Resilient Distributed Integral Control for Multimachine Power Systems with Inherent Input Constraint Satisfaction
Theodoros E. Kavvathas, George C. Konstantopoulos, Charalambos Konstantinou
TL;DR
The paper tackles frequency restoration and proportional real/reactive power sharing in multimachine power systems under generator input constraints. It introduces a distributed bounded-integral-like controller with neighbour-to-neighbour communication and vector-field-based boundedness proofs, leveraging Laplacian coupling matrices $L_T$ and $L_E$ to enforce sharing while respecting bounds. Key contributions include a unified control architecture that remains effective under normal operation and during saturations without structural changes, a rigorous boundedness analysis, and simulation on a 10-bus, 4-machine network demonstrating resilience to load changes and saturation events. This approach enhances cyber-physical stability and resilience, enabling distributed control of modern grids with DERs and operational limits.
Abstract
In this paper, a novel distributed controller for multimachine power systems is proposed to guarantee grid frequency restoration and accurate real and reactive power sharing among the generator units, while maintaining the generator inputs (mechanical torque and field excitation voltage) within given bounds. The boundedness of the controller outputs (generator inputs) is rigorously proven using vector field theory. It is additionally shown that even if one generator input reaches its upper/lower limit, the remaining units can still accomplish the desired control tasks without modifying the controller structure or dynamics; hence introducing enhanced system resilience using the proposed approach. This has been accomplished in a unified control structure while using neighbour-to-neighbour communication, thus maintaining the distributed nature of the controller. An example of a 10-bus, 4-machine power system is simulated to verify the proposed controller performance under sudden changes of the load demand.
