Developing microgrids is an attractive solution for integrating inverter-based resources (IBR) in the power system. Distributed control is a potential strategy for controlling such microgrids. However, a major challenge toward the proliferation of distributed control is cybersecurity. A false data injection (FDI) attack on a microgrid using distributed control can have severe impacts on the operation of the microgrid. Simultaneously, a microgrid needs to be protected from system faults to ensure the safe and reliable delivery of power to loads. However, the irregular response of IBRs to faults makes microgrid protection very challenging. A microgrid is also susceptible to faults inside IBR converters. These faults can remain undetected for a long time and shutdown an IBR. This dissertation first proposes a method that reconstructs communicated signals using their autocorrelation and crosscorrelation measurements to make distributed control more resilient against FDI attacks. Next, this dissertation proposes a protection scheme that works by classifying measured harmonic currents using support vector machines. Finally, this dissertation proposes a protection and fault-tolerant control strategy to diagnose and clear faults that are internal to IBRs. The proposed strategies are verified using time-domain simulation case studies using the PSCAD/EMTDC software package.

Renewable energy resources, such as wind, solar, and geothermal, are interfaced with the grid using DC-to-AC power electronic converters, popularly known as inverters. These “inverterbased resources (IBR)” are mostly distributed and located near consumers. During outages, IBRs can be used to provide power to customers. This gives developers the idea of integrating IBRs in microgrids. A microgrid is a miniature grid that consists of IBRs and customers. A microgrid is normally connected to the grid but can disconnect from the grid and operate on its own. To run efficiently, a microgrid uses fast and reliable communication between IBRs to create a high-performance distributed control strategy. However, this creates cybersecurity concerns for microgrids. This dissertation proposes a cybersecure distributed control strategy to make sure microgrids can keep their advantages. This dissertation also proposes a protection method that relies on machine learning to clear short circuits in the microgrid. Finally, this dissertation proposes a strategy to diagnose failures inside IBRs and ride through them. The proposed solutions are verified using the industry-grade simulation software PSCAD/EMTDC.

Doctor of Philosophy