Dissertation Defense

Model-Predictive Control for Alleviating Transmission Overloads and Voltage Collapse in Large-Scale Electric Power Systems

Jonathon Martin


Emergency control in electric power systems requires rapid identification and implementation of corrective actions. Typically, system operators have performed this service while relying on intuition and predetermined control sequences with limited decision support tools. Automatic control schemes offer the potential to improve this process by quickly analyzing large, complex problems to identify the most effective actions. Model-predictive control (MPC) is one such scheme which has a strong record of success in the process industry and could easily be transferred to power systems applications.

MPC has demonstrated its capabilities in relieving transmission overloads and separately in correcting transformer-driven voltage collapse behaviors on small test networks. However, a comprehensive solution combines both aspects into a single controller formulation. Additionally, most power system networks are large, resulting in computationally challenging problem formulations.

This work addresses these practical considerations by proposing a new linear controller model incorporating voltage magnitude and angle and both active and reactive power. The problem size is reduced by limiting the model to only those devices which are significantly affected by the emergency conditions. The formulation is demonstrated on models of the Californian and Nordic transmission systems.

Sponsored by

Professor Ian Hiskens

Faculty Host

Professor Ian Hiskens