Crowbar circuit action identification method for double-fed wind turbine based on difference hash algorithm

Abstract

The present application relates to the field of power system automation, and discloses a double-fed fan Crowbar circuit action identification method based on a difference hash algorithm, which filters out low-voltage ride-through test results of three-phase symmetrical and asymmetrical faults by reading double-fed fan hardware-in-the-loop simulation or on-site fault ride-through test results; respectively outputs the reactive power and reactive current of the test results as pictures, fits the reactive power and reactive current of the double-fed fan Crowbar circuit that does not act, and outputs as pictures; reduces the picture size, converts the pictures into grayscale pictures, obtains the fingerprints of the pictures by using the difference hash algorithm, calculates the Hamming distance of the reactive power and reactive current, and thus judges whether the double-fed fan Crowbar circuit acts; the method can identify whether the Crowbar circuit acts during low-voltage ride-through from the double-fed fan fault ride-through test results, and the result is not affected by the capacity of the double-fed fan, the low-voltage ride-through depth, the active power output of the fan and the like, and has the advantage of high accuracy.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power system automation, and particularly relates to a Crowbar circuit action identification method for a doubly-fed wind turbine based on a difference hash algorithm. BACKGROUND

[0002] With the construction of new power systems and the grid connection of large-scale new energy, the new energy penetration rate is high in most regions, and it is urgent to build a power grid simulation model containing new energy. The hardware and control strategy of the direct-drive wind turbine and photovoltaic inverter are less different. However, the hardware and control strategy of the low-voltage ride-through of the doubly-fed wind turbine have great differences, and the control strategy has a great influence on the use of the Crowbar circuit. The low-voltage ride-through strategy of the doubly-fed wind turbine is greatly different from each other, and the internal control strategy is the core secret of each family and is not open to the public. Whether the Crowbar circuit is in action during the low-voltage ride-through of the doubly-fed wind turbine is one of the core contents of simulation modeling.

[0003] The existing 'Power System Safety and Stability Guidelines', 'Technical Regulations for Access of Wind Power Plants to Power Systems (First Part: Land-based Wind Power)' and the like require wind turbine generators to carry out simulation modeling work. The existing Crowbar circuit action identification method for the low-voltage ride-through of the doubly-fed wind turbine mainly relies on manual identification or Crowbar circuit action signals, and a few identification methods based on the reactive power and reactive current results of the low-voltage ride-through are also used. However, the method is easily affected by the capacity of the doubly-fed wind turbine and the output of the wind turbine. SUMMARY

[0004] To solve the problems in the prior art, the application provides a Crowbar circuit action identification method for a doubly-fed wind turbine based on a difference hash algorithm, which can accurately identify whether the Crowbar circuit of the doubly-fed wind turbine is in action during the low-voltage ride-through, lay a foundation for modeling and simulation of the doubly-fed wind turbine, and is easy to popularize and apply.

[0005] The technical scheme adopted by the application is as follows:

[0006] The Crowbar circuit action identification method for a doubly-fed wind turbine based on a difference hash algorithm comprises the following steps:

[0007] Step 1, reading the fault voltage ride-through test results:

[0008] The fault voltage ride-through test results of the doubly-fed wind turbine hardware-in-the-loop simulation or the field are read, and the low-voltage ride-through test results of symmetrical and asymmetrical three-phase faults are selected;

[0009] Step 2, selecting reactive power and current data:

[0010] Data selection: based on the low voltage ride through test results of three-phase symmetrical and asymmetrical faults, the reactive power and reactive current data of 200 ms before fault and 500 ms after fault are selected respectively, and the output is a picture;

[0011] Calculation of reactive power and current: read the initial value of reactive power and reactive current before fault and the voltage amplitude during low voltage ride through, calculate the reactive power and reactive current value during low voltage ride through, assume that the Crowbar circuit of double-fed wind turbine does not act during low voltage ride through, fit the reactive power and reactive current data of 200 ms before fault and 500 ms after fault, and output a picture;

[0012] Step 3, picture size reduction:

[0013] Using double-line interpolation method, the test reactive power and reactive current picture and the fitted reactive power and reactive current picture are reduced to 32x32 size;

[0014] Step 4, convert to gray picture:

[0015] Convert the reduced test reactive power and reactive current picture and the fitted reactive power and reactive current picture to gray picture;

[0016] Step 5, get picture fingerprint:

[0017] Get the fingerprints of test reactive power and reactive current gray picture and fitted reactive power and reactive current gray picture using difference hash algorithm;

[0018] Step 6, calculate hamming distance:

[0019] Calculate the hamming distance d1 between test reactive power and fitted reactive power, and the hamming distance d2 between test reactive current and fitted reactive current;

[0020] Step 7, judge whether Crowbar circuit acts:

[0021] Judge whether the Crowbar circuit of double-fed wind turbine acts by the hamming distance value d1 between test reactive power and fitted reactive power and the hamming distance value d2 between test reactive current and fitted reactive current.

[0022] Further, in step 1, the screening method of low voltage ride through test results of three-phase fault of double-fed wind turbine includes the following steps:

[0023] Step 1.1, data screening: based on whether the fundamental positive sequence voltage per unit value in the hardware-in-the-loop simulation or field fault voltage ride through test result of double-fed wind turbine is less than 0.9, if less than 0.9, it is low voltage ride through test data;

[0024] Step 1.2, phase voltage calculation: using full-wave Fourier algorithm, the fundamental positive sequence voltage during fault is calculated as U1, and the three-phase voltage during fault is calculated as U A , U B , U C , respectively.

[0025] Step 1.3, fault judgment: if |U A -U1|, |U B -U1|, |U C -U1| are all less than 0.01, it is low voltage ride through data of three-phase symmetric fault; otherwise, it is low voltage ride through data of asymmetric fault.

[0026] Further, in step 2, the process of calculating the reactive power and reactive current value during low voltage ride through is as follows:

[0027] The reactive current during low voltage ride through is:

[0028]

[0029] The reactive power Q1 during low voltage ride through is:

[0030]

[0031] In the formula, U t is the line voltage amplitude during low voltage ride through; is the reactive current value before fault; I N is the rated current of the device; Q0 is the reactive power before fault; Q1 is the reactive power during low voltage ride through.

[0032] Further, in step 3, the picture size is reduced by using double-line interpolation method, and the function imresize() in matlab software is used to reduce the test reactive power and reactive current picture and the fitted reactive power and reactive current picture to 32x32 size.

[0033] Further, in step 5, the specific steps of using difference hash algorithm to obtain the fingerprint of the picture are as follows:

[0034] Step 5.1, using discrete cosine transform to convert the 32x32 picture after reducing the test reactive power to a 32x32 matrix;

[0035] Step 5.2, keeping the upper left 8x8 matrix of the 32x32 matrix after discrete cosine transform;

[0036] Step 5.3, calculating the average value of the pixel points of the 8x8 matrix after discrete cosine transform;

[0037] ​Step 5.4, record 1 for the pixel points greater than the average value in the 8*8 matrix after the discrete cosine transform, and record 0 for the contrary;

[0038] Step 5.5, obtain the information fingerprint of the test reactive power pattern: combine 64 fingerprint information bits, and keep the sequence consistent.

[0039] Further, in step 6, the Hamming distance is compared whether each bit of the fingerprint is the same;

[0040] If not, the Hamming distance value is added by 1; after the comparison of 64 fingerprint information bits, the Hamming distance value is obtained; the smaller the Hamming distance is, the higher the pattern quantity similarity is.

[0041] Further, in step 7, the Hamming distance value d1 is calculated for the 64 fingerprint information obtained by the test reactive power and the fitted reactive power; the Hamming distance value d2 is calculated for the 64 fingerprint information obtained by the test reactive current and the fitted reactive current; if the Hamming distance values d1 and d2 are less than or equal to 5, it is indicated that the Crowbar circuit of the doubly-fed wind turbine is not in action; otherwise, it is indicated that the Crowbar circuit of the doubly-fed wind turbine is in action.

[0042] Compared with the prior art, the Crowbar circuit action recognition method based on the difference hash algorithm has the following beneficial effects:

[0043] (1) The Crowbar circuit action recognition method based on the difference hash algorithm automatically recognizes whether the Crowbar circuit of the doubly-fed wind turbine is in action according to the reactive power and the reactive current of the fault ride-through test result, uses the perceptual hash algorithm combined with the Hamming distance, is not affected by the capacity of the doubly-fed wind turbine, the low-voltage ride-through depth, the active power output size of the wind turbine, and has the advantage of high accuracy.

[0044] (2) It does not depend on manual recognition of whether the Crowbar circuit is in action during the low-voltage ride-through of the doubly-fed wind turbine, and can distinguish the Crowbar circuit action during the low-voltage ride-through of the three-phase symmetric and asymmetric faults, and is convenient for carrying out the simulation modeling work of the doubly-fed wind turbine.

[0045] (3) It is not affected by the size of the reactive power and the reactive current, and is only related to the trend of the change of the reactive power and the reactive current caused by the Crowbar circuit action, and has the advantages of high accuracy and convenient use.

[0046] (4) The invention uses the difference hash algorithm to recognize the Crowbar circuit action of the doubly-fed wind turbine, has high precision and fast calculation speed, is convenient to use, and is easy to popularize and use. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0048] wherein:

[0049] Figure 1 is a flow chart of the motion recognition method of the present application;

[0050] Figure 2 is a fault ride-through test result waveform of the double-fed wind turbine of application example 1 of the present application;

[0051] Figure 3 is a measured reactive power waveform chart of application example 1 of the present application;

[0052] Figure 4 is a measured reactive current waveform chart of application example 1 of the present application;

[0053] Figure 5 is a fitted reactive power waveform chart of application example 1 of the present application;

[0054] Figure 6 is a fitted reactive current waveform chart of application example 1 of the present application;

[0055] Figure 7 is a fault ride-through test result waveform of the double-fed wind turbine of application example 2 of the present application;

[0056] Figure 8 is a measured reactive power waveform chart of application example 2 of the present application;

[0057] Figure 9 is a measured reactive current waveform chart of application example 2 of the present application;

[0058] Figure 10 is a fitted reactive power waveform chart of application example 2 of the present application;

[0059] Figure 11 is a fitted reactive current waveform chart of application example 2 of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0061] The embodiment provides a Crowbar circuit action recognition method for a doubly-fed wind turbine based on a difference hash algorithm.

[0062] The Crowbar circuit action recognition method for the doubly-fed wind turbine based on the difference hash algorithm does not depend on manual recognition, can automatically and quickly recognize whether the Crowbar is in action during three-phase symmetric and asymmetric low-voltage ride-through, and helps transient model parameter recognition of the doubly-fed wind turbine, thereby laying a foundation for accurate transient simulation modeling of the doubly-fed wind turbine.

[0063] Specifically, as shown in the figure, Figure 1 the Crowbar circuit action recognition method for the doubly-fed wind turbine based on the difference hash algorithm proceeds according to the following steps:

[0064] Step 1, read the fault voltage ride-through test result:

[0065] Read the hardware-in-the-loop simulation or field fault voltage ride-through test result of the doubly-fed wind turbine, and screen low-voltage ride-through test results of three-phase symmetric and asymmetric faults.

[0066] Further, the screening method of the low-voltage ride-through test result of the three-phase fault of the doubly-fed wind turbine specifically includes the following steps:

[0067] Step 1.1, data screening: based on whether the fundamental positive sequence voltage per unit value in the hardware-in-the-loop simulation or field fault voltage ride-through test result of the doubly-fed wind turbine is less than 0.9, if less than 0.9, it is low-voltage ride-through test data.

[0068] Step 1.2, phase voltage calculation: the full-wave Fourier algorithm is used to calculate the fundamental positive sequence voltage per unit value U1 during the fault, and the phase voltage per unit values of three phases during the fault are calculated as U A , U B , and U C , respectively.

[0069] Step 1.3, fault judgment: if |U A -U1|, |U B -U1|, and |U C -U1| are all less than 0.01, it is indicated that the low-voltage ride-through data is three-phase symmetric fault; otherwise, it is asymmetric fault low-voltage ride-through data.

[0070] Step 2, select the reactive power and current data:

[0071] Data selection: Based on the low voltage ride through test results of three-phase symmetrical and asymmetrical faults, the reactive power and reactive current data of 200 ms before fault and 500 ms after fault are selected respectively, and the output is a picture.

[0072] Calculation of reactive power and current: read the initial value of reactive power and reactive current before fault, voltage amplitude during low voltage ride through, calculate the value of reactive power and reactive current during low voltage ride through; assume that the Crowbar circuit of double-fed wind turbine does not act during low voltage ride through, fit the data of reactive power and reactive current of 200 ms before fault and 500 ms after fault, and output a picture.

[0073] Further, the value of reactive power and reactive current during low voltage ride through is calculated, and the specific process is as follows:

[0074] The reactive current during low voltage ride through is:

[0075]

[0076] The reactive power Q1 during low voltage ride through is:

[0077]

[0078] In the formula, U t is the line voltage amplitude during low voltage ride through; is the reactive current value before fault; I N is the rated current of the device; Q0 is the reactive power before fault; Q1 is the reactive power during low voltage ride through.

[0079] Step 3, picture size reduction:

[0080] Using bilinear interpolation method, the test reactive power and reactive current pictures and the fitted reactive power and reactive current pictures are reduced to 32x32 size.

[0081] Specifically, the picture size is reduced by using bilinear interpolation method, and the function imresize() in matlab software is used to reduce the test reactive power and reactive current pictures and the fitted reactive power and reactive current pictures to 32x32 size.

[0082] Step 4, convert to gray picture:

[0083] The reduced test reactive power and reactive current pictures and the fitted reactive power and reactive current pictures are converted to gray pictures for subsequent processing.

[0084] Step 5, get picture fingerprint:

[0085] ​The test reactive power and the reactive current gray scale picture are obtained by using the difference hash algorithm, and the fingerprint of the fitting reactive power and the reactive current gray scale picture is obtained.

[0086] The specific steps of obtaining the fingerprint of the picture by using the difference hash algorithm are as follows:

[0087] Taking the test reactive power picture as an example, the following is introduced:

[0088] Step 5.1, the 32x32 picture after the test reactive power is reduced is converted into a 32x32 matrix by using the discrete cosine transform;

[0089] Step 5.2, the upper left 8x8 matrix of the 32x32 matrix after the discrete cosine transform is reserved;

[0090] Step 5.3, the average value of the pixel points of the 8x8 matrix after the discrete cosine transform is calculated;

[0091] Step 5.4, the pixel points of the 8x8 matrix after the discrete cosine transform are recorded as 1 if they are greater than the average value, and recorded as 0 otherwise;

[0092] Step 5.5, the information fingerprint of the test reactive power picture is obtained: 64 fingerprint information bits are combined, and the order is consistent.

[0093] The fingerprint extraction steps of the test reactive current picture and the fitting reactive power and the reactive current are the same.

[0094] Step 6, calculate the Hamming distance:

[0095] The Hamming distance d1 between the test reactive power and the fitting reactive power is calculated, and the Hamming distance d2 between the test reactive current and the fitting reactive current is calculated.

[0096] Specifically, the Hamming distance is to compare whether each bit of the fingerprint is the same; if not, the Hamming distance value is added by 1; after comparing the 64 fingerprint information bits, the Hamming distance value is obtained. The smaller the Hamming distance is, the higher the similarity of the picture quantity is.

[0097] Step 7, judge whether the Crowbar circuit is in action:

[0098] Whether the Crowbar circuit of the doubly-fed wind turbine is in action is judged by the Hamming distance value d1 between the test reactive power and the fitting reactive power and the Hamming distance value d2 between the test reactive current and the fitting reactive current.

[0099] The Hamming distance value d1 is calculated for the 64 fingerprint information obtained from the test reactive power and the fitted reactive power; the Hamming distance value d2 is calculated for the 64 fingerprint information obtained from the test reactive current and the fitted reactive current. If the Hamming distance values ​​d1 and d2 are both less than or equal to 5, it means that the Crowbar circuit of the doubly fed wind turbine is not operating; otherwise, it means that the Crowbar circuit of the doubly fed wind turbine is operating.

[0100] Through the above steps, a complete identification method is constructed, which can effectively analyze the state of the Crowbar circuit of the doubly fed wind turbine under low voltage ride-through conditions.

[0101] Furthermore, in order to verify the accuracy of the Crowbar circuit action identification method of the doubly fed wind turbine based on the differential hash algorithm, the following actual verification was carried out:

[0102] Actual Verification 1

[0103] Read the on-site fault ride-through test data of A double-fed wind turbine, such as Figure 2 As shown, the screening is for three-phase symmetrical low voltage ride-through test:

[0104] Select the reactive power and reactive current data 200ms before and 500ms after the fault and output them as a picture, such as Figure 3 、 4 As shown;

[0105] Read the initial values ​​of reactive power and reactive current before the fault, and the voltage amplitude during the low voltage ride-through period, calculate the reactive power and reactive current values ​​during the low voltage ride-through period, fit the reactive power and reactive current data 200ms before the fault and 500ms after the fault, and output them as a picture, such as Figure 5 、 6 As shown;

[0106] Use bilinear interpolation to reduce the image size and convert it into grayscale;

[0107] The fingerprints of the test reactive power and reactive current grayscale images and the fitted reactive power and reactive current grayscale images are obtained using the differential hash algorithm;

[0108] Calculate the Hamming distance d1=6 between the test reactive power and the fitted reactive power, and calculate the Hamming distance d2=6 between the test reactive current and the fitted reactive current;

[0109] Since the Hamming distance values ​​d1 and d2 are both greater than 5, the Crowbar circuit of the doubly fed wind turbine operates during the three-phase symmetrical voltage ride-through period.

[0110] Actual Verification 2

[0111] Read the on-site fault ride-through test data of B doubly-fed wind turbine generator set, such asFigure 7 The screening is a three-phase symmetric low voltage ride through test as shown in the figure;

[0112] The reactive power and reactive current data 200 ms before the fault and 500 ms after the fault are selected, and the output is a picture as shown in Figure 8 、 9 ;

[0113] The initial values of the reactive power and reactive current before the fault and the voltage amplitude during the low voltage ride through are read, the reactive power and reactive current values during the low voltage ride through are calculated, the reactive power and reactive current data 200 ms before the fault and 500 ms after the fault are fitted, and the output is a picture as shown in Figure 10 、 11 ;

[0114] The picture size is reduced by using the double-line interpolation method, and the picture is converted into a gray-scale picture;

[0115] The test reactive power and reactive current gray-scale pictures are obtained by using the difference hash algorithm, and the fingerprints of the fitted reactive power and reactive current gray-scale pictures are obtained;

[0116] The Hamming distance d1 between the test reactive power and the fitted reactive power is calculated to be 1, and the Hamming distance d2 between the test reactive current and the fitted reactive current is calculated to be 1;

[0117] Since the Hamming distance values d1 and d2 are both less than 5, the Crowbar circuit of the doubly-fed wind turbine does not act during the three-phase symmetric voltage ride through.

[0118] In summary, the Crowbar circuit action recognition method of the doubly-fed wind turbine based on the difference hash algorithm does not depend on manual recognition, and can automatically and quickly recognize whether the Crowbar acts during the three-phase symmetric and asymmetric low voltage ride through; the Crowbar circuit action of the doubly-fed wind turbine is recognized by using the difference hash algorithm combined with the Hamming distance, and is not affected by the capacity of the doubly-fed wind turbine, the low voltage ride through depth, the size of the active power output of the wind turbine, etc., and has the advantage of high accuracy.

[0119] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, and that the present application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the foregoing description, and it is intended to include all changes falling within the meaning and range of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they belong.

Claims

1. A method for identifying the action of a Crowbar circuit of a doubly-fed wind turbine based on a differential hash algorithm, characterized in that: Comprising the following steps: Step 1, read the fault ride through test results: Read the results of the hardware-in-the-loop simulation or field fault ride through test of the doubly-fed wind turbine, and select the low voltage ride through test results of three-phase symmetric and asymmetric faults; Step 2, select reactive power and current data: Data selection: based on the low voltage ride through test results of three-phase symmetric and asymmetric faults, select the reactive power and current data 200ms before the fault and 500ms after the fault, and output as pictures; Calculate the reactive power and current: read the initial value of the reactive power and current before the fault and the voltage amplitude during the low voltage ride through, calculate the reactive power and current value during the low voltage ride through, assume that the Crowbar circuit of the doubly-fed wind turbine does not act during the low voltage ride through, fit the reactive power and current data 200ms before the fault and 500ms after the fault, and output as pictures; Step 3, reduce the picture size: Use the bilinear interpolation method to reduce the test reactive power and current pictures and the fitted reactive power and current pictures to a size of 32x32; Step 4, convert to grayscale pictures: Convert the reduced test reactive power and current pictures and the fitted reactive power and current pictures to grayscale pictures; Step 5, obtain the picture fingerprint: Use the difference hash algorithm to obtain the fingerprint of the test reactive power and current grayscale pictures and the fitted reactive power and current grayscale pictures; In step 5, the specific steps of obtaining the fingerprint of the picture using the difference hash algorithm are as follows: Step 5.1, use discrete cosine transform to convert the reduced 32x32 test reactive power picture to a 32x32 matrix; Step 5.2, retain the upper left 8x8 matrix of the 32x32 matrix after discrete cosine transform; Step 5.3, calculate the average value of the pixel points of the 8x8 matrix after discrete cosine transform; Step 5.4, record 1 for the pixel points in the 8x8 matrix after discrete cosine transform that are greater than the average value, and record 0 otherwise; Step 5.5, obtain the information fingerprint of the test reactive power picture: combine 64 fingerprint information bits, and maintain consistency in order; Step 6, calculate the Hamming distance: Calculate the Hamming distance d1 between the test reactive power and the fitted reactive power, and calculate the Hamming distance d2 between the test current and the fitted current; Step 7, determine whether the Crowbar circuit is activated: Determine whether the Crowbar circuit of the doubly-fed wind turbine is activated by the Hamming distance values d1 between the test reactive power and the fitted reactive power and d2 between the test current and the fitted current.

2. The method of claim 1, wherein the Crowbar circuit is a Crowbar circuit of a doubly-fed wind turbine. In step 1, the selection method of the low voltage ride through test results of the three-phase fault of the doubly-fed wind turbine includes the following steps: Step 1.1, data selection: based on whether the fundamental positive sequence voltage per unit value in the hardware-in-the-loop simulation or field fault ride through test results of the doubly-fed wind turbine is less than 0.9, if it is less than 0.9, it is low voltage ride through test data; Step 1.2, phase voltage calculation: using full-wave Fourier algorithm, the fundamental positive sequence voltage norm U1 during the fault is calculated, and the three-phase voltage norms U A , B , C respectively during the fault are calculated. Step 1.3, fault judgment: if |U A -U1|, |U B -U1|, |U C -U1| are all less than 0.01, it is explained as low voltage ride through data of three-phase symmetrical fault; otherwise, it is low voltage ride through data of asymmetrical fault.

3. The method of claim 1, wherein the Crowbar circuit is a Crowbar circuit of a doubly-fed wind turbine. In step 2, the process of calculating the reactive power and current values during the low voltage ride through is as follows: Reactive current during low voltage ride through Is: The reactive power Q1 during the low voltage ride through is: wherein U t is the line voltage amplitude during low voltage ride through; is the pre-fault reactive current value; I N is the rated current of the device; Q0is the pre-fault reactive power; Q1is the reactive power during low voltage ride through.

4. The method of claim 1, wherein the Crowbar circuit is a Crowbar circuit of a doubly-fed wind turbine. In step 3, the picture size is reduced by using the double-line interpolation method, and the function imresize() in the matlab software is used to reduce the test reactive power and reactive current pictures and the fitted reactive power and reactive current pictures to the size of 32*32.

5. The method of claim 1, wherein the Crowbar circuit operation identification method based on the difference value hashing algorithm is characterized in that: In step 6, the Hamming distance is compared with each bit of the fingerprint; If not, the Hamming distance value is added by 1; after comparing the 64-bit fingerprint information, the Hamming distance value is obtained; the smaller the Hamming distance is, the higher the graphic quantity similarity is.

6. The method of claim 1, wherein the Crowbar circuit is a Crowbar circuit of a doubly-fed wind turbine. In step 7, if the Hamming distance values d1 and d2 are less than or equal to 5, it is indicated that the Crowbar circuit of the double-fed wind turbine is not in action; otherwise, it is indicated that the Crowbar circuit of the double-fed wind turbine is in action.

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