Biblio

In this work we investigate existing and new metrics for evaluating transient stability of power systems to quantify the impact of distributed control schemes. Specifically, an energy storage system (ESS)-based control scheme that builds on feedback linearization theory is implemented in the power system to enhance its transient stability. We study the value of incorporating such ESS-based distributed control on specific transient stability metrics that include critical clearing time, critical control activation time, system stability time, rotor angle stability index, rotor speed stability index, rate of change of frequency, and control power. The stability metrics are evaluated using the IEEE 68-bus test power system. Numerical results demonstrate the value of the distributed control scheme in enhancing the transient stability metrics of power systems.

In this work we investigate existing and new metrics for evaluating transient stability of power systems to quantify the impact of distributed control schemes. Specifically, an energy storage system (ESS)-based control scheme that builds on feedback linearization theory is implemented in the power system to enhance its transient stability. We study the value of incorporating such ESS-based distributed control on specific transient stability metrics that include critical clearing time, critical control activation time, system stability time, rotor angle stability index, rotor speed stability index, rate of change of frequency, and control power. The stability metrics are evaluated using the IEEE 68-bus test power system. Numerical results demonstrate the value of the distributed control scheme in enhancing the transient stability metrics of power systems.

While power grid systems benefit from utilizing communication network through networked control and protection, the addition of communication exposes the power system to new security vulnerabilities and potential attacks. To mitigate these attacks, such as denial of service, intrusion detection systems (IDS) are often employed. In this paper we investigate the relationship of IDS accuracy performance to the stability of power systems via its impact on communication latency. Several IDS machine learning algorithms are implemented on the NSL-KDD dataset to obtain accuracy performance, and a mathematical model for computing the latency when incorporating IDS detection information during network routing is introduced. Simulation results on the New England 39-bus power system suggest that during a cyber-physical attack, a practical IDS can achieve similar stability as an ideal IDS with perfect detection. In addition, false positive rate has been found to have a larger impact than false negative rate under the simulation conditions studied. These observations can contribute to the design requirements of future embedded IDS solutions for power systems.