Methods and systems for wave energy generation prediction and optimization

Patent metadata

Field	Value
Publication number	US-2020049125-A1
Application number	US-201816102616-A
Country	US
Kind code	A1
Filing date	Aug 13, 2018
Priority date	Aug 13, 2018
Publication date	Feb 13, 2020
Grant date	—

Abstract

Embodiments for managing a wave energy converter (WEC) device by one or more processors are described. At least one environmental characteristic associated with a WEC device in a body of water is received. A prediction of wave conditions on the body of water is calculated based on the at least one environmental characteristic. A signal representative of the prediction of wave conditions is generated.

First claim

1 . A method, by one or more processors, for managing a wave energy converter (WEC) device comprising:
receiving at least one environmental characteristic associated with a WEC device in a body of water; calculating a prediction of wave conditions on the body of water based on the at least one environmental characteristic; and generating a signal representative of the prediction of wave conditions.

2 . The method of claim 1 , wherein the calculating of the prediction of wave conditions is performed with a computing system onboard the WEC device.

3 . The method of claim 2 , wherein the computing system includes a machine learning module.

4 . The method of claim 3 , wherein the machine learning module utilizes a multi-layer perceptron.

5 . The method of claim 1 , further comprising controlling the WEC device based on the prediction of wave conditions.

6 . The method of claim 5 , wherein the WEC device includes a power take-off (PTO), and the controlling of the WEC device includes adjusting a resistance exhibited by the PTO.

7 . The method of claim 1 , wherein the at least one environmental characteristic is associated with at least one of winds and water currents.

8 . A system for managing a wave energy converter (WEC) device comprising:
at least one processor that
receives at least one environmental characteristic associated with a WEC device in a body of water;
calculates a prediction of wave conditions on the body of water based on the at least one environmental characteristic; and
generates a signal representative of the prediction of wave conditions.

9 . The system of claim 8 , wherein the calculating of the prediction of wave conditions is performed with a computing system onboard the WEC device.

10 . The system of claim 9 , wherein the computing system includes a machine learning module.

11 . The system of claim 10 , wherein the machine learning module utilizes a multi-layer perceptron.

12 . The system of claim 8 , wherein the at least one processor further controls the WEC device based on the prediction of wave conditions.

13 . The system of claim 12 , wherein the WEC device includes a power take-off (PTO), and the controlling of the WEC device includes adjusting a resistance exhibited by the PTO.

14 . The system of claim 8 , wherein the at least one environmental characteristic is associated with at least one of winds and water currents.

15 . A computer program product for managing a wave energy converter (WEC) device by one or more processors, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
an executable portion that receives at least one environmental characteristic associated with a WEC device in a body of water; an executable portion that calculates a prediction of wave conditions on the body of water based on the at least one environmental characteristic; and an executable portion that generates a signal representative of the prediction of wave conditions.

16 . The computer program product of claim 15 , wherein the calculating of the prediction of wave conditions is performed with a computing system onboard the WEC device.

17 . The computer program product of claim 16 , wherein the computing system includes a machine learning module.

18 . The computer program product of claim 17 , wherein the machine learning module utilizes a multi-layer perceptron.

19 . The computer program product of claim 15 , wherein the computer-readable program code portions further include an executable portion that controls the WEC device based on the prediction of wave conditions.

20 . The computer program product of claim 19 , wherein the WEC device includes a power take-off (PTO), and the controlling of the WEC device includes adjusting a resistance exhibited by the PTO.

21 . The computer program product of claim 15 , wherein the at least one environmental characteristic is associated with at least one of winds and water currents.

Assignees

• IBM

Inventors

• O'Donncha Fearghal
• Chen Bei
• Mckenna Sean A

Classifications

• F05B2260/821

  Parameter estimation or prediction · CPC title

• F03B15/00 Primary

  Controlling (controlling in general G05 {; regulation of plants characterised by the use of siphons F03B13/086}) · CPC title

• F03B13/14 Primary

  using wave energy · CPC title

• G05B13/027

  using neural networks only · CPC title

• Y02E10/30

  Energy from the sea, e.g. using wave energy or salinity gradient · CPC title