Continuous wind power super-capacity energy storage regulation and control prediction method and system

Abstract

The invention discloses a continuous wind power regulation and control and super-capacity energy storage prediction method. The method comprises the steps of obtaining an original wind speed signal sequence; finding out a plurality of singular points based on the original wind speed signal sequence; performing continuous processing on the plurality of singular points to form a wind speed signal substitution sequence; predicting by using the wind speed signal substitution sequence to obtain a predicted wind speed signal sequence; performing power and energy storage regulation and control on the wind turbine generator based on the predicted wind speed signal sequence; and the super-capacity energy storage system is regulated and controlled, and the error between the actual sending power and the predicted power of the wind power plant is quickly responded and compensated.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid frequency regulation, and in particular to a method and system for continuous wind power regulation and overcapacity energy storage prediction. Background Art

[0002] At present, the proportion of wind power and renewable energy generation has increased in major regional power grids in my country. Due to the randomness of wind speed changes, large-scale wind farms connected to the grid bring difficulties to grid dispatching, which in turn affects the stability of the system. In order to improve the dispatchability of wind farms, the general method uses a neural network method to predict the wind speed for the next day, calculates the power sent by the wind farm to the grid through the predicted wind speed value, and submits the predicted value to the grid dispatching agency. At the same time, an ultra-capacity energy storage system is connected at the outlet of the wind farm to quickly respond to make up for the error between the actual power emitted by the wind farm and the predicted power, thereby improving the credibility of the grid in dispatching wind farm power generation based on wind power forecasts and improving the coordinated operation capabilities between wind farms and power systems.

[0003] However, the traditional prediction method generally directly puts the original wind speed sequence into the neural network for prediction. This direct original wind speed sequence input prediction method will lead to large prediction errors. In order to solve this problem, the present invention proposes a prediction method that can handle the randomness of wind speed signals, reduce the prediction error and improve the prediction accuracy. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a prediction method that can handle the randomness of wind speed signals to improve response accuracy and frequency modulation benefits.

[0005] The purpose of the present invention is to provide a continuous wind power control and overcapacity energy storage prediction method and system. A continuous wind power control and excess energy storage prediction method, characterized by comprising the steps of: S101, obtaining an original wind speed signal sequence; S102, finding a number of singular points based on the original wind speed signal sequence; S103, performing continuous processing on a number of singular points to form a wind speed signal replacement sequence; S104, using the wind speed signal replacement sequence to perform prediction to obtain a predicted wind speed signal sequence; S105. Control the power and energy storage of the wind turbine generator system based on the predicted wind speed signal sequence.

[0006] Further, In step S101, The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N], the wind speed signal sequence is divided into two parts according to the length ratio, the front part is used as the input group sequence, and the back part is used as the test group sequence.

[0007] Further, If the length ratio is 0.9, then the integer sequence [x1,x2,x3.....,x 0.9N ] as the input group, followed by an integer sequence of 10% length [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] as the test group; construct the unknown group sequence, with [x N+1 ,x N+2 ,x N+3 ,...,x N+0.1N ] as the unknown group.

[0008] Further, The above S102, finding a number of singular points based on the original wind speed signal sequence, includes: Calculate the benchmark error w generated by the test group based on the original wind speed signal sequence; The point error group generated by the test group is calculated based on the signal changes of each point in the original wind speed signal sequence; Select the point with larger error from the point error group as the singular point.

[0009] Further, The benchmark error w generated by the test group is calculated based on the original wind speed signal sequence, including: The input group sequence [x1,x2,x3.....,x 0.9N ] Input the neural network for prediction and obtain the predicted value of the test group [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N 、 ]; based on the actual value of the experimental group [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] and the predicted value [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N 、 ]Calculate the root mean square error RMSE between the two as the benchmark error w.

[0010] Further, The point error group generated by the test group is calculated based on the signal changes of each point in the original wind speed signal sequence, including: The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N ], where there are N signal points, the input group sequence [x1,x2,x3.....,x 0.9N ]; The signals at each point in the input group sequence are changed in sequence; First, change x1 in the following way: , gelu() is the activation function, w is the reference error; replace x1 with x1 、 Instead, we get the change signal sequence [x1 、 , x2, x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w1 between the actual value and the predicted value of the test group; Restore x1, change x2, the change method is , get the change signal sequence [x1, x2 、 , x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w2 between the actual value and the predicted value of the test group; And so on, until the error w is obtained 0.9N ; All point errors form a point error group [w1,w2,w3,..., w 0.9N ].

[0011] Further, The 0.9N point errors in the point error group are respectively compared with the reference error w to obtain the error series [w1 / w, w2 / w, w3 / w, ..., w 0.9N / w], select the largest first 0.1N error points from the error series, the original wind speed signal corresponding to the first 0.1N error points needs to be processed continuously, and the corresponding original wind speed signal singular point set is [x q1 ,x q2 ,x q3 ,..,x q0.1N ].

[0012] Further, The above S103, performing continuous processing on a plurality of singular points to form a wind speed signal replacement sequence, comprises: For the original wind speed signal singular point set [x q1 ,x q2 ,xq3 ,..,x q0.1N ], inserting some random numbers between two adjacent singular points; All random numbers determined based on the singular point set of the original wind speed signal are inserted into the original wind speed signal sequence to form a wind speed signal replacement sequence.

[0013] Further, In the continuous x qi and x qi+1 Insert m random numbers, and the method to determine m is , [] indicates rounding, max is the maximum value, min is the minimum value, w qi For x qi The corresponding point error, w qi+1 For x qi+1 The corresponding point error.

[0014] Further, Suppose m random numbers are [a1,a2,a3,..,a m ]; The random number is determined by , where rand() is a random function.

[0015] A continuous wind power control and overcapacity energy storage prediction system, comprising: Signal acquisition module, obtaining original wind speed signal sequence; A search module finds several singular points based on the original wind speed signal sequence; The processing module and several singular points are processed continuously to form a wind speed signal replacement sequence; The prediction module uses the wind speed signal replacement sequence to perform prediction and obtain the predicted wind speed signal sequence; The power control module controls the power and energy storage of the wind turbine based on the predicted wind speed signal sequence.

[0016] The beneficial effects of the present invention are: The present disclosure provides a continuous wind power control and over-capacity energy storage prediction method and system, which can respond in time when the wind speed fluctuates in the wind power system, find singular points, generate alternative signal sequences, and obtain predicted signal sequences, thereby making the prediction results of the control instructions more accurate, regulating the over-capacity energy storage system, and quickly responding to make up for the error between the actual power generated by the wind farm and the predicted power. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A diagram of the method steps. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0020] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0021] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0022] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0023] The disclosed embodiment provides a continuous wind power control and overcapacity energy storage prediction method, such as Figure 1 As shown, including: S101, obtaining an original wind speed signal sequence; S102, finding a number of singular points based on the original wind speed signal sequence; S103, performing continuous processing on a number of singular points to form a wind speed signal replacement sequence; S104, using the wind speed signal replacement sequence to perform prediction to obtain a predicted wind speed signal sequence; S105. Control the power and energy storage of the wind turbine generator system based on the predicted wind speed signal sequence.

[0024] Wherein, step S101, obtaining the original wind speed signal sequence, includes: The original wind speed signal sequence is divided and constructed into signal sequences.

[0025] The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N]. x(t) is a function of time t. The wind speed signal sequence is divided into two parts according to the length ratio, the first part is used as the input group sequence, and the second part is used as the test group sequence.

[0026] If the length ratio value is 0.9, then the integer sequence [x1, x2, x3....., x 0.9N ] as the input group, followed by an integer sequence of 10% length [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] sequences as the experimental group.

[0027] At the same time, a future prediction group sequence is constructed, with [x N+1 ,x N+2 ,x N+3 ,...,x N+0.1N ] is taken as the unknown group, and the unknown group has the same sequence length as the test group.

[0028] During the prediction process, the input group sequence is used as input to predict the values ​​of the test group and the unknown group.

[0029] In another embodiment provided by the present disclosure The volatility of the original wind speed signal sequence is very obvious, so signal processing is required, first of all, to find the singular points.

[0030] The above S102, finding a number of singular points based on the original wind speed signal sequence, includes: Step 1: Calculate the reference error w generated by the test group based on the original wind speed signal sequence.

[0031] The input group sequence [x1, x2, x3....., x 0.9N ] Input the neural network for prediction and obtain the predicted value of the test group [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N 、 Based on the actual value of the experimental group [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] and the predicted value [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N、 ] to calculate the baseline error w (root mean square error RMSE) between the two.

[0032] Step 2: Calculate the point error group w generated by the test group based on the changes of each point signal in the original wind speed signal sequence i .

[0033] The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N ], where there are N signals. Input group sequence [x1,x2,x3.....,x 0.9N ], of which there are 0.9N signal points in total.

[0034] The 0.9N point signal is changed sequentially. First, change x1 in the following way: , gelu() is the activation function, and w is the baseline error in step 1. Replace x1 with x1 、 Instead, we get the change signal sequence [x1 、 , x2, x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w1 between the actual value and the predicted value of the test group; Restore x1, change x2, the change method is , get the change signal sequence [x1, x2 、 , x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w2 between the actual value and the predicted value of the test group; And so on, until the error w is obtained 0.9N All point errors form a point error group [w1,w2,w3,..., w 0.9N ].

[0035] Step 3: From the point error group w i Select the point with larger error as the singular point.

[0036] Point error group w i The error series [w1 / w,w2 / w,w3 / w,...,w 0.9N / w], select the largest first 0.1N error points from the error series, the original wind speed signal corresponding to the first 0.1N error points needs to be processed continuously, and the corresponding original wind speed signal singular point set is [x q1 ,x q2 ,x q3,..,x q0.1N ].

[0037] The above S103, performing continuous processing on a plurality of singular points to form a wind speed signal replacement sequence, comprises: Step 1: The original wind speed signal singular point set [x q1 ,x q2 ,x q3 ,..,x q0.1N ] for continuous processing, For all the singular points in the singular point set of the original wind speed signal, several random numbers are inserted between two adjacent singular points.

[0038] For example, in the continuous x qi and x qi+1 Insert m random numbers, and the method to determine m is , [] indicates rounding, max is the maximum value, min is the minimum value, w qi For x qi The corresponding point error, w qi+1 For x qi+1 The corresponding point error.

[0039] Suppose m random numbers are [a1,a2,a3,..,a m ].

[0040] The method for determining each random number is

[0041] Where rand() is a random function.

[0042] Step 2: insert all random numbers determined based on the singular point set of the original wind speed signal into the original wind speed signal sequence, thereby forming a wind speed signal replacement sequence.

[0043] When inserting, all the singular point sets are inserted into the input group sequence [x1,x2,x3.....,x 0.9N ], thereby forming a sequence of alternative input groups.

[0044] After inserting all the above random numbers into the formed replacement input group sequence, continuous processing is achieved.

[0045] The above S104, using the wind speed signal replacement sequence to perform prediction to obtain a predicted wind speed signal sequence, comprises: By inserting the replacement input group sequence after the random number is inserted into the neural network, a set of predicted wind speed signal sequences [x N+1 ,x N+2 ,x N+3,...,x N+0.1N ].

[0046] In the above S105, power control of the wind turbine is performed based on the predicted wind speed signal sequence.

[0047] Using the predicted wind speed signal sequence [x N+1 ,x N+2 ,x N+3 ,...,x N+0.1N ] to carry out power regulation and energy storage regulation of wind turbines.

[0048] Predicting wind speed signal sequences for power regulation in the field of wind power generation can improve power generation efficiency, ensure grid stability, reduce operation and maintenance costs, and improve economic benefits.

[0049] Improve power generation efficiency and optimize unit operating parameters: There is a nonlinear relationship between wind speed and the output power of wind turbines. By accurately predicting the wind speed signal sequence, the pitch angle, yaw angle and other parameters of the wind turbine can be adjusted in advance according to wind speed changes, so that the wind turbine always maintains the best operating state, captures more wind energy, and converts wind energy into electrical energy more efficiently, thereby improving power generation efficiency.

[0050] Realize early start and shutdown of the unit: Accurate wind speed prediction allows operation and maintenance personnel to know in advance whether the wind speed is about to reach the start or shutdown threshold of the wind turbine. Before the wind speed is about to reach the start wind speed, the unit startup preparation work is done in advance so that the unit can start power generation in time to avoid missing the power generation opportunity; when the wind speed is about to fall below the shutdown wind speed or extreme wind speed may occur, the unit is scheduled to be shut down in advance to protect the unit equipment, and power generation can also be resumed as soon as the wind speed rises, reducing power generation interruption time and improving overall power generation efficiency.

[0051] Ensure grid stability and reduce power fluctuations: Wind speed is random and intermittent. If effective power regulation is not performed, the output power of wind power generation will fluctuate greatly, which will have a serious impact on the stability and reliability of the grid. By predicting the wind speed signal sequence, adjusting the power output in advance and using super-capacity energy storage to work together, the power curve of wind power generation can be smoothed, power fluctuations can be reduced, and the impact on the grid can be reduced.

[0052] To achieve precise dispatch, the grid dispatching department needs to accurately grasp the output of each power generation unit in order to carry out reasonable power dispatching. Accurate wind speed prediction and corresponding power regulation can provide reliable wind power forecast information for grid dispatching, so that the dispatching department can arrange the grid operation mode in advance, reasonably allocate power resources, ensure the safe and stable operation of the grid, and improve the grid's ability to accept wind power.

[0053] Reduce operation and maintenance costs and equipment wear: When the wind speed changes suddenly or exceeds the tolerance of the equipment, the mechanical components and electrical systems of the wind turbine will be subjected to greater stress and impact. Long-term accumulation will accelerate equipment wear, increase the probability of failure and maintenance costs. By using wind speed prediction to control power, measures can be taken in advance to avoid equipment operation under harsh conditions, reduce mechanical fatigue and electrical stress of equipment, extend the service life of equipment, reduce the frequency of equipment maintenance and replacement, and thus reduce operation and maintenance costs.

[0054] Optimize the operation and maintenance plan. Based on the wind speed forecast information, the equipment maintenance plan can be arranged more reasonably. For example, equipment maintenance can be arranged during low wind speed periods to avoid unnecessary downtime maintenance during peak power generation periods and reduce power generation losses caused by maintenance. At the same time, equipment can also be inspected and reinforced in advance according to predicted extreme weather conditions to prevent equipment failures and reduce operation and maintenance costs.

[0055] Improve economic benefits and increase power generation revenue: Through accurate wind speed prediction and reasonable power regulation and energy storage regulation, the power generation efficiency and quality are improved, the wind power generation and grid-connected power are increased, thus bringing more electricity bill income to power generation companies.

[0056] Enhance market competitiveness. In an environment where competition in the power market is increasingly fierce, companies that can provide stable and reliable wind power output are more favored by power grid companies and power users. Accurate wind speed prediction and power control can help improve the market image and competitiveness of wind power companies, enabling them to gain greater advantages in the power market and benefit the investment and development of wind power projects.

[0057] With the above Figure 1 Corresponding to the method shown, the embodiment of the present invention also provides a continuous wind power control and overcapacity energy storage prediction system, including: Signal acquisition module, obtaining original wind speed signal sequence; A search module finds several singular points based on the original wind speed signal sequence; The processing module and several singular points are processed continuously to form a wind speed signal replacement sequence; The prediction module uses the wind speed signal replacement sequence to perform prediction and obtain the predicted wind speed signal sequence; The power control module controls the power and energy storage of the wind turbine based on the predicted wind speed signal sequence.

[0058] The traditional prediction method is often not accurate enough when the original wind speed signal sequence is directly put into the neural network for prediction because the original wind speed signal sequence has strong volatility and randomness. In order to solve the shortcomings of strong volatility and randomness of the original sequence, the present invention proposes a new method. First, the singularity point is found according to the characteristics of the original sequence, and then it is continuous according to the singularity point to form an alternative sequence. The alternative sequence is used to replace the original sequence for prediction to obtain the final prediction result. The method proposed by the present invention can greatly reduce the volatility and randomness of the original sequence and further improve the prediction accuracy.

[0059] In order to further verify the advantages of the present invention, the present invention uses the method of the present invention and the general prediction method to predict the wind speed, using the mean absolute percentage error (MAPE). The results are as follows

[0060] An embodiment of the present disclosure provides a computer device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps provided in any embodiment of the present disclosure are performed.

[0061] The computer device provided in the embodiment of the present application includes a processor, a memory, and a bus. The memory is used to store execution instructions, including internal memory and external memory; the internal memory here is also called internal memory, which is used to temporarily store the operation data in the processor and the data exchanged with the external memory such as the hard disk. The processor exchanges data with the external memory through the internal memory. When the electronic device is running, the processor and the memory communicate through the bus, so that the processor executes the following instructions: Obtain the original wind speed signal sequence; Find several singular points based on the original wind speed signal sequence; Several singular points are processed continuously to form a wind speed signal replacement sequence; The wind speed signal substitution sequence is used for prediction to obtain the predicted wind speed signal sequence; The power and energy storage of wind turbines are controlled based on the predicted wind speed signal sequence.

[0062] The embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps provided in any embodiment of the present disclosure are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0063] Through the description of the above implementation methods, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a read-only CD, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0064] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0065] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the present embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0066] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

[0068] Finally, it should be noted that the above description is only an explanation of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail, those skilled in the art can still modify the technical solutions described above or replace some of the technical features with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A continuous wind power control and excess energy storage prediction method, characterized in that: Includes steps: S101, obtaining an original wind speed signal sequence; S102, finding a number of singular points based on the original wind speed signal sequence; S103, performing continuous processing on a number of singular points to form a wind speed signal replacement sequence; S104, using the wind speed signal replacement sequence to perform prediction to obtain a predicted wind speed signal sequence; S105. Control the power and energy storage of the wind turbine generator system based on the predicted wind speed signal sequence.

2. The continuous wind power control and excess energy storage prediction method according to claim 1 is characterized in that: In step S101, The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N ], the wind speed signal sequence is divided into two parts according to the length ratio, the front part is used as the input group sequence, and the back part is used as the test group sequence.

3. The continuous wind power control and excess energy storage prediction method according to claim 2 is characterized in that: If the length ratio is 0.9, then the integer sequence [x1,x2,x3.....,x 0.9N ] as the input group, followed by an integer sequence of 10% length [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] as the test group; construct the unknown group sequence, with [x N+1 ,x N+2 ,x N+3 ,...,x N+0.1N ] as the unknown group.

4. The continuous wind power control and excess energy storage prediction method according to claim 3 is characterized in that: The above S102, finding a number of singular points based on the original wind speed signal sequence, includes: Calculate the benchmark error w generated by the test group based on the original wind speed signal sequence; The point error group generated by the test group is calculated based on the signal changes of each point in the original wind speed signal sequence; Select the point with larger error from the point error group as the singular point.

5. The continuous wind power control and excess energy storage prediction method according to claim 4 is characterized in that: The benchmark error w generated by the test group is calculated based on the original wind speed signal sequence, including: The input group sequence [x1,x2,x3.....,x 0.9N ] Input the neural network for prediction and obtain the predicted value of the test group [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N 、 ]; based on the actual value of the experimental group [x 0.9N+1 ,x 0.9N+2 ,x 0.9N+3 ,...,x N ] and the predicted value [x 0.9N+1 、 ,x 0.9N+2 、 ,x 0.9N+3 、 ,...,x N 、 ]Calculate the root mean square error RMSE between the two as the benchmark error w.

6. The continuous wind power control and excess energy storage prediction method according to claim 4 is characterized in that: The point error group generated by the test group is calculated based on the signal changes of each point in the original wind speed signal sequence, including: The original wind speed signal sequence is x(t)=[x1,x2,x3,.x i ..,x N ], where there are N signal points, the input group sequence [x1,x2,x3.....,x 0.9N ]; The signals at each point in the input group sequence are changed in sequence; First, change x1. The change method is: , gelu() is the activation function, w is the reference error; replace x1 with x1 、 Instead, we get the change signal sequence [x1 、 , x2, x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w1 between the actual value and the predicted value of the test group; Restore x1, change x2, the change method is , get the change signal sequence [x1, x2 、 , x3,..., x 0.9N ] Input the neural network for prediction and obtain the point error w2 between the actual value and the predicted value of the test group; And so on, until the error w is obtained 0.9N ; All point errors form a point error group <h2 style=";text-align:left;direction:ltr">[w1,w2,w3,..., w<h2 style=";text-align:left;direction:ltr"> 0.9N <h2 style=";text-align:left;direction:ltr"> ]。 7. The continuous wind power control and excess energy storage prediction method according to claim 4 is characterized in that: The 0.9N point errors in the point error group are respectively compared with the reference error w to obtain the error series [w1 / w, w2 / w, w3 / w, ..., w 0.9N / w], select the largest first 0.1N error points from the error series, the original wind speed signal corresponding to the first 0.1N error points needs to be processed continuously, and the corresponding original wind speed signal singular point set is [x q1 ,x q2 ,x q3 ,..,x q0.1N ].

8. The continuous wind power control and excess energy storage prediction method according to claim 7 is characterized in that: The above S103, performing continuous processing on a plurality of singular points to form a wind speed signal replacement sequence, comprises: For the original wind speed signal singular point set [x q1 ,x q2 ,x q3 ,..,x q0.1N ], inserting some random numbers between two adjacent singular points; All random numbers determined based on the singular point set of the original wind speed signal are inserted into the original wind speed signal sequence to form a wind speed signal replacement sequence.

9. The continuous wind power control and excess energy storage prediction method according to claim 8 is characterized in that: In the continuous x qi and x qi+1 Insert m random numbers, and the method to determine m is ， [] indicates rounding, max is the maximum value, min is the minimum value, w qi For x qi The corresponding point error, w qi+1 For x qi+1 The corresponding point error.

10. The continuous wind power control and excess energy storage prediction method according to claim 9 is characterized in that: Suppose m random numbers are [a1,a2,a3,..,a m ]; The random number is determined by , where rand() is a random function.

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