Power Prediction Method, System, Device and Medium Based on Supercapacitor Energy Storage in Wind Farm

Abstract

The present invention relates to the technical field of wind farm power prediction, and particularly relates to a power prediction method, system, device and medium based on over-capacity energy storage in a wind farm. The method includes obtaining actual output power value data at historical moments of the wind farm to form a data set; constructing a prediction model and training the prediction model based on the data in the data set; obtaining output power value data of the wind farm at the current moment, inputting the output power value data of the wind farm at the current moment into the trained prediction model, and predicting the power value at the next moment of the wind farm; wherein the prediction model is constructed by using a regularized aggregation moving algorithm. This prediction method can accurately predict the output power value at the next moment.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm power prediction, and particularly relates to a power prediction method, system, device and medium based on over-capacity energy storage in a wind farm. Background Art

[0002] Wind farms are new energy sources that are currently under development. However, due to their volatility and intermittency, they have an adverse impact on the power grid. Therefore, short-term power prediction is carried out through wind farms to improve the level of wind power consumption. Energy storage devices can suppress the power fluctuations of wind farms, help new energy power stations for consumption, peak shaving, frequency modulation and stable output, thereby reducing energy losses. Therefore, they are widely used in wind farms. Its basic principle is to store excess electric energy through energy storage devices (such as battery packs, supercapacitors or pumped storage power stations) during peak wind power generation, and release electric energy during insufficient wind power generation or peak grid demand, so as to balance power supply and demand and improve the stability and reliability of the power grid.

[0003] Currently, the power prediction method is to utilize the non-linear mapping ability and self-learning ability of neural networks (such as BP neural networks, LSTM, etc.), and establish a complex relationship between wind power and parameters such as meteorological conditions and equipment status through training models to perform wind power prediction. However, different prediction models have limitations in dealing with complex data and non-linear relationships, which may lead to inaccurate prediction results and thus affect the stable operation of wind farms. Summary of the Invention

[0004] The present invention provides a power prediction method, system, device and medium based on over-capacity energy storage in a wind farm, aiming to solve the problem of inaccurate current wind farm power prediction.

[0005] The object of the present invention is achieved by the following technical solutions:

[0006] In a first aspect, the present invention provides a power prediction method based on over-capacity energy storage in a wind farm, including:

[0007] Obtain the output power value data of the wind farm at the current moment, input the output power value data of the wind farm at the current moment into the trained prediction model, predict the power value at the next moment of the wind farm at the current moment, and obtain the predicted power value at the next moment;

[0008] Utilize the predicted power value at the next moment and the actual output power value at the historical moment, input them into the trained prediction model, and predict the output power value within a set time period;

[0009] Use the probability error compensation coefficient to correct the predicted output power value within the set time period to obtain the power prediction result.

[0010] As a further improvement of the present invention, the training method of the trained prediction model includes:

[0011] Obtain the actual output power value data at historical moments of the wind farm to form a data set;

[0012] Construct a prediction model, and train the prediction model based on the data in the data set to obtain a trained prediction model. The prediction model is constructed by using the regularized aggregation moving algorithm. As a further improvement of the present invention, the regularized aggregation moving algorithm is:

[0013]

[0014] In the formula, y m is the predicted power value at the m-th moment, y m-i is the output power value at the (m - i)-th previous moment, y m-n is the output power value at the (m - n)-th previous moment, s i is the slope coefficient corresponding to the output power value at the (m - i)-th previous moment, i = 1, 2, 3,..., n, and n is the total number of actual output power value data in the data set. The slope coefficient is:

[0015]

[0016] In the formula, Mid() is the average value of the slopes, ... respectively represent the time intervals between two output power values.

[0017] As a further improvement of the present invention, the determination method of the total number n of the actual output power value data in the data set is:

[0018] First, judge according to the output power value data at historical moments from m - 4 to m - 1. When the discrimination condition is met, the number of output power value data in the data set is 4. If not, judge by using the output power value data at historical moments from m - 2 to m - 5. If the discrimination condition is met, the number of output power value data is 5. Otherwise, continue to judge according to the discrimination condition until the discrimination condition is met. The discrimination condition is:

[0019]

[0020] In the formula, is the output power value data at the (m - n)-th previous moment in history, is the output power value data at the (m - (n - 1))-th previous moment in history, is the output power value data at the (m - (n - 2))-th previous moment in history, It is the output power value data at the m - (n - 3)th moment before history, where m is the prediction moment and n is the number of output power value data in the dataset, and n ≥ 4.

[0021] As a further improvement of the present invention, the predicted output power value in the set time period is corrected by using the probability error compensation coefficient to obtain the power prediction result. The probability error compensation coefficient is multiplied by each output power value in the predicted set time period respectively to obtain the power prediction result.

[0022] As a further improvement of the present invention, the probability error compensation coefficient is:

[0023]

[0024] In the formula, Q is the probability error compensation coefficient, a is the proportionality coefficient when the prediction error ratio is greater than 1, is the prediction error ratio corresponding to the (m - 10)th moment, is the prediction error ratio corresponding to the (m - 9)th moment, is the prediction error ratio corresponding to the (m - 1)th moment, is the prediction error ratio corresponding to the mth moment, is the predicted power value at the mth moment, is the predicted power value at the (m - 1)th moment, is the predicted power value at the (m - 10)th moment, is the actual output power value at the (m - 10)th moment, is the actual output power value at the mth moment.

[0025] As a further improvement of the present invention, after obtaining the power prediction result, it further includes controlling the capacitor energy storage system in the wind farm to charge and discharge according to the power prediction result to perform power compensation on the wind farm.

[0026] In the second aspect, the present invention also provides a power prediction system based on supercapacitor energy storage in a wind farm, including:

[0027] The first prediction module acquires the output power value data of the wind farm at the current moment, inputs the output power value data of the wind farm at the current moment into the trained prediction model, and predicts the power value at the next moment of the wind farm at the current moment to obtain the predicted power value at the next moment;

[0028] The second prediction module inputs the predicted power value at the next moment and the actual output power value at the historical moment into the trained prediction model to predict the output power value in the set time period;

[0029] The prediction correction module corrects the predicted output power value in the set time period by using the probability error compensation coefficient to obtain the power prediction result.

[0030] In a third aspect, the present invention further provides an electronic device, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the power prediction method based on over-capacity energy storage in a wind farm as described above.

[0031] In a fourth aspect, the present invention further provides a computer-readable storage medium storing at least one instruction, where when the at least one instruction is executed by a processor, the power prediction method based on over-capacity energy storage in a wind farm as described above is implemented.

[0032] The beneficial effects of the present invention are as follows: For the power prediction method based on over-capacity energy storage in a wind farm of the present invention, by inputting the output power value data of the wind farm at the current moment into the trained prediction model to predict the power value at the next moment, it can capture the dynamic changes of the wind farm power in a timely manner using real-time data. This short-term prediction based on real-time data helps to more accurately grasp the power change trend in the next extremely short period of time and reduce the prediction error caused by sudden changes. Using the probability error compensation coefficient to correct the output power value of the set time period obtained by prediction further improves the prediction accuracy.

[0033] Furthermore, the present invention can accurately predict the output power at the next moment through the regularized aggregation moving algorithm. This algorithm can make full use of the time series characteristics in historical data to capture the fluctuation rules and trends of wind power. This algorithm is suitable for processing data with time series characteristics, such as wind power data. Compared with existing prediction methods, obtaining the output power value data of the wind farm at the current moment and inputting it into the trained prediction model can achieve real-time prediction of the power value at the next moment of the wind farm. This real-time prediction ability is of great significance for the dispatching and operation of the power system. By adopting the regularized aggregation moving algorithm and an appropriate model training process, the prediction accuracy of wind power can be significantly improved. Accurate prediction results help the power system better arrange the power generation plan and power dispatching, reducing the operation cost and risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is a schematic flowchart of the power prediction method based on over-capacity energy storage in a wind farm in the embodiments of the present invention;

[0036] Figure 2It is a schematic structural diagram of a power prediction system based on ultra-capacitor energy storage in a wind farm in an embodiment of the present invention;

[0037] Figure 3 It is a schematic structural diagram of an electronic device in an embodiment of the present invention. Specific embodiments

[0038] In order to make the objectives and technical solutions of the present invention clearer and easier to understand, the following further details the present invention in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The concept of the present invention is to provide a power prediction method, system, device and medium based on ultra-capacitor energy storage in a wind farm, mainly including:

[0040] Obtain the output power value data of the wind farm at the current moment, input the output power value data of the wind farm at the current moment into the trained prediction model, predict the power value at the next moment of the wind farm at the current moment, and obtain the predicted power value at the next moment;

[0041] Use the predicted power value at the next moment and the actual output power value at the historical moment, input them into the trained prediction model, and predict the output power value for a set time period;

[0042] Use the probability error compensation coefficient to correct the predicted output power value for the set time period to obtain the power prediction result.

[0043] Through the prediction model of this method, the output power value at each moment can be accurately predicted, and then the output power value for a period of time can be predicted. Using the probability error compensation coefficient to correct the predicted output power value for the set time period further improves the prediction accuracy.

[0044] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings and specific embodiments. Among them, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0045] Embodiment 1

[0046] As Figure 1 shown, this embodiment provides a power prediction method based on ultra-capacitor energy storage in a wind farm, and the prediction method adopts the following specific implementation manners.

[0047] First, obtain the output power value data of the wind farm at the current moment, input the output power value data of the wind farm at the current moment into the trained prediction model, predict the power value at the next moment of the wind farm at the current moment, and obtain the predicted power value at the next moment.

[0048] Specifically, obtain the actual output power value data at historical moments of the wind farm to form a data set. In this embodiment, when predicting the output power value at the m-th moment, it is necessary to obtain the actual output power value data at the previous m - n moments, and then n actual output power value data form a data set.

[0049] Then, the determination method of the number n of output power value data in the data set is as follows: First, judge according to the output power value data at the historical moments of m - 4 to m - 1. When the discrimination condition is met, the number of output power value data in the data set is 4. If not, use the output power value data at the moments of m - 2 to m - 5 to judge. If the discrimination condition is met, the number of output power value data is 5. Otherwise, continue to judge according to the discrimination condition until the discrimination condition is met. The discrimination condition is:

[0050]

[0051] In the formula, is the output power value data at the previous m - n moments in history, is the output power value data at the previous m - (n - 1) moments in history, is the output power value data at the previous m - (n - 2) moments in history, is the output power value data at the previous m - (n - 3) moments in history. m is the prediction moment, n is the number of output power value data in the data set, and n ≥ 4. For example, when n is 5, the corresponding discrimination condition is:

[0052]

[0053] When this discrimination condition is met, the corresponding n is 5. When it is not met, let n = 6 for discrimination.

[0054] Construct a prediction model and train it based on the data in the data set. The prediction model in this embodiment is constructed by using the regularized aggregation moving algorithm.

[0055] Among them, the regularized aggregation moving algorithm is mainly:

[0056]

[0057] In the formula, y m is the predicted power value at the m-th moment, y m-i is the output power value at the previous m - i moment, y m-n is the output power value at the previous m - n moment, s i is the slope coefficient corresponding to the output power value at the previous m - i moment, i = 1, 2, 3,..., n, and n is the total number of actual output power value data in the data set. The slope coefficient is:

[0058]

[0059] In the formula, Mid() is the average value of the slope, ... respectively represent the time intervals between two output power values. For example, the time interval is the interval between the (m - i)-th moment and the (m - i - 1)-th moment in the past.

[0060] During the training process, the output power value y at the historical (m - i)-th moment m-i is used to predict the output power value y at the (m - (i + 1))-th moment. m-(i+1) In this way, in this embodiment, n data in the dataset are respectively trained to obtain corresponding predicted power data. At the same time, based on the predicted power data at any moment and the actual power data at that moment, the prediction error ratio is obtained, that is:

[0061]

[0062] In the formula, is the predicted power value at the m-th moment, is the actual output power value at the m-th moment.

[0063] In this embodiment, the predicted power value at the next moment and the actual output power value at the historical moment are input into the trained prediction model to predict the output power value within a set time period.

[0064] The predicted output power value within the set time period is corrected by using the probability error compensation coefficient to obtain the power prediction result. And this embodiment can correct the error of the prediction model according to the probability error compensation coefficient calculated based on multiple prediction error ratios. The main method is to predict the output power values at multiple future moments according to the prediction model, and multiply the probability error compensation coefficient by each predicted output power value to complete the final error correction. Specifically, the probability error compensation coefficient is:

[0065]

[0066] In the formula, Q is the probability error compensation coefficient, a is the proportionality coefficient when the prediction error ratio is greater than 1, is the prediction error ratio corresponding to the (m - 10)-th moment, is the prediction error ratio corresponding to the (m - 9)-th moment, is the prediction error ratio corresponding to the (m - 1)-th moment, is the prediction error ratio corresponding to the m-th moment, is the predicted power value at the m-th moment, is the predicted power value at the (m - 1)-th moment,... is the predicted power value at time m-10, is the actual output power value at time m-10,..., is the actual output power value at time m. In this embodiment, a refers to the proportion of prediction error ratios greater than 1 among 10 prediction error ratio data.

[0067] By multiplying the product of the probability error compensation coefficient and the power value obtained from the prediction model (i.e., the predicted power value at the next moment), the error of the predicted power value is eliminated.

[0068] Assume that the actual output power value of the wind power is [y1, y2, y3,.., y m (where m is greater than 300). Using the regularized aggregation moving method to calculate the actual output power value [y m-10 , y m-9 , y m-8 ,.., y m , and the obtained predicted values [y` m-10 , y` m-9 , y` m-8 ,.., y` m . The prediction error ratios (y` m-10 - y m-10 ) / y m-10 , (y` m-9 - y m-9 ) / y m-9 , (y` m-8 - y m-8 ) / y m-8 ,.., (y` m - y m ) / y m are obtained respectively. The prediction error ratio is used to compensate the error generated by the predicted value of the wind power output this time.

[0069] By analyzing the power prediction results, calculate the power value that the capacitor energy storage system in the wind farm needs to compensate, and then control the capacitor energy storage system in the wind farm to charge and discharge, so as to perform power compensation on the wind farm, and then achieve economic optimization.

[0070] Specifically, the capacity that the capacitor energy storage can compensate is limited. If it participates in compensating for the defects of wind power prediction (i.e., achieving the purpose of avoiding fines), it cannot participate in secondary frequency modulation and peak shaving and valley filling to achieve the best economic purpose. In order to achieve the best economy, assume that the power that the capacitor energy storage needs to participate in this time is P, where the power that needs to be compensated for power prediction is P1, and the power required for other design economic rewards is P2. If P≥P1+P2, then there is no need to consider the optimization scheme. When P<P1+P2, the power compensated by the capacitor energy storage system for the wind power is:

[0071]

[0072] Based on the expected output of the wind farm energy storage system, the wind farm is further controlled based on the expected output, thereby achieving economic optimization.

[0073] In order to prove the practicality of the method, simulation verification was carried out for the traditional prediction method and the prediction method provided by the present invention, and the specific verification results are shown in Table 1. The prediction value is measured by using the mean absolute error percentage (Mean Absolute Percentage Error). The results are shown in Table 1:

[0074] Table 1

[0075]

[0076] It can be seen from Table 1 that the error percentage of the prediction method of this embodiment is only 12.1, which is more accurate than other prediction methods.

[0077] The prediction method provided by the present invention is further explained below in conjunction with application examples:

[0078] For example, regarding the power prediction application of a wind farm including super-capacity energy storage, the power prediction method provided by the present invention is implemented and applied, as follows:

[0079] Data collection and preprocessing: The output power value of a wind farm within a week is selected as the research object. The wind farm power output value data [y1, y2, ..., y300] (unit: kW) is collected in real time at fixed time intervals (such as 15 minutes) through existing equipment such as power sensors and data collectors in the wind farm. The collected data is synchronously transmitted to the data processing server to form a real-time data stream. Based on the collected power output value data, a power prediction experiment is conducted at 10 moments, each moment interval is 1 hour, and the actual power value fluctuates between 0-500kW.

[0080] Verify the validity of the collected real-time data and remove outliers and missing values. If there is missing data, use interpolation methods (such as linear interpolation and polynomial interpolation) to fill it in to ensure the continuity and integrity of the data.

[0081] Traverse forward from the prediction time m=301, first check whether the power output value corresponding to n=4 meets the discrimination condition. If the calculation result is 0.15, it does not meet the discrimination condition, then set n=5 and continue to check whether it meets the discrimination condition. When n=5, the discrimination condition is met, then the data set [y296, y297, y298, y299, y300], the power value in the data set is [190, 200, 180, 190, 200]. The power output value that meets the discrimination condition is used as the data set for the training data prediction model.

[0082] Next, use the regularized aggregation movement algorithm for prediction:

[0083]

[0084] Take the time interval (unit: hour). When calculating the slope coefficient at each moment in this experiment, i = 1 corresponds to y300:

[0085] ;

[0086] Similarly, ;

[0087] ;

[0088] ; = 7.5.

[0089] Therefore, using the regularized aggregation movement algorithm, we get kW.

[0090] Calculate the prediction error ratio during the training process: [0.05, -0.03, 0.08, -0.02, 0.1, 0.15, -0.05, 0.07, 0.09, -0.01]. The proportion of the prediction error ratio greater than 1 is 0. Therefore, trigger branch. Then, the compensation coefficient is:

[0091]

[0092] Then, the corrected predicted power value is = 185.25kW.

[0093] If the total available power of the capacitor energy storage system P = 500kW; the power to be compensated for the prediction error P 1 = 300kW, the power required for economic reward P 2 = 300kW, then the compensation power is 249.52kW.

[0094] Finally, compare the prediction results of the present invention with those of the traditional prediction model and calculate the mean absolute error percentage. The results show that the error percentage of the prediction method of the present invention is only 12.1, while that of the traditional prediction method reaches 26.4. This effectively verifies the effectiveness and practicality of the present invention.

[0095] By dynamically adjusting the dataset size n and introducing a probabilistic error compensation coefficient Q, combined with the economic power distribution strategy of the energy storage system, this embodiment significantly reduces the power prediction error of the wind farm and realizes the optimal utilization of energy storage resources.

[0096] Embodiment 2

[0097] As Figure 2 shown, a power prediction system based on over-capacity energy storage in a wind farm in this embodiment includes:

[0098] The first prediction module obtains the output power value data of the wind farm at the current moment, inputs the output power value data of the wind farm at the current moment into the trained prediction model, predicts the power value of the next moment of the wind farm at the current moment, and obtains the predicted power value of the next moment;

[0099] The method for determining the number of data collected by the first prediction module in this embodiment is as follows: First, judge according to the output power value data at historical moments m-4 to m-1. When the discrimination condition is met, the number of output power value data in the dataset is 4. If not, use the output power value data at moments m-2 to m-5 for judgment. If the discrimination condition is met, the number of output power value data is 5. Otherwise, continue to judge according to the discrimination condition until the discrimination condition is met. The discrimination condition is:

[0100]

[0101] In the formula, is the output power value data at the historical moment m-n, is the output power value data at the historical moment m-(n-1), is the output power value data at the historical moment m-(n-2), is the output power value data at the historical moment m-(n-3). m is the prediction moment, n is the number of output power value data in the dataset, and n≥4.

[0102] The second prediction module uses the predicted power value of the next moment and the actual output power value of the historical moment, inputs them into the trained prediction model, and predicts the output power value of the set time period. Among them, the prediction model is constructed by using the regularized aggregation moving algorithm:

[0103] The regularized aggregation moving algorithm is:

[0104]

[0105] In the formula, y m is the predicted power value at the mth moment, y m-1 is the output power value at the current moment, y m-n is the output power value at the previous m-n moment, s iis the slope coefficient corresponding to the output power value in the previous m - i moments, where i = 1, 2, 3,..., n, and n is the total number of actual output power value data in the dataset. The slope coefficient is:

[0106]

[0107] In the formula, Mid() is the average value of the slope. ... respectively represent the time intervals between two output power values. The time interval ... is a set value, which is determined according to the time when two output power value data are obtained.

[0108] The prediction correction module uses the probability error compensation coefficient to correct the predicted output power value in the set time period to obtain the power prediction result. This module is constructed based on the probability error compensation coefficient. The error correction step is to multiply the probability error compensation coefficient by each output power value in the predicted set time period respectively. The probability error compensation coefficient in this embodiment is calculated based on multiple prediction error ratios. The probability error compensation coefficient is:

[0109]

[0110] In the formula, Q is the probability error compensation coefficient, a is the proportional coefficient when the prediction error ratio is greater than 1. is the prediction error ratio corresponding to the m - 10 moment. is the prediction error ratio corresponding to the m - 9 moment. is the prediction error ratio corresponding to the m - 1 moment. is the prediction error ratio corresponding to the m moment. is the predicted power value at the m moment. is the predicted power value at the m - 1 moment,..., is the predicted power value at the m - 10 moment. is the actual output power value at the m - 10 moment,..., is the actual output power value at the m moment.

[0111] Embodiment 3

[0112] As Figure 3The present embodiment shown provides an electronic device for implementing the power prediction method based on wind farm over-capacity energy storage in Embodiment 1 above. The electronic device 100 includes at least one processor 102, a memory 101, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the power prediction method based on wind farm over-capacity energy storage 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.

[0113] At least one processor 102 can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), 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.

[0114] The memory 101 in the electronic device 100 stores multiple instructions to implement a power prediction method based on wind farm over-capacity energy storage. The processor 102 can execute the multiple instructions to thereby implement:

[0115] Obtain the output power value data of the wind farm at the current moment, input the output power value data of the wind farm at the current moment into the trained prediction model, predict the power value at the next moment of the wind farm at the current moment, and obtain the predicted power value at the next moment;

[0116] The predicted power value at the next moment and the actual output power value at the historical moment are input into the trained prediction model, and the output power value for a set time period is predicted.

[0117] The probability error compensation coefficient and the predicted output power value for the set time period are used for correction to obtain the power prediction result.

[0118] Embodiment 4

[0119] If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-described embodiment methods 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-described 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 capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM, Read-Only Memory).

[0120] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0121] 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, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps of the functions specified in one block or a plurality of blocks.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A power prediction method based on supercapacitor energy storage in a wind farm, characterized in that, Including: Obtain the output power value data of the wind farm at the current moment, input the output power value data of the wind farm at the current moment into the trained prediction model, predict the power value of the next moment of the wind farm at the current moment, and obtain the predicted power value of the next moment; Use the predicted power value of the next moment and the actual output power value of the historical moment, input them into the trained prediction model, and predict the output power value of the set time period; Use the probability error compensation coefficient and the predicted output power value of the set time period for correction to obtain the power prediction result; The training method of the trained prediction model includes: obtaining the actual output power value data of the wind farm at historical moments to form a data set; Construct a prediction model, train the prediction model based on the data in the data set to obtain a trained prediction model, and the prediction model is constructed by using the regularized aggregation moving algorithm; The regularized aggregation moving algorithm is: where y m is the predicted power value at the m-th moment, y m-i is the output power value at the (m - i)-th previous moment, y m-n is the output power value at the (m - n)-th previous moment, s i is the slope coefficient corresponding to the output power value at the (m - i)-th previous moment, i = 1, 2, 3, ..., n, and n is the total number of actual output power value data in the dataset; The slope coefficient is: where Mid() is the average value of the slope, ... respectively represent the time intervals between two output power values; The determination method of the total number n of the actual output power value data in the data set is: First, judge according to the output power value data at historical moments m - 4 to m - 1. When the discrimination condition is met, the number of output power value data in the data set is 4. If not, use the output power value data at m - 5 to m - 2 for judgment. If the discrimination condition is met, the number of output power value data is 5. Otherwise, continue to judge according to the discrimination condition until the discrimination condition is met. The discrimination condition is: In the formula, is the output power value data at the m - n historical moments before, is the output power value data at the m - (n - 1) historical moments before, is the output power value data at the m - (n - 2) historical moments before, is the output power value data at the m - (n - 3) historical moments before, and m is the prediction moment.

2. The power prediction method based on supercapacitor energy storage in a wind farm according to claim 1, wherein Use the probability error compensation coefficient and the predicted output power value of the set time period for correction to obtain the power prediction result; The power prediction result is to multiply the probability error compensation coefficient by each output power value in the predicted set time period respectively.

3. The power prediction method based on the ultra-capacitor energy storage of a wind farm according to claim 2, wherein The probability error compensation coefficient is: Where Q is the probability error compensation coefficient, and a is the proportionality coefficient when the prediction error ratio is greater than 1. is the prediction error ratio corresponding to the moment m - 10. is the prediction error ratio corresponding to the moment m - 9. is the prediction error ratio corresponding to the moment m - 1. is the prediction error ratio corresponding to the moment m. is the predicted power value at the moment m. is the predicted power value at the moment m - 1. is the predicted power value at the moment m - 10. is the actual output power value at the moment m - 10. is the actual output power value at the moment m.

4. The power prediction method based on the supercapacitor energy storage of a wind farm according to claim 2, wherein After obtaining the power prediction result, it further includes controlling the capacitor energy storage system in the wind farm to charge and discharge according to the power prediction result to perform power compensation on the wind farm.

5. A power prediction system based on supercapacitor energy storage in a wind farm, which is used to implement the power prediction method based on supercapacitor energy storage in a wind farm according to any one of claims 1 to 4, characterized in that Including: A first prediction module, which obtains the output power value data of the wind farm at the current moment, inputs the output power value data of the wind farm at the current moment into the trained prediction model, predicts the power value of the next moment of the wind farm at the current moment, and obtains the predicted power value of the next moment; A second prediction module, which uses the predicted power value of the next moment and the actual output power value of the historical moment, inputs them into the trained prediction model, and predicts the output power value of the set time period; A prediction correction module, which uses the probability error compensation coefficient and the predicted output power value of the set time period for correction to obtain the power prediction result.

6. An electronic device, characterized in that, Including a processor and a memory, the processor is used to execute the computer program stored in the memory to implement the power prediction method based on the super-capacitor energy storage of the wind farm as described in any one of claims 1 - 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the power prediction method based on the super-capacitor energy storage of the wind farm as described in any one of claims 1 - 4.

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