Tag: Arturo Bretas

Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework

The project aims at developing a distributed nonlinear controller for transient stability enhancement. The new control layer will actuate on distributed energy storage systems, be robust to uncertainties in modelling and capable of compensating input time-delay while independent of operating conditions. Furthermore, the robust controller will not require exact knowledge of the system dynamics. Second, bad data analytics based on the innovation approach and cross-layered information provided by distributed software-defined network will be developed. The bad data analytics will consider the inherent interdependencies of the physical processes while providing a countermeasure. Third, an adaptive distributed robust machine learning approach will be developed. The overwhelming majority of supervised machine learning methods require large amounts of carefully labeled training data that is representative of the data distribution to be seen under test.

CPS: Synergy: Distributed coordination of smart devices to mitigate intermittency of renewable generation for a smarter and sustainable power grid

The ultimate goal of the project is to help the electric grid become more reliable even when a large amount of electricity is generated from green, but intermittent – sources such as solar and wind. To deal with this intermittency, inexpensive source of energy storage are required. Instead of investing in batteries, this project seeks to obtain cheap storage by manipulating power demand in consumer loads through intelligent decision-making algorithms. By varying power demand up and down from what a load would nominally consume, the load can be made to behave like a battery, effectively creating a source of Virtual Energy Storage (VES). This kind of virtual storage is cheaper than batteries since it is a software-based solution; little additional hardware is needed. Another aspect of the project is to develop decision-making algorithms to cope with operational issues faced by the power distribution networks (that deliver electricity to neighborhoods) due to increasing use of intermittent solar power.