Dynamic mooring system for water wave energy convertors
11208981 · 2021-12-28
Inventors
Cpc classification
Y02E10/30
GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
F05B2270/80
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F03B13/18
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05B2240/97
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05B2240/917
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05B2270/342
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F05B2270/81
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
F03B13/1885
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
International classification
F03B13/00
MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
Abstract
A dynamic adaptive mooring system for wave energy converters (WEC) is disclosed that has a mooring configuration that has a set of fixed mooring lines, and a set of movable mooring lines. When an incoming wave train interacts with the fixed WECs, a set of wave interference points that have higher wave amplitudes than the incoming wave train are formed downstream of the fixed WECs. The movable WECs are then positioned at the interface points to optimize wave energy transfer.
Claims
1. A dynamic and adaptive mooring system for wave energy converters (WEC), comprising: a) a mooring configuration, anchored to a seafloor, having a set of fixed mooring lines, and a set of movable mooring lines; b) a set of fixed WECs held by the set of fixed mooring lines; c) a set of movable WECs held on the set of movable mooring lines; d) a plurality of sensors to collect a set of data related to a set of wave characteristics of a set of incident waves approaching the set of fixed WECs; e) a moving mechanism for moving the set of movable WECs; f) a processing station having an optimization algorithm receiving the set of data and to determine a location within the mooring configuration that has a highest wave potential energy, and to control the movement of the moving mechanism, and; g) a feedback loop between the plurality of sensors and the processing station to adjust the location of the set of movable WECs to optimize the energy transfer from the ocean waves, and; h) the mooring configuration comprises outer parallel mooring lines having four corners that are being held by four rotatable buoys, wherein each rotatable buoy comprises a top part that is rotatably attached to a bottom part, and wherein the bottom part is directly connected to the mooring lines that may be directly or indirectly attached to the seafloor, and the top part is directly connected to the mooring lines on which the WECs are mounted.
2. The system of claim 1, wherein the set of movable mooring lines are substantially parallel to and located downstream of the set of fixed mooring lines and defining a position plane having an X axis that describes the distance between the set of fixed and movable mooring lines, and a Y axis that describes the distance along the movable mooring line.
3. The system of claim 2, wherein the moving mechanism is configured to move the set of movable WECs along the X axis to change the distance between the sets of fixed and movable WECs.
4. The system of claim 2, wherein the moving mechanism is configured to move the set of movable WECs along the Y axis to change a set of angles between the sets of fixed and movable WECs.
5. The system of claim 1, wherein the plurality of sensors to collect a set of data are installed on the set of fixed WECs.
6. The system of claim 1, wherein the plurality of sensors are selected from a group consisting of cameras, gyroscopes, accelerometers, wave gages, ball floats, radars, sonars, and ultrasound sensors to determine the wave characteristics comprising of an incident direction of the waves, a wave velocity, a wave period, a wave amplitude, and a rate of growth of wave amplitude.
7. The system of claim 1, further having GPS positioning system, satellite photographic process, laser acquisition system and X-band radar system to determine wave characteristics.
8. The system of claim 1, wherein the moving mechanism comprises a motor trolley mechanism installed on the mooring system.
9. The system of claim 1, wherein the processing station is located offshore and having a wireless communication to control the moving mechanism and the operation of the WECs or is located on one of WECs.
10. The system of claim 1, wherein the optimization algorithm comprises of an algorithm that can mathematically predict a set of locations of constructive wave interferences downstream of the set of fixed WECs.
11. The system of claim 10, wherein the optimization algorithm further comprising a gradient ascent/descent algorithm to determine the location of a maximum wave amplitude and a gradient maximum wave amplitude where the maximum potential energy is stored in the waves.
12. The system of claim 10, wherein the optimization algorithm is based on a neural network model to produce a non-linear mapping between a mean generated power and a maximum displacement amplitude with a significant wave height, a wave energy period and a set of control parameters in each time interval to further learn over time to optimize the location of the WECs.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
(1) Embodiments herein will hereinafter be described in conjunction with the appended drawings provided to illustrate and not to limit the scope of the claims, wherein like designations denote like elements, and in which:
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DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
(9) The presently disclosed dynamic and adaptive mooring system for wave energy converters (WEC), comprises of (i) a mooring system configured to hold a set of fixed and movable WEC; (ii) a set of sensors to predict the character and nature of the incoming waves using measurement tools and sensors; (iii) a computer or a processing station to calculate and determine the location of interference points of waves behind the set of fixed WECs, and (iv) a moving mechanism to move the set of movable WECs, using a motor trolley mechanism, to locations that will optimize the energy transfer from the ocean waves to the WECs. These systems work together in a feedback loop, adjusting and finding the optimum placements for the dynamic WECs.
(10) One embodiment of the present invention is shown in
(11) The number of WEC on each line of fixed and movable WECs can be any number and the configuration in
(12) One embodiment of the present system 100 is shown in
(13) There are two types of mooring lines, one is fixed that runs from one anchor point to the other (the first line that faces the incoming waves and the lines at the perimeter of an array connecting the mooring points). The other lines (e.g., the line behind the first fixed line) are connected at the ends on another dynamic motor trolley, which is mounted on the fixed wire that runs from one anchor point to another. This allows for a fixed first line of WEC and a dynamic set of lines behind it, that can adapt or move depending on the wave conditions.
(14) The mooring lines of the present system are controlled using controlled motors and trolley system 301 and 302 (
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(16) In the present system, there are two types of WECs: Standard and equipped. The standard WEC are similar to the conventionally available WECs. The equipped WEC have several sensors and cameras to characterize the incoming waves. These sensors are installed on the tower of the WEC. A standard WEC is an elongated, neutrally or slightly buoyant member that is positioned in the water in an upright orientation. Its buoyancy is designed to locate it at a particular depth beneath the water surface. There is a vertically moving member inside the WEC, which is connected to a mechanical energy transducer to convert vertical movements of the member, in response to over-passing Surface waves, to useful energy. WECs can be in different sizes to accommodate the wave size and energy at the location that they are installed. More details of different embodiments of WECs are provided in the prior art, such as those in U.S. Pat. Nos. 6,768,217, 6,515,375, and 6,798,090.
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(18) Wavelength, wave velocity, wave direction (angle with respect to perpendicular) and wave amplitude is monitored and measured through built in camera, gyroscope, accelerometer and ultrasound sensors (mounted on the first line of generators). This information is processed using the optimization algorithm to mathematically predict the locus of constructive interference (behind the first line of generators).
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(21) In one embodiment, the sensors and actuating motors are on site, and the data collected from the sensors are transmitted continuously via satellite to the ground control station. The computers on ground do the data processing and send back the actuation signals to the motors for movement instructions. The process is automated. In another embodiment, the processing station is installed on one of the WECs.
(22) The trolley system allows for refining the location of the WECs to maximize the energy harvesting. The amplitude and the gradient of the waves are determined at any new location. A built-in gyroscope is able to determine the slope and using an algorithm, such as “gradient ascent/descent” algorithm, the maximum slope and hence the maximum amplitude of the wave around that location is determined. The gradient ascent/descent algorithm, is a first-order iterative optimization algorithm for finding a local maximum/minimum of a differentiable function, which represents the wave. Through this process, an objective verification of maximum wave height is acquired. Maximum wave height directly corresponds to the maximum potential energy stored in the waves. The WECs are then moved to the location with the maximum potential energy, Therefore, by exploiting the maximum wave height, the present system can yield the maximum energy harvesting attainable within a wave energy farm.
(23) The wave amplitude, wave length and wave velocity can be measured by any of currently known methods such as ball float method, GPS positioning mode, satellite photographic process, laser acquisition method and X-band radar method etc. One method that is used in the present system is by a set of accelerometers set on the first line of WECs and a second set of accelerometers set on the second line of WECs. The time of arrival of a wave is recorded by each accelerometer. The average time of arrival of the wave for the first line is subtracted from the average time of the arrival of the wave recorded by the second line. Then the velocity is obtained based on dividing the distance between the two lines and this time difference. The wave length can be determined based on using time difference between two consecutive wave as recorded on one accelerometer. The local wavelength is then the velocity multiply by this time. A wave gauge is used to measure wave height and period. There are a variety of wave gauges. The basic principles of operation of wave gauges are the recording of changes in the hydrostatic pressure produced by passing waves, the oscillations of a buoy floating freely on the surface of a body of water, the transit time of ultrasonic signals from a radiator to a receiver after being reflected from the disturbed surface of the body of water, and the changes in the DC resistance of an electrical-contact or wire transducer as waves pass through them. The individual operating principles are combined in some designs.
(24) An algorithms based on reinforcement learning for the passive and active control of wave energy converters (WECs) is used. The wave height and wave period are recorded and used to teach the algorithm, which then optimizes the location of the WECs. The algorithm learns over time to optimize the location of the WECs. It received the wave information from the sensors and selects optimum location of the second row of the WECs, and send the new locations to the controller to move them to their calculated locations. The algorithm is based on a neural network to produce the non-linear mapping between mean generated power and maximum displacement amplitude with significant wave height, wave energy period and control parameters in each time interval.
(25) The algorithm receives information from the fixed front WECs. This real-time approach provides a robust approach for dealing with constraints on the displacement, velocity, force and power flow. The wave elevation forecasting is very important for the maximization of energy absorption and the meeting of realistic constraints. The neural networks strategy employs information on the predicted wave conditions.
(26) The energy content in waves is subdivided into packets, known as wave groups. With the use of a network of WECs, the information in each wave group is determined. The controller can then adapt the control parameters based on the predicted incoming wave groups. Modifying the present system to the treatment of wave groups makes the control scheme more responsive, with a predicted increase in performance. The prediction of incoming wave groups is also feasible with the use of wave buoys.
(27) With respect to the above description, it is to be realized that the optimum relationships for the parts of the invention in regard to size, shape, form, materials, function and manner of operation, assembly and use are deemed readily apparent and obvious to those skilled in the art, and all equivalent relationships to those illustrated in the drawings and described in the specification are intended to be encompassed by the present invention.
(28) The foregoing is considered as illustrative only of the principles of the invention. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation shown and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.