Self-adaptive sliding amplitude limiting collaborative optimization wind power fluctuation stabilizing method and device

Abstract

The invention discloses a wind power fluctuation stabilizing method and device based on adaptive sliding amplitude limiting collaborative optimization, and relates to the technical field of wind storage fusion access to a power grid. The method comprises: acquiring initial power of a wind turbine generator; according to the initial power of the wind turbine generator, updating a moving average filtering sliding window array, and obtaining a result based on adaptive moving average filtering by using the wind power average value of the current sampling point and the left adjacent interval thereof; updating an amplitude limiting parameter according to the initial power of the wind turbine generator, and limiting a wind power difference value of adjacent sampling periods by using an amplitude limiting filtering method to obtain a result based on amplitude limiting filtering; calculating a weight coefficient according to the results of the adaptive moving average filtering and the amplitude limiting filtering in combination with the wind power 10 min volatility; and finally, according to the weight coefficient, compensating the power of the prediction error and the power of wind power fluctuation stabilization by using the hybrid energy storage system, and realizing wind power fluctuation stabilization based on adaptive sliding amplitude limiting collaborative optimization. The safe and stable operation of the power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation and storage integration into a power grid, and in particular to a method and device for smoothing wind power fluctuations with adaptive sliding amplitude limiting and collaborative optimization. Background Art

[0002] Renewable energy generation can provide clean, abundant and flexible electric energy resources. However, with the gradual increase in wind power penetration, the dynamic behavior of the power system has changed, and the large volatility and uncertainty of wind power itself have brought new challenges to the safe and stable operation of the power grid. In particular, power fluctuations will cause changes in the power grid frequency. Therefore, considering how to reasonably and effectively stabilize wind power output and reduce the impact of its fluctuations on the power system has important theoretical value and practical significance.

[0003] As one of the new driving forces for the development of new quality productivity, energy storage has the characteristics of energy time shifting, flexible layout and fast response speed. It is an important technical means to promote the consumption of new energy, enhance the friendly grid connection of new energy, and improve the active support capacity of new energy. Energy storage can not only release electricity appropriately during peak load periods, but also store part of the electricity during low load periods to achieve the effect of peak shaving and valley filling. By configuring the energy storage system, it is possible to effectively compensate for power prediction errors, smooth wind power fluctuations, and improve the safety and stability of the power system.

[0004] At present, in order to solve the problem of compensating power prediction errors of energy storage systems, low-pass filtering, wavelet decomposition, empirical mode decomposition and discrete Fourier transform are mainly used to decompose wind power, and then allocate the reference power of hybrid energy storage system (HESS). However, the sliding window width of the sliding average filter algorithm directly affects its fluctuation smoothing effect. If the sliding window width is set too large, it will often cause excessive smoothing and increase the energy storage burden; the limiting filter algorithm cannot effectively smooth the power fluctuation of wind power on a time scale of 10 minutes. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and device for smoothing wind power fluctuations through adaptive sliding limiter collaborative optimization, which obtains direct grid-connected power through adaptive sliding limiter collaborative optimization, compensates for prediction errors based on HESS, reduces grid power fluctuations, enhances the active support capability of new energy, and improves the safety and stability of power system operation.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] The present invention provides a method for smoothing wind power fluctuations by adaptive sliding limit collaborative optimization, comprising the following steps:

[0008] Step 101, obtaining the initial power data of the wind turbine generator set at time t;

[0009] Step 102: updating the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtaining the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval;

[0010] Step 103: update the limit parameters according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering;

[0011] Step 104: Calculate a weight coefficient based on the results of the adaptive sliding average filtering and the limiting filtering and in combination with the 10-minute fluctuation rate of wind power;

[0012] Step 105: According to the weight coefficient, the power of the prediction error compensated by the HESS and the power of the wind power fluctuation smoothing are used to realize the wind power fluctuation smoothing based on the adaptive sliding limit collaborative optimization.

[0013] Furthermore, step 101 includes:

[0014] According to the power relationship (1) of the wind-storage fusion power generation system, the grid-connected power of the wind farm at time t is:

[0015]

[0016] Where P t grid P represents the wind-storage integrated grid-connected power at time t; t wind represents the initial wind power at time t; P t hess P represents the HESS active power reference at time t; t scb Represents the active power reference of the supercapacitor bank at time t; P t lfp Indicates the active power reference of the lithium iron phosphate battery pack at time t.

[0017] Further, step 102 includes:

[0018] Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filtering algorithm is:

[0019]

[0020] Where P twind represents the wind power at time t; B represents the sliding window width.

[0021] Further, step 103 includes:

[0022] According to each new wind power sampled, compared with the previous moment, if the change exceeds the limit, the change is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter algorithm is

[0023]

[0024] In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

[0025] Further, step 104 includes:

[0026] α represents the weight coefficient of the sliding average filter algorithm, and its expression is

[0027]

[0028] In the formula, Indicates wind power t-10 time The average power at time t; P represents the average power of wind power from t-20 to t-10; N Indicates the rated power of the wind farm.

[0029] Further, step 105 includes:

[0030] According to the 10-minute power fluctuation rate of wind power, the weight coefficient is set, and the power expression based on adaptive sliding limit collaborative optimization filtering is

[0031] P t grid =αP t sli +(1-α)P t lim (5)

[0032] Furthermore, a weighted algorithm is designed based on the cumulative discharge of HESS, and the limiting parameters are adaptively adjusted to control the cumulative discharge of HESS to recover to the initial value after compensating for wind power fluctuations, thereby reducing the charging and discharging depth of HESS; the positive and negative limiting parameters at time t are

[0033]

[0034] In the formula, ΔP t pos Indicates the positive amplitude limit of wind power fluctuation at time t, ΔP tneg Indicates the negative limit of wind power fluctuation at time t, represents the cumulative discharge of HESS at time t-1, that is,

[0035]

[0036] The adaptive sliding limiting collaborative optimization filtering algorithm can increase the sliding average filter weight and smooth the long-term fluctuation of wind power when the 10-minute scale power fluctuation rate of wind power is greater than the first threshold; when the 10-minute scale power fluctuation rate of wind power is less than the second threshold, the limiting filter weight is increased to ensure that the 1-minute scale power fluctuation rate meets the national standard requirements and reduce the grid-connected power lag time.

[0037] The present invention also provides a wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization, comprising:

[0038] The first acquisition unit is used to acquire the initial power data of the wind turbine generator set at time t;

[0039] A second acquisition unit is used to update the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtain the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval;

[0040] A third acquisition unit is used to update the limit parameter according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering;

[0041] A calculation unit, used to calculate a weight coefficient according to the results of the adaptive sliding average filtering and the limiting filtering, combined with the 10-minute fluctuation rate of wind power;

[0042] The processing unit is used to realize wind power fluctuation smoothing based on adaptive sliding limit collaborative optimization according to the weight coefficient, using the power of the HESS to compensate for the prediction error and the power of the wind power fluctuation smoothing.

[0043] Furthermore, the first acquiring unit is further used for:

[0044] According to the power relationship (1) of the wind-storage fusion power generation system, the grid-connected power of the wind farm at time t is:

[0045]

[0046] Where P t grid P represents the wind-storage integrated grid-connected power at time t; t wind represents the initial wind power at time t; P t hessP represents the HESS active power reference at time t; t scb Represents the active power reference of the supercapacitor bank at time t; P t lfp Indicates the active power reference of the lithium iron phosphate battery pack at time t.

[0047] Furthermore, the second acquiring unit is further used for:

[0048] Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filter is:

[0049]

[0050] Where P t wind represents the wind power at time t; B represents the sliding window width.

[0051] Furthermore, the third acquisition unit is also used for:

[0052] According to each new wind power obtained by sampling, it is compared with the previous moment. If the change exceeds the limit, the change amount is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter is

[0053]

[0054] In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

[0055] Compared with the prior art, the advantages of the present invention are: it can effectively reduce the lag time of the wind speed suppression filtering algorithm result, improve the accuracy of the HESS active power reference, reduce the upper and lower limits of the HESS cumulative discharge, reduce the energy storage configuration capacity, and avoid excessive suppression and increase of unnecessary energy storage capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1A A flow chart of a method for smoothing wind power fluctuations by adaptive sliding limiter collaborative optimization provided by an embodiment of the present invention;

[0058] Figure 1B A flow chart of a method for smoothing wind power fluctuations by adaptive sliding limiter collaborative optimization provided by another embodiment of the present invention;

[0059] Figure 2 A structural diagram of a wind-storage fusion power generation system provided in an embodiment of the present invention;

[0060] Figure 3 A structural diagram of a wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0062] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0063] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0064] See also Figure 1A This embodiment provides a method for smoothing wind power fluctuations by adaptive sliding limit collaborative optimization, including the following steps 101 to 105:

[0065] Step 101: Obtain initial power data of the wind turbine generator set at time t.

[0066] In this embodiment, the wind storage fusion system structure used is as shown in the attached Figure 2 As shown, based on the advantages of power-type energy storage and energy-type energy storage, a HESS combining supercapacitors and lithium iron phosphate batteries is selected to smooth wind power fluctuations.

[0067] exist Figure 2 In, P grid represents the wind-storage integrated grid-connected power, P wind represents the initial wind power, P hess Indicates the HESS active power reference, P scb Represents the active power reference of the supercapacitor bank, P 1fp Indicates the active power reference of lithium iron phosphate battery pack.

[0068] According to the attached Figure 2 The power flow direction shown in the figure can be used to obtain the grid-connected power model:

[0069]

[0070] Where P t grid P represents the wind-storage integrated grid-connected power at time t; t wind represents the initial wind power at time t; P t hess P represents the HESS active power reference at time t; t scb Represents the active power reference of the supercapacitor bank at time t; P t lfp Indicates the active power reference of the lithium iron phosphate battery pack at time t.

[0071] Step 102: update the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtain the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval.

[0072] my country has clear regulations on the changes in active power of wind farms, as shown in Table Ⅰ.

[0073] Table Ⅰ Maximum limits of wind farm active power changes

[0074]

[0075] In this embodiment, the time is in minutes.

[0076] Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filter is:

[0077]

[0078] Where P t wind represents the wind power at time t; B represents the sliding window width.

[0079] Step 103: update the limit parameters according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering.

[0080] In this embodiment, each new wind power obtained by sampling is compared with the previous moment. If the change exceeds the limit, the change amount is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter is

[0081]

[0082] In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

[0083] Step 104: Calculate a weight coefficient based on the results of the adaptive sliding average filtering and the limiting filtering and in combination with the 10-minute fluctuation rate of wind power.

[0084] In this embodiment, α represents the weight coefficient of the sliding average filtering algorithm, and its expression is

[0085]

[0086] In the formula, Indicates wind power t-10 time The average power at time t; P represents the average power of wind power from t-20 to t-10; N Indicates the rated power of the wind farm.

[0087] Step 105: According to the weight coefficient, the power of the prediction error compensated by the HESS and the power of the wind power fluctuation smoothing are used to realize the wind power fluctuation smoothing based on the adaptive sliding limit collaborative optimization.

[0088] In this embodiment, the weight coefficient is set according to the 10-minute power fluctuation rate of wind power, and the power expression based on the adaptive sliding limit collaborative optimization filter is

[0089] P t grid =αP t sli +(1-α)P t lim (5)

[0090] In this embodiment, a weighted algorithm is designed based on the cumulative discharge of HESS, and the limit parameters are adaptively adjusted to control the cumulative discharge of HESS to recover to the initial value after compensating for wind power fluctuations, thereby reducing the charge and discharge depth of HESS; the positive and negative limit parameters at time t are

[0091]

[0092] In the formula, ΔP t pos Indicates the positive amplitude limit of wind power fluctuation at time t, ΔP t neg Indicates the negative limit of wind power fluctuation at time t, represents the cumulative discharge of HESS at time t-1, that is,

[0093]

[0094] In this embodiment, the adaptive sliding limiting collaborative optimization filtering algorithm can increase the sliding average filtering weight to smooth the long-term fluctuations of wind power when the 10-minute scale power fluctuation rate of wind power is large (such as greater than the first threshold value, the first threshold value here can be set according to actual needs, and this embodiment does not impose any restrictions on this); when the 10-minute scale power fluctuation rate of wind power is small (such as less than the second threshold value, the second threshold value here can be set according to actual needs, and this embodiment does not impose any restrictions on this), increase the limiting filtering weight to ensure that the 1-minute scale power fluctuation rate meets the national standard requirements and reduces the grid-connected power lag time.

[0095] See also Figure 1B As shown, at the beginning, i=1; SOC=0.5, where SOC is the state of charge of the battery; first obtain the wind power data at time i, update the sliding window, and obtain the smoothing result P according to formula (2) t sli ; Update the positive and negative limit parameters according to equation (6) and equation (7), and then obtain the smoothing result P according to equation (3) t lim ; Analyze the 10-minute volatility of wind power and obtain the weight coefficient α according to formula (4); then, obtain the grid-connected power P according to formula (5) t grid ; Output the grid-connected power at time i, then i=i+1; continue to obtain the wind power data at time i, and continue to execute the corresponding steps until the wind power fluctuation is smoothed based on the adaptive sliding limit collaborative optimization.

[0096] Compared with the prior art, this embodiment can effectively reduce the lag time of the wind speed suppression filtering algorithm result, improve the accuracy of the HESS active power reference, reduce the upper and lower limits of the HESS cumulative discharge, reduce the energy storage configuration capacity, and avoid excessive suppression and unnecessary increase of energy storage capacity.

[0097] It should be noted that the order of execution of the steps in the above embodiments does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. In practical applications, all the above possible implementation methods can be combined in any way to form possible embodiments of the present invention, which will not be described one by one here.

[0098] Based on the adaptive sliding limit collaborative optimization wind power fluctuation smoothing method provided in the above embodiments, based on the same inventive concept, an embodiment of the present invention further provides an adaptive sliding limit collaborative optimization wind power fluctuation smoothing device.

[0099] Figure 3 1 is a structural diagram of a wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization provided by an embodiment of the present invention. Figure 3 As shown, the wind power fluctuation smoothing device with adaptive sliding limit collaborative optimization may specifically include a first acquisition unit 310 , a second acquisition unit 320 , a third acquisition unit 330 , a calculation unit 340 and a processing unit 350 .

[0100] The first acquisition unit 310 is used to acquire the initial power data of the wind turbine generator set at time t;

[0101] The second acquisition unit 320 is used to update the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtain the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval;

[0102] The third acquisition unit 330 is used to update the limit parameter according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering;

[0103] A calculation unit 340 is used to calculate a weight coefficient according to the results of the adaptive sliding average filtering and the limiting filtering in combination with the 10-minute fluctuation rate of wind power;

[0104] The processing unit 350 is used to realize wind power fluctuation smoothing based on adaptive sliding limit collaborative optimization according to the weight coefficient, using the power of the HESS to compensate for the prediction error and the power of the wind power fluctuation smoothing.

[0105] An embodiment of the present invention provides a possible implementation manner, where the first acquiring unit 310 is further configured to:

[0106] According to the power relationship (1) of the wind-storage fusion power generation system, the grid-connected power of the wind farm at time t is:

[0107]

[0108] Where P t grid P represents the wind-storage integrated grid-connected power at time t; t wind represents the initial wind power at time t; P t hess P represents the HESS active power reference at time t; t scb Represents the active power reference of the supercapacitor bank at time t; P t lfp Indicates the active power reference of the lithium iron phosphate battery pack at time t.

[0109] An embodiment of the present invention provides a possible implementation manner, where the second obtaining unit 320 is further configured to:

[0110] Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filter is:

[0111]

[0112] Where P t wind represents the wind power at time t; B represents the sliding window width.

[0113] An embodiment of the present invention provides a possible implementation manner, wherein the third obtaining unit 330 is further configured to:

[0114] According to each new wind power obtained by sampling, it is compared with the previous moment. If the change exceeds the limit, the change amount is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter is

[0115]

[0116] In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

[0117] An embodiment of the present invention provides a possible implementation manner, wherein the calculation unit 340 is further configured to:

[0118] α represents the weight coefficient of the sliding average filter algorithm, and its expression is

[0119]

[0120] In the formula, Indicates wind power t-10 time The average power at time t; P represents the average power of wind power from t-20 to t-10; N Indicates the rated power of the wind farm.

[0121] An embodiment of the present invention provides a possible implementation manner, wherein the processing unit 350 is further configured to:

[0122] According to the 10-minute power fluctuation rate of wind power, the weight coefficient is set, and the power expression based on adaptive sliding limit collaborative optimization filtering is

[0123] P t grid =αP tsli +(1-α)P t lim (5)

[0124] Furthermore, a weighted algorithm is designed based on the cumulative discharge of HESS, and the limiting parameters are adaptively adjusted to control the cumulative discharge of HESS to recover to the initial value after compensating for wind power fluctuations, thereby reducing the charging and discharging depth of HESS; the positive and negative limiting parameters at time t are

[0125]

[0126] In the formula, ΔP t pos Indicates the positive amplitude limit of wind power fluctuation at time t, ΔP t neg Indicates the negative limit of wind power fluctuation at time t, represents the cumulative discharge of HESS at time t-1, that is,

[0127]

[0128] The adaptive sliding limiting collaborative optimization filtering algorithm can increase the sliding average filter weight and smooth the long-term fluctuation of wind power when the 10-minute scale power fluctuation rate of wind power is greater than the first threshold; when the 10-minute scale power fluctuation rate of wind power is less than the second threshold, the limiting filter weight is increased to ensure that the 1-minute scale power fluctuation rate meets the national standard requirements and reduce the grid-connected power lag time.

[0129] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

Claims

1. A wind power fluctuation smoothing method with adaptive sliding limit collaborative optimization, characterized in that: The following steps are involved: Step 101, obtaining the initial power data of the wind turbine generator set at time t; Step 102: updating the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtaining the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval; Step 103: update the limit parameters according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering; Step 104: Calculate a weight coefficient based on the results of the adaptive sliding average filtering and the limiting filtering and in combination with the 10-minute fluctuation rate of wind power; Step 105: According to the weight coefficient, the power of the prediction error compensated by the HESS and the power of the wind power fluctuation smoothing are used to realize the wind power fluctuation smoothing based on the adaptive sliding limit collaborative optimization.

2. The method for wind power fluctuation smoothing with adaptive sliding limit collaborative optimization according to claim 1 is characterized in that: In the step 101, obtaining the initial power data of the wind turbine generator set at time t includes: According to the power relationship (1) of the wind-storage fusion power generation system, the grid-connected power of the wind farm at time t is: In the formula, represents the wind-storage integrated grid-connected power at time t; represents the initial wind power at time t; represents the HESS active power reference at time t; Represents the active power reference of the supercapacitor bank at time t; Indicates the active power reference of the lithium iron phosphate battery pack at time t.

3. The method for wind power fluctuation smoothing with adaptive sliding limit collaborative optimization according to claim 2 is characterized in that: In step 102, the sliding window array of the sliding average filter is updated, and the result based on the adaptive sliding average filter is obtained by using the average wind power of the current sampling point and its left adjacent interval, including: Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filter is: In the formula, represents the wind power at time t; B represents the sliding window width.

4. The method for wind power fluctuation smoothing with adaptive sliding limit collaborative optimization according to claim 2 is characterized in that: In step 103, the limiting parameters are updated, and the wind power difference between adjacent sampling periods is limited by the limiting filtering method to obtain the result based on the limiting filtering, including: According to each new wind power obtained by sampling, it is compared with the previous moment. If the change exceeds the limit, the change amount is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter is In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

5. A method for smoothing wind power fluctuations by adaptive sliding limiter collaborative optimization according to any one of claims 3-4, characterized in that: In step 104, according to the results of the adaptive sliding average filtering and the limiting filtering, combined with the 10-minute fluctuation rate of wind power, a weight coefficient is calculated, including: α represents the weight coefficient of the sliding average filter algorithm, and its expression is In the formula, Indicates wind power t-10 time The average power at time t; P represents the average power of wind power from t-20 to t-10; N Indicates the rated power of the wind farm.

6. The method for smoothing wind power fluctuations by adaptive sliding limiter collaborative optimization according to claim 5 is characterized in that: In step 105, the power of the prediction error compensated by the HESS and the power of the wind power fluctuation smoothing are used to realize the wind power fluctuation smoothing based on the adaptive sliding limit collaborative optimization, including: According to the 10-minute power fluctuation rate of wind power, the weight coefficient is set, and the power expression based on adaptive sliding limit collaborative optimization filtering is Furthermore, a weighted algorithm is designed based on the cumulative discharge of HESS, and the limiting parameters are adaptively adjusted to control the cumulative discharge of HESS to recover to the initial value after compensating for wind power fluctuations, thereby reducing the charging and discharging depth of HESS; the positive and negative limiting parameters at time t are In the formula, Indicates the positive limit of wind power fluctuation at time t, Indicates the negative limit of wind power fluctuation at time t, represents the cumulative discharge of HESS at time t-1, that is, The adaptive sliding limiting collaborative optimization filtering algorithm can increase the sliding average filter weight and smooth the long-term fluctuation of wind power when the 10-minute scale power fluctuation rate of wind power is greater than the first threshold; when the 10-minute scale power fluctuation rate of wind power is less than the second threshold, the limiting filter weight is increased to ensure that the 1-minute scale power fluctuation rate meets the national standard requirements and reduce the grid-connected power lag time.

7. An adaptive sliding limiter collaboratively optimized wind power fluctuation smoothing device, characterized in that: include: The first acquisition unit is used to acquire the initial power data of the wind turbine generator set at time t; A second acquisition unit is used to update the sliding window array of the sliding average filter according to the initial power data of the wind turbine generator set, and obtain the result based on the adaptive sliding average filter by using the average wind power of the current sampling point and its left adjacent interval; A third acquisition unit is used to update the limit parameter according to the initial power data of the wind turbine generator set, and use the limit filtering method to limit the wind power difference between adjacent sampling periods to obtain a result based on the limit filtering; A calculation unit, used to calculate a weight coefficient according to the results of the adaptive sliding average filtering and the limiting filtering, combined with the 10-minute fluctuation rate of wind power; The processing unit is used to realize wind power fluctuation smoothing based on adaptive sliding limit collaborative optimization according to the weight coefficient, using the power of the HESS to compensate for the prediction error and the power of the wind power fluctuation smoothing.

8. The wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization according to claim 7 is characterized in that: The first acquisition unit is further used for: According to the power relationship (1) of the wind-storage fusion power generation system, the grid-connected power of the wind farm at time t is: In the formula, represents the wind-storage integrated grid-connected power at time t; represents the initial wind power at time t; represents the HESS active power reference at time t; Represents the active power reference of the supercapacitor bank at time t; Indicates the active power reference of the lithium iron phosphate battery pack at time t.

9. The wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization according to claim 8 is characterized in that: The second acquisition unit is further used for: Each new wind power signal obtained by sampling is stored in the window array, and the data at the end of the array is removed. The arithmetic mean of the array is calculated as the wind power smoothing grid-connected power at the current moment. The grid-connected power based on the adaptive sliding average filter is: In the formula, represents the wind power at time t; B represents the sliding window width.

10. The wind power fluctuation smoothing device with adaptive sliding limiter collaborative optimization according to claim 8, characterized in that: The third acquisition unit is further used for: According to each new wind power obtained by sampling, it is compared with the previous moment. If the change exceeds the limit, the change amount is used as the maximum limit and replaced with the original value output. The grid-connected power based on the limit filter is In the formula, ΔP pos Indicates the positive amplitude limit of wind power fluctuation; ΔP neg Indicates the negative limit of wind power fluctuation.

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