A power prediction method, device, medium and product of a wave energy device

Abstract

The present application discloses a power prediction method, device, medium and product of a wave energy device, which relates to the field of output power prediction. The method comprises obtaining water depth topography data and wind field data of a target sea area; solving the wave field at the wave energy device by using an improved wave energy balance control equation according to the water depth topography data and wind field data; determining the output power of the wave energy device as the initial output power by using a quadratic linear interpolation method according to the wave height and wave period at the wave energy device; and correcting the initial output power by using a directional weighted function according to the wave direction at the wave energy device to obtain a corrected output power. The present application can improve the accuracy of output power prediction of a wave energy device.

Description

Technical Field

[0001] The present application relates to the field of output power prediction, and in particular to a power prediction method, equipment, medium and product for a wave energy device. Background Art

[0002] As an important form of marine energy, wave energy is almost everywhere in the ocean and exists all the time. Since wave energy is easily affected by meteorological and environmental conditions, it has great volatility and randomness. Its large-scale access to the grid will have adverse effects such as impact on the power grid and increase the difficulty of grid dispatch. Establishing a power prediction method for wave energy farms is of practical significance for grid-connected dispatch and energy supply for coastal cities, peninsula cities, and island cities.

[0003] The third generation of wave models represented by SWAN can input the coastline and water depth information of the sea area into the model, and through reasonable division of the grid, accurately predict the wave field information of the sea area. There are many patents and papers in this regard. The SNL-SWAN model modifies the obstacle module in the SWAN mode. By rewriting the propagation coefficient Kt, the power matrix can be brought into the model, and then the energy acquisition effect of the wave energy device can be characterized, and the output power of the wave energy device can be predicted. However, at present, megawatt-class wave energy devices such as the "Nankun" adopt a three-sided design, and their energy acquisition performance is different when facing waves in different directions. The propagation direction of the wave has a very significant impact on specific wave energy devices, such as the "pendulum" device. There are relatively large differences in the energy acquisition performance in different wave directions. The existing power prediction method cannot reflect the influence of the wave direction, resulting in a large deviation in the output power prediction result. Summary of the invention

[0004] The purpose of this application is to provide a power prediction method, equipment, medium and product for a wave energy device, which can improve the accuracy of output power prediction of the wave energy device.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a method for predicting power of a wave energy device, comprising:

[0007] Obtain water depth topography data and wind field data of the target sea area;

[0008] According to the water depth topography data and wind field data, the improved wave energy balance control equation is used to solve the wave field at the wave energy device; the wave energy device is located in the target sea area, and the parameters of the wave field include wave height, wave direction and wave period; the improved wave energy balance control equation is obtained by introducing an energy propagation coefficient into the convection term of the wave energy balance control equation;

[0009] The output power of the wave energy device is determined by using a quadratic linear interpolation method according to the wave height and wave period at the wave energy device as the initial output power;

[0010] According to the wave direction at the wave energy device, the initial output power is corrected using a directional weighting function to obtain a corrected output power; the parameters in the directional weighting function include the main wave direction, the reverse weight coefficient and the wave direction standard deviation, and the parameters in the directional weighting function are obtained by combining a water tank experiment with an optimization algorithm.

[0011] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the power prediction method for a wave energy device described in any one of the above.

[0012] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power prediction method for a wave energy device described in any one of the above.

[0013] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting power of a wave energy device.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] The present application provides a power prediction method, device, medium and product for a wave energy device. The power prediction method of the present application obtains a power matrix of the wave energy device by using a water tank experiment, obtains an initial output power by using a quadratic linear interpolation method according to the wave height and wave period at the layout position, and on this basis, considers the influence of wave direction, adds a direction weighting function, obtains the main wave direction, reverse weight coefficient and wave direction standard deviation in the direction weighting function by combining the water tank experiment with an optimization algorithm, ensures the accuracy of the direction weighting function, and finally obtains the output power considering the influence of wave direction, that is, the corrected output power. The corrected output power characterizes the influence of wave propagation direction (wave direction) on the energy acquisition of the wave energy device, and can more accurately predict the output power of the wave energy device. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is an application environment diagram of a power prediction method for a wave energy device in one embodiment of the present application;

[0018] Figure 2 A schematic flow chart of a power prediction method for a wave energy device provided in one embodiment of the present application;

[0019] Figure 3 A schematic diagram of the arrangement of a wave energy device provided in one embodiment of the present application;

[0020] Figure 4 A schematic diagram of a pendulum wave energy device and wave direction provided in one embodiment of the present application;

[0021] Figure 5 A flow chart of determining parameters in a directional weighting function provided in an embodiment of the present application;

[0022] Figure 6 A schematic diagram of a directional weighting function curve provided in an embodiment of the present application;

[0023] Figure 7 A schematic diagram of a method for converting the wave direction of a directional weighting function provided in another embodiment of the present application;

[0024] Figure 8 A comparison diagram of output power prediction curves provided in an embodiment of the present application;

[0025] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] The power prediction method of the wave energy device provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the water depth terrain data and wind field data to the server 104. The server 104 uses the improved wave energy balance control equation according to the water depth terrain data and wind field data to solve the wave field at the wave energy device; according to the wave height and wave period at the wave energy device, the quadratic linear interpolation method is used to determine the output power of the wave energy device as the initial output power; according to the wave direction at the wave energy device, the directional weighted function is used to correct the initial output power to obtain the corrected output power. The server 104 can feedback the obtained corrected output power to the terminal 102. In addition, in some embodiments, the power prediction method of the wave energy device can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the water depth topography data and the wind field data, or the server 104 can obtain the water depth topography data and the wind field data from the data storage system and process them.

[0029] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0030] In an exemplary embodiment, Figure 2 As shown, a power prediction method for a wave energy device is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0031] Step 201, obtaining water depth topographic data and wind field data of the target sea area.

[0032] Step 202, according to the water depth topography data and wind field data, using the improved wave energy balance control equation to solve the wave field at the wave energy device; the wave energy device is located in the target sea area, and the parameters of the wave field include wave height, wave direction and wave period; the improved wave energy balance control equation is obtained by introducing the energy propagation coefficient into the convection term of the wave energy balance control equation.

[0033] Step 203, using a quadratic linear interpolation method according to the wave height and wave period at the wave energy device, determine the output power of the wave energy device as the initial output power.

[0034] Step 204, according to the wave direction at the wave energy device, the initial output power is corrected using a directional weighting function to obtain a corrected output power; the parameters in the directional weighting function include the main wave direction, the reverse weight coefficient and the wave direction standard deviation, and the parameters in the directional weighting function are obtained by combining a water tank experiment with an optimization algorithm.

[0035] Implementing the above steps 201 to 204 can improve the accuracy of the output power prediction of the wave energy device.

[0036] In another exemplary embodiment of the present application, in order to predict the power of the wave energy device, it is first necessary to determine the wave energy device to be deployed and the deployment sea area. In the embodiment of the present application, it is necessary to input the geographical information such as the water depth and coastline of the sea area and the boundary data such as the driving wind field to reconstruct the wave propagation process. The waves will interact with the seabed during the propagation process. The water depth will affect the propagation speed and direction of the waves, and will affect a series of physical phenomena such as the refraction, diffraction and breaking of the waves. The wind field data of the sea area is the main driving force for wave generation. The intensity and duration of the wind will directly affect the energy of the wave field input to the sea area. The wind field data not only affects the generation of waves, but also affects the propagation process of the waves. Therefore, it is necessary to obtain the water depth terrain data and wind field data. Collect the geographical information data such as the coastline and water depth of the sea area, and collect the boundary data such as the driving wind field. Then the above step 201 is replaced by the following steps 301 to 303.

[0037] Step 301, the source of the bathymetry data can be: the nautical chart data released by China or the ETOPO1v1 data released by the National Geographic Data Center (NGDC) of the United States. The bathymetry data is compiled and stored in the bathymetry.bot file. The file contains the bathymetry data, which is arranged in row order from left to right, and each row of data is separated by a space.

[0038] Step 302, the driving wind field data is sea surface wind data containing time series. The CCMP (CrossCalibrate Multi-Platform) sea surface wind field dataset or the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) can be used. The file name can be defined as: wind_data.wnd. The data includes the time step, initial time, and wind speed and direction at each grid node.

[0039] In another exemplary embodiment of the present application, in order to solve the wave field, a calculation model needs to be established. First, an input file needs to be set, and the file should contain physical parameters such as the grid division method and boundary conditions. The model uses nonlinear partial differential equations such as the energy wave energy balance control equation to simulate the generation, propagation and related effects of waves. The model uses the numerical method of the finite difference method to solve these equations, and then the continuous mathematical problems can be discretized into discrete problems that can be processed by the computer. In order to meet the requirements of discretization, the grid division form must be defined, including information such as the grid starting point, length and number. Read the water depth data of the sea area and interpolate it to the corresponding grid nodes. Read the input wind field file at the boundary area of ​​the sea area. Then the above step 202 is replaced by the following steps 401 to 403.

[0040] Step 401, the purpose of grid division is to divide the continuous simulation area into discrete cells, so as to obtain the physical parameters in each cell. In this embodiment, the simulation area can be discretized by defining a series of parameters such as grid type, starting point coordinates, spacing and quantity. After discretization, the physical quantity of each cell can be expressed as a value on the grid node.

[0041] Step 402, the driven wind field file is input at the boundary condition, and the file covers the information of wind speed and wind direction during the simulation time period. In the model, the wind field generates waves by driving, through the source term Describing the relationship between wind speed and wave energy, the source term in the wave energy balance control equation is as follows:

[0042] ;

[0043] ;

[0044] Where N is the wave number spectral density, t is the time variable, is the group velocity, is the convection term, is the energy propagation coefficient, is the source term, is the frequency, is the wind generation term, is the nonlinear interaction term, is the dissipation term, is the gradient operator, specifically the partial derivatives in the x, y, and z directions.

[0045] At each time step, the source term is calculated based on the wind field data. , add this energy term to the wave energy balance control equation and update the wave field.

[0046] Step 403, in a simplified calculation, the boundary condition can be directly defined as a wave spectrum similar to the JONSWAP spectrum, which is a spectrum describing the energy distribution of ocean waves, and the expression is as follows:

[0047] ;

[0048] in, is the frequency The energy density at is the spectral parameter, g is the gravitational acceleration, is the spectrum peak frequency, is the peak enhancement factor, Make Related parameters (when hour, ).

[0049] The JONSWAP spectrum defines the wave energy distribution on the boundary. These energy distributions are introduced into the SWAN model as input conditions. By setting the frequency distribution, direction distribution and model initial conditions, the wave energy of each direction and frequency on the boundary is determined. If the boundary condition is set to be only the JONSWAP spectrum, the propagation of waves in the model is only affected by nonlinear interactions, energy dissipation and other physical processes.

[0050] Step 404, define the wave energy device deployment coordinates and direction information in the model. In this embodiment, the wave energy device is defined as a line segment, such as Figure 3 As shown in the figure, the solid line part represents the grid divided in SWAN, and the dotted line part represents the deployed wave energy device. The location information of the wave energy device can be defined by the coordinates of two points. After defining the location information of the wave energy device, its directional weighting function parameters need to be defined.

[0051] Step 405, the energy acquisition characteristic data of the wave energy device, specifically, the energy acquisition characteristic data is a wave energy power matrix, the file name can be named: POWER.txt, which contains the energy acquisition power values ​​of the wave energy device corresponding to different wave heights and wave periods. The file first specifies the width of the wave energy device, then the wave height, then the wave period, and finally the output power corresponding to each working condition of the wave energy device.

[0052] The following further explains how to obtain the power matrix of a wave energy device. The definition of wave direction is similar to that of wind direction, which is the direction from which the waves come. First, complete the setting of the main wave direction. For example, the main wave direction of a pendulum wave energy device is defined as 90°. Figure 4 shown.

[0053] Then, using the physical experimental model of the water tank experiment (the physical experimental model is a scaled-down model of the wave energy device), the incoming wave direction is maintained at 90° in the water tank, and the wave height and wave period are changed respectively. For example, the wave height variation range is 0.3~2m, and the wave period variation range is 3~10s. The wave height increases gradually with a step length of 0.1m, and the wave period increases gradually with a step length of 0.5s. By changing the wave height and wave period in the water tank, the output power matrix of the wave energy device under different wave heights and wave periods is obtained.

[0054] Step 406: The model uses the wave action spectrum balance equation to describe the wave generation, propagation and evolution process. Each grid node in the model is controlled by this equation. The wave action spectrum balance equation can be expressed as:

[0055] .

[0056] Where N is the wave number spectral density, is the group velocity, is the convection term, is the source term, is the frequency.

[0057] The model rewrites the wave action balance equation to achieve the addition of wave energy devices at specific grid nodes. After rewriting, the wave energy balance control equation at the wave energy device is:

[0058] ;

[0059] in, is the energy propagation coefficient.

[0060] The convection term in the above equation is By multiplying To correct, it reflects the impact of wave energy devices on wave energy in different wave directions. is the energy propagation coefficient, which is a square term in the equilibrium equation. The expression of the energy propagation coefficient is as follows:

[0061] ;

[0062] In the above formula, is the energy absorbed by the wave energy device at each frequency, is the energy of the incident wave at each frequency. Energy propagation coefficient Used to describe the proportion of wave energy that is not absorbed when passing through the WEC.

[0063] Source Term The calculation formula is: ;in, is the wind generation term, is the nonlinear interaction term, For dissipation items.

[0064] In another exemplary embodiment of the present application, the specific implementation of step 203 is as follows:

[0065] First, specify the calculation time. For example, specify the model calculation time to start at 0:0:00 on January 1, 2010, with a time step of 3600 seconds (1 hour) and end at 0:0:00 on February 28, 2010. After starting the model, the wave field (wave height, wave period, wave direction) at each grid node can be obtained. At the location of the wave energy device, the output power corresponding to the specific wave height and wave period is obtained by quadratic linear interpolation. The formula is as follows:

[0066] ;

[0067] in, The wave height is And the wave period is When the output power of the wave energy device is is the wave height at the wave energy device, and Wave height The highest and lowest wave heights in the interval. is the wave period at the wave energy device, and The wave period The maximum wave period and the minimum wave period of the interval, The wave height is And the wave period is When the output power of the wave energy device is The wave height is And the wave period is When the output power of the wave energy device is The wave height is And the wave period is When the output power of the wave energy device is The wave height is And the wave period is The output power of the wave energy device when .

[0068] Based on the wave height and wave period data at the wave energy device, the output power P of the wave energy device is obtained. However, the influence of the wave propagation direction is not included. For example, for a pendulum wave energy device, different wave directions have a very large impact on the output power.

[0069] In another exemplary embodiment of the present application, according to the wave direction at the wave energy device , a new directional weighting function is added to characterize the influence of wave direction on energy capture. The function contains three parameters: main wave direction , standard deviation of wave direction And the reverse weight wr. The specified parameters can be flexibly modified according to the characteristics of each device.

[0070] In order to obtain the directional weighted function, it is necessary to collect the energy acquisition characteristic data (power matrix) of the wave energy device, and determine the main wave direction, wave direction standard deviation and reverse weight parameters of the wave energy device; in order to predict the output power of the wave energy device, it is necessary to define the location information of the wave energy device in the model. The location of the wave energy device determines its direct interaction with the wave field. Different locations will have different effects on wave propagation and development. According to their working principles, there are many types of wave energy devices, the most common of which are oscillating body type, overriding wave type and oscillating water column type. There are significant differences in the energy acquisition characteristics of different types of wave energy devices. The model needs to read the energy acquisition characteristic file of the wave energy device and define the relevant parameters of its wave direction weight function.

[0071] In the embodiment of the present application, a water tank experiment is used to determine the power matrix (energy acquisition characteristics). For example, starting from a wave height of 0.5 meters, the wave period is first set to 3s, and then the wave period is increased at intervals of 0.5s, and the energy acquisition power of all wave periods at 0.5 meters is obtained. Then, the wave height is increased at intervals of 0.5m, and the above process is repeated, and finally the wave height is cut off at 4m. In this example, the specific experimental conditions are determined according to the characteristics of each device, for example, it can cover a wave height of [0.1-5] meters and a wave period of [2-8]s.

[0072] The main wave direction is the data determined in the design stage of the wave energy device, which can be verified by water tank experiments. For example, when the main wave direction is determined to be 90° in the design, a water tank experiment can be used to test the wave direction of 70-100° to determine whether the wave energy device has the maximum output power at 90° under the same working conditions (wave height, wave period). If not, the wave direction when the output power is maximum is determined as the main wave direction. If so, the main wave direction during design (i.e. 90°) is determined as the main wave direction.

[0073] The specified parameters can be flexibly modified according to the characteristics of each wave energy device. The definition is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] in, is the directional weighting function, is the wave direction, ranging from 0° to 30°, is the main wave direction (e.g. 90°), is the standard deviation of the wave direction, which determines the width of the wave energy distribution. and are all intermediate variables. is the reverse weight, ranging from [0,1].

[0078] The final output power of the considered wave downward is obtained, that is, the corrected output power is: , output to the specified file, where To correct the output power, is the initial output power, is the directional weighting function, For wave direction.

[0079] The following further explains how the wave direction standard deviation and reverse weight in the directional weighting function in the above steps are obtained.

[0080] like Figure 5 As shown in the figure, based on the power matrix, the optimal wave working condition of the wave energy device is selected by physical experiment. For example, assuming that the main wave direction is 90°, the optimal working condition of the wave energy device is a wave height of 1.5m and a wave period of 6s. Its output power is taken as the first output power. , that is 100kW.

[0081] First, we need to obtain the reverse weight coefficient wr, change the wave direction to 270°, and keep the optimal working condition of the wave energy device at a wave height of 1.5m and a wave period of 6s. Its output power is used as the second output power , then the reverse weight coefficient calculation formula is: .

[0082] 1) Keep the wave height at 1.5m and the wave period at 6s, and only change the wave direction to 80° to obtain the third output power corresponding to 80° .

[0083] 2) Keep the wave height at 1.5m and the wave period at 6s, and only change the wave direction to 70° to obtain the third output power corresponding to 70° .

[0084] And so on, we can get a series of , …and the corresponding , ….in, , They are the first wave direction and the second wave direction respectively. , They are the third output powers corresponding to the first wave direction and the second wave direction respectively.

[0085] After determining the main wave direction and reverse weight coefficient, first set the initial value of the wave direction standard deviation, for example, =10°; Substitute it into the directivity weighting function. Calculate the output power value related to the wave direction , and this value corresponds to a real measurement value .

[0086] Then, the error function needs to be defined: In this embodiment, mean squared error (MSE) is selected as a common indicator for measuring the difference between the predicted value and the actual value. The formula is as follows:

[0087] ;

[0088] ;

[0089] in, is the mean square error function, Wave direction The corresponding third output power is, The input is the wave direction The output power obtained by correcting the first output power with the directional weighting function is: is the first output power, Indicates that the input is wave direction The directional weighting function of is the number of different wave directions in the pool experiment, and i represents the i-th wave direction.

[0090] Then, the error convergence threshold is set by minimizing the error function, for example ,in, The error is calculated using the mean square error function, and the maximum number of iterations is set to 1000 to prevent the optimization algorithm from falling into an infinite loop and to find the most suitable wave direction standard deviation that meets the conditions.

[0091] For example, for a pendulum wave energy device, the main wave direction is defined as 90°. Through a water tank test, it is found that the output power is 100 kW at 90°. First, the reverse weight coefficient wr is found. When the wave direction of the water tank test is 270°, the output power of the wave energy device is 90 kW, then wr=90 / 100=0.9.

[0092] When the wave direction is adjusted to 80°, the power generation is measured to be 90kW, when the wave direction is 70°, the power generation is measured to be 80kW, when the wave direction is 100°, the power generation is measured to be 90kW, when the wave direction is 110°, the power generation is measured to be 80kW. According to the calculation of this model, the standard deviation of the wave direction can be obtained: =28.93°.

[0093] Then we get the directional weighted function curve as Figure 6 shown.

[0094] About the final calculation It should be further explained that the wave direction of the above directional weighted function is a theoretical wave direction. The actual wave direction is different from the real wave direction due to the placement angle of the wave energy device and needs to be converted to the real wave direction.

[0095] like Figure 7 As shown in Figure 2, the actual deployment angle of the wave energy device in the sea area is (range [0,360]), then the actual wave direction in the local sea area is The conversion formula is as follows:

[0096] .

[0097] For example, the power matrix of a wave energy device has three wave height values: 0.5 m, 1.0 m, and 1.5 m, and four wave period values: 6.0 s, 7.0 s, 8.0 s, and 9.0 s. The corresponding power matrix is ​​shown in Table 1.

[0098] Table 1 Power matrix parameters of wave energy device (power / kW)

[0099]

[0100] Determine the interval of wave height and wave period: Assume that the current wave height is H s = 1.2 m, the wave period is T p =7.5 seconds.

[0101] Find the wave height H s The lowest wave height (H low = 1.0 m) and the maximum wave height (H high = 1.5 m). Find the wave period T p The minimum wave period of the interval (T low = 7.0 seconds) and the maximum wave period (T high = 8.0 seconds).

[0102] It can be interpolated that when the wave height is 1.2 meters and the wave period is 7.5 seconds, the initial output power of the wave energy device is about 157.5kW.

[0103] Assume that the wave direction at this grid node is is 270°, when the above-mentioned directivity weighting function is used, the corrected output power can be obtained: for kW.

[0104] In order to illustrate the technical effect of the present application, the embodiment of the present application also compares the power prediction effect after adding the directional weighting function and when not adding the directional weighting function. According to historical detection data and practical experience, when the wave direction is not considered, the calculated power of the wave energy device is often larger than the actual output power. In the embodiment of the present application, a directional weighting function is set to correct it. Figure 8 As shown in the figure, the output power of a certain sea area from 18:00 to 19:00 the next day, a total of 26 hours, is predicted. The main wave direction of the wave energy device is 90°. During this period, the corresponding wave direction of the wave energy device varies between [76°, 104°], and the calculation result of the direction weighting function is between [0.89, 1.0]. Without considering the wave direction, the cumulative output power (i.e., the initial output power P) for 26 hours is 4534.2kW. After adding the direction weighting function, the corrected output power It is 4230.3kW, a decrease of 6.7%, which is closer to the actual result.

[0105] Exemplarily, the water tank experiment in the embodiment of the present application may use a scaled model of a wave energy device to conduct a physical water tank experiment.

[0106] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store water depth topography data and wind field data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power prediction method for a wave energy device is implemented.

[0107] Those skilled in the art will understand that Fig. 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0108] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0109] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0112] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A power prediction method for a wave energy device, characterized in that: The power prediction method of the wave energy device comprises: Obtain water depth topography data and wind field data of the target sea area; According to the water depth topography data and wind field data, the improved wave energy balance control equation is used to solve the wave field at the wave energy device; the wave energy device is located in the target sea area, and the parameters of the wave field include wave height, wave direction and wave period; the improved wave energy balance control equation is obtained by introducing an energy propagation coefficient into the convection term of the wave energy balance control equation; The output power of the wave energy device is determined by using a quadratic linear interpolation method according to the wave height and wave period at the wave energy device as the initial output power; According to the wave direction at the wave energy device, the initial output power is corrected by using a directional weighting function to obtain a corrected output power; the parameters in the directional weighting function include the main wave direction, the reverse weight coefficient and the wave direction standard deviation, and the parameters in the directional weighting function are obtained by combining a water tank experiment with an optimization algorithm; The improved wave energy balance control equation is: ； Where N is the wave number spectral density, is the group velocity, is the convection term, is the energy propagation coefficient, is the source term, is the frequency, t is the time variable, is the gradient operator; Source Term The calculation formula is: ； in, is the wind generation term, is the nonlinear interaction term, is the dissipation term; Energy propagation coefficient The calculation formula is: ； in, is the energy absorbed by the wave energy device at each frequency, is the energy of the incident wave at each frequency; The initial output power is corrected using a directional weighting function, and the formula for obtaining the corrected output power is: ； in, To correct the output power, is the initial output power, is the directional weighting function, is the wave direction; The directional weighting function is: ； ； ； in, The main wave direction, is the standard deviation of the wave direction, and are all intermediate variables. is the reverse weight.

2. The power prediction method of a wave energy device according to claim 1, characterized in that: The method of combining the pool experiment with the optimization algorithm to obtain the parameters in the directional weighted function is: According to the power matrix of the wave energy device, determine the main wave direction of the wave energy device and the wave height and wave period under the optimal working conditions, and conduct tank experiments; The process of the pool experiment is as follows: The wave height and wave period of the wave in the water tank experiment are set to the wave height and wave period under the optimal working condition and remain unchanged, and the output power of the wave energy device under the main wave direction condition and the reverse direction condition of the main wave direction are measured respectively as the first output power and the second output power; Calculating a ratio of the second output power to the first output power as a reverse weight coefficient; The wave height and wave period of the water tank experiment are set to the wave height and wave period under the optimal working condition and remain unchanged, and the output power of the wave energy device under different wave directions is measured respectively to obtain the third output power corresponding to different wave directions; According to the third output power corresponding to different wave directions, the standard deviation of the wave direction is solved with the minimization of the mean square error function as the optimization goal; the mean square error function is used to characterize the error between the third output power and the output power obtained by correcting the first output power based on the directional weighted function.

3. The power prediction method of a wave energy device according to claim 2, characterized in that: The mean square error function is: ； ； in, is the mean square error function, Wave direction The corresponding third output power is, The input is the wave direction The output power obtained by correcting the first output power with the directional weighting function is: is the first output power, Indicates that the input is wave direction The directional weighting function of is the number of different wave directions in the pool experiment, and i represents the i-th wave direction.

4. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power prediction method for a wave energy device according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power prediction method of the wave energy device according to any one of claims 1 to 3 is implemented.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the power prediction method of the wave energy device according to any one of claims 1 to 3 is implemented.

Images