Electrical transformer failure prediction
First Claim
1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
- receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables;
partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset;
receive an analysis type indicator defined by a user;
compute a worth value for each of the plurality of variables;
select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is greater than 200 based on the received analysis type indicator;
select a first model based on the received analysis type indicator;
train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers;
validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data;
select a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a neural network model;
train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers;
validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data;
compare the fit by the trained first model to the fit by the trained second model;
select a probability of failure model as the validated first model or the validated second model based on the comparison;
receive electrical system data for a transformer;
execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and
update a failure probability for the transformer based on the computed probability of failure.
1 Assignment
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Accused Products
Abstract
A computing device predicts a probability of a transformer failure. An analysis type indicator defined by a user is received. A worth value for each of a plurality of variables is computed. Highest worth variables from the plurality of variables are selected based on the computed worth values. A number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator. A first model and a second model are also selected based on the received analysis type indicator. Historical electrical system data is partitioned into a training dataset and a validation dataset that are used to train and validate, respectively, the first model and the second model. A probability of failure model is selected as the first model or the second model based on a comparison between a fit of each model.
24 Citations
30 Claims
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1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is greater than 200 based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a neural network model; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure.
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2. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a third model based on the received analysis type indicator, wherein the selected first model is a decision tree model, the selected second model is a neural network model, and the selected third model is a backward regression model; train the selected third model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained third model using the validation dataset to statistically assess the fit by the trained third model to the historical electrical system data; compare the fit by the trained third model to the fit by the trained second model and the fit by the trained first model; select a probability of failure model as the validated first model, the validated second model, or the validated third model based on the comparisons; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (3)
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4. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a third model based on the received analysis type indicator; train the selected third model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained third model using the validation dataset to statistically assess the fit by the trained third model to the historical electrical system data; compare the fit by the trained third model to the fit by the trained second model and the fit by the trained first model; create a fourth model as an ensemble model based on the received analysis type indicator using the trained third model, the trained second model, and the trained first model; train the created fourth model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained fourth model using the validation dataset to statistically assess the fit by the trained fourth model to the historical electrical system data; select a probability of failure model as the validated first model, the validated second model, or the validated third model based on the comparisons; compare the fit by the trained fourth model to the fit by the selected probability of failure model; update the probability of failure model selection as the validated fourth model or the selected probability of failure model based on the comparison between the trained fourth model and the selected probability of failure model; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (5)
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6. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a stepwise regression model; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (7)
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8. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a third model based on the received analysis type indicator, wherein the selected first model is a decision tree model, the selected second model is a backward regression model, and the selected third model is a logistic regression model; train the selected third model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained third model using the validation dataset to statistically assess the fit by the trained third model to the historical electrical system data; compare the fit by the trained third model to the fit by the trained second model and the fit by the trained first model; select a probability of failure model as the validated first model, the validated second model, or the validated third model based on the comparisons; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (9)
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10. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; select second highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the second highest worth variables is limited to a second predetermined number based on the received analysis type indicator; select a third model based on the received analysis type indicator; train the selected third model using values from the training dataset of the selected second highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained third model using the validation dataset to statistically assess the fit by the trained third model to the historical electrical system data; compare the fit by the trained third model to the fit by the selected probability of failure model; update the probability of failure model selection as the validated third model or the selected probability of failure model based on the comparison between the trained third model and the selected probability of failure model; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:
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receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; supplement the received historical electrical system data with values computed from one or more of the plurality of data points defined for each observation, wherein the values computed from one or more of the plurality of data points are counts associated with a plurality of electric meters that are summed over a predefined time interval, wherein the counts are further summed for the transformer to which the plurality of electric meters is connected; partition the supplemented historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (22, 23)
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24. A computing device comprising:
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a processor; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a stepwise regression model; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (25, 26)
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27. A method of predicting a probability of a transformer failure, the method comprising:
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receiving historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partitioning, by a computing device, the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receiving an analysis type indicator defined by a user; computing, by the computing device, a worth value for each of the plurality of variables; selecting, by the computing device, highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator; selecting, by the computing device, a first model based on the received analysis type indicator; training, by the computing device, the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validating, by the computing device, the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; selecting, by the computing device, a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a stepwise regression model; training, by the computing device, the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validating, by the computing device, the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; comparing, by the computing device, the fit by the trained first model to the fit by the trained second model; selecting, by the computing device, a probability of failure model as the validated first model or the validated second model based on the comparison; receiving electrical system data for a transformer; executing, by the computing device, the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and updating, by the computing device, a failure probability for the transformer based on the computed probability of failure. - View Dependent Claims (28, 29, 30)
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Specification