EP-4484985-B1 - SYSTEM AND METHOD FOR EVALUATING MODELS FOR PREDICTIVE FAILURE OF RENEWABLE ENERGY ASSETS

Inventors

• WANG, Yajuan
• GABOR, Solymosi
• KIM, YOUNGHUN

Dates

Publication Date

20260513

Application Date

20191227

Claims (15)

1. A non-transitory computer readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising: Receiving (902) first historical sensor data of a first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of any number of renewable energy assets, the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset during the first time period; generating a first set of failure prediction models using the first historical sensor data, each of the first set of failure prediction models being trained (906) by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure; evaluating (908) each failure prediction model of the first set of failure prediction models to determine a positive prediction value, the positive prediction value being determined based on curvature analysis of the failure prediction models using different lead time windows and observation time windows; selecting at least one failure prediction model of the first set of failure prediction models based on the positive prediction values and the lead time windows to create a first selected failure prediction model, the first selected failure prediction model including the lead time window before a predicted failure; receiving first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset; applying (912) the first selected failure prediction model to the current sensor data to generate a first failure prediction a failure of at least one component of the one or more components; comparing (914) the first failure prediction to a first trigger criteria; and generating (916) and transmitting a first alert based on the comparison of the failure prediction to the first trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.
2. The non-transitory computer readable medium of claim 1, wherein the renewable energy asset is a wind turbine or a solar panel.
3. The non-transitory computer readable medium of claim 1, wherein each of the first set of failure prediction models predict failure of a component of the renewable asset.
4. The non-transitory computer readable medium of claim 3, the method further comprising: selecting a first trigger threshold from a plurality of trigger thresholds based on the component, wherein each different trigger threshold of the plurality of trigger threshold is directed to a different component or group of components; or filtering the first historical sensor data to retrieve a portion of the historical sensor data related to the component, the generating the first set of failure prediction models using the first historical sensor data comprising generating the first set of failure prediction models using the portion of the first historical sensor data.
5. The non-transitory computer readable medium of claim 3, the method further comprising: generating a second set of failure prediction models using the first historical sensor data, each of the second set of failure prediction models being trained by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure, the second set of failure prediction models being for predicting a fault of a component that is different than the first set of failure prediction models; evaluating each failure prediction model of the second set of failure prediction models to determine a positive prediction value; selecting at least one failure prediction model of the second set of failure prediction models based on the positive prediction values and the lead time windows to create a second selected failure prediction model, the second selected failure prediction model including the lead time window before a predicted failure; receiving first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset; applying the second selected failure prediction model to the current sensor data to generate a second failure prediction; comparing the second failure prediction to a second trigger criteria; and generating and transmitting a second alert based on the comparison of the failure prediction to the second trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.
6. The non-transitory computer readable medium of claim 5, the method further comprising filtering the second historical sensor data to retrieve a portion of the historical sensor data related to the component, the generating the second set of failure prediction models using the first historical sensor data comprising generating the first second of failure prediction models using the portion of the first historical sensor data.
7. The non-transitory computer readable medium of claim 1, wherein selecting at least one failure prediction model of the first set of failure prediction models further comprises receiving a selection of the selected failure prediction model using the curvature analysis from an authorized digital device.
8. A system (104), comprising: at least one processor (1202); and memory (1210) containing instructions, the instructions being executable by the at least one processor (1202) to: receive first historical sensor data of a first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of any number of renewable energy assets (112), the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset (112) during the first time period; generate a first set of failure prediction models using the first historical sensor data, each of the first set of failure prediction models being trained by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure; evaluate each failure prediction model of the first set of failure prediction models to determine a positive prediction value, the positive prediction value being determined based on curvature analysis of the failure prediction models using different lead time windows and observation time windows; select at least one failure prediction model of the first set of failure prediction models based on the evaluation of the positive prediction values and the lead time windows to create a first selected failure prediction model, the first selected failure prediction model including the lead time window before a predicted failure; receive first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset (112); apply the first selected failure prediction model to the current sensor data to generate a first failure prediction a failure of at least one component of the one or more components; compare the first failure prediction to a first trigger criteria; and generate and transmitting a first alert based on the comparison of the failure prediction to the first trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.
9. The system of claim 8, wherein the renewable energy asset (112) is a wind turbine or a solar panel.
10. The system of claim 8, wherein each of the first set of failure prediction models predict failure of a component of the renewable asset (112).
11. The system of claim 10, the instructions being further executable by the at least one processor to: select a first trigger threshold from a plurality of trigger thresholds based on the component, wherein each different trigger threshold of the plurality of trigger threshold is directed to a different component or group of components; or filter the first historical sensor data to retrieve a portion of the historical sensor data related to the component, the generating the first set of failure prediction models using the first historical sensor data comprising generating the first set of failure prediction models using the portion of the first historical sensor data.
12. The system of claim 8, the instructions being further executable by the at least one processor to: generate a second set of failure prediction models using the first historical sensor data, each of the second set of failure prediction models being trained by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure, the second set of failure prediction models being for predicting a fault of a component that is different than the first set of failure prediction models; evaluate each failure prediction model of the second set of failure prediction models to determine a positive prediction value; select at least one failure prediction model of the second set of failure prediction models based on the comparison of the positive prediction values and the lead time windows to create a second selected failure prediction model, the second selected failure prediction model including the lead time window before a predicted failure; receive first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset (112); apply the second selected failure prediction model to the current sensor data to generate a second failure prediction; compare the second failure prediction to a second trigger criteria; and generate and transmit a second alert based on the comparison of the failure prediction to the second trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.
13. The system of claim 12, the instructions being further executable by the at least one processor to: filter the second historical sensor data to retrieve a portion of the historical sensor data related to the component, the generating the second set of failure prediction models using the first historical sensor data comprising generating the first second of failure prediction models using the portion of the first historical sensor data.
14. The system of claim 8, wherein selecting at least one failure prediction model of the first set of failure prediction models further comprises receiving a selection of the selected failure prediction model using the curvature analysis from an authorized digital device.
15. A method comprising: Receiving (902) first historical sensor data of a first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of any number of renewable energy assets, the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset during the first time period; generating a first set of failure prediction models using the first historical sensor data, each of the first set of failure prediction models being trained (906) by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure; evaluating (908) each failure prediction model of the first set of failure prediction models to determine a positive prediction value, the positive prediction value being determined based on curvature analysis of the failure prediction models using different lead time windows and observation time windows; selecting at least one failure prediction model of the first set of failure prediction models based on the evaluation of the positive prediction values and the lead time windows to create a first selected failure prediction model, the first selected failure prediction model including the lead time window before a predicted failure; receiving first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset; applying (912) the first selected failure prediction model to the current sensor data to generate a first failure prediction a failure of at least one component of the one or more components; comparing (914) the first failure prediction to a first trigger criteria; and generating (916) and transmitting a first alert based on the comparison of the failure prediction to the first trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.

Description

Field of the Invention Embodiments of the present invention(s) relate generally to forecasting failure of renewable energy assets and, in particular, evaluating models to predict failures of one or more renewable energy assets to increase lead time before failure and improve accuracy. Description of Related Art Detection and prediction of failure in one or more components of an asset of an electrical network has been difficult. Detection of a failure of a component of an asset is tedious and high in errors. In this example, an asset is a device for generating or distributing power in an electrical network. Examples of assets can include, but is not limited to, a wind turbine, solar panel power generator, converter, transformer, distributor, and/or the like. Given that detection of a failure of a component of an asset may be difficult to determine, increased accuracy of prediction of future failures compounds problems. US 2017/074250 A1 proposes a wind turbine condition monitoring method and system, comprising acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data; training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report. US 2012/191633 A1 proposes a computer-implemented reservoir prediction system, method, and software, provided for failure prediction for artificial lift systems, such as sucker rod pump systems. The method includes a production well associated with an artificial lift system and data indicative of an operational status of the artificial lift system. Summary An example nontransitory computer readable medium comprises executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising receiving first historical sensor data of a first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of any number of renewable energy assets, the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset during the first time period, generating a first set of failure prediction models using the first historical sensor data, each of the first set of failure prediction models being trained by different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, the lead time window including a period of time before a predicted failure, evaluating each failure prediction model of the first set of failure prediction models using at least a confusion matrix including metrics for true positives, false positives, true negatives, and false negatives as well as a positive prediction value, comparing the confusion matrix and the positive prediction value of each of the first set of failure prediction models, selecting at least one failure prediction model of the first set of failure prediction models based on the comparison of the confusion matrixes, the positive prediction values, and the lead time windows to create a first selected failure prediction model, the first selected failure prediction model including the lead time window before a predicted failure, receiving first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset, applying the first selected failure prediction model to the current sensor data to generate a first failure prediction a failure of at least one component of the one or more components, comparing the first failure prediction to a first trigger criteria, and generating and transmitting a first alert based on the comparison of the failure prediction to the first trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction. In some embodiments, the renewable energy asset is a wind turbine or a solar panel. Each of the first set of failure prediction models may predict failure of a component of the renewable asset. The method may further comprise selecting the first trigger threshold from a plurality of trigger thresholds based on the component, wherein each different trigger threshold of the plurality of trigger threshold is directed to a different component or group of components. In various embodiments, the method further comprise filtering the first historical sensor data to retrieve a portion of the historical sensor data related to the component, the generating the first set of failure prediction models using the first historical sensor data comprising generating the first set of failu