A numerical calculation method for wind turbine generator speed parameter

Abstract

The application discloses a numerical calculation method of a wind turbine generator speed parameter, and belongs to the technical field of wind farm data processing. The method comprises the following steps: obtaining operation data of a wind turbine generator; wherein the operation data of the wind turbine generator comprises active power data and generator speed data; calculating a rated speed of a wind driven generator based on the operation data; calculating a grid-connected speed of the wind driven generator based on the operation data; and performing speed curve fitting based on the operation data to obtain a speed curve; wherein the speed curve is used to determine whether the speed of the generator matches the output power, and further determine whether the generator is normal. The technical scheme of the application can efficiently utilize SCADA data of the wind turbine generator, and perform numerical calculation on parameters such as the grid-connected speed and the rated speed of the wind turbine generator, so as to solve the problems that the visual method is not accurate enough and cannot be automated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind farm data processing, in particular to a numerical calculation method of wind turbine generator speed parameters. BACKGROUND

[0002] With the continuous progress of technology and the gradual reduction of production cost, wind power has become an important part of the scale growth of new energy. As the core component of the wind turbine, the performance and operation health of the generator are directly related to the power generation efficiency and power supply quality of the whole unit. Generally, in the equipment procurement technical agreement of the wind turbine, the wind turbine manufacturer will provide the main parameters of the wind turbine, such as rated power, rated voltage, rated speed, grid-connected speed, power factor, etc. Calculating and comparing the actual rated speed and grid-connected speed of the wind turbine is an important part of detecting whether the wind turbine meets the technical requirements.

[0003] However, in actual work, we found that the actual rated speed and grid-connected speed of some wind turbines in the wind farm do not match the technical agreement, and there are cases of low configuration and high use or high configuration and low use. However, the owner and other maintenance personnel lack a method to analyze and numerically calculate the grid-connected speed and rated speed of the wind turbine, and therefore have not discovered such problems. Therefore, there is an urgent need for a calculation method that can calculate the grid-connected speed and rated speed of the wind turbine according to the actual operation data of the wind turbine, and determine whether the speed of the wind turbine matches the corresponding power and whether there is an abnormality.

[0004] In the prior art, the grid-connected speed and rated speed of the wind turbine are plotted as a power-generator speed scatter plot, and then the grid-connected speed and rated speed of the wind turbine are estimated by manually observing the data concentration area of the scatter plot. Although this method is intuitive, it lacks intelligence and accuracy, is easily affected by human factors, and is not conducive to batch calculation of multiple units in the entire wind farm. SUMMARY

[0005] The present application provides a numerical calculation method of wind turbine generator speed parameters to solve the technical problem that the existing method of determining the grid-connected speed and rated speed of the wind turbine is intuitive, lacks intelligence and accuracy, is easily affected by human factors, and is not conducive to batch calculation of multiple units in the entire wind farm.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In one aspect, the present application provides a numerical calculation method of wind turbine generator speed parameters, comprising:

[0008] acquiring operation data of the wind turbine; wherein, the operation data of the wind turbine comprises active power data of the wind turbine and generator speed data of the wind turbine;

[0009] calculating rated speed of the wind turbine based on the operation data;

[0010] calculating grid-connected speed of the wind turbine based on the operation data;

[0011] performing speed curve fitting based on the operation data to obtain a speed curve; wherein, the speed curve is used to determine whether the speed of the generator matches the output power, and further determine whether the generator is normal.

[0012] Further, calculating rated speed of the wind turbine based on the operation data comprises:

[0013] obtaining a rated speed value R1 from power dimension based on the operation data;

[0014] obtaining a rated speed value R2 from speed dimension based on the operation data;

[0015] taking the maximum value between R1 and R2 as the rated speed of the wind turbine.

[0016] Further, obtaining a rated speed value R1 from power dimension based on the operation data comprises:

[0017] extracting active power data and generator speed data with time resolution of T minutes from the operation data, and aligning the extracted active power data and generator speed data in time dimension, and using the aligned active power data and generator speed data to form a first data set; removing active power data and corresponding generator speed data in the first data set which is not greater than 0 to obtain a second data set; wherein, T is a preset value;

[0018] selecting active power data and corresponding generator speed data in the second data set which is greater than a first preset power threshold, and equally dividing the selected data into N intervals according to the size of the active power, and for each interval, obtaining the maximum generator speed value; wherein, N is a preset value;

[0019] reordering the calculated maximum generator speed values of each interval in ascending order to form a third data set;

[0020] removing the first n and last n values in the third data set to obtain a fourth data set; wherein, n is a preset value;

[0021] The average value of the data in the fourth data set is taken as a rated speed value R1 obtained from the power dimension.

[0022] Further, based on the operation data, a rated speed value R2 is obtained from the speed dimension, comprising:

[0023] The screened generator speed data is equally divided into multiple speed partitions; wherein, when the screened generator speed data is equally divided, the number of speed partitions M needs to satisfy 10≤M≤300;

[0024] For each speed partition, the ratio of the amount of data in it to the total amount of generator speed data is calculated;

[0025] The speed partitions with a ratio less than a preset ratio threshold are removed; for the remaining speed partitions, the maximum probability speed of each speed interval is calculated using a kernel density estimation algorithm;

[0026] The maximum value of all maximum probability speeds is taken as a rated speed value R2 obtained from the speed dimension.

[0027] Further, based on the operation data, the grid-connected speed of the wind turbine is calculated, comprising:

[0028] Based on the operation data, a grid-connected speed value R3 is obtained from the power dimension;

[0029] Based on the operation data, a grid-connected speed value R4 is also obtained from the speed probability dimension;

[0030] The minimum value of R3 and R4 is taken as the grid-connected speed of the wind turbine.

[0031] Further, based on the operation data, a grid-connected speed value R3 is obtained from the power dimension, comprising:

[0032] The active power data and generator speed data with a time resolution of T minutes in the operation data are extracted, and the extracted active power data and generator speed data are aligned in the time dimension, and the aligned active power data and generator speed data are used to form a fifth data set; the active power data and the corresponding generator speed data in the fifth data set that are not greater than 0 are removed to obtain a sixth data set; wherein, T is a preset value;

[0033] The active power data and the corresponding generator speed data in the sixth data set that are less than a second preset power threshold are screened out, and the screened data are equally divided into N intervals according to the size of the active power; for each interval divided, the maximum generator speed value is obtained; wherein, N is a preset value;

[0034] The calculated maximum generator speed values of each interval are reordered in ascending order to form a seventh data set from the maximum generator speed values of the reordered intervals;

[0035] The first n and last n values in the seventh data set are removed to obtain an eighth data set; wherein n is a preset value;

[0036] The average value of the data in the eighth data set is taken as a grid-connected speed value R3 obtained from the power dimension.

[0037] Further, based on the operation data, a grid-connected speed value R4 is also obtained from the speed probability dimension, including:

[0038] The screened generator speed data is equally partitioned to obtain multiple speed partitions; wherein when the screened generator speed data is equally partitioned, the number M of speed partitions needs to satisfy 10≤M≤300;

[0039] For each speed partition, the ratio of the amount of data in it to the total amount of generator speed data is calculated, and the kernel density estimation algorithm is used to calculate the maximum probability speed and the maximum probability active power of each speed interval;

[0040] The speed partitions are reordered in descending order according to the ratio;

[0041] The first partition K that meets the following conditions is screened from the reordered speed partitions t :

[0042] (1) The maximum probability speed of the speed partition is less than 0.75*MR;

[0043] (2) The maximum probability active power of the speed partition is less than 0.2*MP;

[0044] Wherein MP represents the maximum active power data in the sixth data set; MR represents the maximum generator speed data in the sixth data set;

[0045] The maximum probability speed of K t is taken as a grid-connected speed value R4 obtained from the speed probability dimension.

[0046] Further, based on the operation data, a speed curve fitting is performed to obtain a speed curve, including:

[0047] The active power data and generator speed data with a time resolution of T minutes in the operation data are extracted, and the active power data and generator speed data are aligned in the time dimension, then the active power data and the corresponding generator speed data not greater than 0 are removed to obtain a ninth data set; wherein T is a preset value;

[0048] The active power data in the ninth data set is equally divided into multiple power partitions;

[0049] For each power partition, the ratio of the data amount to the total active power data amount is calculated, and the partitions with a ratio less than a preset ratio threshold are removed, and for the remaining power partitions, the maximum probability power and the maximum probability rotating speed corresponding to each partition are calculated using a kernel density estimation algorithm.

[0050] The maximum probability power and the maximum probability rotating speed corresponding to each partition are used to fit a power-rotating speed curve, wherein the abscissa of the curve is the active power, and the ordinate is the generator rotating speed.

[0051] In another aspect, the present application also provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above method.

[0052] In another aspect, the present application also provides a computer-readable storage medium, which stores at least one instruction, which is loaded and executed by the processor to implement the above method.

[0053] The technical solution provided by the present application is dedicated to the numerical calculation of the actual rotating speed parameter of a wind turbine, and aims to efficiently utilize the SCADA data of the wind turbine, and to carry out numerical calculation on the grid-connection rotating speed and the rated rotating speed of the generator of the wind turbine, so as to solve the problems of inaccuracy of the visual method and lack of automation.

[0054] The technical solution provided by the present application brings at least the following beneficial effects:

[0055] 1. The present application selects the data corresponding to the power interval in a targeted manner, such as selecting the data corresponding to the power interval not lower than 80% of the maximum power when calculating the rated rotating speed, and selecting the data corresponding to the power interval not higher than 2% of the maximum power when calculating the grid-connection rotating speed. The targeted selection of the power interval greatly avoids the influence of abnormal data of the unit on the calculation result.

[0056] 2. The rated rotating speed and the grid-connection rotating speed in the present application are calculated using two algorithms respectively, i.e. the maximum rotating speed value is calculated by power partition, the maximum probability rotating speed value is calculated by introducing KDE in the rotating speed partition, and the results calculated by the two methods are compared and determined, which further improves the accuracy of the results and the adaptability of the calculation method.

[0057] 3, The application introduces the partition probability and the KDE algorithm to fit the rotating speed curve and generates various application modes: on the one hand, whether the abnormality between the rotating speed of the generator unit and the corresponding output power can be determined (whether the rotating speed curve is smooth is determined); on the other hand, the longitudinal comparison of the rotating speed curve of the over unit in time can determine whether the rotating speed of the generator and the corresponding output power exist time degradation; finally, the transverse comparison of the rotating speed curves of different units of the same type can determine the differences between the units and whether there is a batch quality problem. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0059] Figure 1 is the execution flow diagram of the numerical calculation method of the wind turbine generator rotating speed parameter provided by the embodiment of the present application;

[0060] Figure 2 is the flow diagram of fitting the rotating speed curve provided by the embodiment of the present application;

[0061] Figure 3 is the rotating speed parameter calculation result diagram of the normally running double-fed asynchronous generator unit provided by the embodiment of the present application;

[0062] Figure 4 is the rotating speed parameter calculation result diagram of the trial running period double-fed asynchronous generator unit provided by the embodiment of the present application;

[0063] Figure 5 is the rotating speed parameter calculation result diagram of the normally running direct-drive synchronous generator unit provided by the embodiment of the present application;

[0064] Figure 6 is the rotating speed parameter calculation result diagram of the abnormally running direct-drive synchronous generator unit provided by the embodiment of the present application;

[0065] Figure 7 is the fitting diagram of the rotating speed curve provided by the embodiment of the present application;

[0066] Figure 8 is the comparison result diagram of the rotating speed curves of the multiple units of the same type (showing the consistency of the normal unit) provided by the embodiment of the present application;

[0067] Figure 9 is the comparison result diagram of the rotating speed curves of the multiple units of the same type (comparison between the normal unit and the abnormal unit) provided by the embodiment of the present application;

[0068] Figure 10 is a system block diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0070] First, it should be noted that in the embodiments of the present application, the words such as "exemplarily", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplary" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "exemplarily" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be either one of the two.

[0071] First Embodiment

[0072] The present embodiment provides a numerical calculation method of wind turbine generator speed parameter, which uses the actual operation data of wind turbine to numerically calculate the actual grid-connected speed and rated speed of wind turbine. The method can be realized by an electronic device, which can be a terminal or a server. The method comprises the following steps:

[0073] 1. Obtain the operation data of wind turbine;

[0074] It should be noted that in the current situation, the SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control system) system is widely used in wind farms to collect and record the operation data of wind turbine, including power, generator speed, wind speed, wind direction, blade angle and other parameters. In this regard, the present embodiment selects the active power data and generator speed data recorded by the SCADA system as the processing object.

[0075] In addition, it should be noted that the grid-connected speed and the rated speed of the wind turbine are two important indicators, the generator speed data recorded by the SCADA system is a series of discrete data from 0 to the maximum speed, and the speed sensor may also record incorrect data due to failure, therefore, the specific values of the grid-connected speed and the rated speed of the generator cannot be directly obtained from the recorded speed data, and statistical analysis processing is required. Traditionally, the speed data and the power data or the speed data and the wind speed data are plotted as XY scatter plots, the grid-connected speed and the rated speed of the generator are estimated by observing the data concentration band of the scatter plot, and whether the speed of the generator is abnormal is observed, and this method is often used for cause analysis when the output of the wind turbine is abnormal. However, this method is not accurate enough on the one hand, and on the other hand, it cannot be applied to scenarios that require automatic analysis or batch calculation.

[0076] To solve the above problems, the embodiment divides the data, obtains the maximum value and the maximum probability value of each partition, and comprehensively obtains the grid-connected speed and the rated speed, and plots a speed curve for detecting whether the speed of the generator is normal.

[0077] 2. Based on the operation data, calculating the rated speed of the wind turbine;

[0078] It should be noted that the wind turbine has a speed range, the rated speed is generally equal to the maximum speed of the wind turbine, and the speed of the wind turbine at the maximum power generally corresponds to the rated speed of the generator, but the power output of the generator at the rated speed does not only correspond to the maximum power of the wind turbine, but also corresponds to a wider power interval. In addition, the speed corresponding to the maximum power cannot be simply equated to the rated speed of the generator, because some abnormal scatter points may occur in the SCADA recording process. Therefore, the partition concept needs to be introduced and the influence of abnormal data needs to be eliminated. The specific calculation process is as shown in Figure 1 , which includes:

[0079] S1: Extracting the active power data {P i} and the generator speed data {R i} recorded by the SCADA with a time resolution of 10 minutes, forming a data set S={(P i, R i )}, eliminating the data corresponding to P i ≤0, and calculating the maximum active power value MP=max(P i ) and the maximum generator speed value MR=max(R i ).

[0080] S2: Screening the SCADA data corresponding to P i >0.8MP, and dividing the data into 10 intervals I j, j = 1, 2,..., 10. For each power interval I j , find the maximum rotational speed MR_I j .

[0081] MR_I j = max(R i ) R i ∈I j (1)

[0082] S3: Form a set {MR_I j} with the 10 calculated MR_I j values, and reorder them from small to large to get [MR_I1, MR_I2,..., MR_I 10 ], where MR_I1≤ MR_I2≤... ≤ MR_I 10 .

[0083] S4: To avoid the impact of abnormal data, remove the first 2 and last 2 of the 10 values, and finally leave 6 data {MR_I3, MR_I4, MR_I5, MR_I6, MR_I7, MR_I8}.

[0084] S5: Average the 6 data, i.e. get the average maximum rotational speed of the generator in the [0.8MP, MP] interval, and set it as R1:

[0085]

[0086] At this point, a rated speed R1 is obtained from the power dimension.

[0087] S6: Divide the rotational speeds {R i} into equal parts to get a partition set {K i}. When partitioning, set an appropriate partition step size according to the MR value, so that the number of partitions m satisfies 10≤ m≤ 300.

[0088] S7: For each rotational speed partition K j , calculate the ratio of the data amount in this partition to the total rotational speed data amount, and remove the partitions with a ratio less than 1% to reduce the impact of abnormal data such as jumps.

[0089] S8: For the remaining rotational speed partitions K j , use the Kernel Density Estimation (KDE) algorithm to calculate the maximum probability rotational speed MR_K jKernel Density Estimation (KDE) algorithm is a commonly used non-parametric estimation method, which estimates the probability density by smoothing the sample data. The Gaussian function is selected as the kernel function, and the standard deviation of the data is used as the bandwidth. Therefore, the probability density function is:

[0090]

[0091] where, is the estimated probability density function value at data point x, n is the data quantity, and σ is the bandwidth (here, the standard deviation of the data).

[0092] MR_K j is the K j th maximum speed in the K th speed partition.

[0093] S9: Find the maximum speed of all speed partitions, denoted as R2:

[0094] R2=Max(MR_K j ) (4)

[0095] Thus, a rated speed value is obtained from the speed dimension.

[0096] S10: Finally, the rated speed R rated of the generator is taken as the maximum of the two speed values, specifically:

[0097] R rated =max(R1,R2) (5)

[0098] 3. Based on the operation data, the grid-connected speed of the wind turbine is calculated.

[0099] It should be noted that the grid-connected speed is the critical speed of the generator when the wind turbine is connected to the grid. Since the grid-connected speed corresponds to the wind turbine just connected to the grid, the corresponding power is not high. According to this feature, it seems that the maximum probability value of the generator speed in a small power interval near 0 power can be used as the grid-connected speed, just like when the rated speed is processed. However, through a large number of actual data of different wind farms and different models, it is found that relying solely on this method has great drawbacks. First, because individual manufacturers' units have abnormal data of ultra-high speed in the interval near 0 power (perhaps due to sensor abnormalities, grid connection debugging, etc.); second, even for normal units, the speed concentration band near 0 power is not at the grid-connected speed, but is more concentrated at the speed before grid connection. Therefore, the maximum density speed value corresponding to the small interval near 0 power cannot be used as the grid-connected speed value.

[0100] Through observation and analysis of a large number of unit data of different manufacturers and different models, it is found that the grid-connected speed of the generator accounts for a large proportion in the whole speed range of the generator, in order to accurately calculate the grid-connected speed of the generator, the following method is proposed: firstly, the maximum speed within 2% of the maximum power (note that it is not the maximum probability speed) is calculated; secondly, the speed range meeting the corresponding speed condition and power condition is found, and the maximum probability speed is calculated; finally, the smaller value of the speeds obtained by the two methods is taken as the grid-connected speed, and the specific calculation process is as shown in Figure 1 , which comprises the following steps:

[0101] S1: extract the active power data {P i} and the generator speed data {R i} with a time resolution of 10 minutes of SCADA records, to form a data set S={(P i, R i )}, eliminate the data corresponding to P i ≤0, and calculate the maximum active power value MP=max(P i ) and the maximum generator speed value MR=max(R i ).

[0102] S2: screen out the SCADA data corresponding to P i <0.02MP, and divide the data into 10 intervals J k according to the active power, k=1,2,...,10. For each interval J k , the maximum speed MR_J k of the interval is obtained.

[0103] S3: the 10 MR_J k values calculated are composed into a set {MR_J k}, and are reordered from small to large to obtain [MR_J1,MR_J2,...,MR_J 10 ], wherein MR_J1≤MR_J2≤...≤MR_J 10 .

[0104] S4: in order to avoid the influence of abnormal data, the first two and the last two of the 10 values are eliminated, and finally 6 data {MR_J3,MR_J4,MR_J5,MR_J6,MR_J7,MR_J8} are left.

[0105] S4: the 6 data are averaged, and the maximum average speed of the generator in the [0,0.02MP] interval is obtained, which is set as R3:

[0106]

[0107] At this point, a grid-connected speed value R3 is obtained from the power dimension.

[0108] S5: Dividing the rotating speed set {R i} into equal parts, and the number of parts m satisfies 10≤m≤300.

[0109] S6: For each rotating speed part K j , calculating the data volume ratio ρ_K j , the maximum probability rotating speed MR_K j , and the maximum probability active power MP_K j . The calculation of MR_K j and MP_K j both adopts the kernel density estimation (KDE) algorithm, and the Gaussian function is selected as the kernel function, and the standard deviation of the data is used as the bandwidth. See formula (3) for details.

[0110] The calculation of the data volume ratio ρ_K j is as follows:

[0111]

[0112] where n_K j is the data volume contained in the rotating speed part K j , and N is the total data volume of all parts:

[0113] N = ∑n_K j (8)

[0114] S7: Reordering the rotating speed part set {K j} from large to small according to the ratio ρ_K j .

[0115] S8: Finding the first part K t that satisfies the following conditions:

[0116] (1) The maximum probability rotating speed of the part is less than 0.75*MR;

[0117] (2) The maximum probability active power of the part is less than 0.2*MP.

[0118] The formula of the above conditions is:

[0119] MR_K t ≤0.75×MR and MP_K t ≤0.2×MP (9)

[0120] S9: The maximum probability rotating speed of the part K t is R4.

[0121] R4 = MR_K t (10)

[0122] So far, a grid-connected speed value R4 is obtained from the speed probability dimension.

[0123] S10: Finally, the grid-connected speed of the generator takes the smaller value of the two grid-connected speed values, specifically:

[0124] R cutin = min(R3, R4) (11)

[0125] 4. Based on the operation data, a speed curve is fitted to obtain a speed curve;

[0126] It should be noted that the speed curve can be used to determine whether the speed and output power of the generator match, and further determine whether the generator is normal. For this purpose, the present embodiment proposes to fit the power-speed curve or the speed-power curve using the maximum probability density method, which is the same method, only the parameters of the bin need to be adjusted.

[0127] The speed curve fitting process will be described below taking the power-speed curve as an example, as shown in FIG. 1, which includes: Figure 2

[0128] S1: Extract the active power data {P i} and the generator speed data {R i} recorded by SCADA with a time resolution of 10 minutes, form a data set S = {(P i, R i )}, and eliminate the data corresponding to P i ≤ 0.

[0129] S2: Divide the active power data {P i} into equal parts, with a step size of 50 kW or 100 kW, to obtain a power partition set {U i}.

[0130] S3: For each power partition U j , calculate the ratio of the data amount of the partition to the total active power data amount, eliminate the partitions with a ratio less than 1%, and reduce the influence of abnormal data such as jumps.

[0131] S4: For the remaining power partitions U j , use the kernel density estimation (KDE) algorithm to calculate the maximum probability power MP_U i and the maximum probability speed MR_U j corresponding to each interval, and obtain a data pair (MP_U i , MR_U j ) for each interval.

[0132] S5: All power partitions U j ​The above data form a set Ps, through which the rotating speed curve can be fitted. In the fitting, the interpolation spline value can be selected according to whether the curve needs to be smoothed.

[0133] Ps = {(MP_U i , MR_U j )} (12)

[0134] From whether the fitted rotating speed curve is smooth, it can be judged whether there is an abnormality between the generator rotating speed and the output power of the unit; between different units of the same type, the rotating speed curves can be compared to determine whether there is a large difference between the units, thereby helping to judge the quality reliability of the units of the same type; for the same unit, the rotating speed curves of different operation periods (such as the first year of operation, the second year of operation, the third year of operation, …, and the time of out of warranty) can be compared to judge whether the quality of the generator of the unit has decreased.

[0135] Next, the effectiveness of the method of the present application is verified through actual cases.

[0136] Case 1: normally running doubly-fed asynchronous generator unit

[0137] Taking a 5MW unit of a certain wind farm as an example, the rated power of the unit is 5000kW, the rated wind speed is 9.5m / s, the grid-connected rotating speed of the generator is 1050rpm, the rated rotating speed of the generator is 1750rpm, the operating data of active power and generator rotating speed recorded by the SCADA system of the unit in a complete year are selected, the sampling frequency is 10 minutes, and there are a total of 50509 data, after excluding the data whose power is less than or equal to 0, there are still 34506 data.

[0138] Four rotating speed data are calculated according to the method of the present application:

[0139] R1 = 1751.62rpm

[0140] R2 = 1749.71rpm

[0141] R3 = 1050.17rpm

[0142] R4 = 1050.21rpm

[0143] From the above data, it can be seen that for the normally running unit, R1 is close to R2, and R3 is close to R4, which shows that the results are similar for different numerical calculation methods. According to formula (5) and formula (11), the actual rated rotating speed of the generator of the unit is 1751.62rmp and the actual grid-connected rotating speed is 1050.17rpm, which meets the design parameters, as shown in Figure 3

[0144] Case 2: doubly-fed asynchronous generator unit in trial operation period ​

[0145] Take a 6.25MW unit in a wind farm as an example, the rated power of the unit is 6250kW, the rated wind speed is 9.5m / s, the grid-connected speed of the generator is 1080rpm, the rated speed of the generator is 1780rpm, the SCADA system data of the unit in the trial operation period less than 2 months is selected, including active power, generator speed, etc., the sampling frequency is 10 minutes, a total of 7966 data, after excluding the data with power less than or equal to 0, there are still 5567 data.

[0146] Four speed data are calculated according to the method of the application:

[0147] R1=1776.50rpm

[0148] R2=1651.00rpm

[0149] R3=1667.14rpm

[0150] R4=1078.00rpm

[0151] From the above data, for the unit in the trial operation period, since the operation is unstable and needs to be debugged, etc., the data recorded by the SCADA exist a large number of abnormal data, thereby causing a large difference between the speed values calculated by different methods, for example, the R2 value 1651.00rpm and the designed rated speed parameter 1780rpm exist a large difference, since R2 is obtained by the maximum probability method, it is indicated that the generator speed in the debugging period is not completely released; the R3 value 1667.14rpm and the designed grid-connected speed 1080rpm are obviously different, since R3 is obtained by the maximum value method, it is indicated that there are a large number of high-speed abnormal points near 0 power in the debugging period, which may be caused by trial operation or speed sensor debugging, etc.

[0152] Although there are obvious differences between R1 and R2, R3 and R4, but after taking the maximum value and the minimum value respectively, the correct actual rated speed data and grid-connected speed data are still obtained, which are 1776.5rpm and 1078.0rpm respectively, which are consistent with the design parameters, as shown in Figure 4

[0153] Case 3: normally running direct-drive synchronous generator unit

[0154] ​Case 1 and Case 2 are both doubly-fed asynchronous generator sets, which use a variable speed gearbox to convert the low speed of the impeller into high speed to drive the generator. The speed of the generator is high. The direct-drive synchronous generator set has no gearbox structure, and the speed of the generator is equal to the speed of the impeller, so the speed is low. Taking a 1.55MW unit in a wind farm as an example, the rated power of the unit is 1550kW, the rated wind speed is 11.1m / s, the generator grid-connected speed is 9.9rpm, the rated speed of the generator is 17.3rpm, and about 5 months of SCADA system data of the unit are selected, including active power, generator speed, etc. The sampling frequency is 10 minutes, and a total of 20676 data are obtained. After removing the data with power less than or equal to 0, there are still 19748 data.

[0155] Four speed data are calculated by the method of the application:

[0156] R1 = 17.19

[0157] R2 = 17.07

[0158] R3 = 9.96

[0159] R4 = 9.88

[0160] From the above values, for a normally operating synchronous generator set, R1 and R2 are similar, and R3 and R4 are similar, indicating that different numerical calculation methods have similar results. According to formula (5) and formula (11), the actual rated speed of the generator of the unit is 17.19rpm, and the actual grid-connected speed is 9.88rpm, which meets the design parameters, as shown in Table 1. Figure 5

[0161] Case 4: Abnormal operation of direct-drive synchronous generator set

[0162] Taking a 4.5MW direct-drive synchronous unit in a wind farm as an example, the rated power of the unit is 4500kW, the rated wind speed is 10.8m / s, the generator grid-connected speed is 6.0rpm, and the rated speed of the generator is 9.5rpm. About 5 months of SCADA system data of the unit are selected, including active power, generator speed, etc. The sampling frequency is 10 minutes, and a total of 20543 data are obtained. After removing the data with power less than or equal to 0, there are still 14269 data.

[0163] Four speed data are calculated by the method of the application:

[0164] R1 = 9.53rpm

[0165] R2 = 9.50rpm

[0166] R3 = 6.00rpm

[0167] R6 = 6.01rpm​

[0168] According to formula (5) and formula (11), the actual rated speed of the generator of the unit is 9.53 rpm, and the actual grid-connected speed is 6.0 rpm, which meets the design parameters, as shown in the following table. Figure 6 Therefore, even for the abnormal running unit, the method of the present application can still accurately calculate the grid-connected speed and rated speed of the generator.

[0169] Cases 1 to 4 prove that the method of the present application has strong adaptability and can adapt to various generator working conditions. On the one hand, the method of the present application selects a specific power interval according to the power characteristics corresponding to the rated speed and the grid-connected speed, so as to avoid the abnormal data interval to a great extent; on the other hand, the method of the present application respectively calculates the maximum speed value in the power interval and the maximum probability speed value in the speed interval, and then compares and verifies, so as to further avoid the influence of abnormal data.

[0170] Case 5: Fitting and comparison of speed curve

[0171] Taking the unit in case 1 as an example, the speed curve is fitted by using the method of the present application, as shown in the following figure. Figure 7 It can be seen from the figure that the fitted speed curve is smooth, and there is no large protrusion or mutation. Figure 7

[0172] Figure 8 The comparison of the speed curves of the same type of units in the wind farm is shown, and it can be seen from the figure that the speed curves of the units of the same type are basically coincident, and each unit maintains excellent stability and consistency during operation, which indicates that the quality of the units of the same type is reliable and stable. Figure 8

[0173] Figure 9 The speed curve figure of another 4.5 MW direct-drive type unit in another wind farm is shown, and the data is from about 5 months of operation. Figure 9 It can be seen from the figure that there is a problem between the generator speed and the output power of the units except F02 unit, and since the problem exists in multiple units of the same type, it is necessary to check whether there is a batch problem, which provides a basis for troubleshooting for the operation and maintenance personnel.

[0174] It can be seen from the comparison of Figure 8 and Figure 9 that the speed curve of the generator of the normally running unit is smooth, and if the speed curve is mutated, it indicates that there is a problem between the generator speed and the output power of the unit, and the problem needs to be checked.

[0175] ​​In summary, the embodiment provides a numerical calculation method of the wind turbine generator speed parameter, according to the operation data recorded by SCADA, the maximum value in the partition and the maximum probability value in the partition are used to calculate the grid-connected speed, the rated speed and the fitting generator speed curve of the wind turbine generator. When calculating, according to whether the rated speed or the grid-connected speed is calculated, the corresponding data of the power interval is selected, which can greatly avoid the influence of abnormal data of the unit; and by using the power partition to obtain the maximum speed value, introducing the KDE in the speed partition to obtain the maximum probability speed value, and comparing and determining the results calculated by the two methods, the accuracy of the results and the adaptability of the calculation method can be further improved; the problems of the "visual method" such as inaccuracy and non-automation are solved.

[0176] Second embodiment

[0177] The embodiment provides an electronic device, such as Figure 10 As shown in the figure, the electronic device comprises a processor and a memory; wherein the processor and the memory can be connected through a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to realize the method of the first embodiment. In addition, the electronic device can also comprise a transceiver, and the processor and the transceiver can be connected through a communication bus, and the transceiver is used for communicating with other devices.

[0178] Next, the various constituent components of the electronic device will be specifically introduced: Figure 10

[0179] ​The processor is the control center of the electronic device. The electronic device can include multiple processors. Each of the processors can be a single-CPU or a multi-CPU. The processor can be one processor or a collective term of multiple processing elements. For example, the processor can be one or more central processing units (CPUs), other general purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement one or more embodiments of the present application, such as one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor can be a microprocessor or any conventional processor, or the like. The processor can perform various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0180] In a specific implementation, as an embodiment, the processor can include one or more CPUs, such as CPU0 and CPU1 shown in FIG. 7, of course, this is only an exemplary description. Figure 10

[0181] The memory is used to store software programs for implementing the solution of the present application, and is controlled by the processor to perform the implementation. The specific implementation can refer to the method embodiments described above, and will not be described here.

[0182] ​Optionally, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may be integrated with the processor or may exist independently, and may be accessed through the interface circuit of the electronic device (…). Figure 10 (Not shown in the image) is coupled to the processor; however, this embodiment of the invention does not impose specific limitations on this.

[0183] The transceiver may include a receiver and a transmitter. Figure 10 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver can be integrated with the processor or exist independently, and is connected through the interface circuit of the electronic device (…). Figure 10 (Not shown in the image) is coupled to the processor, and this embodiment of the invention does not specifically limit this.

[0184] In addition, it should be noted that, Figure 10 The structure of the electronic device shown is not intended to limit the device. Actual devices may include more or fewer components than shown, or combine certain components, or have different component arrangements. Furthermore, the technical effects achieved by this electronic device when performing the method of the first embodiment described above can be referenced to the technical effects described in the first embodiment; therefore, they will not be repeated here.

[0185] Third Embodiment

[0186] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0187] Moreover, it should be noted that the present application can be provided as a method, an apparatus, or a computer program product. Therefore, the embodiments of the present application can take the form of an entirely or partially hardware embodiment, an entirely or partially software embodiment, or an embodiment combining software and hardware aspects. Furthermore, when implemented in software, the embodiments of the present application can take the form of a computer program product including one or more computer program instructions embodied in a computer-usable storage medium. The computer program instructions can be stored in a computer-usable storage medium, or transmitted from one computer-usable storage medium to another, for example, from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center through a wired (for example, infrared, wireless, microwave, or the like) manner. The computer-usable storage medium can be any available medium or a collection of medium(s) accessible by a computer or a data storage device such as a server, a data center, or the like, including one or more of a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0188] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and a combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device that implements the flow Figure 1 The flow or flows and / or blocks in the flowcharts and / or block diagrams Figure 1 The device that implements the function specified in the flow or flows and / or blocks.

[0189] The computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing terminal devices to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction devices that implement the flow Figure 1 The flow or flows and / or blocks in the flowcharts and / or block diagrams Figure 1the functions specified in the individual block or blocks. Such computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable devices to generate a computer-implemented process, thus the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart block(s). Figure 1 the functions specified in the individual block or blocks. Such computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable devices to generate a computer-implemented process, thus the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart block(s). Figure 1 the functions specified in the individual block or blocks. Such computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable devices to generate a computer-implemented process, thus the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart block(s).

[0190] It should also be noted that, in the present document, the terms such as first and second, etc. are merely used to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or terminal device. Without more limitations, the element defined by the statement "including a…", does not exclude the presence of other identical elements in the process, method, article or terminal device including the element. In addition, the term "and / or" is merely a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " in the present document generally represents an "or" relationship between the front and rear associated objects, but it can also represent an "and / or" relationship, which can be understood in the context before and after. "One or more" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0191] In addition, it can be understood that in various embodiments of the present application, the size of the sequence number of the above processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0192] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or in a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0193] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of functional modules / units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms. The units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present, or two or more units can be integrated in one unit.

[0194] If the method is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0195] Finally, it should be noted that the above description is only the preferred embodiment of the application, it should be pointed out that although the preferred embodiment of the application has been described, for those skilled in the art, once the basic creative concept of the application is known, several improvements and refinements can be made without departing from the principles of the application, and these improvements and refinements should also be considered as the protection scope of the application. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the application.

Claims

1. A numerical method for calculating a wind turbine generator speed parameter, characterized by, The method comprises the following steps: acquiring operation data of a wind turbine; wherein the operation data of the wind turbine comprises active power data of the wind turbine and generator speed data of the wind turbine; calculating a rated speed of the wind turbine based on the operation data; calculating a grid-connected speed of the wind turbine based on the operation data; performing speed curve fitting based on the operation data to obtain a speed curve; wherein the speed curve is used to determine whether the speed of the generator matches the output power, and further determine whether the generator is normal; calculating a rated speed of the wind turbine based on the operation data, comprising: based on the operation data, obtaining a rated rotating speed value from a power dimension R 1; based on the operation data, obtaining a rated speed value from a rotational speed dimension R 2; Take R 1 and R the maximum value in 2 as the rated speed of the wind turbine; based on the operation data, obtaining a rated rotating speed value from a power dimension R 1. comprising: extracting active power data and generator speed data with a time resolution of T minutes from the operation data, and aligning the extracted active power data and generator speed data in the time dimension, to form a first data set using the aligned active power data and generator speed data; removing active power data and its corresponding generator speed data not greater than 0 in the first data set to obtain a second data set; wherein, T is a preset value; The active power data greater than the first preset power threshold and the corresponding generator speed data in the second data set are screened out, and the screened data are equally divided into N intervals according to the size of the active power, for each interval, the maximum generator speed value is obtained; wherein, N is a preset value; reordering the calculated maximum generator speed values of each interval in ascending order to form a third data set from the reordered maximum generator speed values of each interval; Before removing the third dataset n Individual and after n We obtain the fourth dataset by taking values; where, n This is the default value; taking the average value of the data in the fourth data set as a rated speed value R1 obtained from the power dimension; obtaining a rated speed value R2 from the speed dimension based on the operation data, comprising: The screened generator rotating speed data is equally divided to obtain multiple rotating speed partitions; wherein, when the screened generator rotating speed data is equally divided, the number of rotating speed partitions M needs to satisfy 10≤ M ≤300; for each speed partition, calculating the ratio of the data amount within it to the total generator speed data amount; eliminating speed partitions with a ratio less than a preset ratio threshold; for the remaining speed partitions, calculating the maximum probability speed of each speed interval using the kernel density estimation algorithm; taking the maximum value of all maximum probability rotational speeds as the rated rotational speed value from the rotational speed dimension R 2.

2. The numerical method of calculating the wind turbine generator speed parameter as claimed in claim 1, wherein, calculating a grid-connected speed of the wind turbine based on the operation data, comprising: Based on the operation data, a grid-connected rotational speed value is obtained from the power dimension R 3; Based on the operation data, a grid-connected speed value is also obtained from the speed probability dimension R 4; Take R 3 and R the minimum value in 4 as the grid-connected speed of the wind turbine; Based on the operation data, a grid-connected rotating speed value is obtained from a power dimension R 3. comprising: extract active power data and generator speed data with a time resolution of T minutes from the operation data, and align the extracted active power data and generator speed data in the time dimension, to form a fifth data set using the aligned active power data and generator speed data; remove active power data and its corresponding generator speed data not greater than 0 in the fifth data set to obtain a sixth data set; wherein, T is a preset value; The active power data less than the second preset power threshold and the corresponding generator speed data in the sixth data set are screened out, and the screened data are equally divided into N intervals according to the size of the active power. For each interval, the maximum generator speed value is obtained. N is a preset value. reordering the calculated maximum generator speed values of each interval in ascending order to form a seventh data set from the reordered maximum generator speed values of each interval; Before removing the seventh dataset n Individual and after n The values ​​are used to obtain the eighth dataset; among them, n This is the default value; averaging the values of the data in the eighth data set as the grid-connected rotational speed value obtained from the power dimension R 3; Based on the operation data, a grid-connected speed value is also obtained from the speed probability dimension R 4. Comprising: The screened generator rotating speed data is equally divided to obtain multiple rotating speed partitions; wherein, when the screened generator rotating speed data is equally divided, the number of rotating speed partitions M needs to satisfy 10≤ M ≤300; for each speed partition, calculating the ratio of the data amount within it to the total generator speed data amount, and calculating the maximum probability speed and maximum probability active power of each speed interval using the kernel density estimation algorithm; reordering the speed partitions in descending order according to the ratio; selecting a first partition from the sorted speed partitions that satisfies the following conditions K t : (1) The maximum probability rotating speed of the rotating speed partition is less than ; (2) the maximum probability active power of the rotating speed partition is less than ; wherein, MP represents the maximum active power data in the sixth data set; MR represents the maximum generator speed data in the sixth data set; With K t the maximum probability speed as the grid-connected speed value obtained from the speed probability dimension R 4.

3. The numerical method of calculating the wind turbine generator speed parameter as claimed in claim 1, wherein, performing speed curve fitting based on the operation data to obtain a speed curve, comprising: The active power data and the generator speed data with a time resolution of T minutes in the extraction operation data are extracted, and the active power data and the generator speed data are aligned in the time dimension, and then the active power data and the corresponding motor speed data not greater than 0 are removed to obtain a ninth data set; wherein, T is a preset value; equally partitioning the active power data in the ninth data set to obtain multiple power partitions; for each power partition, calculating the ratio of the data amount within it to the total active power data amount, eliminating partitions with a ratio less than a preset ratio threshold, and for the remaining power partitions, calculating the maximum probability power and maximum probability speed corresponding to each partition using the kernel density estimation algorithm; fitting a power-speed curve using the maximum probability power and maximum probability speed corresponding to each partition; wherein the abscissa of the curve is the active power and the ordinate is the generator speed.

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