A method, system and device for predicting the capacity factor of a horizontal axis tidal current energy device

Abstract

The application discloses a kind of horizontal axis tidal current energy power generation device capacity coefficient prediction method, system and equipment, it is related to tidal current energy power generation device field test and analysis field, this method includes: obtaining the tidal current observation data set of target sea area and the electric power data set of horizontal axis tidal current energy power generation device output;Interpolation is carried out to tidal current observation data, so that tidal current observation data and electric power data one-to-one correspondence, construct matching data set;Matching data set is carried out quality control, obtain target data set;Target data set is sequentially grouped and divided into equivalent direction and equivalent flow velocity, obtain prediction data set;Capacity coefficient prediction model of different equivalent direction different equivalent flow velocity interval is constructed according to prediction data set, and then the current capacity coefficient of horizontal axis tidal current energy power generation device is predicted.The application can accurately and scientifically predict the capacity coefficient of horizontal axis tidal current energy power generation device under any equivalent flow velocity condition in any equivalent direction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of in-situ testing and analysis of tidal current power generation devices, and particularly relates to a horizontal-axis tidal current power generation device capacity coefficient prediction method, system and equipment. BACKGROUND

[0002] As a clean and renewable marine energy, tidal current energy has the advantages of high energy density and strong stability. The horizontal-axis tidal current power generation device is the mainstream equipment for current tidal current energy development and utilization. The capacity coefficient, as a core index for measuring the energy utilization efficiency and project economy of the power generation device, is of great significance for site selection planning, investment evaluation and operation optimization of tidal current power generation projects.

[0003] However, the existing horizontal-axis tidal current power generation device capacity coefficient prediction technology has many deficiencies. First, the existing method can only calculate the capacity coefficient of the horizontal-axis tidal current power generation device during the entire test period (i.e. only one capacity coefficient value can be obtained), which is not conducive to fully understanding the operation effect of the tested tidal current power generation device under different flow conditions. Second, the tidal flow and electric power data used by the existing method often have low matching degree. A large amount of electric power data lacks corresponding tidal flow data support, resulting in that the tidal data and the output electric power data of the power generation device cannot be matched one by one, directly affecting the accuracy and representativeness of subsequent analysis. Third, the existing method ignores the changes of the operation characteristics of the power generation device under the rising tide and falling tide tidal current environment, and lacks systematic quality control of the test data, resulting in that the elimination effect of abnormal data is not ideal, further reducing the reliability of the capacity coefficient calculation result. Finally, the tidal direction has significant periodicity and volatility. The existing prediction method does not consider the differences in tidal characteristics under different equivalent directions, resulting in that the calculation result of the capacity coefficient deviates greatly from the actual situation, and it is difficult to meet the engineering application requirements.

[0004] Therefore, there is an urgent need for a horizontal-axis tidal current power generation device capacity coefficient prediction method that can solve the problems of data missing, data mismatching, low data quality and not considering the influence of equivalent directions. SUMMARY

[0005] The purpose of the present application is to provide a horizontal-axis tidal current power generation device capacity coefficient prediction method, system and equipment, which can accurately and scientifically predict the capacity coefficient of the horizontal-axis tidal current power generation device under any equivalent flow rate condition in any equivalent direction.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a horizontal-axis tidal current power generation device capacity coefficient prediction method, comprising:

[0008] The tidal current observation data set of the target sea area and the electric power data set output by the horizontal axis tidal current energy power generation device are acquired; each tidal current observation data in the tidal current observation data set comprises a tidal current observation time, an equivalent flow velocity and an equivalent direction input into a horizontal axis tidal current energy power generation device impeller sweeping cross section range; and each electric power data in the electric power data set comprises an electric power measurement time and an electric power.

[0009] The tidal current observation data in the tidal current observation data set is interpolated to make the tidal current observation data correspond to the electric power data one by one, and a matching data set is constructed; each matching data in the matching data set comprises an electric power measurement time, an electric power, an equivalent flow velocity and an equivalent direction.

[0010] The matching data set is subjected to quality control to obtain a target data set.

[0011] The target data set is sequentially subjected to grouping division of the equivalent direction and the equivalent flow velocity to obtain a prediction data set; the prediction data set comprises electric powers and equivalent flow velocities of multiple equivalent flow velocity intervals in multiple direction grouping data sets.

[0012] A capacity coefficient prediction model of different equivalent directions and different equivalent flow velocity intervals is constructed according to the prediction data set.

[0013] According to the current equivalent direction and the current equivalent flow velocity, a corresponding capacity coefficient prediction model is adopted to predict a current capacity coefficient of the horizontal axis tidal current energy power generation device.

[0014] In a second aspect, the present application provides a horizontal axis tidal current energy power generation device capacity coefficient prediction system, comprising:

[0015] A data acquisition module is configured to acquire a tidal current observation data set of a target sea area and an electric power data set output by a horizontal axis tidal current energy power generation device; each tidal current observation data in the tidal current observation data set comprises a tidal current observation time, an equivalent flow velocity and an equivalent direction input into a horizontal axis tidal current energy power generation device impeller sweeping cross section range; and each electric power data in the electric power data set comprises an electric power measurement time and an electric power; the measurement frequency of the electric power data is greater than the observation frequency of the tidal current observation data.

[0016] A data interpolation module is configured to interpolate the tidal current observation data in the tidal current observation data set to make the tidal current observation data correspond to the electric power data one by one, and construct a matching data set; each matching data in the matching data set comprises an electric power measurement time, an electric power, an equivalent flow velocity and an equivalent direction.

[0017] A quality control module is configured to perform quality control on the matching data set to obtain a target data set.

[0018] a grouping module, configured to group the target data set in equivalent directions and equivalent flow velocities in sequence to obtain a prediction data set, wherein the prediction data set comprises electric power and equivalent flow velocities of multiple equivalent flow velocity intervals in multiple direction grouping data sets;

[0019] a model construction module, configured to construct a capacity coefficient prediction model of different equivalent directions and different equivalent flow velocity intervals according to the prediction data set;

[0020] a coefficient prediction module, configured to predict a current capacity coefficient of the horizontal axis tidal current energy power generation device by using a corresponding capacity coefficient prediction model according to a current equivalent direction and a current equivalent flow velocity.

[0021] In a third 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, and the processor executes the computer program to implement the horizontal axis tidal current energy power generation device capacity coefficient prediction method.

[0022] According to the specific embodiments provided by the present application, the present application has the following technical effects: by interpolating the tidal current observation data, the tidal current observation data and the electric power data are one-to-one corresponding, the reliability of the calculation result of the capacity coefficient of the horizontal axis tidal current energy power generation device is ensured, the problem of weak representativeness of the calculation result caused by small amount of data is solved, and a mathematical model between the capacity coefficient and the equivalent flow velocity of the horizontal axis tidal current energy power generation device, i.e. the capacity coefficient prediction model, is constructed, which can accurately and scientifically predict the capacity coefficient of the horizontal axis tidal current energy power generation device under any equivalent flow velocity condition in any equivalent direction, thereby improving the utilization efficiency of the tidal current energy resources and promoting the large-scale development and utilization of the tidal current energy resources. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 A flowchart of a horizontal axis tidal current energy power generation device capacity coefficient prediction method according to an embodiment of the present application.

[0025] Figure 2 A schematic diagram of part of the tidal current observation data set in an embodiment of the present application.

[0026] Figure 3 A comparison diagram of the equivalent flow velocity and equivalent direction rose diagram after interpolation and the equivalent flow velocity and equivalent direction rose diagram before interpolation in an embodiment of the present application.

[0027] Figure 4 A graph showing the change of equivalent direction in the data set with data sequence number in an embodiment of the present application.

[0028] Figure 5 A regression model of the power curve during the flood tide and a scatter plot of the output power during the flood tide in an embodiment of the present application.

[0029] Figure 6 A mathematical model of the capacity coefficient of the horizontal axis tidal current energy power generation device and the equivalent flow velocity in the horizontal axis tidal current energy power generation device in the 12th equivalent direction in an embodiment of the present application.

[0030] Figure 7 A functional module diagram of a horizontal axis tidal current energy power generation device capacity coefficient prediction system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0032] In order to make the above objectives, characteristics and advantages of the present application more apparent, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0033] In an exemplary embodiment, as shown in Figure 1 , a horizontal axis tidal current energy power generation device capacity coefficient prediction method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or can be executed by a terminal and a server together. In the embodiments of the present application, the method comprises the following steps 101 to 106.

[0034] Step 101, obtaining a tidal current observation data set of a target sea area and an electric power data set output by a horizontal axis tidal current energy power generation device.

[0035] As shown in Figure 2 , each tidal current observation data in the tidal current observation data set comprises a tidal current observation time , an equivalent flow velocity input into a horizontal axis tidal current energy power generation device impeller swept cross-sectional range, and an equivalent direction . i The sequence number of the tidal current observation data is , is the total number of the tidal current observation data sets. Each of the electric power data in the electric power data set comprises an electric power measurement time and the electric power .

[0036] In one specific application example, the step 101 comprises the following steps 11 to 13.

[0037] Step 11, obtaining a raw tidal current observation data set of a target sea area.

[0038] The present application directly reads raw tidal current observation data obtained by a tidal current observation device in a test sea area. Each of the raw tidal current observation data in the raw tidal current observation data set comprises a tidal current observation time , a vertical profile flow velocity of a tidal current flow velocity , a vertical profile flow direction of a tidal current flow direction , and an acute angle included angle between a swept cross section of a horizontal-axis tidal current energy power generation device and the true north . k is the serial number of the measurement profile, which takes a value of , is the total number of the vertical profiles of the tidal current flow velocity and the tidal current flow direction.

[0039] Step 12, calculating an equivalent flow velocity and an equivalent direction input into a swept cross section range of a horizontal-axis tidal current energy power generation device impeller according to the vertical profile flow velocity of the tidal current flow velocity , the vertical profile flow direction of the tidal current flow direction , and the acute angle included angle between the swept cross section of the horizontal-axis tidal current energy power generation device and the true north , to obtain a tidal current observation data set.

[0040] Specifically, the equivalent flow velocity and the equivalent direction input into the swept cross section range of the horizontal-axis tidal current energy power generation device impeller are calculated by using the following formula:

[0041] ;

[0042] ;

[0043] wherein, is an absolute value symbol, A is a vertical profile serial number of a lowermost end of the horizontal-axis tidal current energy power generation device impeller, B is a vertical profile serial number of an uppermost end of the horizontal-axis tidal current energy power generation device impeller, is a coefficient weight of the equivalent flow velocity of the k th measurement profile, is a coefficient weight of the equivalent flow velocity of the kThe layered area of ​​a measurement profile on the swept surface of the impeller of a horizontal axial tidal power generation device. S The swept area of ​​the impeller of the horizontal axis tidal power generation device. For the first k Vertical profile velocity of the tidal current velocity at a measurement section. For the first k The vertical profile of the tidal current direction measured in the measurement section. For characteristic flow direction values, The eigenvalues ​​are the eigenvalues ​​along the x-axis. These are the eigenvalues ​​along the y-axis.

[0044] This application considers the equivalent flow velocity within the swept cross-section of the impeller of the horizontal axial tidal power generation device. and the equivalent direction within the swept cross section of the impeller of the tidal power generation device During the calculation, the ratio of the area of ​​the velocity measurement layer to the total impeller area was introduced as a weighting factor for calculating the equivalent velocity. Compared with existing calculation methods, this method improves the accuracy and representativeness of the calculation results for equivalent flow velocity and equivalent direction.

[0045] Furthermore, this application defines the equivalent direction within the swept cross-section range of the impeller of the tidal power generation device. In the calculation, the "arctan2()" function is used to calculate its characteristic flow direction value, enabling the calculation results to automatically identify flow direction data located in the entire quadrant (0°~360°) without manual correction. This is more reliable than the calculation results of the existing "arctan()" function. For example: ① u=1, v=1 (northeast direction) → arctan(1 / 1)=45°, arctan2(1,1)=45° (consistent results). ② u=-1, v=-1 (southwest direction) → arctan((-1) / (-1))=45° (incorrect, should be 225°), arctan2(-1,-1)=-135° (-135°+360°=225°, correct).

[0046] This application calculates the... k Layered area of ​​a measurement profile on the swept surface of the impeller of a horizontal axial tidal power generation device. First, the swept surface of the impeller of the horizontal axis tidal power generation device is considered as a circle and placed in a rectangular coordinate system, with the center of the circle located at the origin of the rectangular coordinate system. Since the velocity measurement profile is divided from bottom to top, the position of the circle on the y-axis is: bottom at... , at the top . R is the radius of the horizontal axis tidal current power generation device impeller, which is provided by the tidal current power generation device research and development unit, or can be measured on site.

[0047] Secondly, when the measurement profile is , then the circle is divided into multiple horizontal strip intervals from the bottom to the top of the circle at intervals of height, and the number of horizontal strip intervals is . Wherein, the symbol represents the upward rounding, represents the number of horizontal strip intervals that can be divided. The horizontal strip interval number is represented by , and its value is .

[0048] Thirdly, the bottom longitudinal coordinate of the first horizontal strip interval can be set as , and the top longitudinal coordinate is , then the definitions of and are as follows: . It can be seen that when , ; when , .

[0049] Finally, the calculation formula of the layered area of the measurement profile on the swept surface of the horizontal axis tidal current power generation device impeller is as follows:

[0050] .

[0051] In the above formula, the value range of the arcsin function is , and when the bottom longitudinal coordinate is or the top longitudinal coordinate is below the x-axis, although the calculation results of and are negative, the multiple horizontal strip intervals divided by the circle can still be calculated through the above formula. The horizontal strip interval is the layered area of the measurement profile on the swept surface of the horizontal axis tidal current power generation device impeller.

[0052] Step 13, directly read the electric power measurement time and the electric power output by the tidal current power generation device obtained by the electric power measurement device of the horizontal axis tidal current power generation device. j is the serial number of the data obtained by the electric power measurement device, and its value is , is the total number of data obtained by the electric power measurement device.

[0053] Step 102, interpolating the tidal flow observation data in the tidal flow observation data set to make the tidal flow observation data correspond to the electric power data one by one, and constructing a matching data set. The comparison of the tidal flow observation data before and after interpolation is shown in the following table. Figure 3

[0054] In one specific application example, step 102 includes the following steps 21 to 27.

[0055] Step 21, pairing the tidal flow observation data set and the electric power data set according to the principle that the tidal flow observation time and the electric power measurement time correspond to each other, to obtain a plurality of groups of preliminary time synchronization data. Each group of preliminary time synchronization data includes the tidal flow observation data of two adjacent tidal flow observation times and the electric power data between the two tidal flow observation times.

[0056] Since the data acquisition frequency of the electric power measurement equipment of the tidal flow energy power generation device is higher than the data acquisition frequency of the tidal flow observation equipment in the on-site test and analysis work of the tidal flow energy power generation device, it can be analyzed that since the sampling frequency of the tidal flow observation data is lower than the sampling frequency of the electric power data, the tidal flow observation data set will have a regular part of time without tidal flow observation data corresponding to the electric power data in the electric power data set. This loss is not due to the loss of tidal flow observation data caused by the failure of the observation equipment. Therefore, when the observation time of the tidal flow observation data is increased from to , the electric power measurement equipment of the tidal flow energy power generation device has already acquired n the electric power output by the tidal flow energy power generation device .

[0057] When the observation time of the tidal flow observation data is synchronized with the measurement time of the horizontal axis tidal flow energy power generation device output electric power data, that is, , the corresponding time of the tidal flow energy power generation device output electric power is . Among them, is the serial number of the first tidal flow energy power generation device output electric power data synchronized with the tidal flow observation time.

[0058] Then, the serial number of the horizontal axis tidal flow energy power generation device output electric power data synchronized with the initial tidal flow observation time can be represented by , and its value is , is the total number of data acquired by the electric power measurement equipment.

[0059] In order to facilitate understanding, the application gives the matching condition of any adjacent group of data in the preliminary time synchronization data as follows:

[0060] ​ .

[0061] This formula clearly shows that there is no one-to-one correspondence between the power flow observation dataset represented by the first row and the electric power dataset represented by the second row. That is, when the observation time of the power flow observation data in the power flow observation dataset... The electrical power data set will contain There is no corresponding power flow observation data for the group's power data. Among them, express The data corresponding to each moment is included in the statistics, while Data corresponding to a given time point is not included in the statistics.

[0062] Therefore, the next step is to conduct interpolation research on tidal current observation data. This application, compared to existing technologies, is able to interpolate missing tidal current observation data in the first row of the tidal current observation dataset, instead of discarding power data in the second row of the power data that does not correspond to tidal current observation data, as is done in existing technologies. By increasing the amount of data in the dataset, the reliability of the capacity factor calculation results for the horizontal axis tidal current power generation device is ensured, and the problem of weak representativeness of the calculation results due to insufficient data volume is solved.

[0063] This application first reads the power flow observation times from the power flow observation dataset. The equivalent flow velocity input to the impeller sweep section of the horizontal axis tidal power generation device The equivalent direction of the input to the impeller sweep section of the horizontal axis tidal power generation device Electric power measurement time in the electric power dataset The electrical power output of the horizontal axis tidal power generation device Then, the power output of all horizontal axis tidal power generation devices in the tidal current observation dataset is compared with that in the power output dataset. Corresponding equivalent flow rate and equivalent direction Perform interpolation.

[0064] Step 22: For any set of initial synchronization time data, take the first electrical power in the initial time synchronization data as the first existing independent variable, take the first equivalent flow velocity in the initial time synchronization data as the first existing dependent variable, take all the electrical power in the initial time synchronization data as the first interpolation independent variable, interpolate the equivalent flow velocity to obtain the equivalent flow velocity corresponding to each first interpolation independent variable.

[0065] First, extract the equivalent flow rate from the initial synchronization time data. and equivalent flow rate Corresponding electrical power .in, Represents equivalent flow rate Corresponding electrical power The sequence number, whose value range is: , The equivalent flow rate in the initial synchronization time data The total number of corresponding electrical power data.

[0066] Secondly, it will be compared with the equivalent flow rate. Corresponding electrical power Set as the first existing independent variable Equivalent flow rate Set as the first existing dependent variable The electrical power in the initial synchronization time data Set as the first interpolation variable Using the Akima data interpolation module in Origin software, the "variable to be interpolated" is set to... Set the "existing independent variable" to Set the "existing dependent variable" to After setting up the data, begin calculating the interpolation results. The results, i.e., the first dependent variable, can be output. It is easy to see that each first interpolation variable requires an interpolation independent variable. Each will correspond to a first dependent variable. .

[0067] Finally, the calculated According to The corresponding principle is to assign data sequence numbers, that is... ,in, The range of values ​​for: It's easy to know. This is the interpolated equivalent flow velocity, and its symbolic expression is: .

[0068] Step 23: Calculate the x-axis component and y-axis component of the first equivalent direction in the initial synchronization time data.

[0069] Specifically, the equivalent direction is first extracted from the initial synchronization time data. and equivalent direction Corresponding tidal current observation time Then calculate the equivalent directions respectively. Components on the x-axis and equivalent direction Components on the y-axis : .

[0070] Step 24: Take the sequence number of the power flow observation time of the first equivalent direction in the initial synchronization time data as the second existing independent variable, take the component of the first equivalent direction on the x-axis in the initial synchronization time data as the second existing dependent variable, take the sequence number of all power measurement times in the initial synchronization time data as the second interpolation independent variable, and interpolate the equivalent direction on the x-axis to obtain the x-axis equivalent direction component corresponding to each second interpolation independent variable.

[0071] Specifically, it will be related to the equivalent direction. Corresponding tidal current observation time Serial number i Set as the second existing independent variable , equivalent direction Components on the x-axis Set as the second existing dependent variable The power measurement time in the initial synchronization time data Serial number Set as the second interpolation variable Then, call the cubic spline data interpolation module in the Origin software and set the "variable to be interpolated" to... Set the "existing independent variable" to Set the "existing dependent variable" to After setting up the data, begin calculating the interpolation results. The results, i.e., the second dependent variable, can then be output. It is easy to see that each second interpolation variable requires an interpolation independent variable. Each will correspond to a second dependent variable. The calculated According to The corresponding principle is to assign data sequence numbers, that is... ,in The range of values ​​for: It's easy to know. That is, the equivalent direction The interpolated data for the components on the x-axis has the following symbolic expression: .

[0072] Step 25: Take the sequence number of the power flow observation time of the first equivalent direction in the initial synchronization time data as the third existing independent variable, take the component of the first equivalent direction on the y-axis in the initial synchronization time data as the third existing dependent variable, take the sequence number of all power measurement times in the initial synchronization time data as the second interpolation independent variable, and interpolate the equivalent direction on the y-axis to obtain the y-axis equivalent direction component corresponding to each second interpolation independent variable.

[0073] Specifically, the sequence number of the current flow observation time corresponding to the equivalent direction i is set as the third existing independent variable , the component of the equivalent direction on the y-axis is set as the third existing dependent variable , the sequence number of the electric power measurement time in the initial synchronization time data is set as the third interpolated independent variable . Then, the cubic spline data interpolation module in the Origin software is called, the "interpolated variable" is set as , the "existing independent variable" is set as , and the "existing dependent variable" is set as . After the data is set, the interpolation result calculation of the data is started, and the calculation result, i.e., the third dependent variable , can be output. It is easy to know that each third interpolated independent variable corresponds to a third dependent variable . The calculated is assigned a data sequence number according to the principle corresponding to the interpolated independent variable , i.e., , wherein the value range of is: It is easy to know that the dependent variable is the interpolation data of the component of the equivalent direction on the y-axis, and the symbolic expression is .

[0074] Step 26, according to the equivalent direction component of the x-axis corresponding to each second interpolated independent variable and the equivalent direction component of the y-axis corresponding to each third interpolated independent variable, the equivalent direction corresponding to each second interpolated independent variable is calculated. The calculation formula is:

[0075] .

[0076] wherein is an intermediate quantity, and is the equivalent direction corresponding to the nth interpolated independent variable.

[0077] Step 27, according to each group of initial synchronization time data, the equivalent flow rate corresponding to each first interpolated independent variable in each group of initial synchronization time data, and the equivalent direction corresponding to each second interpolated independent variable, a matching data set is constructed.

[0078] Specifically, the electric power measurement time in the initial synchronization time data is read​​​​and electric power , reading the interpolated equivalent flow rate and the interpolated equivalent direction , and arranging the read data in ascending order according to the data serial number , wherein ranges from 1 to 1000. Each of the matching data in the finally constructed matching data set includes the electric power measurement time , the electric power , the equivalent flow rate and the equivalent direction .

[0079] Step 103: performing quality control on the matching data set to obtain a target data set.

[0080] In one specific application example, step 103 includes steps 31 to 34.

[0081] Step 31: dividing the matching data set into a flood data set and an ebb data set according to the equivalent direction.

[0082] Specifically, a professional scatter plot drawing module in Origin software is called, the electric power measurement time in the matching data set is set as the independent variable, and the equivalent direction in the matching data set is set as the dependent variable, thereby generating a relationship graph of the equivalent direction versus time. Through analysis of the relationship graph, the range of the equivalent direction of the flood tide and the range of the equivalent direction of the ebb tide can be determined more easily.

[0083] Then, according to the range of the equivalent direction of the flood tide, the equivalent direction in the matching data set and the electric power measurement time , the electric power and the equivalent flow rate corresponding thereto are extracted, and the data are arranged in ascending order according to the measurement time and re-allocated with data serial numbers, i.e. , to form the flood data set. The value range of is , and the total number of data serial numbers in the flood data set is . The data in the flood data set include the flood data serial number , the data serial number in the matching data set, the electric power measurement time , the electric power , the equivalent flow rate and the equivalent direction .

[0084] Similarly, according to the equivalent direction range of the ebb tide, the equivalent direction in the matching data set is extracted and the corresponding electric power measurement time , the electric power and the equivalent flow rate are arranged in ascending order according to the measurement time of the data, and the data sequence number is redistributed, that is , to form the flood tide data set. Among them , the value range of , is the total number of data sequence numbers in the ebb tide data set. The data in the ebb tide data set includes: ebb tide data sequence number , data sequence number in the matching data set, electric power measurement time , electric power , equivalent flow rate and equivalent direction .

[0085] As shown in Figure 4 , the equivalent direction in the matching data set is mainly concentrated in three regions. The first region is: 0° to 30°; The second region is: 120° to 210°; The third region is: 330° to 360°. Through analysis of the generated relationship graph, it can be judged that: the first region and the third region belong to the equivalent direction range of the flood tide; The second region belongs to the equivalent direction range of the ebb tide. Therefore, the data in the matching data set can be divided into the flood tide data set and the ebb tide data set by Figure 4 .

[0086] Step 32, taking the electric power in the flood tide data set as the dependent variable and the equivalent flow rate in the flood tide data set as the independent variable, constructing an electric power curve regression model during the flood tide, and performing data quality control on the flood tide data set according to the electric power curve regression model during the flood tide, to obtain the quality-controlled flood tide data set.

[0087] Specifically, step 32 includes the following steps (1)~(5).

[0088] (1) Read the electric power and the equivalent flow rate in the flood tide data set.

[0089] (2) Set the electric power as the dependent variable , and set the equivalent flow rate as the independent variable , and determine the parameters and the parameters a in the electric power curve regression equation b, thereby constructing the regression model of the power curve during the flood tide. In order to balance the efficiency and accuracy in the iteration process, the maximum number of iterations is set to 600, and the threshold of the change of the model parameters is set to .

[0090] (3) Substitute each equivalent flow rate in the flood tide data set into the regression model of the power curve during the flood tide, to obtain the calculated value of the power model of each equivalent flow rate in the flood tide data set . .

[0091] (4) According to the calculated value of the power model of each equivalent flow rate in the flood tide data set and the power corresponding to each equivalent current in the flood tide data set, calculate the discriminant coefficient of each equivalent current : .

[0092] (5) Remove the matching data with the discriminant coefficient of the equivalent current greater than the set threshold from the flood tide data set, to obtain the quality-controlled flood tide data set.

[0093] Specifically, when , the equivalent flow rate and the corresponding data sequence number in the flood tide data set , the data sequence number in the matching data set , the power measurement time , the power , and the equivalent direction are retained. Conversely, when , the equivalent flow rate and the corresponding data sequence number in the flood tide data set , the data sequence number in the matching data set , the power measurement time , the power , and the equivalent direction are removed.

[0094] This cycle is repeated to complete the quality control of the entire flood tide data set. As shown in Figure 5 , by constructing the regression model of the power curve during the flood tide, the abnormal data in the flood tide data set can be removed by setting the discriminant coefficient of the abnormal data. Similarly, this method can also be applied to the identification of abnormal data in the ebb tide data set.

[0095] ​​Step 33, taking the electric power in the ebb data set as the dependent variable and the equivalent flow rate in the ebb data set as the independent variable, constructing an electric power curve regression model during the ebb period, and performing data quality control on the ebb data set according to the electric power curve regression model during the ebb period to obtain a quality-controlled ebb data set.

[0096] Specifically, step 33 includes the following steps (1)~(5).

[0097] (1) Reading the electric power and the equivalent flow rate in the ebb data set.

[0098] (2) Taking the electric power as the dependent variable and the equivalent flow rate as the independent variable , using an orthogonal distance regression iterative algorithm to determine the parameters and the parameters in the electric power curve regression equation , thereby constructing an electric power curve regression model during the ebb period. In order to balance efficiency and accuracy during the iteration process, the maximum number of iterations is set to 600, and the model parameter change threshold is set to .

[0099] (3) Substituting each equivalent flow rate in the ebb data set into the electric power curve regression model during the ebb period to obtain the electric power model calculation value for each equivalent flow rate in the ebb data set.

[0100] (4) Calculating the discriminant coefficient for each equivalent current according to the electric power model calculation value for each equivalent flow rate in the ebb data set and the electric power corresponding to each equivalent current in the ebb data set: .

[0101] (5) Removing the matching data in the ebb data set with a discriminant coefficient of the equivalent current greater than the set threshold to obtain a quality-controlled ebb data set.

[0102] Specifically, when , the equivalent flow rate in the ebb data set and its corresponding ebb data sequence number , data sequence number in the matching data set, electric power measurement time , electric power , and equivalent direction Conversely, when the equivalent flow rate in the ebb data set and the corresponding ebb data sequence number , the data sequence number in the matching data set , the electric power measurement time , the electric power and the equivalent direction are removed.

[0103] This cycle is repeated to complete the quality control of the entire ebb data set.

[0104] Step 34, according to the quality-controlled flood data set and the quality-controlled ebb data set, construct the target data set.

[0105] Specifically, read the flood data sequence number , the data sequence number in the matching data set , the electric power measurement time , the electric power , the equivalent direction and the equivalent flow rate in the quality-controlled flood data set. Read the ebb data sequence number , the data sequence number in the matching data set , the electric power measurement time , the electric power , the equivalent flow rate and the equivalent direction in the quality-controlled ebb data set. Remove the flood data sequence number and the ebb data sequence number respectively. Then arrange all the data in the two data sets corresponding to the data sequence number in ascending order of the data sequence number , thereby forming the target data set.

[0106] The data in the target data set includes the data sequence number of the target data set and the corresponding electric power measurement time , electric power , equivalent flow rate and equivalent direction .

[0107] It should be noted that: since steps 32 and 33 carry out the discrimination and removal of abnormal data, it may lead to the data sequence number in the matching data set not being continuous. Therefore, it is necessary to reassign the data sequence number. The re-assigned data sequence number is represented by , wherein the value range of is: , Total number of data sequence in the target data set.

[0108] Step 104, grouping and dividing the target data set according to equivalent direction and equivalent flow rate to obtain a prediction data set. The prediction data set includes electric power and equivalent flow rate of multiple equivalent flow rate intervals in multiple direction grouping data sets.

[0109] In one specific application example, step 104 includes steps 41 and 42.

[0110] Step 41, dividing the equivalent direction in the target data set into multiple equivalent direction intervals according to a fixed equivalent direction range, and dividing the target data set into multiple direction grouping data sets according to the equivalent direction intervals.

[0111] Specifically, reading electric power , equivalent flow rate and equivalent direction in the target data set. Setting a certain fixed equivalent direction range , dividing the target data set into multiple equivalent direction intervals. The number of divided equivalent direction intervals is: . Wherein, symbol represents rounding up, is the total number of divided equivalent direction intervals. Use m to represent the sequence number of the divided equivalent direction interval, which takes value .

[0112] According to the divided equivalent direction interval, the read equivalent direction and the corresponding electric power and equivalent flow rate are divided into the corresponding equivalent direction interval. Then: the electric power in each equivalent direction interval can be represented by the letter , and the equivalent flow rate in each equivalent direction interval can be represented by the letter . The final direction grouping data set includes the electric power in the first m direction and the equivalent flow rate in the first m direction .

[0113] Wherein, in order to divide the entire circumference uniformly, the set value should be divisible by 360. The setting of equivalent direction interval adopts the form of "left closed right open", that is, the data located at the starting point of the corresponding interval is divided into the interval, while the data located at the end point of the interval is not divided into the interval, thereby solving the problem of dividing the data at the interval division point.

[0114] Step 42: For any directional grouped dataset, divide the directional grouped dataset into multiple equivalent flow velocity intervals according to a fixed equivalent flow velocity range.

[0115] Specifically, a certain equivalent flow velocity range is set. For each direction group of the dataset, an equivalent velocity interval is divided. Then, the... m The number of equivalent velocity intervals that can be divided into the directional grouped datasets in each direction is: .in, For the first m The maximum equivalent flow velocity in each direction, For the first m Minimum equivalent velocity in each direction, symbol Indicates rounding up. For the first m The total number of equivalent velocity intervals that can be divided into directional grouping datasets in each direction. (Using...) Indicates the first m The index of the equivalent velocity interval into which the directional grouping dataset is divided in each direction, and its value is... .

[0116] According to the m Equivalent flow in each direction , will correspond to the first m Electric power in each direction All were classified as number one. m The equivalent velocity intervals of the directional grouping dataset in each direction. In the middle. Then: the first m The first direction grouping dataset in the i-th direction The electrical power within each equivalent flow velocity range can be represented by the letter... It means that the first m The first direction grouping dataset in the i-th direction The equivalent velocity within a given equivalent velocity range can be represented by the letter […]. express.

[0117] The final prediction dataset includes: m In the directional grouping dataset of the i-th direction, the th... Electric power within each equivalent flow velocity range and the m In the directional grouping dataset of the direction, the _th _ ... The equivalent velocity within each equivalent velocity range .

[0118] It should be particularly pointed out that, similar to the division of the equivalent direction interval, the equivalent flow velocity interval divided by the direction grouping data set in each direction also adopts the form of "left closed and right open", that is, the data located at the starting point of the corresponding interval is divided into the interval, and the data located at the end point of the interval is not divided into the interval, thereby solving the problem of dividing the data at the interval division point.

[0119] As shown in Figure 6 , the present application divides the equivalent direction range into 15°, that is, the entire circumference is divided into 24 equivalent directions. Then, Figure 6 the range of the 12th equivalent direction in the formula (1) is: 165° to 180°. The direction grouping data set in the 12th equivalent direction range is selected to construct the mathematical model between the capacity coefficient of the horizontal axis tidal current power generation device and the equivalent flow velocity, which makes it possible to calculate and predict the capacity coefficient of the horizontal axis tidal current power generation device under any equivalent flow velocity condition in any equivalent direction.

[0120] Step 105, constructing the capacity coefficient prediction model of different equivalent directions and different equivalent flow velocity intervals according to the prediction data set.

[0121] In one specific application example, step 105 includes the following steps 51 to 53.

[0122] Step 51, for any equivalent flow velocity interval in any direction grouping data set, calculating the average value of the electric power in the equivalent flow velocity interval.

[0123] Specifically, first, the electric power m in the first equivalent flow velocity interval in the direction grouping data set in the 1st direction and the equivalent flow velocity in the first equivalent flow velocity interval in the direction grouping data set in the 1st direction are read. m .

[0124] Then, the average value of the electric power in the first equivalent flow velocity interval in the direction grouping data set in the 1st direction and the average value of the equivalent flow velocity in the first equivalent flow velocity interval in the direction grouping data set in the 1st direction are calculated by using the following formula: m m :

[0125] .

[0126] .

[0127] wherein,​​​​​​​​ For the first m In the directional grouping dataset of the direction, the _th _ ... Electric power within each equivalent flow velocity range The total number.

[0128] Step 52: Calculate the capacity factor within the equivalent flow velocity range based on the average electrical power within the equivalent flow velocity range and the rated electrical power of the horizontal axial tidal power generation device.

[0129] Specifically, setting the first m In the directional grouping dataset of the direction, the _th _ ... The capacity coefficient within each equivalent velocity range is represented by a letter. The rated power of a horizontal axis tidal current power generation device is indicated by letters. This indicates. Then The calculation formula is: Among them, the rated power of the horizontal axis tidal power generation device. It can be provided by the research and development unit of the horizontal axis tidal power generation device.

[0130] Step 53: Using the capacity coefficient within the equivalent velocity range as the dependent variable and the equivalent velocity within the equivalent velocity range as the independent variable, construct a capacity coefficient prediction model for the equivalent velocity range.

[0131] Specifically, read the first m In the directional grouping dataset of the direction, the _th _ ... Capacity coefficient within each equivalent flow velocity range and the m In the directional grouping dataset of the i-th direction, the th... equivalent velocity within each equivalent velocity range .Will Set as dependent variable ,Will Set as independent variable Define the independent variable. With dependent variable The nonlinear mathematical expression between them is: The parameters in the nonlinear mathematical expression are determined based on the Levenberg-Marquardt iterative optimization algorithm in Origin software. , , and To balance efficiency and accuracy during the iteration process, the maximum number of iterations for the Levenberg-Marquardt iterative optimization algorithm was set to 600, and the threshold for model parameter changes was set to [value missing]. .

[0132] the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction m the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction m the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction The mathematical model (i.e. the capacity coefficient prediction model) between the first equivalent flow velocity and the capacity coefficient in the first equivalent flow velocity interval in the first direction group data set in the first direction is:

[0133] .

[0134] wherein, represents the cut-in flow velocity of the horizontal axis tidal current power generation device, represents the cut-out flow velocity of the horizontal axis tidal current power generation device, and the definition domain of the mathematical model is provided, which provides a boundary condition for the rationality of the calculation result of the capacity coefficient of the horizontal axis tidal current power generation device.

[0135] In step 106, the current capacity coefficient of the horizontal axis tidal current power generation device is predicted by using the corresponding capacity coefficient prediction model according to the current equivalent direction and the current equivalent flow velocity.

[0136] The Origin software used in the present application can also use data processing software such as MATLAB.

[0137] The present application improves the accuracy and reliability of the calculation result of the capacity coefficient of the horizontal axis tidal current power generation device, and makes it possible to predict the capacity coefficient of the horizontal axis tidal current power generation device.

[0138] The existing calculation method can only calculate the capacity coefficient of the horizontal axis tidal current power generation device during the entire test period, i.e. only one capacity coefficient value can be obtained. The method proposed in the present application makes it possible to calculate and predict the capacity coefficient of the horizontal axis tidal current power generation device under any equivalent flow velocity condition in any equivalent direction.

[0139] In order to make the existing capacity coefficient calculation result more comparable with the capacity coefficient calculation result proposed in the present application, the present application calculates the capacity coefficient of the horizontal axis tidal current power generation device during the entire test period by using the existing method. On the other hand, the present application calculates the capacity coefficient of the horizontal axis tidal current power generation device under all equivalent flow velocity conditions in all equivalent directions, i.e. the average value during the entire test period, as shown in Table 1. The comparison results in Table 1 are all average values of the capacity coefficient during the entire test period.

[0140] Table 1 Comparison table of capacity coefficient results of horizontal axis tidal current power generation device

[0141]

[0142] ​Based on the same inventive concept, the embodiment of the present application further provides a horizontal axis tidal current energy generation device capacity coefficient prediction system for implementing the horizontal axis tidal current energy generation device capacity coefficient prediction method. Figure 7 As shown in the figure, the horizontal axis tidal current energy generation device capacity coefficient prediction system comprises a data acquisition module 701, a data interpolation module 702, a quality control module 703, a grouping division module 704, a model construction module 705 and a coefficient prediction module 706.

[0143] The data acquisition module 701 is configured to acquire a tidal current observation data set of a target sea area and an electric power data set output by a horizontal axis tidal current energy generation device. Each tidal current observation data in the tidal current observation data set comprises a tidal current observation time, an equivalent flow velocity and an equivalent direction input into a swept section range of a horizontal axis tidal current energy generation device impeller. Each electric power data in the electric power data set comprises an electric power measurement time and an electric power. The measurement frequency of the electric power data is greater than the observation frequency of the tidal current observation data.

[0144] The data interpolation module 702 is configured to interpolate the tidal current observation data in the tidal current observation data set, so that the tidal current observation data correspond to the electric power data one by one, and construct a matching data set. Each matching data in the matching data set comprises an electric power measurement time, an electric power, an equivalent flow velocity and an equivalent direction.

[0145] The quality control module 703 is configured to perform quality control on the matching data set, and obtain a target data set.

[0146] The grouping division module 704 is configured to sequentially perform grouping division on the target data set according to the equivalent direction and the equivalent flow velocity, and obtain a prediction data set. The prediction data set comprises electric power and equivalent flow velocity in multiple equivalent flow velocity intervals in multiple direction grouping data sets.

[0147] The model construction module 705 is configured to construct capacity coefficient prediction models of different equivalent directions and different equivalent flow velocity intervals according to the prediction data set.

[0148] The coefficient prediction module 706 is configured to predict a current capacity coefficient of the horizontal axis tidal current energy generation device by using a corresponding capacity coefficient prediction model according to a current equivalent direction and a current equivalent flow velocity.

[0149] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0150] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps in the above method embodiments.

[0151] In an exemplary embodiment, a computer program product is provided, comprising a computer program, the computer program, when executed by a processor, implements the steps in the above method embodiments.

[0152] In summary, compared with the prior art, the beneficial effects of the present application include at least the following points.

[0153] (1) The calculation method of the equivalent flow velocity and the equivalent direction of the horizontal axis tidal current power generation device is proposed, which improves the accuracy and reliability of the calculation results.

[0154] Specifically, in step 12, when calculating the equivalent flow velocity and the equivalent direction input to the horizontal axis tidal current power generation device impeller swept section range, by introducing the ratio of the flow velocity measurement layer area to the entire impeller area as the coefficient weight of calculating the equivalent flow velocity and the equivalent direction (i.e. ), compared with the existing calculation method, the accuracy and reliability of the calculation results of the equivalent flow velocity and the equivalent direction are improved.

[0155] In the calculation of the equivalent direction input to the tidal current power generation device impeller swept section range, the "arctan2(y / x)" function is used to calculate the characteristic flow direction value, so that the calculation result can automatically identify the flow direction data located in the full quadrant (0°~360°), without manual correction. Compared with the calculation result of the existing "arctan(y / x)" function, it is more reliable.

[0156] The calculation formula of the layered area of the measurement profile on the swept surface of the horizontal axis tidal current power generation device impeller is proposed, which makes it possible to quickly and accurately calculate the layered area of the measurement profile on the swept surface of the horizontal axis tidal current power generation device impeller, avoids the use of complex integral algorithm, and improves the calculation efficiency of the layered area.

[0157] (2) The data interpolation algorithm based on the output power variation characteristics of the horizontal axis tidal current power generation device is proposed, which ensures the reliability of the calculation results of the capacity coefficient of the horizontal axis tidal current power generation device, and solves the problem of weak representativeness of the calculation results caused by small amount of data.

[0158] ​​Specifically, step 22 proposes an interpolation method for equivalent flow velocity and equivalent direction data. Compared with the existing technology, this method solves the problem of low matching degree between power flow velocity and electric power, increases the total amount of data involved in the calculation, ensures the reliability of the capacity coefficient calculation results, and solves the problem of weak representativeness of the calculation results due to the small amount of data.

[0159] Furthermore, in the interpolation algorithm for the equivalent flow velocity, unlike existing technologies, this application proposes to correlate the equivalent flow velocity with the... Corresponding electrical power Set as independent variable Equivalent flow rate Set as dependent variable This interpolation method is used because the output power of the horizontal axis tidal power generation device is known. The equivalent current velocity of the test sea area is necessarily the same. The correlation is evident. Therefore, it can be inferred that changes in the output power data necessarily contain information about changes in the equivalent current velocity data of the test sea area. Thus, this application proposes an interpolation method based on the equivalent current velocity data derived from the characteristics of output power variation, ensuring the reliability of the capacity coefficient calculation results for the horizontal axis tidal power generation device.

[0160] (3) A data quality control method based on the operating characteristics of the horizontal axis tidal power generation device during high tide and low tide is proposed. By splitting the matching dataset into high tide dataset and low tide dataset, and on this basis, combined with the output power characteristics of the horizontal axis tidal power generation device during high tide and low tide, a power curve regression model during high tide and low tide is constructed. By setting the discrimination coefficient of abnormal data, abnormal data in high tide dataset and low tide dataset can be removed, thereby improving the accuracy of capacity coefficient calculation results.

[0161] (4) A method for predicting the capacity coefficient of a horizontal axis tidal current power generation device based on equivalent direction partitioning is proposed. First, the constructed target dataset is divided into multiple directional group datasets according to the equivalent direction. On this basis, each directional group dataset is further divided into velocity intervals according to a certain equivalent velocity range, which makes it possible to calculate the capacity coefficient of the horizontal axis tidal current power generation device under different equivalent directions. This avoids the problem of weak representativeness of the capacity coefficient calculation results caused by the periodicity and fluctuation of the tidal current direction, and improves the scientificity and reliability of the capacity coefficient calculation results.

[0162] (5) The prediction method of the capacity coefficient of the horizontal axis tidal current power generation device is systematically proposed, and the mathematical model between the capacity coefficient of the horizontal axis tidal current power generation device and the equivalent flow speed data is constructed, which makes it possible to calculate and predict the capacity coefficient of the horizontal axis tidal current power generation device under any equivalent flow speed condition in any equivalent direction. Compared with the existing calculation method which can only calculate the capacity coefficient of the horizontal axis tidal current power generation device during the entire test period (i.e., only one capacity coefficient value can be obtained), the present application can enable the tidal current power generation device research and development unit to clearly understand the operation effect of the tested tidal current power generation device under different flow speed conditions, facilitate the device research and development unit to optimize and improve the tidal current power generation device, and thus improve the utilization efficiency of tidal current energy resources and promote the large-scale development and utilization of tidal current energy resources.

[0163] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0164] The technical features of the above embodiments can be combined arbitrarily, and to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0165] The principles and implementation modes of the present application are described by applying specific examples in the present text, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. A method for predicting the capacity factor of a horizontal axis tidal current power generation device, characterized in that, The method includes: Acquire a tidal current observation dataset and an electrical power dataset output by a horizontal axis tidal current power generation device for the target sea area; each tidal current observation data in the tidal current observation dataset includes the tidal current observation time, the equivalent flow velocity input to the impeller sweep section of the horizontal axis tidal current power generation device, and the equivalent direction; each electrical power data in the electrical power dataset includes the electrical power measurement time and the electrical power. Interpolate the tidal current observation data in the tidal current observation dataset to make the tidal current observation data correspond one-to-one with the electric power data, and construct a matching dataset; each matching data in the matching dataset includes electric power measurement time, electric power, equivalent flow velocity and equivalent direction; The matching dataset is divided into a high tide dataset and a low tide dataset based on the equivalent direction. Using the electrical power in the high tide dataset as the dependent variable and the equivalent flow velocity in the high tide dataset as the independent variable, a regression model of the electrical power curve during high tide is constructed. Each equivalent flow velocity in the high tide dataset is substituted into the regression model of the electrical power curve during high tide to obtain the calculated value of the electrical power model for each equivalent flow velocity in the high tide dataset. Based on the calculated value of the electrical power model for each equivalent flow velocity in the high tide dataset and the electrical power corresponding to each equivalent current in the high tide dataset, the discriminant coefficient for each equivalent current is calculated. ;in, Let be the discriminant coefficient of the equivalent current in the high tide dataset. for The calculated value of the electric power model, for The corresponding electrical power, The equivalent current velocity in the high tide dataset is used to remove matching data whose discrimination coefficient of the equivalent current in the high tide dataset is greater than a set threshold, thus obtaining the high tide dataset after quality control. Using the electrical power in the ebb tide dataset as the dependent variable and the equivalent flow velocity in the ebb tide dataset as the independent variable, a regression model of the electrical power curve during ebb tide is constructed. Each equivalent flow velocity in the ebb tide dataset is substituted into the regression model of the electrical power curve during ebb tide to obtain the calculated value of the electrical power model for each equivalent flow velocity in the ebb tide dataset. Based on the calculated value of the electrical power model for each equivalent flow velocity in the ebb tide dataset and the electrical power corresponding to each equivalent current in the ebb tide dataset, the discriminant coefficient for each equivalent current is calculated. ;in, The discriminant coefficients for the equivalent current in the ebb tide dataset are: for The calculated value of the electric power model, for The corresponding electrical power, The equivalent flow velocity in the ebb tide dataset is used to remove matching data whose discrimination coefficient of the equivalent current in the ebb tide dataset is greater than a set threshold, thus obtaining the ebb tide dataset after quality control. Based on the high tide dataset and low tide dataset after quality control, construct the target dataset; The target dataset is sequentially divided into groups based on equivalent direction and equivalent velocity to obtain a prediction dataset; the prediction dataset includes the electrical power and equivalent velocity of multiple equivalent velocity intervals in multiple directional group datasets; Based on the predicted dataset, construct capacity coefficient prediction models for different equivalent flow velocity ranges in different equivalent directions; Based on the current equivalent direction and current equivalent flow velocity, the current capacity factor of the horizontal axis tidal power generation device is predicted using the corresponding capacity factor prediction model.

2. The method for predicting the capacity factor of a horizontal axis tidal power generation device according to claim 1, characterized in that, Obtain the tidal current observation dataset for the target sea area, including: Obtain the raw tidal current observation dataset of the target sea area; each raw tidal current observation data in the raw tidal current observation dataset includes the tidal current observation time, the vertical profile velocity of the tidal current velocity, the vertical profile direction of the tidal current direction, and the acute angle between the swept section of the horizontal axis tidal current power generation device and true north. Based on the vertical profile velocity of the tidal current, the vertical profile direction of the tidal current, and the acute angle between the swept section of the horizontal axis tidal power generation device and true north, the equivalent velocity and equivalent direction input into the swept section of the impeller of the horizontal axis tidal power generation device are calculated to obtain the tidal current observation dataset.

3. The method for predicting the capacity factor of a horizontal axis tidal power generation device according to claim 2, characterized in that, The equivalent velocity and equivalent direction of the flow input to the impeller swept cross section of the horizontal axial tidal power generation device are calculated using the following formulas: ； ； ； in, The equivalent flow velocity input to the impeller sweep cross-section of the horizontal axis tidal power generation device. The equivalent direction within the swept cross-section of the impeller of the horizontal axis tidal power generation device is the input direction. i The serial number of the tidal current observation data. It is the absolute value symbol. A This is the vertical section number of the lowest end of the impeller of the horizontal axis tidal power generation device. B This is the vertical section number of the uppermost end of the impeller of the horizontal axis tidal power generation device. For the first k The coefficient weights of the equivalent flow velocity of each measurement profile. For the first k The layered area of ​​a measurement profile on the swept surface of the impeller of a horizontal axial tidal power generation device. S The swept area of ​​the impeller of the horizontal axis tidal power generation device. For the first k Vertical profile velocity of the tidal current velocity at a measurement section. For the first k The vertical profile of the tidal current direction measured in the measurement section. The acute angle between the swept section of the horizontal axis tidal power generation device and true north. For characteristic flow direction values, The eigenvalues ​​are the eigenvalues ​​along the x-axis. The eigenvalues ​​are located in the y-axis direction. For the first k The bottom ordinate of the horizontal bar intervals of the measurement profile. For the first k The top ordinate of the horizontal bar intervals of the measurement profile. The radius of the impeller of the horizontal axis tidal power generation device.

4. The method for predicting the capacity factor of a horizontal axis tidal power generation device according to claim 1, characterized in that, Interpolating the power flow observation data in the power flow observation dataset to ensure a one-to-one correspondence between the power flow observation data and the electrical power data, a matching dataset is obtained, including: The power flow observation dataset and the power output dataset are paired according to the principle that the power flow observation time and the power output measurement time correspond to each other to obtain multiple sets of preliminary time synchronization data; each set of preliminary time synchronization data includes power flow observation data for two adjacent power flow observation times and power output data between the two power flow observation times; For any set of initial synchronization time data, the first electrical power in the initial time synchronization data is taken as the first existing independent variable, the first equivalent flow velocity in the initial time synchronization data is taken as the first existing dependent variable, and all electrical power in the initial time synchronization data is taken as the first independent variable to be interpolated. The equivalent flow velocity is interpolated to obtain the equivalent flow velocity corresponding to each first independent variable to be interpolated. Calculate the x-axis component and y-axis component of the first equivalent direction in the initial synchronization time data; The sequence number of the power flow observation time in the first equivalent direction in the initial synchronization time data is taken as the second existing independent variable, the x-axis component of the first equivalent direction in the initial synchronization time data is taken as the second existing dependent variable, and the sequence number of all power measurement times in the initial synchronization time data is taken as the second interpolation independent variable. Interpolation is performed on the equivalent direction on the x-axis to obtain the x-axis equivalent direction component corresponding to each second interpolation independent variable. The sequence number of the power flow observation time in the first equivalent direction in the initial synchronization time data is taken as the third existing independent variable, the component of the first equivalent direction on the y-axis in the initial synchronization time data is taken as the third existing dependent variable, and the sequence number of all power measurement times in the initial synchronization time data is taken as the third interpolation independent variable. Interpolation is performed on the equivalent direction on the y-axis to obtain the y-axis equivalent direction component corresponding to each third interpolation independent variable. Calculate the equivalent direction corresponding to each second interpolated independent variable based on the x-axis equivalent direction component corresponding to each second interpolated independent variable and the y-axis equivalent direction component corresponding to each third interpolated independent variable. A matching dataset is constructed based on each set of initial synchronization time data, the equivalent flow velocity corresponding to each first interpolation independent variable in each set of initial synchronization time data, and the equivalent direction corresponding to each second interpolation independent variable.

5. The method for predicting the capacity factor of a horizontal axis tidal power generation device according to claim 1, characterized in that, The target dataset is sequentially divided into equivalent directions and equivalent flow velocities to obtain a prediction dataset, including: According to a fixed range of equivalent directions, the equivalent directions in the target dataset are divided into multiple equivalent direction intervals, and the target dataset is divided into multiple directional grouped datasets according to the equivalent direction intervals. For any directional grouped dataset, the directional grouped dataset is divided into multiple equivalent flow velocity intervals according to a fixed equivalent flow velocity range.

6. The method for predicting the capacity factor of a horizontal axis tidal power generation device according to claim 1, characterized in that, Based on the predicted dataset, a capacity coefficient prediction model is constructed for each equivalent flow velocity range, including: For any equivalent velocity interval in any directional grouped dataset, calculate the average electrical power within the equivalent velocity interval; Calculate the capacity factor within the equivalent flow velocity range based on the average power value within the equivalent flow velocity range and the rated power of the horizontal axial tidal power generation device. A capacity coefficient prediction model for the equivalent flow velocity range is constructed by using the capacity coefficient within the equivalent flow velocity range as the dependent variable and the equivalent flow velocity within the equivalent flow velocity range as the independent variable.

7. A capacity factor prediction system for a horizontal axis tidal power generation device, characterized in that, The system executes the capacity factor prediction method for a horizontal axial tidal power generation device as described in any one of claims 1-6, and the system comprises: The data acquisition module is used to acquire tidal current observation datasets and electrical power datasets output by the horizontal axis tidal current power generation device for the target sea area. Each tidal current observation data in the tidal current observation dataset includes the tidal current observation time, the equivalent flow velocity input to the impeller sweep cross section of the horizontal axis tidal current power generation device, and the equivalent direction. Each electrical power data in the electrical power dataset includes the electrical power measurement time and the electrical power. The measurement frequency of the electrical power data is greater than the observation frequency of the tidal current observation data. The data interpolation module is used to interpolate the tidal current observation data in the tidal current observation dataset, so that the tidal current observation data corresponds one-to-one with the electric power data, and constructs a matching dataset; each matching data in the matching dataset includes electric power measurement time, electric power, equivalent flow velocity and equivalent direction; The quality control module is used to perform quality control on the matching dataset to obtain the target dataset; The grouping module is used to sequentially group the target dataset according to equivalent direction and equivalent velocity to obtain a prediction dataset; the prediction dataset includes the electrical power and equivalent velocity of multiple equivalent velocity intervals in multiple directional group datasets; The model building module is used to build capacity coefficient prediction models for different equivalent directions and different equivalent flow velocity ranges based on the prediction dataset. The coefficient prediction module is used to predict the current capacity coefficient of the horizontal axis tidal power generation device based on the current equivalent direction and the current equivalent flow velocity using the corresponding capacity coefficient prediction model.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the capacity factor of a horizontal axial tidal power generation device according to any one of claims 1-6.

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