Abstract: Systems, methods, and instructions of computer-readable media may include obtaining, at a client machine, a user-selected configuration parameter for an orbit simulation; sending, from the client machine to a remote system, via a network connection, a first set of configuration parameters for the orbit simulation, wherein the first set of configuration parameters comprise the user-selected configuration parameter; receiving, at the client device from the remove device, via the network connection, a stream of orbital data comprising points along an orbit, wherein the points along the orbit are determined by the remote system based on the first set of configuration parameters; and presenting, at a display, a dynamic rendering of the orbit simulation, wherein the orbit simulation is based on the stream of orbital data.

Abstract: A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

Abstract: A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.