"Thinking machines" needed to power smart grid
Plans to develop the "smart" grid"--a system that uses intelligent computer networks to manage electric power--cannot succeed without the creation of new "thinking machines" that can learn and adapt to new situations, from power outages along the grid to fluctuations in the power supply, according to IEEE's Ganesh Kumar Venayagamoorthy, a power engineering expert and professor at Missouri University of Science and Technology, in an article published in the latest issue of IEEE's Smart Grid newsletter.
These new machines must take on almost human-like intelligent characteristics, such as the ability to make decisions, adapt to unfamiliar situations, learn from changes in their environments and make sense of how all of the electricity flows through the nation's power grid, according to Venayagamoorthy.
"And those capabilities, in turn, will depend on subsystems that continuously improve their knowledge of grid dynamics, and not just gather data," Venayagamoorthy said in his article, which examines the current state of computational intelligence--a field of computing that deals with creating systems that can make decisions "in complex, uncertain and changing environments"--and discusses that field's potential to give rise to computational systems thinking machines (CSTM).
"As the smart grid evolves over time, and as we become more dependent on intermittent sources of energy such as wind and solar power, we will see that traditional technology will not work," he said.
Through the National Science Foundation's Office of Emerging Frontiers in Research and Innovation funded Brain2Grid project, Venayagamoorthy is exploring the use living neural networks composed of thousands of brain cells from laboratory rats to control simulated power grids in the lab. From those studies, he hopes to create a "biologically inspired" computer program to manage and control complex power grids in Mexico, Brazil and elsewhere.
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