Floating type wind power generation control method and system

Abstract

The invention discloses a floating type wind power generation control method and system. The floating type wind power generation control method comprises the steps that historical operation data and generating capacity data of a floating type wind power generator are obtained and preprocessed; analyzing the preprocessed data, and determining operation parameters influencing the generating capacity of the generator in the historical operation data; constructing a generating capacity prediction model based on the operation parameters and a preset neural network model, and predicting to obtain generating capacity prediction data; acquiring real-time generating capacity data, and analyzing a difference condition between the real-time generating capacity data and the generating capacity prediction data; evaluating the power generation performance of the generator according to the difference condition to obtain a power generation performance evaluation value; and determining an adjustment coefficient according to the power generation performance evaluation value to adjust and control the operation parameters of the generator. According to the method, the generating capacity is predicted by constructing the generating capacity prediction model, difference analysis is carried out in combination with real-time monitoring data, operation parameters are adjusted based on difference evaluation, and intelligent optimization of generating control of the floating type wind driven generator is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of floating wind power generation, and in particular to a floating wind power generation control method and system. Background Art

[0002] Floating wind power generation is a technology that uses wind energy to generate electricity. Compared with traditional ground-based wind power generation, floating wind power generation has higher wind speed and stability, and can be deployed in a wider sea area. However, floating wind power generation also faces many challenges, such as the influence of environmental factors such as wind speed, wind direction, and waves, as well as technical issues such as the stability and control of the floating platform. In order to overcome these challenges, floating wind power generation requires an effective control method to ensure the safe operation of the system and maximize power generation efficiency.

[0003] However, traditional floating wind turbine power generation control methods usually adopt control strategies with fixed operating parameters, which cannot be flexibly adjusted, resulting in low power generation efficiency. At the same time, during the energy conversion process, traditional methods may have problems with large energy loss. For example, some operating parameters are not adjusted in time, resulting in the failure to effectively convert wind energy into electrical energy. In addition, traditional methods lack intelligent control and cannot be dynamically adjusted according to the operating status, making it difficult to achieve optimal power generation control. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a floating wind power generation control method and system, comprising:

[0005] Obtain historical operation data and power generation data of the floating wind turbine, and pre-process the historical operation data and power generation data;

[0006] Analyze the pre-processed historical operation data and power generation data to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine;

[0007] A power generation prediction model is constructed based on the operating parameters and the preset neural network model, and power generation prediction data of the floating wind turbine in the future is predicted according to the power generation prediction model;

[0008] Acquire real-time power generation data corresponding to the power generation forecast data in time, and analyze the difference between the power generation forecast data and the real-time power generation data;

[0009] The power generation performance of the floating wind turbine is evaluated according to the difference, and a power generation performance evaluation value of the floating wind turbine is obtained;

[0010] The adjustment coefficient is determined according to the power generation performance evaluation value of the floating wind turbine generator, and the operating parameters of the floating wind turbine generator are adjusted and controlled according to the adjustment coefficient.

[0011] Furthermore, the preprocessing of the historical operation data and the power generation data includes:

[0012] The historical operation data and power generation data are preprocessed, which includes removing erroneous data points and duplicate data points, filling in missing data points, and standardizing the data.

[0013] Furthermore, the pre-processed historical operation data and power generation data are analyzed to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine, including:

[0014] The pre-processed historical operation data is divided into several groups of operation parameter data groups according to parameter types, and the correlation coefficient between each group of operation parameter data groups and the power generation data is calculated respectively;

[0015] The operating parameter data groups with correlation coefficients greater than a preset value are screened out and determined as operating parameters in the historical operating data that affect the power generation of the floating wind turbine generator.

[0016] Furthermore, the power generation prediction model is constructed based on the operating parameters and the preset neural network model, and the power generation prediction data of the floating wind turbine in the future period is predicted according to the power generation prediction model, including:

[0017] Construct a data set based on the operating parameters, and input the data set into a preset neural network model to construct an initial model for power generation prediction;

[0018] The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for power generation prediction;

[0019] The initial power generation prediction model is trained and tested until the initial power generation prediction model meets the preset convergence conditions, thereby obtaining the power generation prediction model;

[0020] The power generation forecast data of the floating wind turbine in the future is predicted based on the obtained power generation forecast model.

[0021] Further, the acquiring of real-time power generation data corresponding to the power generation forecast data in time, and analyzing the difference between the power generation forecast data and the real-time power generation data, includes:

[0022] Acquire real-time power generation data corresponding to the power generation forecast data in time, and perform pairwise difference calculation between the power generation forecast data and the real-time power generation data according to time points to obtain power generation deviation data;

[0023] The power generation deviation data is plotted into a power generation deviation curve, and the peak value and the trough value in the power generation deviation curve are extracted, and a descending curve segment corresponding to each peak value to the trough value and an ascending curve segment corresponding to each trough value to the peak value are determined;

[0024] The descending slope and the first average value of each descending curve segment and the ascending slope and the second average value of the ascending curve segment are calculated respectively, and the descending slope and the first average value and the ascending slope and the second average value are used as the difference between the power generation prediction data and the real-time power generation data.

[0025] Further, the power generation performance of the floating wind turbine generator is evaluated according to the difference to obtain the power generation performance evaluation value of the floating wind turbine generator, including:

[0026] Obtaining a descending slope and a first average value as well as an ascending slope and a second average value, and presetting a standard average value;

[0027] Calculate the differences between the first average value and the second average value and the standard average value respectively, and evaluate and value the differences to obtain the evaluation value of the corresponding curve segment;

[0028] Calculate the average slope value of the slopes corresponding to all curve segments, and calculate the ratio of the slope corresponding to each curve segment to the average slope value, and use the ratio as the weight corresponding to each curve segment;

[0029] The evaluation value of the power generation performance of the floating wind turbine is obtained by performing a weighted addition calculation based on the evaluation value and weight corresponding to the power generation control parameter of each curve segment.

[0030] Furthermore, the step of determining the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine and adjusting and controlling the operating parameters of the floating wind turbine according to the adjustment coefficient includes:

[0031] A corresponding relationship between an adjustment coefficient and a power generation performance evaluation value interval is preset, and a corresponding adjustment coefficient is associated with each power generation performance evaluation value interval;

[0032] Acquire the power generation performance evaluation value, and select the adjustment coefficient corresponding to the power generation performance evaluation value interval based on the mapping relationship between the power generation performance evaluation value interval to which the difference belongs and the adjustment coefficient-power generation performance evaluation value interval correspondence relationship;

[0033] Each parameter in the operation parameters of the floating wind turbine generator is multiplied and adjusted one by one according to the adjustment coefficient, so as to complete the adjustment control of the power generation control parameters of the floating wind turbine generator.

[0034] The present invention also provides a floating wind power generation control system, comprising:

[0035] An acquisition module is used to acquire historical operation data and power generation data of the floating wind turbine and pre-process the historical operation data and power generation data;

[0036] A determination module is used to analyze the pre-processed historical operation data and power generation data to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine;

[0037] A prediction module is used to construct a power generation prediction model based on operating parameters and a preset neural network model, and predict power generation prediction data of the floating wind turbine in a future period of time according to the power generation prediction model;

[0038] An analysis module is used to obtain real-time power generation data corresponding to the power generation forecast data in time, and analyze the difference between the power generation forecast data and the real-time power generation data;

[0039] An evaluation module is used to evaluate the power generation performance of the floating wind turbine according to the difference, and obtain an evaluation value of the power generation performance of the floating wind turbine;

[0040] The control module is used to determine the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine generator, and adjust and control the operating parameters of the floating wind turbine generator according to the adjustment coefficient.

[0041] Compared with the prior art, the floating wind power generation control method and system according to the embodiment of the present invention have the following beneficial effects:

[0042] By cleaning, converting and analyzing historical operation data and power generation data, the present invention can better understand the characteristics and correlation of the data, and provide a more reliable basis for subsequent modeling and prediction;

[0043] The present invention can help understand the key factors of power generation change by determining the operating parameters that affect the power generation of floating wind turbines in historical operating data, and provide an important reference for optimizing the operation of the generator;

[0044] The present invention can help predict the power generation in the future by establishing a power generation prediction model, provide a reasonable expectation of future power generation, and help formulate operation plans and resource allocation;

[0045] By comparing the difference between the predicted data and the real-time data, the present invention can evaluate the accuracy and stability of the prediction model, and provide a basis for model improvement and optimization;

[0046] The present invention evaluates and calculates the power generation performance according to the difference, which can more accurately understand the performance of the generator and provide support for operation and maintenance and management decisions;

[0047] The present invention determines the adjustment coefficient according to the power generation performance evaluation value, and adjusts and controls the operating parameters of the floating wind turbine generator, which is helpful to optimize the performance and efficiency of the generator. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the flow structure of the floating wind power generation control method in an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of the composition of the floating wind power generation control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0051] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0052] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0053] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0054] like Figure 1As shown, in an embodiment of the present application, a floating wind power generation control method is provided, including: S100: acquiring historical operation data and power generation data of a floating wind turbine generator, and preprocessing the historical operation data and power generation data; S200: analyzing the preprocessed historical operation data and power generation data, and determining the operation parameters in the historical operation data that affect the power generation of the floating wind turbine generator; S300: constructing a power generation prediction model based on the operation parameters and a preset neural network model, and predicting the power generation prediction data of the floating wind turbine generator in a future period of time according to the power generation prediction model; S400: acquiring real-time power generation data corresponding to the power generation prediction data in time, and analyzing the difference between the power generation prediction data and the real-time power generation data; S500: evaluating the power generation performance of the floating wind turbine generator according to the difference, and obtaining the power generation performance evaluation value of the floating wind turbine generator; S600: determining the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine generator, and adjusting and controlling the operation parameters of the floating wind turbine generator according to the adjustment coefficient.

[0055] Furthermore, the present invention can better understand the characteristics and correlations of the data by cleaning, converting and analyzing the historical operating data and power generation data, and provide a more reliable basis for subsequent modeling and prediction; the present invention can help understand the key factors of power generation changes by determining the operating parameters that affect the power generation of floating wind turbines in historical operating data, and provide an important reference for optimizing the operation of the generator; the present invention can help predict the power generation in a future period of time by establishing a power generation prediction model, provide a reasonable expectation of future power generation, and help formulate operation plans and resource allocation; the present invention can evaluate the accuracy and stability of the prediction model by comparing the difference between the predicted data and the real-time data, and provide a basis for model improvement and optimization; the present invention evaluates and calculates the power generation performance according to the difference, and can more accurately understand the performance of the generator, and provide support for operation and maintenance and management decisions; the present invention determines the adjustment coefficient according to the power generation performance evaluation value, and adjusts and controls the operating parameters of the floating wind turbine, which helps to optimize the performance and efficiency of the generator.

[0056] In an embodiment of the present application, a floating wind power generation control method is provided, wherein the preprocessing of historical operation data and power generation data includes: preprocessing the historical operation data and power generation data, the preprocessing includes removing erroneous data points and duplicate data points, and filling missing data points, and standardizing the data.

[0057] Specifically, check the data and remove possible erroneous data points, such as abnormal values ​​beyond a reasonable range. At the same time, it is also necessary to identify and remove duplicate data points to avoid interference with subsequent analysis and modeling. There may be some missing data points in the historical operation data and power generation data, which may be caused by equipment failure, sensor failure or data recording errors. For missing data points, interpolation, mean filling, regression filling and other methods can be used to fill the missing values ​​to ensure the integrity and continuity of the data. Standardization can help unify data of different scales or dimensions to the same standard scale, which helps to eliminate the dimensional effects between different variables and makes the data easier to compare and analyze. The standardization methods that can be used include Z-score standardization, maximum and minimum value standardization, etc. In general, preprocessing historical operation data and power generation data is an important step in data analysis and modeling. By removing erroneous data, filling missing data and standardization, the quality and reliability of the data can be improved, providing a reliable basis for subsequent data analysis and modeling.

[0058] In an embodiment of the present application, a floating wind power generation control method is provided, wherein the pre-processed historical operating data and power generation data are analyzed to determine the operating parameters in the historical operating data that affect the power generation of the floating wind turbine, including: dividing the pre-processed historical operating data into several groups of operating parameter data groups according to parameter type, and calculating the correlation coefficient between each group of operating parameter data groups and the power generation data; screening out the operating parameter data groups whose correlation coefficients are greater than a preset value, and determining them as the operating parameters in the historical operating data that affect the power generation of the floating wind turbine.

[0059] Specifically, according to the specific operating parameter types of floating wind turbines, the pre-processed historical operating data are divided into different parameter type groups, such as wind speed, blade angle, rotor speed, generator temperature, etc. For each group of operating parameter data groups, the correlation coefficient between the parameter group and the power generation data can be calculated. The correlation coefficient can be used to measure the strength of the linear relationship between two variables. Its value range is between -1 and 1. The larger the absolute value, the stronger the correlation. According to the preset correlation coefficient threshold, the operating parameter data groups with correlation coefficients greater than the preset value are screened out. These operating parameter data groups with large correlation coefficients can be determined as the operating parameters that affect the power generation of floating wind turbines in the historical operating data, which can help understand the degree of influence of different operating parameters on power generation, and help optimize the operating strategy and improve power generation efficiency. Through the above steps, the historical operating data can be analyzed to find out the operating parameters that have a greater impact on the power generation of floating wind turbines, thereby providing guidance and basis for subsequent optimization and improvement.

[0060] In an embodiment of the present application, a floating wind power generation control method is provided, wherein a power generation prediction model is constructed based on operating parameters and a preset neural network model, and power generation prediction data of a floating wind turbine in a future period of time is predicted based on the power generation prediction model, including: constructing a data set based on the operating parameters, and inputting the data set into a preset neural network model to construct an initial power generation prediction model; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the initial power generation prediction model; training and testing the initial power generation prediction model until the initial power generation prediction model meets the preset convergence conditions to obtain the power generation prediction model; and predicting the power generation prediction data of the floating wind turbine in a future period of time based on the obtained power generation prediction model.

[0061] Specifically, according to the key operating parameters that affect the power generation screened above, a data set is constructed, which includes the operating parameters in the historical operating data and the corresponding power generation data, and the constructed data set is input into the preset neural network model; the constructed data set is divided into a training set and a test set according to a certain ratio, usually 80% of the data can be used as a training set and 20% of the data can be used as a test set, and the training set and the test set are respectively input into the initial power generation prediction model for training and testing. During the training process, the training can be carried out according to the preset convergence conditions, such as setting parameters such as training rounds and learning rates, and monitoring the performance of the model on the test set. Through multiple training and adjustment of model parameters, until the model meets the preset convergence conditions on the test set, a power generation prediction model is obtained; using the obtained power generation prediction model, the operating parameter data of the floating wind turbine in the future period is input, and the power generation can be predicted, thereby providing a reference for future power generation planning and operation. The above steps can help build and train the power generation prediction model, and use the model to predict future power generation, providing decision support for the planning and operation of wind power generation.

[0062] In an embodiment of the present application, a floating wind power generation control method is provided, wherein the real-time power generation data corresponding to the power generation prediction data in time and analyzing the difference between the power generation prediction data and the real-time power generation data include: acquiring the real-time power generation data corresponding to the power generation prediction data in time, and performing pairwise difference calculations between the power generation prediction data and the real-time power generation data according to time points to obtain power generation deviation data; plotting the power generation deviation data into a power generation deviation curve, extracting the peak value and the trough value in the power generation deviation curve, and determining the descending curve segment corresponding to each peak value to the trough value and the rising curve segment corresponding to each trough value to the peak value; respectively calculating the descending slope and the first average value of each descending curve segment and the rising slope and the second average value of the rising curve segment, and taking the descending slope and the first average value as well as the rising slope and the second average value as the difference between the power generation prediction data and the real-time power generation data.

[0063] Specifically, the real-time power generation data is obtained through the monitoring system or sensor, and the power generation forecast data and the real-time power generation data are calculated by difference according to the time point to obtain the power generation deviation data; the power generation deviation data is plotted into a power generation deviation curve to visualize and analyze the difference. By analyzing the power generation deviation curve, the peak value and the trough value can be determined. These extreme points correspond to the time points when the difference between the power generation forecast data and the real-time power generation data is large; for each descending curve segment corresponding to the peak value to the trough value and each ascending curve segment corresponding to the trough value to the peak value, the descending slope and the first average value, as well as the ascending slope and the second average value can be calculated respectively. These values ​​can help understand the difference between the power generation forecast data and the real-time power generation data; according to the calculated descending slope, ascending slope and average values, the difference between the power generation forecast data and the real-time power generation data can be analyzed and explained, so as to find out the factors affecting the accuracy of power generation forecast. Through the above steps, the difference between the power generation forecast data and the real-time power generation data can be deeply analyzed, so as to provide reference and basis for improving the power generation forecast model and optimizing power generation operation.

[0064] In an embodiment of the present application, a floating wind power generation control method is provided, wherein the power generation performance of the floating wind turbine is evaluated according to the difference to obtain a power generation performance evaluation value of the floating wind turbine, including: obtaining a descending slope and a first average value as well as an ascending slope and a second average value, and presetting a standard average value; respectively calculating the difference between the first average value and the second average value and the standard average value, and evaluating the difference to obtain an evaluation value of the corresponding curve segment; calculating the average slope value of the slopes corresponding to all curve segments, and calculating the ratio of the slope corresponding to each curve segment to the average slope value, and using the ratio as the weight corresponding to each curve segment; performing weighted addition calculation based on the evaluation value and weight corresponding to the power generation control parameter of each curve segment to obtain the power generation performance evaluation value of the floating wind turbine.

[0065] Specifically, calculate the difference between the first average value and the second average value and the standard average value: according to the standard average value you set in advance, calculate the difference between the first average value and the second average value and the standard average value respectively; according to the size and direction of the difference, divide the difference into several levels by designing the evaluation value standard, and assign corresponding evaluation scores to each level for subsequent evaluation; average the slopes corresponding to all curve segments to obtain the average slope value, compare the slope corresponding to each curve segment with the average slope value, and calculate their ratio, which can be used as a weight; according to the evaluation value and weight corresponding to the power generation control parameter of each curve segment, perform weighted addition calculation to obtain the power generation performance evaluation value of the floating wind turbine generator. According to the obtained power generation performance evaluation value, the power generation performance of the floating wind turbine generator can be evaluated, the factors affecting the power generation performance can be found, and corresponding improvement and optimization suggestions can be put forward. Through the above steps, the power generation performance of the floating wind turbine generator can be quantitatively evaluated, which can help power generation operators and engineers better understand the performance of the generator and make targeted adjustments and optimizations.

[0066] In an embodiment of the present application, a floating wind power generation control method is provided, wherein an adjustment coefficient is determined according to a power generation performance evaluation value of the floating wind turbine generator, and an operating parameter of the floating wind turbine generator is adjusted and controlled according to the adjustment coefficient, including: presetting an adjustment coefficient-power generation performance evaluation value interval correspondence relationship, wherein the adjustment coefficient-power generation performance evaluation value interval correspondence relationship is associated with a corresponding adjustment coefficient for each power generation performance evaluation value interval; obtaining the power generation performance evaluation value, and selecting an adjustment coefficient corresponding to the power generation performance evaluation value interval based on a mapping relationship between the power generation performance evaluation value interval to which the difference belongs within the adjustment coefficient-power generation performance evaluation value interval correspondence relationship; and multiplying and adjusting each parameter in the operating parameter of the floating wind turbine generator one by one according to the adjustment coefficient, thereby completing the adjustment control of the power generation control parameters of the floating wind turbine generator.

[0067] Specifically, according to the range of the power generation performance evaluation value, the corresponding adjustment coefficient is designed. The evaluation value range can be divided into several intervals, and the corresponding adjustment coefficient is preset for each interval. According to the power generation performance evaluation value obtained in the above steps, it is mapped to the preset power generation performance evaluation value interval, and the corresponding adjustment coefficient is obtained. According to the mapped power generation performance evaluation value interval, the corresponding adjustment coefficient is selected. This adjustment coefficient will be used to adjust the operating parameters of the floating wind turbine generator; the selected adjustment coefficient is applied to the operating parameters of the floating wind turbine generator, and each parameter is adjusted by multiplication one by one, so that the operating parameters of the generator can be automatically adjusted according to the real-time performance evaluation value. During the adjustment process, it is necessary to monitor the operating status and performance of the generator in real time, and make feedback adjustments according to the actual situation to ensure that the generator operates in the best state. Through the above steps, it is possible to automatically adjust the operating parameters of the floating wind turbine generator according to the real-time power generation performance evaluation value, so that it can maintain the best power generation performance under different working conditions, improve power generation efficiency and extend the life of the equipment. This automatic adjustment control can improve the stability and reliability of the system, reduce human intervention, and better adapt to different environmental and working conditions.

[0068] like Figure 2 As shown, in an embodiment of the present application, a floating wind power generation control system is provided, including: an acquisition module, used to acquire historical operation data and power generation data of a floating wind turbine generator, and pre-process the historical operation data and power generation data; a determination module, used to analyze the pre-processed historical operation data and power generation data, and determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine generator; a prediction module, used to construct a power generation prediction model based on the operation parameters and a preset neural network model, and predict the power generation prediction data of the floating wind turbine generator in a future period of time according to the power generation prediction model; an analysis module, used to acquire real-time power generation data corresponding to the power generation prediction data in time, and analyze the difference between the power generation prediction data and the real-time power generation data; an evaluation module, used to evaluate the power generation performance of the floating wind turbine generator according to the difference, and obtain the power generation performance evaluation value of the floating wind turbine generator; a control module, used to determine the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine generator, and adjust and control the operation parameters of the floating wind turbine generator according to the adjustment coefficient.

[0069] In summary, the embodiment of the present invention provides a floating wind power generation control method and system, which includes: acquiring and preprocessing the historical operation data and power generation data of the floating wind turbine generator; analyzing the preprocessed data to determine the operating parameters that affect the power generation of the generator in the historical operation data; constructing a power generation prediction model based on the operating parameters and a preset neural network model, and predicting the power generation prediction data; acquiring real-time power generation data, and analyzing the difference between the real-time power generation data and the power generation prediction data; evaluating the power generation performance of the generator according to the difference to obtain a power generation performance evaluation value; determining the adjustment coefficient according to the power generation performance evaluation value to adjust and control the operating parameters of the generator. The present invention predicts the power generation by constructing a power generation prediction model, and performs difference analysis in combination with real-time monitoring data, and adjusts the operating parameters based on the evaluation of the difference, thereby realizing intelligent optimization of the power generation control of the floating wind turbine generator.

[0070] Finally, it should be noted that: Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0071] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0072] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0073] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A floating wind power generation control method, characterized in that: include: Obtain historical operation data and power generation data of the floating wind turbine, and pre-process the historical operation data and power generation data; Analyze the pre-processed historical operation data and power generation data to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine; A power generation prediction model is constructed based on the operating parameters and the preset neural network model, and power generation prediction data of the floating wind turbine in the future is predicted according to the power generation prediction model; Acquire real-time power generation data corresponding to the power generation forecast data in time, and analyze the difference between the power generation forecast data and the real-time power generation data; The power generation performance of the floating wind turbine is evaluated according to the difference, and a power generation performance evaluation value of the floating wind turbine is obtained; The adjustment coefficient is determined according to the power generation performance evaluation value of the floating wind turbine generator, and the operating parameters of the floating wind turbine generator are adjusted and controlled according to the adjustment coefficient.

2. A floating wind power generation control method according to claim 1, characterized in that: The preprocessing of the historical operation data and the power generation data includes: The historical operation data and power generation data are preprocessed, which includes removing erroneous data points and duplicate data points, filling in missing data points, and standardizing the data.

3. A floating wind power generation control method according to claim 2, characterized in that: The pre-processed historical operation data and power generation data are analyzed to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine, including: The pre-processed historical operation data is divided into several groups of operation parameter data groups according to parameter types, and the correlation coefficient between each group of operation parameter data groups and the power generation data is calculated respectively; The operating parameter data groups with correlation coefficients greater than a preset value are screened out and determined as operating parameters in the historical operating data that affect the power generation of the floating wind turbine generator.

4. A floating wind power generation control method according to claim 3, characterized in that: The power generation prediction model is constructed based on the operating parameters and the preset neural network model, and the power generation prediction data of the floating wind turbine in the future period is predicted according to the power generation prediction model, including: Construct a data set based on the operating parameters, and input the data set into a preset neural network model to construct an initial model for power generation prediction; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial model for power generation prediction; The initial power generation prediction model is trained and tested until the initial power generation prediction model meets the preset convergence conditions, thereby obtaining the power generation prediction model; The power generation forecast data of the floating wind turbine in the future is predicted based on the obtained power generation forecast model.

5. A floating wind power generation control method according to claim 4, characterized in that: The acquiring of real-time power generation data corresponding to the power generation prediction data in time, and analyzing the difference between the power generation prediction data and the real-time power generation data, includes: Acquire real-time power generation data corresponding to the power generation forecast data in time, and perform pairwise difference calculation between the power generation forecast data and the real-time power generation data according to time points to obtain power generation deviation data; The power generation deviation data is plotted into a power generation deviation curve, and the peak value and the trough value in the power generation deviation curve are extracted, and a descending curve segment corresponding to each peak value to the trough value and an ascending curve segment corresponding to each trough value to the peak value are determined; The descending slope and the first average value of each descending curve segment and the ascending slope and the second average value of the ascending curve segment are calculated respectively, and the descending slope and the first average value and the ascending slope and the second average value are used as the difference between the power generation prediction data and the real-time power generation data.

6. A floating wind power generation control method according to claim 5, characterized in that: The method of evaluating the power generation performance of the floating wind turbine generator according to the difference to obtain the power generation performance evaluation value of the floating wind turbine generator includes: Obtaining a descending slope and a first average value as well as an ascending slope and a second average value, and presetting a standard average value; Calculate the differences between the first average value and the second average value and the standard average value respectively, and evaluate and value the differences to obtain the evaluation value of the corresponding curve segment; Calculate the average slope value of the slopes corresponding to all curve segments, and calculate the ratio of the slope corresponding to each curve segment to the average slope value, and use the ratio as the weight corresponding to each curve segment; The evaluation value and weight corresponding to the power generation control parameter of each curve segment are weighted added to obtain the power generation performance evaluation value of the floating wind turbine.

7. A floating wind power generation control method according to claim 7, characterized in that: The method of determining the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine generator and adjusting and controlling the operating parameters of the floating wind turbine generator according to the adjustment coefficient includes: A corresponding relationship between an adjustment coefficient and a power generation performance evaluation value interval is preset, and a corresponding adjustment coefficient is associated with each power generation performance evaluation value interval; Acquire the power generation performance evaluation value, and select the adjustment coefficient corresponding to the power generation performance evaluation value interval based on the mapping relationship between the power generation performance evaluation value interval to which the difference belongs and the adjustment coefficient-power generation performance evaluation value interval correspondence relationship; Each parameter in the operation parameters of the floating wind turbine generator is multiplied and adjusted one by one according to the adjustment coefficient, so as to complete the adjustment control of the power generation control parameters of the floating wind turbine generator.

8. A floating wind power generation control system, characterized in that: include: An acquisition module is used to acquire historical operation data and power generation data of the floating wind turbine and pre-process the historical operation data and power generation data; A determination module is used to analyze the pre-processed historical operation data and power generation data to determine the operation parameters in the historical operation data that affect the power generation of the floating wind turbine; A prediction module is used to construct a power generation prediction model based on operating parameters and a preset neural network model, and predict power generation prediction data of the floating wind turbine in a future period of time according to the power generation prediction model; An analysis module is used to obtain real-time power generation data corresponding to the power generation forecast data in time, and analyze the difference between the power generation forecast data and the real-time power generation data; An evaluation module is used to evaluate the power generation performance of the floating wind turbine according to the difference, and obtain an evaluation value of the power generation performance of the floating wind turbine; The control module is used to determine the adjustment coefficient according to the power generation performance evaluation value of the floating wind turbine generator, and adjust and control the operating parameters of the floating wind turbine generator according to the adjustment coefficient.

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