Service location anomalies

Patent metadata

Field	Value
Publication number	US-12406192-B2
Application number	US-202217732788-A
Country	US
Kind code	B2
Filing date	Apr 29, 2022
Priority date	Jun 29, 2021
Publication date	Sep 2, 2025
Grant date	Sep 2, 2025

Abstract

Disclosed techniques include using machine learning to detect an electrical anomaly in a power distribution system. In an example, a method includes accessing voltage measurements measured at an electric metering device and over a time period. The method further includes calculating, from voltage measurements and for each time window of a set of time windows, a corresponding average voltage and a corresponding minimum voltage. The method further includes applying a machine learning model to the average voltages and the minimum voltages. The machine learning model is trained to identify one or more predetermined electrical anomalies from voltages. The method further includes receiving, from the machine learning model, a classification indicating an identified anomaly. The method further includes based on the classification, sending an alert to a utility operator.

First claim

What is claimed is:

1. A method of using machine learning to detect an electrical anomaly in a power distribution system, the method comprising:
accessing a first plurality of voltage measurements measured at an electric metering device;
calculating, from the first plurality of voltage measurements and for each of a first plurality of time windows, a first corresponding average voltage and a first corresponding minimum voltage;
training a machine learning model by:
accessing a set of training data pairs, wherein each training data pair comprises one or more of (i) a training set of average voltages and a set of minimum voltages, (ii) a training set of average voltages of all the electric metering devices connected to a distribution transformer, or (iii) a training set of average voltages of one electric metering device that is behind the distribution transformer and an expected classification that indicates one or more electrical anomalies;
providing one training data pair of the set of training data pairs to the machine learning model;
receiving, from the machine learning model, a determined classification;
calculating a loss function by comparing the determined classification and the expected classification; and

adjusting internal parameters of the machine learning model to minimize the loss function;
applying the machine learning model to the first average voltages and the first minimum voltages, wherein the machine learning model is trained to identify, from voltage measurements, a first voltage signature that corresponds to the electrical anomaly;
receiving, from the machine learning model, a first classification indicating a first loose connection;
based on the first classification, sending a first alert to a utility operator;
calculating, from a second plurality of voltage measurements and for each of a second plurality of time windows, a second corresponding average voltage and a second corresponding minimum voltage, wherein the second plurality of time windows occur before the first plurality of time windows;
applying the machine learning model to the first average voltages, the first minimum voltages, first voltage signature, the second average voltages, and the second minimum voltages;
receiving, from the machine learning model, a second classification identifying a second voltage signature indicating a second loose connection; and
based on the second classification, sending a second alert to the utility operator.

2. The method of claim 1 , wherein the second plurality of voltage measurements are measured at an additional electric metering device.

3. The method of claim 1 , wherein the first voltage signature includes a first decrease in a minimum voltage over a time period and a second decrease in average voltage over the time period, and wherein the second decrease is less than the first decrease.

4. The method of claim 1 , wherein the second plurality of voltage measurements are measured at the electric metering device.

5. The method of claim 1 , further comprising applying the machine learning model to topology information that associates the electric metering device with one or more distribution transformers that are electrically connected to the electric metering device via a distribution line.

6. The method of claim 1 , wherein accessing the first plurality of voltage measurements comprises transmitting a request to the electric metering device and receiving the first plurality of voltage measurements from the electric metering device.

7. The method of claim 1 , wherein the set of training data pairs further comprise topology information that associates one or metering devices with one or more distribution transformers.

8. A non-transitory computer-readable storage medium storing computer-executable program instructions, wherein when executed by a processing device, the computer-executable program instructions cause the processing device to perform operations comprising:
accessing a first plurality of voltage measurements measured at an electric metering device;
calculating, from the first plurality of voltage measurements and for each of a first plurality of time windows, a first corresponding average voltage and a first corresponding minimum voltage;
training a machine learning model by:
accessing a set of training data pairs, wherein each training data pair comprises one or more of (i) a training set of average voltages and a set of minimum voltages (ii) a training set of average voltages of all the electric metering devices connected to a distribution transformer, or (iii) a training set of average voltages of one electric metering device that is behind the distribution transformer and an expected classification that indicates one or more electrical anomalies;
providing one training data pair of the set of training data pairs to the machine learning model;
receiving, from the machine learning model, a determined classification;
calculating a loss function by comparing the determined classification and the expected classification; and
adjusting internal parameters of the machine learning model to minimize the loss function;

applying the machine learning model to the first average voltages and the first minimum voltages, wherein the machine learning model is trained to identify, from voltage measurements, a first voltage signature that corresponds to an electrical anomaly;
receiving, from the machine learning model, a first classification indicating a first anomaly;
based on the first classification, sending a first alert to a utility operator;
calculating, from a second plurality of voltage measurements and for each of a second plurality of time windows, a second corresponding average voltage and a second corresponding minimum voltage, wherein the second plurality of time windows occur before the first plurality of time windows;
applying the machine learning model to the first average voltages, the first minimum voltages, the second average voltages, and the second minimum voltages;
receiving, from the machine learning model, a second classification identifying a second voltage signature indicating a second anomaly; and
based on the second classification, sending a second alert to the utility operator.

9. The non-transitory computer-readable storage medium of claim 8 , wherein one or more of the first anomaly and the second anomaly relate to a loose connection associated with the electric metering device.

10. The non-transitory computer-readable storage medium of claim 8 , wherein one or more of the first anomaly and the second anomaly is represented by a first decrease in a minimum voltage over a time period and a second decrease in average voltage over the time period, and wherein the second decrease is less than the first decrease.

11. The non-transitory computer-readable storage medium of claim 8 , wherein one or more of the first anomaly and the second anomaly is represented by one or more correlations between one or more peaks or valleys of the first minimum voltages with one or more peaks or valleys of the first average voltages.

12. The non-transitory computer-readable storage medium of claim 8 , wherein when executed by a processing device, the computer-executable program instructions cause the processing device to apply the machine learning model to topology information that associates the electric metering device with one or more distribution transformers that are electrically connected to the electric metering device via a distribution line.

13. The non-transitory computer-readable storage medium of claim 8 , wherein accessing the first plurality of voltage measurements comprises transmitting

Assignees

• Landis & Gyr Innovations Inc
• Landis & Gyr Technology Inc

Inventors

• Garza Carlos Iga

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• H02J13/12

  Monitoring network conditions, e.g. electrical magnitudes or operational status · CPC title

• H02J13/10

  characterised by displaying of information or by user interaction, e.g. supervisory control and data acquisition [SCADA] systems · CPC title

• G01R31/086

  in power transmission or distribution networks, i.e. with interconnected conductors · CPC title

• G01R19/2513

  Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging · CPC title