JP-7855617-B2 - Service location anomaly

Inventors

• ガルザ，カルロス イガ

Assignees

• ランディス・ギア・テクノロジー・インコーポレイテッド

Dates

Publication Date

20260508

Application Date

20220606

Priority Date

20220429

Claims (20)

1. A method of using machine learning to detect electrical anomalies in a power distribution system, The aforementioned method, Accessing a first set of voltage measurements taken by an electric metering device, From the first plurality of voltage measurements, calculate the first corresponding average voltage and the first corresponding minimum voltage for each of the first plurality of time windows, The method involves applying a machine learning model to a first average voltage and a first minimum voltage, wherein the machine learning model is trained to identify a first voltage signature corresponding to an electrical anomaly from the voltage measurement values. The machine learning model receives a first classification indicating a first poorly connected connection, Based on the first classification described above, a first alarm is transmitted to the utility operator, Calculating a second corresponding average voltage and a second corresponding minimum voltage for each of a second set of time windows from a second set of voltage measurements, wherein the second set of time windows occurs before the first set of time windows, and the second set of time windows occurs before the first set of time windows. The machine learning model is applied to the first average voltage, the first minimum voltage, the first voltage signature, the second average voltage, and the second minimum voltage. The machine learning model receives a second classification that identifies a second voltage signature indicating a second poor contact connection. A method comprising transmitting a second alarm to a utility operator based on the second classification described above.
2. The aforementioned second set of voltage measurements are measured by an additional electric metering device. The method according to claim 1.
3. The first voltage signature includes a first decrease in minimum voltage over a certain period and a second decrease in average voltage over the same period. The second decrease is smaller than the first decrease. The method according to claim 1.
4. The aforementioned second set of voltage measurements are measured by an electric metering device. The method according to claim 1.
5. The machine learning model is further applied to topology information relating the electric meter device to one or more distribution transformers electrically connected to the electric meter device via a distribution line. The method according to claim 1.
6. Accessing the first plurality of voltage measurements includes sending a request to the electric meter device and receiving the first plurality of voltage measurements from the electric meter device. The method according to claim 1.
7. The aforementioned method, Training a machine learning model by accessing a set of training data pairs, wherein each training data pair is: (i) A set of average voltages to learn and a set of minimum voltages, (ii) A learning set of the average voltage of all electric metering devices connected to the distribution transformer, or (iii) A learning set of average voltages from one electric meter unit behind a distribution transformer, and an expected classification indicating one or more electrical anomalies. Including one or more of the following, The machine learning model is provided with one of the training data pairs from the set of training data pairs. The machine learning model receives the determined classification, This involves comparing the determined classification with the expected classification to calculate the loss function, and The intrinsic parameters of the aforementioned machine learning model are adjusted to minimize the loss function, Further including, The method according to claim 1.
8. The set of training data pairs further includes topology information relating one or more distribution transformers to one or more metering devices. The method according to claim 7.
9. A non-temporary 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 a predetermined operation. The aforementioned operation is, Accessing a first set of voltage measurements taken by an electric metering device, From the first plurality of voltage measurements, calculate the first corresponding average voltage and the first corresponding minimum voltage for each of the first plurality of time windows, The method involves applying a machine learning model to a first average voltage and a first minimum voltage, wherein the machine learning model is trained to identify a first voltage signature corresponding to an electrical anomaly from the voltage measurement values. From the aforementioned machine learning model, a first classification indicating a first anomaly is received, Based on the first classification described above, a first alarm is transmitted to the utility operator, The method involves calculating a second corresponding average voltage and a second corresponding minimum voltage for each of a second set of time windows from a second set of voltage measurements, wherein the second set of time windows occurs before the first set of time windows. The machine learning model is applied to the first average voltage, the first minimum voltage, the second average voltage, and the second minimum voltage. The machine learning model receives a second classification that identifies a second voltage signature indicating a second anomaly, Based on the second classification described above, a second alarm is transmitted to the utility operator, Non-temporary computer-readable storage media, including [specific data/information].
10. One or more of the first and second abnormalities are related to a contact failure in the electric meter device. A non-temporary computer-readable storage medium according to claim 9.
11. One or more of the first and second abnormalities are represented by a first decrease in the minimum voltage over a certain period and a second decrease in the average voltage over that period. The second decrease is smaller than the first decrease. A non-temporary computer-readable storage medium according to claim 9.
12. One or more of the first and second abnormalities are represented by one or more correlations between one or more peaks or valleys of the first minimum voltage and one or more peaks or valleys of the first average voltage. A non-temporary computer-readable storage medium according to claim 9.
13. When executed by the aforementioned processing unit, a computer-executable program instruction causes the processing unit to apply a machine learning model to topology information relating one or more distribution transformers electrically connected to the electric meter device via a distribution line to the electric meter device. A non-temporary computer-readable storage medium according to claim 9.
14. Accessing the first plurality of voltage measurement values includes sending a request to the electric meter device and receiving the plurality of voltage measurement values from the electric meter device. A non-temporary computer-readable storage medium according to claim 9.
15. When executed by the aforementioned processing unit, the computer executable program instruction causes the processing unit to train a machine learning model in the following manner, and the manner is: Accessing a set of training data pairs, where each training data pair is: (i) A set of average voltages and a set of minimum voltages, (ii) A set of average voltages of multiple electric meter devices connected to a distribution transformer, (iii) A set of average voltages from one electric meter device behind a distribution transformer, and an expected classification indicating one or more electrical anomalies, Including one or more of the following, The machine learning model is provided with a set of training data pairs, The machine learning model receives the determined classification, This involves comparing the determined classification with the expected classification to calculate the loss function, and The intrinsic parameters of the aforementioned machine learning model are adjusted to minimize the loss function, including, A non-temporary computer-readable storage medium according to claim 9.
16. A system for detecting electrical anomalies in a resource allocation system, The aforementioned system, A headend system including computing devices and machine learning models, Equipped with multiple electric meter devices, Each of the aforementioned electric meter devices is equipped with a sensor, Each of the aforementioned electric meter devices is electrically connected to a distribution transformer located upstream of each of the aforementioned electric meter devices. Each of the aforementioned electric meter devices is, Multiple voltage measurement values are obtained from each sensor of the aforementioned electric meter device. Each of the multiple voltage measurements is configured to provide the headend system. The headend system includes a machine learning model, Access the first set of voltage measurement values measured by the first of the set of electricity metering devices, From the first plurality of voltage measurements, a first corresponding average voltage and a first corresponding minimum voltage are calculated for each of the first plurality of time windows. The machine learning model is configured to be applied to a first average voltage and a first minimum voltage, and the machine learning model is trained to identify a first voltage signature corresponding to an electrical anomaly from the voltage measurement. The headend system is From the machine learning model, a first classification indicating a first poorly connected connection is received. Based on the first classification, it is configured to send a first alarm to the utility operator, the first alarm identifies a first electric meter device, The headend system is Access the second set of voltage measurements taken by the second electric meter device among the set of electric meter devices, The system is configured to calculate a second corresponding average voltage and a second corresponding minimum voltage for each of the second multiple time windows from the second multiple voltage measurements, wherein the second multiple time windows occur before the first multiple time windows. The headend system is The machine learning model is applied to the first average voltage, the first minimum voltage, the second average voltage, and the second minimum voltage. From the machine learning model, a second classification is received that identifies a second voltage signature indicating a second poor contact connection. Based on the second classification, it is configured to send a second alarm to the utility operator, the alarm identifying a second electric meter device. system.
17. The headend system is further configured to train a machine learning model with multiple voltage measurements from at least one electric metering device. The system according to claim 16.
18. The headend system is further configured to apply topology information to a machine learning model that associates multiple electric metering devices with one or more distribution transformers electrically connected upstream of the multiple electric metering devices via distribution lines. The first abnormality is that the difference between the average voltage of the multiple electric meters and the daily average voltage of one of the multiple electric meters is greater than or equal to a threshold. The system according to claim 16.
19. The first voltage signature includes a first decrease in the lowest voltage over a certain period and a second decrease in the average voltage over a certain period. The second decrease is smaller than the first decrease. The system according to claim 16.
20. Receiving the aforementioned multiple voltage measurement values includes transmitting a request to each electric meter device and receiving the multiple voltage measurement values from each electric meter device. The system according to claim 16.

Description

This application relates to machine learning for detecting anomalies in power distribution systems. This application, which cross-references related applications , claims the interests of U.S. Provisional Application No. 63/216,375 filed on 29 June 2021 and U.S. Patent Application No. 17/732,788 filed on 29 April 2022, both of which are incorporated in their entirety by reference. Electricity is supplied to consumers through the distribution system. The distribution system is complex, and the availability of electricity is crucial for customers. Therefore, if a malfunction in the distribution system is left unrepaired, it can increase downtime, wear out parts, and drive up service costs. An exemplary communication network topology of a power distribution system according to one aspect of this disclosure is shown.An exemplary power distribution network relating to one aspect of this disclosure is shown.A flowchart illustrating an exemplary process for detecting anomalies using a machine learning model, according to one aspect of this disclosure, is shown.This shows how statistical voltage data obtained from a metering device is calculated according to one aspect of this disclosure.A flowchart illustrating an exemplary process for detecting anomalies using a machine learning model, according to one aspect of this disclosure, is shown.A flowchart illustrating an exemplary process for training a machine learning model using supervised learning to detect anomalies, relating to aspects of this disclosure, is provided.This is a graph showing voltage measurements related to poorly connected connections, according to one aspect of this disclosure.This is a graph showing voltage measurements related to seasonal overload, according to one aspect of this disclosure.This graph shows voltage measurements related to excessive voltage drop due to a long secondary transmission line, as disclosed herein.An exemplary computing device relating to one aspect of this disclosure is shown. Detailed Description : Aspects of the present invention relate to the use of machine learning to detect electrical anomalies in an electrical system by learning and identifying voltage patterns (signatures) in voltage measurements obtained by a metering device (meter) installed in an end-user's home. Anomalies include, but are not limited to, poor contact in the connection between the meter and meter socket in the end-user's home, seasonal overloads (e.g., overloads that occur only seasonally), and long secondary lines (e.g., connections from distribution transformers to the end-user's home). Each of these anomalies may result in an identifiable voltage signature within the end-user's home. Therefore, the advantages of certain embodiments include early identification of electrical anomalies, which contribute to fault avoidance, improved system efficiency, and improved system reliability in the form of improved Mean System Interruption Frequency Index (SAIFI) or Mean System Interruption Time Index (SAIDI) scores. For example, once an anomaly is identified, proactive action can be taken to resolve its cause and avoid unplanned power outages, resulting in these advantages. In addition, or alternatively, after identifying an electrical anomaly, the disclosed system can retrospectively analyze measurement data from one or more meters to determine additional patterns indicating the anomaly. An untrained machine learning model that is unaware of the voltage signatures that identify anomalies may not be able to identify such patterns, but a machine learning model that is aware of voltage signatures can be trained to identify such voltage signatures. In this respect, the disclosed solution can alert to the occurrence of electrical problems earlier. For example, a trained machine learning model with the ability to identify voltage signatures that match a poor contact can identify patterns from the corresponding electric meter data months or even years before the poor contact becomes a serious problem. The following non-limiting example is provided for illustrative purposes. Voltage measurements are collected by a metering device at a specific frequency (e.g., every 15 minutes). Examples of suitable metering devices include smart meters or Advanced Measurement Infrastructure (AMI) meters. The metering device transmits voltage data to a headend system via a communication network, either together with or separately from metering data such as power consumption. Continuing this example, the headend system receives voltage measurements from a metering device and derives statistical data from these measurements. For example, statistical data such as the average daily voltage or the lowest daily voltage are calculated over a certain period (e.g., one month). This statistical data is provided to a machine learning model. The machine learning model, pre-trained to detect one or more anomalies such as poor contact or seasonal overload from the voltage data and/or de