WIND TURBINE MAINTENANCE OPTIMIZER

• US 20140244328A1
• Filed: 02/21/2014
• Published: 08/28/2014
• Est. Priority Date: 02/22/2013
• Status: Abandoned Application

• Current Assignee: Vestas Wind Systems A/S
• Original Assignee: Vestas Wind Systems A/S
• Inventors: ZHOU, Yu, MOHAMED SALLEH, Mohamed Faisal Bin, PEDERSEN, Lars Rath
• Application Number:
  US14/186,854
• Publication Number:
  US 20140244328A1
• Time in Patent Office: Days
• US Class Current: 705/7.13
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: Y02A 30/00 Adapting or protecting infrastructure or their operation
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

A service planning engine may predict the future wind speed at a wind farm as well as the future price of energy of the utility grid coupled to the wind turbines. Based on the predicted values, the service planning engine identifies times to perform maintenance when lost revenue is minimized—e.g., when the predicted price of energy is low. Furthermore, the service planning engine may calculate a service priority associated with the turbine to determine when to perform service. The service priority may be based on a status signal associated with the component to be serviced or the time window stipulated in the service contract for performing the maintenance. In one embodiment, the service planning engine also considers resource constraints such as labor costs (e.g., work schedules, overtime costs, availability of technicians with certain skills, availability of components, shipping costs, and the like).

Claims

1. A method of scheduling a maintenance task in a power plant, comprising:

generating, based on one or more processors, a predicted cost of electrical power associated with a utility grid coupled to the power plant for a time window;

identifying a time slot within the time window for performing the maintenance task;

upon determining that the predicted cost during the identified time slot is expected to be below a cost threshold, scheduling the maintenance task in a maintenance schedule; and

upon determining that the predicted cost during the identified time slot is expected to be above the cost threshold, determining whether the maintenance task can be postponed based on a service deadline associated with the maintenance task.

2. The method of claim 1, further comprising:

comparing an accuracy score for a range of predicted cost values below the cost threshold to a predefined accuracy threshold, the accuracy score representing the likelihood that an actual cost of electrical power during the identified time slot will fall within the range of predicted cost values;

upon determining the accuracy score satisfies the predefined accuracy threshold, scheduling the maintenance task in the maintenance schedule.

3. The method of claim 1, further comprising:

generating predicted weather conditions at the power plant for the time window;

upon determining that the predicted weather conditions during the time slot are safe for performing the maintenance task, scheduling the maintenance task in the maintenance schedule.

4. The method of claim 1, wherein scheduling the maintenance task in the maintenance schedule comprises:

generating a priority score associated with the maintenance task, the priority score based on at least one of a type of component associated with the maintenance task, a status signal associated with the maintenance task, and a period of time remaining to perform the maintenance task by the service deadline; and

determining an ordering of a plurality of maintenance tasks by comparing the priority score associated with the maintenance task to respective priority scores of the plurality of maintenance tasks.

5. The method of claim 4, further comprising:

upon determining another maintenance task is already scheduled to be performed in the time slot, comparing the priority score of the maintenance task to a priority score of the another maintenance task.

6. The method of claim 1, wherein identifying the time slot for performing the maintenance task comprises:

determining whether a resource necessary for performing the maintenance task is available during the time slot, the resource is one of an availability of a technician that performs the maintenance task or an availability of spare parts used in the maintenance task.

7. The method of claim 1, further comprising, before determining whether the predicted cost is expected to be below the cost threshold:

characterizing the maintenance task as one of an optimized service task and a non-optimized service task based on a severity level derived from one or more status signals associated with the maintenance task that are received from the power plant, wherein the optimized service task is scheduled using the predicted cost and the non-optimized service task is not scheduled based on the predicted cost.

8. The method of claim 1, wherein the power plant is a wind turbine and the service deadline is defined in a service contract associated with performing maintenance on the wind turbine.

9. A system, comprising:

a computer processor; and

a memory containing a program that, when executed on the computer processor, performs an operation for scheduling maintenance tasks in a power plant, comprising;

generating a predicted cost of electrical power associated with a utility grid coupled to the power plant for a time window;

identifying a time slot within the time window for performing the maintenance task;

upon determining that the predicted cost during the identified time slot is expected to be below a cost threshold, scheduling the maintenance task in a maintenance schedule; and

upon determining that the predicted cost during the identified time slot is expected to be above the cost threshold, determining whether the maintenance task can be postponed based on a service deadline associated with the maintenance task.

10. The system of claim 9, the operation further comprising:

comparing an accuracy score for a range of predicted cost values below the cost threshold to a predefined accuracy threshold, the accuracy score representing the likelihood that an actual cost of electrical power during the identified time slot will fall within the range of predicted cost values;

upon determining the accuracy score satisfies the predefined accuracy threshold, scheduling the maintenance task in the maintenance schedule.

11. The system of claim 9, the operation further comprising:

generating predicted weather conditions at the power plant for the time window;

upon determining that the predicted weather conditions during the time slot are safe for performing the maintenance task, scheduling the maintenance task in the maintenance schedule.

12. The system of claim 9, wherein scheduling the maintenance task in the maintenance schedule comprises:

generating a priority score associated with the maintenance task, the priority score based on at least one of a type of component associated with the maintenance task, a status signal associated with the maintenance task, and a period of time remaining to perform the maintenance task by the service deadline; and

determining an ordering of a plurality of maintenance tasks by comparing the priority score associated with the maintenance task to respective priority scores of the plurality of maintenance tasks.

13. The system of claim 12, the operation further comprising:

upon determining another maintenance task is already scheduled to be performed in the time slot, comparing the priority score of the maintenance task to a priority score of the another maintenance task.

14. The system of claim 9, wherein the power plant is a wind turbine and the service deadline is defined in a service contract associated with performing maintenance on the wind turbine.

15. A computer program product for scheduling maintenance tasks in a power plant, the computer program product comprising:

a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code comprising computer-readable program code configured to;

generate a predicted cost of electrical power associated with a utility grid coupled to the power plant for a time window;

identify a time slot within the time window for performing the maintenance task;

upon determining that the predicted cost during the identified time slot is expected to be below a cost threshold, schedule the maintenance task in a maintenance schedule; and

upon determining that the predicted cost during the identified time slot is expected to be above the cost threshold, determine whether the maintenance task can be postponed based on a service deadline associated with the maintenance task.

16. The computer program product of claim 15, further comprising computer-readable program code configured to:

compare an accuracy score for a range of predicted cost values below the cost threshold to a predefined accuracy threshold, the accuracy score representing the likelihood that an actual cost of electrical power during the identified time slot will fall within the range of predicted cost values;

upon determining the accuracy score satisfies the predefined accuracy threshold, schedule the maintenance task in the maintenance schedule.

17. The computer program product of claim 15, further comprising computer-readable program code configured to:

generate predicted weather conditions at the power plant for the time window;

upon determining that the predicted weather conditions during the time slot are safe for performing the maintenance task, scheduling the maintenance task in the maintenance schedule.

18. The computer program product of claim 15, wherein scheduling the maintenance task in the maintenance schedule comprises computer-readable program code configured to:

generate a priority score associated with the maintenance task, the priority score based on at least one of a type of component associated with the maintenance task, a status signal associated with the maintenance task, and a period of time remaining to perform the maintenance task by the service deadline; and

determine an ordering of a plurality of maintenance tasks by comparing the priority score associated with the maintenance task to respective priority scores of the plurality of maintenance tasks.

19. The computer program product of claim 18, further comprising computer-readable program code configured to:

upon determining another maintenance task is already scheduled to be performed in the time slot, compare the priority score of the maintenance task to a priority score of the another maintenance task.

20. The computer program product of claim 15, wherein the power plant is a wind turbine and the service deadline is defined in a service contract associated with performing maintenance on the wind turbine.