lot more - nodes are in the so called 'hidden layer(s)' in between)

You train the net to do one task.
Training works by setting some input, looking at the output an then adjusting the weights according to an error function (which is calculated based on the output vs. the expected/correct output)
(that's just a VERY rough explanation. There's a lot of other shenannigans you can do with NNs, but that's the basic gist of it)

Once you're satisfied with the performance you 'freeze' the weights and use the net.

So there's still quite a functional differnec between the brain an NNs
QuixoteJ
2 / 5 (5) Apr 18, 2013
[Irukanji]There are some CPUs on spacecraft leaving the solar system which were built to last a long time...well shielded, resistant to cold(or warmed...)...and can withstand large blows without significant damage...
Fry one of the adders in these CPU's and that particular function never comes back. The human brain can adapt and recover to full functionality after similar damage (robustness).
[Requiem]No, it is the most intuitive understanding platform that we know of existing. Computation is something entirely different, and computers are much better at it in general.
Brain is still better as a *robust* computational platform, though. Robustness is the ability to continue the job in the presence of significant perturbations, which, I argue, the brain accomplishes almost infinitely better than any artificial CPU. The job is "life" (wicked difficult, by the way) and the perturbations are constant and inumerable (sometimes life-threatening). Yet the brain endures. No?
socean
not rated yet Apr 18, 2013
I for one welcome our BIANN overlords :)
If we made a BIANN from biological neurons complex enough that it became intelligent/self aware, would it be artificial intelligence or not?


Would you bring it home to meet your mom?
betterexists
1 / 5 (4) Apr 21, 2013
Who lived on this earth and first saw 1-day old embryo? and deciphered the whole mechanism....Copulation, male-female gametes...their fusion, zy how about goats/Sheep.

Might have some naturally aborted fetus...even that...why so nosy? Female stuff, isn't it?????

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