Wide Area Monitoring and Control »
Jing Dai, Ronald G Harley, Ganesh Kumar Venayagamoorthy and Steve M. Potter
BIANN uses biologically plausible spiking neuron models. It bridges the gap between oversimplified ANNs and living neural networks. Effective encoding, decoding and training mechanisms for BIANN still need to be developed. A reservoir computing based training approach is proposed for the BIANN to serve as a novel modeling and control tool for practical applications. The BIANN is able to provide accurate one and five steps-ahead predictions of the rotor speed and terminal voltage of a generator in a SMIB, for online monitoring purposes.
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.
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.
Bipul Luitel and Ganesh K. Venayagamoorthy
Intelligence 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.
Jiaqi Liang, Ganesh Kumar Venayagamoorthy and Ronald G Harley
High penetration of intermittent renewables adds uncertainty and variablity. Static OPF cannot handle fast stochastic/dynamic events. Secondary frequency and voltage control cannot guarantee system-wide security. Coordinated AC power flow control solution replaces existing linear secondary frequency and voltage control, interacts with dynamics of load and local controllers. It simultaneously considers economy, stability, and security in real-time control. It also handles fast stochastic events (eg. wind variations, and contingencies).
Jacqi Liang, Ganesh K. Venayagamoorthy and Ronald G. Harley
To achieve a high penetration level of intermittent renewable energy, power system stability and security need to be ensured dynamically as the system operating condition continuously changes. A DSOPF control algorithm using adaptive critic designs (ACDs) is proposed as a solution to control the smart grid in an environment with high short-term uncertainty and variability.