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EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid

The electric power grid is a complex system under semi-autonomous distributed control. It has spatial and/or temporal complexity, nonconvexity, nonlinearity, nonstationarity and uncertainty. Control algorithms do not yet exist at sufficient scale to guarantee stability over a wide range of nonlinear operating conditions, as witnessed by the August 2003 blackout.

It has been shown that Adaptive Critic Design (ACD) based neurocontrollers can significantly improve the stability of a power system when they are applied in real time control loops. While these and other approaches have achieved ACD based adaptive optimal control, none scale up for controlling very large power systems in real-time. The Venayagamoorthy’s EFRI team will combine neurobiology with engineering to develop new approaches to emulate brain-like capabilities for fast real-time control and decision making. These will revolutionize nonlinear adaptive optimal control of very large systems, including the electric power grid. These advances will be made by studying learning and adaptation for in vitro neuronal networks interfaced to computing machines; applying new closed-loop hybrid neural architectures that are capable of learning to optimize, predict and control; improving the understanding of their simulation on new massively parallel systems; and validating these architectures on several power system testbeds, using complex, scalable simulations and real-time hardware. The overall goal is to utilize biological network mechanisms to make control of complex interactive systems, such as the power grid, more brain-like.

Neurocontrol offers nonlinear adaptive optimal control, which is particularly useful for nonlinear stochastic power networks, which would otherwise need different mathematical models and changing parameters for each network topology. Neural network architectures have worked well on small power systems, but do not replicate the powerful capabilities of the brain to learn, and compute in a massively parallel fashion, allowing for adaptive optimal decision making, prediction and pattern recognition among other things. Learning how the brain actually performs these functions, and transforming this knowledge into algorithms on a chip, will constitute a breakthrough in theory and application. Neurocontrol of the power grid with new architectures and algorithms will therefore increase real-time responsiveness to changing power loads and component failures, improve the dynamic and transient behavior of the power network, and improve grid reliability, ensuring local and wide area stability, reduce greenhouse gas emissions and assisting human experts in control rooms. The findings of this project shall provide a better understanding of multiple-time base system identifiers and controllers, and their interactions and scalability for large systems. The project outcomes will have broad implications for all types of complex systems design. Advances in understanding the brain’s learning mechanisms at the network level could affect many areas of neuroscience and lead to new educational approaches.