Abstract: A system for servicing cable includes a field-end assembly and a live-end assembly, each mounted on a support structure having at least one drive mechanism operable to cause relative linear movement between the field-end assembly and the live-end assembly. A drilling-and-shorting assembly can create a short circuit in a field-end of the cable, and a continuity tester can test the integrity of the short-circuit. The field end of the cable can then be ejected from the system, and an end-cap-cradle assembly can position an end cap on a live end of an electrical cable and test its installation.

Abstract: A system for servicing cable includes a field-end assembly and a live-end assembly, each mounted on a support structure having at least one drive mechanism operable to cause relative linear movement between the field-end assembly and the live-end assembly. A drilling-and-shorting assembly can create a short circuit in a field-end of the cable, and a continuity tester can test the integrity of the short-circuit. The field end of the cable can then be ejected from the system, and an end-cap-cradle assembly can position an end cap on a live end of an electrical cable and test its installation.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric.

Abstract: A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric.

Abstract: A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

Abstract: A computer-implemented method and system performing allocating capital assets for managing a plurality of utility service components. The method includes ranking each of the utility service components based on data retrieved corresponding to the utility service components, calculating a base failure metric for each of the utility service components, receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user, analyzing the selected utility service component under a plurality of improvement scenarios, calculating an estimated failure metric of the selected utility service component based on each of the improvement scenarios, and displaying comparison information between the base failure metric and the estimated failure metric.