Search for: All records

The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems. 

Memristors have recently received significant attention as device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at memristor terminals make them promising candidates to efficiently perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merits of memristor crossbars with the advantages of an operator splitting method, the alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The strength of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristor-based power iteration method can further be applied to principal component analysis. The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence.