Scalable Monitoring and DSOPF Control for Smart Grids

Karthikeyan Balasubramaniam and Ganesh Kumar Venayagamoorthy

An adaptive, optimal, real-time controller based on adaptive critics design called dynamic stochastic optimal power flow (DSOPF) controller is proposed. Stochastic nature in power system can arise as a result of load and generation stochastic behaviors and due to random noise in PMU data which arises due to communication noise and measurement error. DSOPF controller can perform real-time control action but system wide information cannot be made available to DSOPF controller in real-time because of power system communication delays which can range from a few milliseconds to several seconds depending on distance and communication media.

If state variables can be predicted ahead of time, then communication delay can be compensated for. Hence, a scalable wide area monitoring system that can predict state variables ahead of time is developed. Scalability is achieved by using cellular architecture called cellular computational network (CCN). This module can effectively compensate for communication delays and hence can enable DSOPF controller to perform real-time control with system wide information.

Scalable Integrated Situational Awareness System for Smart Grid

Bipul Luitel and Ganesh Kumar Venayagamoorthy

In a smart grid, monitoring of system variables such as voltages and speed deviations of generators is important for assessing its stability, and making proper control decisions. Development of wide area monitoring system is, hence, important for situational awareness; especially in a smart grid where integration of renewable resources, distributed generation and bidirectional power flows can lead to instabilities if proper control action is not taken at the right time, place and context.

Emergent Intelligence in Cellular Neural Networks

Bipul Luitel and Ganesh K. Venayagamoorthy

Emergent Intelligence in Cellular Neural NetworksIntelligence in CNN emerges from a group of cells that are learning subsystems, all of which either utilize the same or  a different learning method, that either adapt themselves in synchrony with the other subsystems or independent of the others in their own pace. Intelligence in CNN emerges over time through progressive learning and adaptation of these distributed interacting subsystems represented as cells.