Decentralized frequency control of power systems with deep penetration of wind-based generation

The introduction of the highly variable and uncertain renewable resources into the power grid is calling for more control and regulation of the power system dynamics. In particular, the automatic generation control, which is responsible for maintaining the nominal system frequency and the scheduled real power interchange, needs to be modified to include unmodeled system dynamics and to account for disturbances from renewable resources. In this thesis, we work on a nonlinear differential algebraic model of the power system which takes into account the effect of the power network and includes wind power injections. We then propose two decentralized controllers that each would stabilize the system frequency and power interchange. The first controller is based on linear quadratic (LQ) optimal control followed by an optimization algorithm to increase the sparsity of the feedback gains. The other controller is designed using the theory of overlapping control and the inclusion principle. Each controller is applied separately on a 3-machine 6-bus 2-wind turbine nonlinear model, and the simulation is carried out using Simulink. A power flow program is run at each automatic generation control (AGC) cycle to update the power flow variables. Results show that we can design decentralized controllers for each control area that can successfully track the desired frequency regardless of the disturbances associated with wind-based generation. Furthermore, we show that the performance of these controllers is comparable to that of a centralized controller

Security of cyber-physical systems: A control-theoretic perspective

Motivated by the attacks on control systems through the cyber (digital) part, we study how signal attacks injected through actuators and/or sensors affect control system stability and performance. We ask the questions: What are the different types and scenarios of signal attacks? When are the attacks stealthy and unbounded? How to compute the worst stealthy bounded attacks? How to defend against such attacks through controller design? How to identify and estimate signal attacks before significant performance loss happens? We answer the above questions in this thesis using tools from control theory. We show that it is necessary to use a sampled-data framework to accurately assess the vulnerabilities of control systems. In addition, we show that the most lethal attacks are related to the structure of the system (location of zeros and poles, number of inputs and outputs). We show that dual rate control is a powerful tool to defend against these vulnerabilities, and we provide a related controller design. Furthermore, we show that the worst stealthy bounded attacks can be computed by an iterative linear program, and we show how to lessen their effects through iterative controller design. Finally, we study the trade-off between control and estimation of signal attacks and provide several controller designs utilizing the power of dual rate sampling