Power and Energy

Electric power systems are comprised of cyber and physical components that are crucial to grid resiliency. Data from both components should be collected when modeling power systems: data from communication networks and intrusion detection systems; physical telemetry from sensors and field devices.

In order to obtain the ex-ante least-cost schedule of energy generation and reserves for online generating units, the system operator addresses a dynamic decision-making process known as the economic dispatch (ED) problem. Current industry practice involves adopting a deterministic two-stage optimization framework that relies on a one-day-ahead horizon and a forecast of uncertain parameters. The optimal solution to the resulting problem thus yields a generation schedule for the entire day ahead.

This dataset contains electrical measurements collected in the context of the paper "An Embedded Deep Learning NILM System: A Year-long Field Study in Real Houses".

Despite numerous advantages of space vector pulse width modulation (SVPWM) methods, their implementation on multilevel inverters (MLI) is onerous. Due to the ever-increasing use of MLIs in power electronic applications, it is essential to implement efficient modulation procedures aiming to keep control sampling periods sufficiently short. Here, a novel and straightforward SVPWM Strategy is introduced, which uses algebraic functions throughout the procedure and avoids using hardware-consuming trigonometric functions. Look-up tables and region detection are no more necessary.