SBIR/STTR Award attributes
This effort will develop and demonstrate methods for remotely assessing the status of electrical grids based on the illumination from lights powered by those grids. It will lead to the development of low-cost sensors which can be passively assess grid health over a large scale. This effort will assess grid health by exploiting recent advances in high-spatial, high-temporal resolution imaging and the emergence of Deep Learning (DL) techniques in signal processing. High resolution imaging will be used to capture of the rapidly evolving grid signature while DL will provide an efficient method for processing the large amounts of data that will result. Combined, this will result in a probabilistic assessment of the state of the power grid, which will enable end users to predict the additional load an existing grid can accept, to anticipate impending grid failure and to prepare for the associated socio-economic repercussions of such events.
