System and method for evaluating models for predictive failure of renewable energy assets

Patent metadata

Field	Value
Publication number	US-11734474-B2
Application number	US-202117219724-A
Country	US
Kind code	B2
Filing date	Mar 31, 2021
Priority date	Dec 27, 2018
Publication date	Aug 22, 2023
Grant date	Aug 22, 2023

Abstract

An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

First claim

The invention claimed is:

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 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 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 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.

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.

5. The non-transitory computer readable medium of claim 3 , the method further comprising 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.

6. 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.

7. The non-transitory computer readable medium of claim 6 , 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.

8. 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.

9. A system, comprising:
at least one processor; and
memory containing instructions, the instructions being executable by the at least one processor 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, 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;
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 m

Assignees

• Utopus Insights Inc

Inventors

• Wang Yajuan
• Solymosi Gabor
• Kim Younghun

Classifications

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• H02S50/00 Primary

  Monitoring or testing of PV systems, e.g. load balancing or fault identification · CPC title

• Y02E10/50

  Photovoltaic [PV] energy · CPC title

• G06F2113/06

  Wind turbines or wind farms · CPC title