Method and System for Adjusting Charging Rate of Supercapacitor Energy Storage Based on Acquisition Error Adjustment

Abstract

The present invention provides a method and system for adjusting the charging rate of a supercapacitor energy storage based on acquisition error adjustment, belonging to the technical field of wind power generation. Specifically: judging the error type of each data point in the original wind speed sequence; correcting the data points with errors in the original wind speed sequence; inputting the corrected wind speed sequence into a GRU model for prediction to obtain a wind speed prediction result; dynamically adjusting the charging rate of the wind turbine to the supercapacitor energy storage system based on the wind speed prediction result. By processing the original wind speed sequence of the wind turbine in the supercapacitor energy storage system, correcting the data points with acquisition delay and acquisition data deviation, and correcting the error points, the problems of acquisition deviation and acquisition delay existing in the acquisition data caused by the inherent imperfections of the existing wind speed acquisition equipment are effectively solved, thereby improving the accuracy of wind speed prediction based on these data, and adjusting the charging rate in real time based on the prediction result to balance the power generation and energy storage efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and particularly relates to a method and system for adjusting the charging rate of supercapacitor energy storage based on acquisition error adjustment. Background Art

[0002] Due to the randomness and unpredictability of wind speed changes, the grid connection of large-scale wind farms has brought unprecedented challenges to grid dispatching. This uncertainty not only increases the complexity of dispatching work but may also have an adverse impact on the stability of the power system. To address this problem, improving the dispatchability of wind farms has become the focus of current research. A common and effective method is to use the neural network method to predict the wind speed for the next day. This method constructs a complex neural network model and uses a large amount of historical wind speed data as training samples to learn the laws and trends of wind speed changes, thereby achieving the prediction of future wind speed.

[0003] Based on the predicted wind speed values, the power sent by the wind farm to the grid can be further calculated, and these predicted values can be submitted to the grid dispatching agency in a timely manner. In this way, the grid dispatching agency can formulate power generation plans and dispatching strategies based on these predicted data to ensure the stable operation of the power system. To further improve the credibility of grid dispatching of wind farms based on wind power prediction, a supercapacitor energy storage system also needs to be connected at the outlet of the wind farm. This system can quickly respond to the error between the actual power generated by the wind farm and the predicted power, and balance this difference through energy storage and discharge, thereby ensuring that the power provided by the wind farm to the grid is stable and reliable.

[0004] However, there are still some problems that cannot be ignored in traditional wind speed prediction methods. These methods generally predict based on historical wind speed sequences, ignoring the volatility and randomness of the wind, and it is difficult to accurately capture the changing laws of wind speed sequences. The collection of historical wind speed sequences relies on various instruments and equipment, and these instruments / equipment for collecting historical wind speed sequences themselves have some problems such as large acquisition errors and data delays. These problems will lead to inaccurate, biased or lagged historical data, which will further affect the accuracy when put into the prediction model for prediction. If the historical data received by the prediction model itself is inaccurate, then no matter how advanced and precise the model itself is, its prediction results will inevitably have large errors.

[0005] In view of the above problems, Chinese Patent Application No. CN112183814A discloses a short-term wind speed prediction method. By using the variational mode decomposition method, the fluctuating original wind speed sequence is decomposed into multiple stationary wind speed subsequences with specific characteristics. Each wind speed subsequence is predicted separately, and finally the prediction results of each wind speed subsequence are combined to obtain the predicted wind speed. However, the efficiency and accuracy of the variational mode decomposition method in this patent still need to be improved, and the accuracy and real-time performance of the prediction method for wind speed subsequences also need to be further improved.

[0006] Chinese Patent Application No. CN114897260A discloses a method for modeling a short-term wind speed prediction model based on an LSTM (Long Short-Term Memory) neural network. By preprocessing, the fluctuating characteristics with significant patterns in the original wind speed sequence data are removed, and the hyperparameters of the LSTM neural network model are optimized and selected by the Bayesian optimization algorithm to obtain a short-term wind speed prediction model. However, there is still room for improvement in the preprocessing method, the Bayesian optimization algorithm, and the selection of hyperparameters to further improve the convergence speed and prediction effect of the model.

[0007] Therefore, in order to improve the accuracy of wind speed prediction, in addition to continuously improving the prediction model and method, it is also necessary to strengthen the research and improvement of the instruments / devices for collecting historical wind speed sequences, reduce their collection errors and delay problems, so as to ensure that the collected historical data is accurate and reliable. Only in this way can a more accurate data basis be provided for wind speed prediction, and further improve the dispatchability of wind farms and the stability of power systems. Summary of the Invention

[0008] The object of the present invention is to overcome the problem that the existing wind speed collection equipment has inherent imperfections, resulting in collection deviations and delays in the collected wind speed data, which affects the accuracy of short-term wind speed prediction based on these data, and provides a method and system for adjusting the charging rate of supercapacitor energy storage based on collection error adjustment.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] In the first aspect, the present invention provides a method for adjusting the charging rate of supercapacitor energy storage based on collection error adjustment, including the following steps:

[0011] Obtain the original wind speed sequence of the wind turbine in the supercapacitor energy storage system;

[0012] Judge whether each data point in the original wind speed sequence belongs to the error type of collection data deviation, collection delay or both, and obtain a judgment result;

[0013] Based on the judgment result, correct the corresponding data points in the original wind speed sequence;

[0014] Input the corrected wind speed sequence into the GRU model for prediction to obtain the wind speed prediction result;

[0015] Dynamically adjust the charging rate of the wind turbine to the supercapacitor energy storage system based on the wind speed prediction result.

[0016] In the step of obtaining the original wind speed sequence of the wind turbine in the supercapacitor energy storage system, the obtained original wind speed sequence is: ;

[0017] Among them, represents the th data point, , represents the number of data points in the original wind speed sequence;

[0018] In the step of judging whether each data point in the original wind speed sequence belongs to the error types of acquisition data deviation, acquisition delay, or both to obtain the judgment result, the method of judging whether each data point in the original wind speed sequence belongs to the acquisition data deviation is as follows:

[0019] a. Divide the data points in the original wind speed sequence, take the first 0.9N data points as training data, and the last 0.1N data points as test data;

[0020] b. Train the GRU model based on the training data, predict the test data based on the trained GRU model to obtain the predicted value, and calculate the root mean square error between the predicted value and the actual value, denoted as ;

[0021] c. Perform random scaling transformation on each data point in the first 0.9N - 1 data points , and the expression of the random scaling transformation is: =rand(0.9,1.1)· , add the transformed data point to the training data, and take the training data added with as the new training data; train the GRU model based on the new training data to obtain a new GRU model; predict the test data based on the new GRU model and calculate the new root mean square error, denoted as ;

[0022] d. Calculate the ratio between and , find the data point corresponding to when i is satisfied , Data points with data acquisition deviation.

[0023] In the step of determining whether each data point in the original wind speed sequence belongs to the error types of data acquisition deviation, acquisition delay, or both, and obtaining the determination result, the method for determining whether each data point in the original wind speed sequence belongs to the acquisition delay is as follows:

[0024] I. Construct an approximation function ;

[0025]

[0026] where is the summation subscript, representing different terms; represents the first fitting coefficient, represents the second fitting coefficient, represents the time parameter, represents the base of the natural logarithm;

[0027] Continuously adjust , and the plus or minus signs between them, so that when t = 1, ; when t = 2, ; when t = 3, ;..., when t = N, ;

[0028] II. The acquisition delay data points in the original wind speed sequence satisfy the following conditions:

[0029] Condition 1: and have opposite signs;

[0030] Condition 2: and ;

[0031] where and represent consecutive time points, and t + 0.5 represents and 's midpoint; represents the second-order derivative of , represents the second-order derivative of , represents the first-order derivative of , represents the first-order derivative of , represents the first-order derivative of ;

[0032] Mark the data points that meet the above two conditions as , then For collecting data points with delay.

[0033] In the step of determining whether each data point in the original wind speed sequence belongs to the error types of acquisition data deviation, acquisition delay, or both, and obtaining the determination result, the method for determining that a data point belongs to the error type of both is as follows:

[0034] When there is an intersection between the data points with acquisition data deviation and the data points with acquisition delay, the intersection is the error type with both acquisition data deviation and acquisition delay.

[0035] In the step of correcting the corresponding data points in the original wind speed sequence based on the determination result, the correction method for the data points with acquisition data deviation is as follows:

[0036]

[0037] Among them, sigmoid is the first activation function, tanh is the second activation function.

[0038] In the step of correcting the corresponding data points in the original wind speed sequence based on the determination result, the correction method for the data points with acquisition delay is as follows:

[0039] ;

[0040] Among them, represents the -th iteration of the function ; ∈{+1, -1}, indicating an equal-probability random selection of positive and negative signs.

[0041] In the step of correcting the corresponding data points in the original wind speed sequence based on the determination result, the correction method for the intersection of the data points with poor acquisition accuracy and the data points with acquisition delay is as follows:

[0042] Let be the intersection of the data points with acquisition data deviation and the data points with acquisition delay, then the following method is used to correct :

[0043]

[0044] Among them, , representing the first weight coefficient; , representing the second weight coefficient.

[0045] In a second aspect, the present invention provides a system for adjusting the charging rate of an ultra-capacity energy storage based on acquisition error adjustment, including:

[0046] An acquisition module for acquiring the original wind speed sequence of a wind turbine in an ultra-capacitor energy storage system;

[0047] A judgment module for judging whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, to obtain a judgment result;

[0048] A correction module for correcting the corresponding data points in the original wind speed sequence based on the judgment result;

[0049] A prediction module for inputting the corrected wind speed sequence into a GRU model for prediction to obtain a wind speed prediction result;

[0050] An adjustment module for dynamically adjusting the charging rate of the wind turbine to the ultra-capacitor energy storage system based on the wind speed prediction result.

[0051] In a third aspect, the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the ultra-capacitor energy storage charging rate adjustment method based on acquisition error adjustment are implemented.

[0052] In a fourth aspect, the present invention provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the ultra-capacitor energy storage charging rate adjustment method based on acquisition error adjustment are implemented.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention provides a method for adjusting the charging rate of a supercapacitor energy storage based on acquisition error adjustment, which includes the following steps: obtaining the original wind speed sequence of a wind turbine in a supercapacitor energy storage system; determining whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, to obtain a determination result; based on the determination result, correcting the corresponding data points in the original wind speed sequence; inputting the corrected wind speed sequence into a gated recurrent unit (GRU) model for prediction to obtain a wind speed prediction result; and dynamically adjusting the charging rate of the wind turbine to the supercapacitor energy storage system based on the wind speed prediction result. By processing the original wind speed sequence of the wind turbine in the supercapacitor energy storage system, determining and correcting the data points with acquisition delay and acquisition data deviation, and correcting the error points, the problem of acquisition deviation and acquisition delay in the acquisition data caused by the inherent imperfection of the existing wind speed acquisition equipment is effectively solved, thereby improving the accuracy of wind speed prediction based on these data. The charging rate is adjusted in real time based on the prediction result to balance the power generation and energy storage efficiency; and the correction method is simple and efficient, without the need for complex optimization algorithms, avoiding the defects of the existing methods in preprocessing and optimization algorithms, and improving the convergence speed of the prediction model.

[0055] Furthermore, using the GRU model for wind speed prediction fully considers the volatility and randomness of the wind speed sequence, can accurately capture the change law of the wind speed sequence, and has a better prediction effect than traditional physical models, statistical models and other methods.

[0056] Furthermore, the GRU model has a relatively simple structure and relatively easy selection of hyperparameters, avoiding the disadvantages of complex model structure and difficult selection of hyperparameters in the existing wind speed prediction methods based on deep learning models, and improving the real-time performance of prediction.

[0057] Furthermore, there is no need to perform complex decomposition on the wind speed sequence, avoiding the deficiencies of the existing variational mode decomposition and other methods in terms of decomposition efficiency and accuracy. Description of the Drawings

[0058] Figure 1 It is a flowchart of the method of the present invention;

[0059] Figure 2 It is a system structure diagram of the present invention;

[0060] Figure 3 It is a system diagram of Embodiment 7 in the present invention. Detailed Embodiments

[0061] To further understand the content of the present invention, the following describes the present invention in detail with reference to the drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0062] Example 1

[0063] As Figure 1 shown, a method for adjusting the charging rate of a supercapacitor energy storage based on acquisition error adjustment includes the following steps:

[0064] S1: Obtain the original wind speed sequence of the wind turbine in the supercapacitor energy storage system;

[0065] S2: Determine whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, to obtain a judgment result;

[0066] S3: Based on the judgment result, correct the corresponding data points in the original wind speed sequence;

[0067] S4: Input the corrected wind speed sequence into the GRU model for prediction to obtain a wind speed prediction result;

[0068] S5: Dynamically adjust the charging rate of the wind turbine to the supercapacitor energy storage system based on the wind speed prediction result.

[0069] Example 2

[0070] Let the original wind speed sequence (wind speed data collected by the device / instrument), , where represents the th data point, , represents the number of data points in the original wind speed sequence;

[0071] Use these N data to predict the future 0.1N wind speed sequences, with a sampling interval of 15 minutes, that is, the time between the i-th point and the i + 1-th point is 15 minutes.

[0072] Judge whether there is a problem of acquisition data deviation in the wind speed sequence collected by the device / instrument:

[0073] Acquisition data deviation: Assume the actual wind speed is 5 m / s. Due to the inherent imperfections of the wind speed acquisition device, the collected data may be higher or lower, for example, the collected data is 4.9 m / s or 5.1 m / s.

[0074] The specific judgment method is as follows:

[0075] a. Data preparation: Divide the data points in the original wind speed sequence, use the first 0.9N data points as the training set to train the GRU model; use the last 0.1N data points as the test set to evaluate the prediction performance;

[0076] b. Initial prediction and error calculation:

[0077] Train a GRU model based on training data;

[0078] Predict the prediction data based on the trained GRU model to obtain a predicted value;

[0079] Calculate the root mean square error between the predicted value and the actual value, denoted as ;

[0080] c. Data perturbation and error analysis:

[0081] For each data point in the first 0.9N - 1 data Perform a random scaling change, with the range being 0.9 times to 1.1 times the original value. The expression for the random scaling change is: = rand(0.9, 1.1)· , represents the data point after the change;

[0082] After scaling each data point once, add the data point after the change to the training data, and use the training data after adding as the new training data; retrain the GRU model based on the new training set to obtain a new GRU model, and predict the test data based on the new GRU model;

[0083] For the predicted value after each scaling change, calculate the new root mean square error, denoted as ;

[0084] d. Identify points with unqualified recognition accuracy

[0085] For the new root mean square error , calculate its ratio to the initial error , that is ;

[0086] Find the data point when it satisfies i corresponding to , is the data point with a deviation in the collected data.

[0087] The correction method for the data point with a deviation in the collected data is as follows:

[0088]

[0089] Among them, sigmoid is the first activation function, tanh is the second activation function.

[0090] Example 3

[0091] Based on Example 2, determine whether there is a problem of data acquisition delay in the wind speed sequence collected by the device / instrument:

[0092] There is a data acquisition delay. The delay problem of the acquired data will cause the wind speed data that should have been acquired at a certain moment to become the wind speed value at (the next time step, representing the time step size, representing the random variation). For example, the wind speed data at 30 min is 6 m / s, and the wind speed data at 30.5 min is 7 m / s. Then the wind speed sequence that should have acquired the data of 6 m / s at 30 min becomes 7 m / s.

[0093] The method for determining whether each data point in the original wind speed sequence belongs to the acquired data delay is as follows:

[0094] I. Construct an approximate function ;

[0095]

[0096] Among them, is the summation subscript, representing different terms; represents the first fitting coefficient, represents the second fitting coefficient, represents time, represents the base of the natural logarithm;

[0097] Continuously adjust , and the plus and minus signs between them, so that when t = 1, ; when t = 2, ; when t = 3, ;..., when t = N, . This effect is the ideal effect. When 95% of the cases are satisfied, it can be considered that this is successfully constructed.

[0098] II. The acquired delay data points in the original wind speed sequence satisfy the following conditions:

[0099] Condition 1: and have opposite signs;

[0100] Condition 2: and ;

[0101] Among them, and represent consecutive time points, and t + 0.5 represents and the midpoint; denotes the second-order derivative with respect to denotes the second-order derivative with respect to denotes the first-order derivative with respect to denotes the first-order derivative with respect to denotes the first-order derivative;

[0102] Data points that meet the above two conditions are denoted as , then are data points with acquisition delay.

[0103] The correction method for data points with acquisition delay is as follows:

[0104]

[0105] where denotes the -th iteration of the function ; ∈{+1, -1}, indicating an equal-probability random selection of positive or negative signs.

[0106] Example 4

[0107] Based on Example 3, the method for determining that a data point belongs to the error type with both characteristics is: the intersection of data points with acquisition data deviation and data points with acquisition delay.

[0108] The correction method for the intersection of data points with acquisition data deviation and data points with acquisition delay is as follows:

[0109] Let be the intersection of data points with acquisition data deviation and data points with acquisition delay, then for the following method is used for correction:

[0110]

[0111] where , denotes the first weight coefficient; , denotes the second weight coefficient.

[0112] Example 5

[0113] Based on the above examples, the corrected wind speed sequence is obtained. The corrected wind speed sequence is input into the GRU model for prediction to obtain the wind speed prediction result. Based on the wind speed prediction result, the charging rate of the wind turbine to the super-capacity energy storage system is dynamically adjusted. The specific method is as follows:

[0114] Standardize the corrected wind speed sequence to eliminate the dimension difference;

[0115] Divide the corrected wind speed sequence into a training set, a validation set, and a test set. The training set is used to train the GRU model, the validation set is used to adjust the network parameters, and the test set is used to evaluate the network performance;

[0116] Design the architecture of the GRU model, including an input layer, a GRU layer, and a fully connected layer; select appropriate network parameters such as the number of network layers, the number of GRU units, and the activation function according to the task requirements and data characteristics; preferably in this embodiment, design a network with two GRU layers, and each layer contains 256 GRU units; the activation function is selected sigmoid and tanh , the learning rate is set to 0.001, the batch size is 64, the regularization method selects L2 regularization, the weight decay coefficient is 0.0001, and the Dropout probability is set to 0.5.

[0117] Use the training set data to train the designed GRU model, update the GRU model parameters through the backpropagation algorithm to minimize the prediction error; use the validation set data to monitor the performance of the model to avoid overfitting and obtain the trained GRU model;

[0118] Input the test set data into the trained GRU model to obtain the wind speed prediction result;

[0119] According to the wind speed prediction result, calculate the power output corresponding to the predicted wind speed;

[0120] According to the predicted power output and the current state of the super-capacitor energy storage system, dynamically adjust the charging rate of the wind turbine to the super-capacitor energy storage system.

[0121] Embodiment 6

[0122] A super-capacitor energy storage charging rate adjustment system based on acquisition error adjustment, comprising:

[0123] An acquisition module for acquiring the original wind speed sequence of the wind turbine in the super-capacitor energy storage system;

[0124] A judgment module for judging whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, and obtaining a judgment result;

[0125] A correction module: for correcting the corresponding data points in the original wind speed sequence based on the judgment result;

[0126] A prediction module for inputting the corrected wind speed sequence into the GRU model for prediction to obtain a wind speed prediction result;

[0127] An adjustment module for dynamically adjusting the charging rate of a wind turbine to a supercapacitor energy storage system based on the wind speed prediction result.

[0128] Embodiment 7

[0129] As Figure 3 shown, the present invention also provides an electronic device 100 for a supercapacitor energy storage charging rate adjustment method based on acquisition error adjustment; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0130] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the supercapacitor energy storage charging rate adjustment method based on acquisition error adjustment described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0131] The at least one processor 102 can be a Central Processing Unit (CPU), or can also be other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0132] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for adjusting the charging rate of an over-capacity energy storage based on acquisition errors. The processor 102 can execute the plurality of instructions to implement:

[0133] Obtain the original wind speed sequence of the wind turbine in the over-capacity energy storage system;

[0134] Determine whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, and obtain a judgment result;

[0135] Based on the judgment result, correct the corresponding data points in the original wind speed sequence;

[0136] Input the corrected wind speed sequence into the GRU model for prediction to obtain a wind speed prediction result;

[0137] Dynamically adjust the charging rate of the wind turbine to the over-capacity energy storage system based on the wind speed prediction result.

[0138] Embodiment 8

[0139] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, and a read-only memory (ROM, Read-Only Memory).

[0140] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0141] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one or more flows Figure 1 or more flows and / or blocks Figure 1 or more blocks.

[0142] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more flows Figure 1 or more flows and / or blocks Figure 1 or more blocks.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows Figure 1 or more flows and / or blocks Figure 1 or more blocks.

[0144] It should be noted that although the present invention has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed present invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the term "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0145] Although the present invention has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the invention. Accordingly, this specification and the drawings are merely exemplary illustrations of the invention defined by the appended claims and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment, characterized in that: The steps include: Obtain the original wind speed sequence of the wind turbine generator in the super-capacity energy storage system; Determine whether each data point in the original wind speed sequence belongs to the error type of acquisition data deviation, acquisition delay or both, and obtain a judgment result; The method for determining whether each data point in the original wind speed sequence belongs to the collection data deviation is as follows: a. Divide the data points in the original wind speed sequence, taking the first 0.9N data points as training data and the last 0.1N data points as test data; b. Train the GRU model based on the training data, predict the test data based on the trained GRU model, get the predicted value, and calculate the root mean square error between the predicted value and the actual value, denoted as ; c. For each data point in the first 0.9N-1 data points Perform random scaling changes. The expression for random scaling changes is: =rand(0.9,1.1) , the changed data points Add training data, add The training data is used as the new training data; the GRU model is trained based on the new training data to obtain a new GRU model; the test data is predicted based on the new GRU model, and the new root mean square error is calculated, which is recorded as ; d. Calculation and The ratio between them is found to satisfy hour i The corresponding data points , To collect data points for data deviation; The method for determining whether each data point in the original wind speed sequence belongs to the collection delay is as follows: I. Constructing approximate functions ; in, To sum the subscripts, indicate different terms; represents the first fitting coefficient, represents the second fitting coefficient, Represents the time parameter, represents the base of natural logarithms; Keep adjusting , And the positive and negative signs between them make t=1, ; When t=2, ; When t=3, ; ..., when t=N, ; II. Original wind speed series The collection delay data points in meet the following conditions: Condition 1: and The signs are opposite; Condition 2: And ; in, and Represents continuous time points, t+0.5 represents and The midpoint of Express The second-order derivative, Express The second-order derivative, Express First-order derivative, Express First-order derivative, Express First-order derivative; The data points that meet the above two conditions are recorded as ,but To collect delayed data points; Based on the judgment result, correcting the corresponding data points in the original wind speed sequence; The corrected wind speed sequence is input into the GRU model for prediction to obtain the wind speed prediction result; The charging rate of wind turbines to the super-capacity energy storage system is dynamically adjusted based on wind speed prediction results.

2. The method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment according to claim 1, characterized in that: In the step of obtaining the original wind speed sequence of the wind turbine generator in the ultra-capacity energy storage system, the original wind speed sequence obtained is: ; in, Indicates data points, , Represents the number of data points in the original wind speed series.

3. The method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment according to claim 2 is characterized in that: In the step of determining whether each data point in the original wind speed sequence belongs to an error type of acquisition data deviation, acquisition delay, or both, and obtaining a determination result, a method for determining whether a data point belongs to an error type of both is as follows: When there is an intersection between the data points of the acquisition data deviation and the data points of the acquisition delay, the intersection is an error type in which both the acquisition data deviation and the acquisition delay exist.

4. The method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment according to claim 3 is characterized in that: In the step of correcting the corresponding data points in the original wind speed sequence based on the judgment result, the correction method for the data points of the collected data deviation is as follows: in, sigmoid is the first activation function, tanh is the second activation function.

5. The method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment according to claim 4 is characterized in that: In the step of correcting the corresponding data points in the original wind speed sequence based on the judgment result, the correction method for the delayed data points is as follows: ； in, Representation function of Iterations; ∈{+1,-1}, indicating that the positive and negative signs are randomly selected with equal probability.

6. The method for adjusting the charging rate of supercapacity energy storage based on acquisition error adjustment according to claim 5 is characterized in that: In the step of correcting the corresponding data points in the original wind speed sequence based on the judgment result, the correction method for the intersection of the data points with poor acquisition accuracy and the data points with acquisition delay is as follows: set up is the intersection of the data point of the data deviation and the data point of the data delay, then Use the following method to make corrections: in, , represents the first weight coefficient; , represents the second weight coefficient.

7. A supercapacity energy storage charging rate adjustment system based on acquisition error adjustment, based on the supercapacity energy storage charging rate adjustment method based on acquisition error adjustment according to claim 1, characterized in that: include: An acquisition module, used to acquire the original wind speed sequence of the wind turbine generator in the ultra-capacity energy storage system; A judgment module is used to judge whether each data point in the original wind speed sequence belongs to the error type of acquisition data deviation, acquisition delay or both, and obtain a judgment result; Correction module: used for correcting corresponding data points in the original wind speed sequence based on the judgment result; The prediction module is used to input the corrected wind speed sequence into the GRU model for prediction to obtain the wind speed prediction result; The adjustment module is used to dynamically adjust the charging rate of the wind turbine to the super-capacity energy storage system based on the wind speed prediction result.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for adjusting the charging rate of super-capacity energy storage based on acquisition error adjustment described in any one of claims 1 to 6 are implemented.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting the charging rate of super-capacity energy storage based on acquisition error adjustment according to any one of claims 1 to 6 are implemented.

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