{
  "$type": "site.standard.document",
  "description": "Methods, systems, and computer readable mediums determining a system state of a power system using a convolutional neural network using a convolutional neural network are disclosed. One method includes converting power grid topology data corresponding to a power system into a power system matrix…",
  "path": "/patents/1264380",
  "publishedAt": "2020-06-11T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06N3/04",
    "University of Tennessee Research Foundation"
  ],
  "textContent": "Methods, systems, and computer readable mediums determining a system state of a power system using a convolutional neural network using a convolutional neural network are disclosed. One method includes converting power grid topology data corresponding to a power system into a power system matrix representation input and applying the power system matrix representation input to a plurality of convolutional layers of a deep convolutional neural network (CNN) structure in a sequential manner to generate one or more feature maps. The method further includes applying the one or more feature maps to a fully connected layer (FCL) operation for generating a respective one or more voltage vectors representing a system state of the power system.",
  "title": "METHODS, SYSTEMS, AND COMPUTER READABLE MEDIUMS FOR DETERMINING A SYSTEM STATE OF A POWER SYSTEM USING A CONVOLUTIONAL NEURAL NETWORK"
}