System And Method For Grading Electricity Distribution Network Feeders Susceptible To Impending Failure
First Claim
1. A system for facilitating maintenance of feeders in an electricity distribution network, the system comprising:
- values of a plurality of feeder attributes related to past performance history;
a particular dataset for training a model configured to determine the relative susceptibilities to failure of the feeders based on the values of the plurality of feeder attributes, wherein the particular dataset for training comprises data on feeders from a failure history database and other feeders that are selected based on their situational association with a specific failed feeder and/or selected based on a similarity measure used in statistical causal inference; and
a machine learning engine configured to train the model and to apply the trained model to the values of a plurality of feeder attributes so as to generate rankings of the feeders indicative of their relative susceptibilities to failure; and
a decision support application configured to provide the rankings of the feeders available to operators and engineers so as to facilitate maintenance of the feeders.
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Accused Products
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.
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Citations
29 Claims
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1. A system for facilitating maintenance of feeders in an electricity distribution network, the system comprising:
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values of a plurality of feeder attributes related to past performance history; a particular dataset for training a model configured to determine the relative susceptibilities to failure of the feeders based on the values of the plurality of feeder attributes, wherein the particular dataset for training comprises data on feeders from a failure history database and other feeders that are selected based on their situational association with a specific failed feeder and/or selected based on a similarity measure used in statistical causal inference; and a machine learning engine configured to train the model and to apply the trained model to the values of a plurality of feeder attributes so as to generate rankings of the feeders indicative of their relative susceptibilities to failure; and a decision support application configured to provide the rankings of the feeders available to operators and engineers so as to facilitate maintenance of the feeders. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for facilitating maintenance of feeders in an electricity distribution network, the method comprising:
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providing a failure prediction model; training the failure prediction model on values of a plurality of feeder attributes related to past performance history; applying the trained failure prediction model to values of the plurality of feeder attributes so as to generate rankings of the feeders indicative of their relative susceptibilities to failure; and providing the rankings of the feeders to operators and engineers so as to facilitate maintenance of the feeders. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A computer-readable medium for facilitating maintenance of feeders in an electricity distribution network, the computer-readable medium comprising a set of instructions for:
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training a failure prediction model on values of a plurality of feeder attributes related to past performance history; applying the trained failure prediction model to values of the plurality of feeder attributes so as to generate rankings of the feeders indicative of their relative susceptibilities to failure; and providing the rankings of the feeders to operators and engineers so as to facilitate maintenance of the feeders. - View Dependent Claims (20, 21, 22, 23, 24, 25)
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26. A system for assessing likelihood of failure in an “
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distribution network, the distribution network having a plurality of networked or interlinked components involved in transmission of the items to end-users or recipients, the system comprising;a failure-prediction model configured to determine the relative susceptibility-to-failures of individual network components based on an input data set of network component attribute values; and a machine learning engine configured to train the model and to apply the trained model to the input data set so as to generate a list of the network components ranked by their relative susceptibility-to-failures. - View Dependent Claims (27)
- items”
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28. A method for assessing likelihood of failure in an “
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distribution network, the distribution network having a plurality of networked or interlinked components involved in transmission of the items to end-users or recipients, the method comprising;providing a failure-prediction model configured to determine the relative susceptibility-to-failures of individual network components based on an input data set of network component attribute values; training the model on a training data set of network component attribute values; and applying the trained model to the input data set so as to generate a list of the network components ranked by their relative susceptibility-to-failures. - View Dependent Claims (29)
- items”
Specification