A method, system, device and medium for quantitatively evaluating wind bias

Abstract

The present application belongs to the field of wind power generation, and specifically discloses a wind deviation quantitative evaluation method, system, device and medium, comprising: obtaining sampling data, performing standardization processing, cleaning unit operation data, excluding the influence of factors such as control difference, turbulence intensity difference and blade damage after eliminating abnormal data, and taking the screened unit as an evaluation object; taking the power coefficient CP expected value as a characteristic parameter to reduce the influence of power fluctuation and randomness on wind deviation evaluation; calculating the effective wind speed interval of wind deviation, and selecting the constant CP control wind speed interval of the unit to reduce the data amount requirement; calculating the expected CP under each wind deviation angle and performing normalization processing, and finally performing wind deviation curve fitting to further weaken the fluctuation influence; deriving the fitted curve and calculating the inflection point, comparing with the set early warning judgment rule, issuing an alarm and giving a quantitative evaluation conclusion, and guiding the wind deviation correction.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation, specifically relating to a method, system, equipment, and medium for quantitative assessment of wind deviation. Background Technology

[0002] Due to factors such as long-term operational errors of the wind vane, installation errors of the wind vane, and measurement errors, the unit's wind alignment deviation can occur. The power generation capacity or efficiency CP value of a normal unit should reach its optimum when the angle between the wind vane and the nacelle is close to 0 degrees. If the performance is optimal at other angles, it proves that the unit has a certain wind alignment deviation. Wind alignment deviation will cause the unit's power generation loss, with a loss rate of θ, where θ is the deviation angle. For example, when the wind alignment deviation is 10°, the unit's power loss is about 4.5%.

[0003] Currently, after operating for a period of time, the units in service generally exhibit a slight deviation from the wind direction. Based on the analysis of wind direction deviations of more than 600 units, it was found that the deviation angle is generally in the range of 3°-6°, with a power loss of about 0.5%-1.63%. It is necessary to conduct regular wind vane calibration for mechanical anemometers using matching wind-following fixtures during annual and semi-annual inspections, and to regularly inspect and calibrate the parameters of ultrasonic anemometers.

[0004] Because wind is random and fluctuating, the power output of wind turbines also fluctuates. Currently, in the industry, when assessing wind deviation, wind speed and active power are generally used as evaluation parameters. The deviation angle of the point where the active power is the largest under different wind speed conditions is used as a condition to qualitatively judge the wind deviation. Considering the fluctuation of active power, this brings great uncertainty to the evaluation results, and the wind deviation assessment of wind turbines is inefficient and highly random. Alternatively, power generation capacity (i.e., the ratio of actual active power to theoretical active power) is used as the evaluation parameter, and the deviation angle of the point where the power generation capacity is the largest is used as a condition to judge the wind deviation. Although this method reduces the impact of power fluctuations to some extent, it requires a large amount of data to support it and is not suitable for short-term wind deviation assessment. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method, system, equipment and medium for quantitative assessment of wind deviation, so as to solve the problems of low efficiency, strong randomness, large uncertainty and high data requirements in the assessment of wind deviation by wind turbine units in the prior art.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] In a first aspect, the present invention provides a method for quantitatively assessing wind deviation, comprising:

[0008] S1: Acquire sampling data;

[0009] S2: Calculate the average air density based on the sampling data obtained in step S1, standardize the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed range.

[0010] S3: Based on the evaluation wind speed range obtained in step S2, divide the evaluation range into partitions according to the wind deviation, calculate the expected CP for each wind deviation range, and normalize the expected CP based on the expected CP for each wind deviation range to obtain the normalized expected CP.

[0011] S4: Based on the normalized expected value CP obtained in step S3, perform polynomial fitting to obtain the polynomial fitting curve. Based on the polynomial fitting curve, perform quantitative evaluation of the deviation and classify and issue early warnings for the wind deviation angle.

[0012] Furthermore, the specific steps in step S1 for obtaining the sampled data include:

[0013] S11: Obtain the number of benchmark turbines in the wind farm, the installed capacity of each benchmark turbine, and the total capacity of non-benchmark turbines in the power station;

[0014] S12: Obtain the complete annual historical operating data of the wind farm units, including 1-minute and 10-minute operating data; obtain the complete annual historical unit operating logs, shutdown records, fault records, and blade inspection records of the wind farm units. The operating data includes: wind speed, maximum wind speed, minimum wind speed, wind speed standard deviation, active power, maximum active power, minimum active power, active power standard deviation, rotor speed, ambient temperature, generator speed, blade angle, nacelle position, wind direction, PLC status, shutdown records, fault records, and blade inspection records.

[0015] S13: Obtain complete historical operating data for the year, including temperature, air pressure, and humidity data for the same period.

[0016] Furthermore, the formula for calculating the average air density in step S2 is as follows:

[0017]

[0018] Where: ρ 10min The derived average air density over 10 minutes;

[0019] T 10min This represents the average absolute temperature obtained from a 10-minute measurement.

[0020] B 10min This is the average air pressure obtained from a 10-minute measurement.

[0021] R0 is the dry gas constant, 287.05 J / (kg·K);

[0022] The formula for standardizing the measured wind speed in step S2 is as follows:

[0023]

[0024] V ave Standardized wind speed;

[0025] V 10min The average cabin wind speed over 10 minutes;

[0026] ρ0 is the reference air density.

[0027] Furthermore, the cleaning of the unit operation data in step S2 includes:

[0028] Based on the standardized data obtained from the calculation, the unit operation data is cleaned to remove abnormal data and obtain the cleaned unit operation data.

[0029] Data in the 10-minute average that falls under the following categories will be deleted: data where the wind speed exceeds the wind turbine's operating range; data where the wind turbine is shut down due to a fault; data where the turbine is manually shut down during testing or maintenance; data during shutdown or startup; data where the power is reduced; and data during grid curtailment.

[0030] Check the PLC status of the unit, select the operating data where the wind turbine status is in normal power generation state, and discard the rest of the data;

[0031] The analysis of unit control parameter consistency, unit turbulence intensity, and blade inspection data in step S2 specifically includes:

[0032] Control parameter consistency analysis: Based on the unit operation data after cleaning, the consistency of the control parameters is judged by scatter plots of speed-blade angle, speed-power, and speed-torque. Units with consistency deviations are not subject to wind deviation analysis to eliminate the influence of control factors.

[0033] Unit turbulence intensity analysis: By analyzing the turbulence intensity of the unit, a turbulence intensity range is defined. Units with turbulence intensity exceeding the design turbulence intensity across the entire wind speed range and at 15 m / s are excluded to reduce the impact of wind conditions. The turbulence intensity calculation formula is as follows:

[0034] I i =σ i / V ave，i

[0035] Among them: I i This is the turbulence intensity data for the i-th 10-minute period;

[0036] V ave，i This is the i-th 10-minute standardized average wind speed data;

[0037] σ i This represents the i-th 10-minute standard deviation data point;

[0038] Blade inspection data analysis: By reviewing the blade inspection records, units with defective blades are excluded, and wind deviation analysis is not performed to reduce the impact of blades on the assessment conclusions;

[0039] The method for selecting the wind speed evaluation range in step S2 is as follows:

[0040] According to the control strategy of variable speed and variable pitch wind turbine generator sets, there is a constant CP. max Within the operating range or maximum wind energy capture range, the blade pitch angle remains constant. The generator speed is controlled to follow the wind speed variation, maintaining a constant tip speed ratio and a constant wind energy utilization coefficient. This wind speed range forms the basis for wind speed range selection. A scatter plot of the unit's wind speed-speed is drawn, using two turning points as benchmarks to determine the constant CP (critical velocity coefficient). max Work area scope, for CP max The working area is reduced by 5% for both the upper and lower limits to determine the final assessment wind speed range.

[0041] Furthermore, step S3, which involves dividing the evaluation range according to the wind deviation based on the evaluation wind speed range obtained in step S2, specifically includes:

[0042] Given the data and wind speed ranges, the wind energy utilization coefficient (CP) is calculated for each wind deviation range. The wind deviation range is divided into 1° intervals, with the midpoint of the interval rounded to an integer. The calculation formula is as follows:

[0043]

[0044] Where: V i，j This refers to the j-th 10-minute standardized average wind speed data within the i-th deviation interval.

[0045] P i，j This represents the j-th 10-minute average active power data within the i-th deviation interval;

[0046] ρ0 is the reference air density;

[0047] A represents the swept area of ​​the wind turbine;

[0048] CP i，j The wind energy utilization coefficient for the j-th 10-minute interval in the i-th deviation interval;

[0049] The calculation of the expected CP for each wind deviation interval in step S3 specifically includes:

[0050] Based on the calculated wind energy utilization coefficient CP, calculate the expected CP for each wind deviation interval;

[0051] The wind deviation interval is divided into 1° intervals, and the wind speed within each interval is divided into 0.5 m / s intervals. The midpoint of the interval is rounded to the nearest integer. The formula for calculating the average CP value of the wind speed interval is as follows:

[0052]

[0053] Among them: CP ave，i，k The average value of CP in the k-th wind speed interval of the i-th deviation interval;

[0054] N i，k Let k be the number of data points in the k-th wind speed interval of the i-th deviation interval, where k = 1, 2, 3…n;

[0055] CP i，k，j The wind energy utilization coefficient for the j-th 10-minute interval within the k-th wind speed interval of the i-th deviation interval;

[0056] The formula for calculating the expected CP at each wind deviation angle is:

[0057]

[0058] Among them: CP hope，i Let CP be the expected value for the i-th deviation interval;

[0059] CP ave，i，k The average value of CP in the k-th wind speed interval of the i-th deviation interval;

[0060] N i，k Let k be the number of data points in the k-th wind speed interval of the i-th deviation interval, where k = 1, 2, 3…n;

[0061] N i The total number of data points in the i-th deviation interval;

[0062] In step S3, the expected CP is normalized according to each wind deviation interval to obtain the normalized expected CP. This specifically includes:

[0063] Based on the expected CP at each deviation angle, the maximum expected CP is found, and then the normalized CP value at each deviation angle is calculated using the following formula:

[0064]

[0065] Among them: CP 归一化，i Let CP be the normalized expected value of the i-th deviation interval;

[0066] CP hope，maxThe maximum expected CP for each deviation angle of the unit;

[0067] CP hope，i Let CP be the expected value of the i-th deviation interval.

[0068] Furthermore, in step S4, based on the normalized expected value CP obtained in step S3, polynomial fitting is performed to obtain the polynomial fitting curve, specifically including:

[0069] Based on the normalized expected CP obtained in step S3, and taking the actual unit's wind deviation range as the basis, with the wind deviation angle as the independent variable and the expected CP for each wind deviation interval as the dependent variable, a polynomial fitting is performed to obtain the deviation angle-expected CP fitting curve.

[0070] The wind deviation angle is taken as -6° to 6°. With the wind deviation angle as the independent variable and the expected CP for each wind deviation interval as the dependent variable, a quadratic polynomial is fitted. The calculation formula is as follows:

[0071] CP 归一化 =Ax 2 +Bx+C

[0072] Among them: CP 归一化 The normalized expected value CP for the deviation interval;

[0073] A is the fitting coefficient for the quadratic term;

[0074] B is the fitting coefficient for the linear term;

[0075] C is the fitting constant term;

[0076] x is the windward deviation angle.

[0077] Furthermore, in step S4, the deviation is quantitatively evaluated based on the polynomial fitting curve, and a graded early warning is issued for the wind deviation angle. This specifically includes:

[0078] Based on the obtained deviation angle-expected CP fitting curve, the windward deviation angle is quantitatively evaluated. The derivative of the quadratic fitting curve is calculated, and the inflection point is determined. The calculation formula is as follows:

[0079] dCP 归一化 / dx=2Ax+B=0

[0080] Among them: CP 归一化 The normalized expected value CP for the deviation interval;

[0081] A is the fitting coefficient for the quadratic term;

[0082] B is the fitting coefficient for the linear term;

[0083] x is the windward deviation angle;

[0084] The inflection point or deviation quantification value is:

[0085]

[0086] Where: x 偏差角 This is the inflection point, and also the quantified value of the deviation;

[0087] A is the fitting coefficient for the quadratic term;

[0088] B is the fitting coefficient for the linear term;

[0089] Set early warning thresholds and issue graded early warnings based on the wind deviation angle;

[0090] Based on operational data and the unit's yaw characteristics, the warning threshold is set to three levels, when 2°≤|x 偏差角 If |x ≤ 3°, only a prompt is needed; if 3° < |x 偏差角 If |x| ≤ 5°, issue a warning; if |x| ≤ 5°, issue a warning. 偏差角 If the temperature exceeds 5°, an alarm will be triggered.

[0091] Secondly, the present invention provides a system for quantitatively assessing wind deviation, comprising:

[0092] The sampling data acquisition module is used to acquire sampling data;

[0093] The standardized calculation and evaluation wind speed range selection module is used to calculate the average air density based on the acquired sampling data, perform standardized calculations on the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed ranges.

[0094] The expected CP normalization module is used to divide the evaluation interval into partitions according to the wind deviation based on the obtained evaluation wind speed interval, calculate the expected CP for each wind deviation interval, and normalize the expected CP based on the expected CP for each wind deviation interval to obtain the normalized expected CP.

[0095] The deviation quantitative assessment and wind deviation angle classification early warning module is used to perform polynomial fitting based on the obtained normalized expected value CP to obtain a polynomial fitting curve, and to perform quantitative assessment of the deviation based on the polynomial fitting curve and classify and issue early warnings for wind deviation angle.

[0096] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements a method for quantitatively assessing wind deviation as described above.

[0097] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a method for quantitatively assessing wind deviation as described above.

[0098] The present invention has at least the following beneficial effects:

[0099] This invention first cleans the unit's operating data, removing abnormal data and eliminating the influence of factors such as control differences, turbulence intensity differences, and blade damage, using the screened units as evaluation objects. Then, the expected power coefficient (CP) is used as a feature parameter to reduce the impact of power fluctuations and randomness on the wind deviation assessment. The effective wind speed range for wind deviation is calculated, and the constant CP control wind speed range for the unit is selected to reduce the amount of data required. The expected CP at each wind deviation angle is calculated and normalized. Finally, the wind deviation curve is fitted to further reduce the impact of fluctuations. The derivative of the fitted curve is calculated and the inflection point is compared with the set early warning judgment rules to issue an alarm and provide a quantitative assessment conclusion, guiding the correction of wind deviation. Attached Figure Description

[0100] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0101] Figure 1 This is a flowchart of a method for quantitatively assessing wind deviation according to the present invention.

[0102] Figure 2 This is a schematic diagram illustrating the control curve analysis between the unit speed and blade angle of the present invention;

[0103] Figure 3 This is a schematic diagram illustrating the control curve analysis between the unit speed and power of the present invention;

[0104] Figure 4 This is a schematic diagram of the fitting curve for Unit 1.

[0105] Figure 5 This is a schematic diagram of the fitting curve for Unit 2.

[0106] Figure 6 This is a schematic diagram of the fitting curve for Unit 12#.

[0107] Figure 7 This is a schematic diagram of the fitting curve for unit #24;

[0108] Figure 8 This is a schematic diagram of the fitting curve for unit #25;

[0109] Figure 9 This is a schematic diagram of a system module for quantitatively assessing wind deviation. Detailed Implementation

[0110] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0111] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0112] Example 1

[0113] like Figure 1 As shown, a method for quantitatively assessing wind deviation includes:

[0114] S1: Acquire sampling data;

[0115] S11: Obtain the number of benchmark turbines in the wind farm, the installed capacity of each benchmark turbine, and the total capacity of non-benchmark turbines in the power station;

[0116] S12: Obtain the complete annual historical operating data of the wind farm turbines, including 1-minute and 10-minute operating data; obtain the complete annual historical operating logs, shutdown records, fault records, and blade inspection records of the wind farm turbines, including: wind speed, maximum wind speed, minimum wind speed, wind speed standard deviation, active power, maximum active power, minimum active power, active power standard deviation, rotor speed, ambient temperature, generator speed, blade angle, nacelle position, wind direction, PLC status, shutdown records, fault records, and blade inspection records, etc.

[0117] S13: Obtain complete annual historical operating data, including temperature, air pressure, and humidity data for the same period;

[0118] The data should cover the entire wind speed range as much as possible, and should be at least 1.5 times the wind speed corresponding to the cut-in wind speed to 85% of the rated power. Each 0.5 m / s wind speed range should be no less than 30 minutes, and the entire wind speed range should include at least 180 hours of sampling data, that is, 1080 valid data points (10 minutes of data). The specific situation needs to be judged based on the actual data. In principle, the more valid data, the better.

[0119] S2: Calculate the average air density based on the sampling data obtained in step S1, standardize the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed range.

[0120] S21: Calculate the average air density, measure the wind speed and perform standardized calculations to obtain standardized data;

[0121] Generally, air density varies with temperature and exhibits periodic changes with the seasons. Therefore, it is necessary to convert wind speed to a reference density to reduce the influence of temperature on wind speed and to facilitate comparative analysis with wind speeds at the reference density. The formula for calculating the 10-minute average air density is:

[0122]

[0123] Where: ρ 10min The derived average air density over 10 minutes;

[0124] T 10min This represents the average absolute temperature obtained from a 10-minute measurement.

[0125] B 10min The average air pressure is obtained from a 10-minute actual measurement. If a 10-minute air pressure is not available, the local average air pressure from the feasibility study report can be used instead.

[0126] R0 is the dry gas constant, 287.05 J / (kg·K);

[0127] The standardized formula for wind speed is:

[0128]

[0129] V ave Standardized wind speed;

[0130] V 10min The average cabin wind speed over 10 minutes;

[0131] ρ0 is the reference air density, typically the average annual density at the site or the standard air density of 1.225 kg / m³. 3 .

[0132] S22: Based on the standardized data calculated in step S21, clean the unit operation data, remove abnormal data, and obtain the cleaned unit operation data.

[0133] The cleaning standard is:

[0134] ① For data where the wind speed exceeds the operating range of the wind turbine generator, the filtering criteria are: cut-in wind speed - cut-out wind speed;

[0135] ② Wind turbine generator shutdown data caused by wind turbine generator failure. The screening criteria are: average active power ≥ 10kw and generator speed ≥ cut-in speed - 50rpm.

[0136] ③ Manual shutdown data during testing or maintenance operations, the filtering criteria are: check the shutdown record time period and remove that time period;

[0137] ④ Data during shutdown or startup phases should be filtered based on the following criteria: wind speed-blade angle (optimal CP phase), wind speed-rotation speed (constant speed operation phase), and wind speed-active power (rated power phase) characteristic curves of the unit during normal operation. In addition, data points where the generator has been running for less than 600 seconds should be filtered out.

[0138] ⑤ Reduced power operation data: Unit protection reduces power operation due to icing, overheating, excessive vibration, etc. The screening criteria are: based on the characteristic curves of wind speed-blade angle (optimal CP stage), wind speed-speed (constant speed operation stage), wind speed-active power (rated power stage) during normal operation of the unit, set conditions for deletion;

[0139] ⑥ Operational data during power grid rationing: The filtering criteria are: you can check the PLC status to delete the data under power rationing, or you can set conditions to delete the data based on the characteristic curves of wind speed-blade angle (optimal CP stage), wind speed-rotation speed (constant speed operation stage), and wind speed-active power (rated power stage) when the unit is running normally.

[0140] Data exhibiting the above conditions will be deleted from the 10-minute average.

[0141] ⑦ Normal operating data of the unit: Check the PLC status of the unit, select the operating data where the wind turbine status is normal power generation, and discard the rest of the data;

[0142] S23: Analyze the consistency of unit control parameters, unit turbulence intensity, and blade inspection data;

[0143] Control parameter consistency analysis: Based on the unit operation data after cleaning, the consistency of control parameters such as speed-blade angle, speed-power, and speed-torque is judged by scatter plots. Units with consistency deviations are not subject to wind deviation analysis to eliminate the influence of control factors.

[0144] Unit Turbulence Intensity Analysis: Considering the impact of turbulence intensity on the power curve—specifically, the power curve tends to slightly increase at low wind speeds and slightly decrease before reaching rated power—it is necessary to eliminate units with abnormal turbulence intensity. This is achieved by analyzing the unit's turbulence intensity, defining a turbulence intensity range, and eliminating units with turbulence intensity exceeding the design turbulence intensity (based on a 10-minute average) across the entire wind speed range. This reduces the influence of wind conditions. The turbulence intensity calculation formula is as follows:

[0145] I i =σ i / V ave，i

[0146] Among them: I i This is the turbulence intensity data for the i-th 10-minute period;

[0147] V ave，i This is the i-th 10-minute standardized average wind speed data;

[0148] σ i This represents the i-th 10-minute standard deviation data.

[0149] Blade inspection data analysis: Considering that blade condition has a significant impact on unit efficiency, by reviewing the blade inspection records, units with defective blades are excluded, and wind deviation analysis is not performed to reduce the impact of blades on the evaluation conclusions.

[0150] S24: Combine the unit control logic to evaluate the selection of wind speed range;

[0151] According to the control strategy of variable speed and variable pitch wind turbine generator sets, there is a constant CP. max Within the operating range or maximum wind energy capture range, the blade pitch angle remains constant. The generator speed is controlled to follow the wind speed variation, maintaining a constant tip speed ratio (i.e., a constant wind energy utilization coefficient) to maximize wind energy capture. This wind speed range forms the basis for wind speed range selection. A scatter plot of wind speed-speed can be drawn, using two turning points as benchmarks to determine the constant tip speed ratio (CP). max The working area, considering the uncertainty of wind speed measurement, is for CP max The working area is appropriately reduced by 5% for both the upper and lower limits, to determine the final assessment wind speed range.

[0152] S3: Based on the evaluation wind speed range obtained in step S2, divide the evaluation range into partitions according to the wind deviation and calculate the expected CP. Based on the expected CP of each wind deviation range, normalize the expected CP to obtain the normalized expected CP.

[0153] S31: Calculation of wind energy utilization coefficient;

[0154] Given the data and wind speed ranges, the wind energy utilization coefficient (CP) is calculated for each wind deviation range. The wind deviation range is divided into 1° intervals, with the midpoint of the interval rounded to an integer. The calculation formula is as follows:

[0155]

[0156] Where: V i，j This refers to the j-th 10-minute standardized average wind speed data within the i-th deviation interval.

[0157] P i，j This represents the j-th 10-minute average active power data within the i-th deviation interval;

[0158] ρ0 is the reference air density;

[0159] A represents the swept area of ​​the wind turbine;

[0160] CP i，j The wind energy utilization coefficient for the j-th 10-minute interval in the i-th deviation interval;

[0161] S32: Based on the wind energy utilization coefficient CP calculated in step S31, calculate the expected CP for each wind deviation interval;

[0162] The wind deviation interval is divided into 1° intervals, and the wind speed within each interval is divided into 0.5 m / s intervals. The midpoint of the interval is rounded to the nearest integer. The formula for calculating the average CP value of the wind speed interval is as follows:

[0163]

[0164] Among them: CP ave，i，k The average value of CP in the k-th wind speed interval of the i-th deviation interval;

[0165] N i，k Let k be the number of data points in the k-th wind speed interval of the i-th deviation interval, where k = 1, 2, 3…n;

[0166] CP i，k，j The wind energy utilization coefficient for the j-th 10-minute interval within the k-th wind speed interval of the i-th deviation interval;

[0167] The formula for calculating the expected CP at each wind deviation angle is:

[0168]

[0169] Among them: CP hope，i Let CP be the expected value for the i-th deviation interval;

[0170] CP ave，i，k The average value of CP in the k-th wind speed interval of the i-th deviation interval;

[0171] N i，k Let k be the number of data points in the k-th wind speed interval of the i-th deviation interval, where k = 1, 2, 3…n;

[0172] N i The total number of data points in the i-th deviation interval;

[0173] S33: Normalize the expected CP to obtain the normalized expected CP;

[0174] Based on the expected CP at each deviation angle, the maximum expected CP is found, and then the normalized CP value at each deviation angle is calculated using the following formula:

[0175]

[0176] Among them: CP 归一化，i Let CP be the normalized expected value of the i-th deviation interval;

[0177] CP hope，max The maximum expected CP for each deviation angle of the unit;

[0178] CP hope，i Let CP be the expected value for the i-th deviation interval;

[0179] S4: Based on the normalized expected value CP obtained in step S3, perform polynomial fitting to obtain the polynomial fitting curve. Based on the polynomial fitting curve, perform quantitative evaluation of the deviation and classify and issue early warnings for the wind deviation angle.

[0180] S41: Based on the normalized expected CP obtained in step S3, take the actual unit's wind deviation range as the basis, the wind deviation angle as the independent variable, and the expected CP for each wind deviation interval as the dependent variable, perform polynomial fitting to obtain the polynomial fitting curve, namely the deviation angle-expected CP fitting curve.

[0181] The wind deviation angle is taken as -6° to 6°. With the wind deviation angle as the independent variable and the expected CP for each wind deviation interval as the dependent variable, a quadratic polynomial is fitted. The calculation formula is as follows:

[0182] CP 归一化 =Ax 2 +Bx+C

[0183] Among them: CP 归一化 The normalized expected value CP for the deviation interval;

[0184] A is the fitting coefficient for the quadratic term;

[0185] B is the fitting coefficient for the linear term;

[0186] C is the fitting constant term;

[0187] x is the windward deviation angle;

[0188] S42: Based on the deviation angle-expected CP fitting curve obtained in step S41, the wind deviation angle is quantitatively evaluated, the derivative of the quadratic fitting curve is calculated, and the inflection point is calculated. The calculation formula is as follows:

[0189] dCP 归一化 / dx=2Ax+B=0

[0190] Among them: CP 归一化 The normalized expected value CP for the deviation interval;

[0191] A is the fitting coefficient for the quadratic term;

[0192] B is the fitting coefficient for the linear term;

[0193] x is the windward deviation angle;

[0194] The inflection point or deviation quantification value is:

[0195]

[0196] Where: x 偏差角 This is the inflection point, and also the quantified value of the deviation;

[0197] A is the fitting coefficient for the quadratic term;

[0198] B is the fitting coefficient for the linear term;

[0199] S43: Set warning thresholds and issue graded warnings for wind deviation angles;

[0200] Specifically, based on years of operational data from over 600 generating units and their yaw characteristics, the warning threshold is set at three levels: when 2°≤|x 偏差角 If |x ≤ 3°, only a prompt is needed; if 3° < |x 偏差角 If |x| ≤ 5°, issue a warning; if |x| ≤ 5°, issue a warning. 偏差角 If the temperature exceeds 5°, an alarm will be triggered.

[0201] Example 2

[0202] like Figure 9 As shown, a quantitative assessment system for wind deviation includes:

[0203] The sampling data acquisition module is used to acquire sampling data;

[0204] The standardized calculation and evaluation wind speed range selection module is used to calculate the average air density based on the acquired sampling data, perform standardized calculations on the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed ranges.

[0205] The expected CP normalization module is used to divide the evaluation interval into partitions according to the wind deviation based on the obtained evaluation wind speed interval, calculate the expected CP for each wind deviation interval, and normalize the expected CP based on the expected CP for each wind deviation interval to obtain the normalized expected CP.

[0206] The deviation quantitative assessment and wind deviation angle classification early warning module is used to perform polynomial fitting based on the obtained normalized expected value CP to obtain a polynomial fitting curve, and to perform quantitative assessment of the deviation based on the polynomial fitting curve and classify and issue early warnings for wind deviation angle.

[0207] Example 3

[0208] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a method for quantitative assessment of wind deviation as described in Embodiment 1.

[0209] Example 4

[0210] The present invention provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements a method for quantitative assessment of wind deviation as described in Embodiment 1.

[0211] Example 5

[0212] A quantitative assessment of wind deviation was conducted on five turbines at a wind farm.

[0213] Table 1. Statistical table of wind energy utilization coefficients of 5 wind turbines in a wind farm from December 1, 2021 to December 1, 2022.

[0214]

[0215]

[0216] Table 1 shows that the actual power output (CP) of the wind farm fluctuates around the guaranteed CP curve, with the overall power efficiency ranging from 0.42 to 0.58. This indicates that wind speed and power fluctuations have a significant impact on the CP curve, necessitating the calculation of the expected CP and considering the turbine's response to wind and control curves (such as...). Figures 2-3 As shown in the image, no abnormalities were found during the inspection of the blades and other parts.

[0217] Table 2. Turbulence intensity analysis of 5 turbines at a wind farm from December 1, 2021 to December 1, 2022.

[0218]

[0219]

[0220] Table 2 shows the turbulence intensity of a wind farm turbine across all wind speed ranges and at a representative turbulence intensity of 15 m / s. It can be seen that the average turbulence intensity across all wind speed ranges is concentrated between 0.109 and 0.123, which is less than the 0.131 reported in the feasibility study, and therefore does not need to be eliminated. The representative turbulence intensity at different heights at V = 15 m / s is concentrated between 0.07 and 0.095, which is less than the 0.102 reported in the feasibility study, and therefore does not need to be eliminated. A review of the on-site blade inspection report revealed no abnormalities in the blades of turbines #1, #2, #12, #24, and #25, therefore they do not need to be eliminated.

[0221] Table 3. Wind speed-rotation speed statistics for 5 turbine units in a wind farm.

[0222]

[0223]

[0224] Table 3 shows the wind speed-speed operation characteristics within the range of 4.5 m / s to 10.5 m / s. The constant CPmax operating range is determined based on two turning points, namely 1080 rpm and 1750 rpm. Considering the fluctuation of statistical data at the turning points of the actual unit, the upper and lower limits of the operating range are reduced by 5% to obtain the final evaluation wind speed range, namely 4.725 m / s to 9.975 m / s.

[0225] Table 4: Expected CP Statistics for 5 Generator Units in a Wind Farm

[0226]

[0227]

[0228] Table 4 shows the expected CP and maximum expected CP of the five units within the wind angle range of -6° to 6°, calculated according to the expected CP evaluation algorithm based on the final evaluated wind speed range of 4.725m / s to 9.975m / s.

[0229] Table 5. Normalized Statistical Table of Expected CP for 5 Generator Units in a Wind Farm

[0230]

[0231]

[0232] Table 5 shows the normalization of expected CP within the -6° to -6° wind angle for the five units after normalization calculation. As can be seen from Table 5, the normalization trend of expected CP varies greatly among the units, with the range basically between 0.9 and 1.0. In addition, it can be found that due to the influence of fluctuations, the wind angle corresponding to the point normalized to 1 is not necessarily the angle with wind deviation, and further fitting calculation is required.

[0233] Table 6. Statistical table of normalized fitting curves for expected CP of 5 units in a wind farm.

[0234] Unit number Expected CP normalized fitting curve function 1# <![CDATA[CP 归一化 =-0.0018x 2 +0.0099x+0.9835]]> 2# <![CDATA[CP 归一化 =-0.0014x 2 -0.0018x+0.9935]]> 12# <![CDATA[CP 归一化 =-0.0017x 2 +0.0037x+0.9923]]> 24# <![CDATA[CP 归一化 =-0.0015x 2 +0.0013x+0.9957]]> 25# <![CDATA[CP 归一化 =-0.0021x 2 +0.0055x+0.9959]]>

[0235] Based on the trend of wind angle-cp variation, a quadratic polynomial is fitted, where CP 归一化 Here, cp is the normalized value, and x is the angle of wind.

[0236] Table 7. Statistical Table of Wind Deviation Angle Quantification Evaluation Results for 5 Generator Units in a Wind Farm

[0237]

[0238] Table 7 compares the deviation angles corresponding to points where the normalized expected value cp is 1 with the deviation angles obtained after finding the inflection point of the fitted curve. It shows that due to the fluctuations in unit output and wind speed measurements, relying solely on the maximum value to determine the wind deviation angle introduces a certain error and has low accuracy. The result obtained through the fitted curve effectively reduces the impact of fluctuations and has higher accuracy. The fitted curves for each unit are shown below. Figures 4-8 As shown, by Figures 4-8 It can be seen that there is a difference between the maximum point and the inflection point of the fitted curve.

[0239] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0240] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0241] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0242] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0243] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0244] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0245] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively assessing wind deviation, characterized in that, include: S1: Acquire sampling data; S2: Calculate the average air density based on the sampling data obtained in step S1, standardize the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed range. S3: Based on the evaluation wind speed range obtained in step S2, divide the evaluation range into partitions according to the wind deviation, calculate the expected CP for each wind deviation range, and normalize the expected CP based on the expected CP for each wind deviation range to obtain the normalized expected CP. S4: Based on the normalized expected value CP obtained in step S3, perform polynomial fitting to obtain the polynomial fitting curve. Based on the polynomial fitting curve, perform quantitative evaluation of the deviation and classify and warn the wind deviation angle. Given the data and wind speed ranges, the wind energy utilization coefficient (CP) is calculated for each wind deviation range. The wind deviation range is divided into 1° intervals, with the midpoint of the interval rounded to an integer. The calculation formula is as follows: in: For the first The j-th 10-minute standardized average wind speed data within the deviation interval; For the first The j-th 10-minute average active power data within each deviation interval; For reference air density; The area swept by the wind turbine; For the first Wind energy utilization coefficient for the j-th 10-minute interval of the deviation interval; The calculation of the expected CP for each wind deviation interval in step S3 specifically includes: Based on the calculated wind energy utilization coefficient CP, calculate the expected CP for each wind deviation interval; The wind deviation interval is divided into 1° intervals, and the wind speed within each interval is divided into 0.5 m / s intervals. The midpoint of the interval is rounded to the nearest integer. The formula for calculating the average CP value of the wind speed interval is as follows: in: For the first The average value of CP in the kth wind speed interval of the deviation interval; For the first The number of data points in the k-th wind speed interval of each deviation interval, k=1,2,3...n; For the first Wind energy utilization coefficient in the j-th 10-minute interval within the k-th wind speed interval of the deviation interval; The formula for calculating the expected CP at each wind deviation angle is: in: For the first Expected CP for each deviation interval; For the first The average value of CP in the kth wind speed interval of the deviation interval; For the first The number of data points in the k-th wind speed interval of each deviation interval, k=1,2,3...n; For the first The total number of data points in each deviation interval.

2. The method for quantitatively assessing wind deviation according to claim 1, characterized in that, The specific steps for obtaining the sampled data in step S1 include: S11: Obtain the number of benchmark turbines in the wind farm, the installed capacity of each benchmark turbine, and the total capacity of non-benchmark turbines in the power station; S12: Obtain the complete annual historical operating data of the wind farm units, including 1-minute and 10-minute operating data; obtain the complete annual historical unit operating logs, shutdown records, fault records, and blade inspection records of the wind farm units. The operating data includes: wind speed, maximum wind speed, minimum wind speed, wind speed standard deviation, active power, maximum active power, minimum active power, active power standard deviation, rotor speed, ambient temperature, generator speed, blade angle, nacelle position, wind direction, PLC status, shutdown records, fault records, and blade inspection records. S13: Obtain complete historical operating data for the year, including temperature, air pressure, and humidity data for the same period.

3. The method for quantitatively assessing wind deviation according to claim 1, characterized in that, The formula for calculating the average air density in step S2 is as follows: in: The derived average air density over 10 minutes; This represents the average absolute temperature obtained from a 10-minute measurement. This is the average air pressure obtained from a 10-minute measurement. The dry gas constant is 287.05 J / (kg·K); The formula for standardizing the measured wind speed in step S2 is as follows: Standardized wind speed; The average cabin wind speed over 10 minutes; For reference air density.

4. The method for quantitatively assessing wind deviation according to claim 1, characterized in that, The cleaning of unit operating data in step S2 includes: Based on the standardized data obtained from the calculation, the unit operation data is cleaned to remove abnormal data and obtain the cleaned unit operation data. Data in the 10-minute average that falls under the following categories will be deleted: data where the wind speed exceeds the wind turbine's operating range; data where the wind turbine is shut down due to a fault; data where the turbine is manually shut down during testing or maintenance; data during shutdown or startup; data where the power is reduced; and data during grid curtailment. Check the PLC status of the unit, select the operating data where the wind turbine status is in normal power generation state, and discard the rest of the data; The analysis of unit control parameter consistency, unit turbulence intensity, and blade inspection data in step S2 specifically includes: Control parameter consistency analysis: Based on the unit operation data after cleaning, the consistency of the control parameters is judged by scatter plots of speed-blade angle, speed-power, and speed-torque. Units with consistency deviations are not subject to wind deviation analysis to eliminate the influence of control factors. Unit turbulence intensity analysis: By analyzing the turbulence intensity of the unit, a turbulence intensity range is defined. Units with turbulence intensity exceeding the design turbulence intensity across the entire wind speed range and at 15 m / s are excluded to reduce the impact of wind conditions. The turbulence intensity calculation formula is as follows: in: For the first 10-minute turbulence intensity data; For the first One 10-minute standardized average wind speed data; For the first 10-minute standard deviation data; Blade inspection data analysis: By reviewing the blade inspection records, units with defective blades are excluded, and wind deviation analysis is not performed to reduce the impact of blades on the assessment conclusions; The method for selecting the wind speed evaluation range in step S2 is as follows: According to the control strategy of variable speed and variable pitch wind turbine generator sets, there is a constant CP. max Within the operating range or maximum wind energy capture range, the blade pitch angle remains constant. The generator speed is controlled to follow the wind speed variation, maintaining a constant tip speed ratio and a constant wind energy utilization coefficient. This wind speed range forms the basis for wind speed range selection. A scatter plot of the unit's wind speed-speed is drawn, using two turning points as benchmarks to determine the constant CP (critical velocity coefficient). max Work area scope, for CP max The working area is reduced by 5% for both the upper and lower limits to determine the final assessment wind speed range.

5. The method for quantitatively assessing wind deviation according to claim 1, characterized in that, Step S3, which involves dividing the evaluation range into zones based on the wind deviation obtained in step S2, specifically includes: In step S3, the expected CP is normalized according to each wind deviation interval to obtain the normalized expected CP. This specifically includes: Based on the expected CP at each deviation angle, the maximum expected CP is found, and then the normalized CP value at each deviation angle is calculated using the following formula: in: For the first Normalized expected value CP for each deviation interval; This represents the maximum expected CP for each deviation angle of the unit. For the first The expected CP for each deviation interval.

6. The method for quantitatively assessing wind deviation according to claim 5, characterized in that, In step S4, based on the normalized expected value CP obtained in step S3, polynomial fitting is performed to obtain the polynomial fitting curve, specifically including: Based on the normalized expected CP obtained in step S3, and taking the actual unit's wind deviation range as the basis, with the wind deviation angle as the independent variable and the expected CP for each wind deviation interval as the dependent variable, a polynomial fitting is performed to obtain the deviation angle-expected CP fitting curve. The wind deviation angle is taken as -6° to 6°. With the wind deviation angle as the independent variable and the expected CP for each wind deviation interval as the dependent variable, a quadratic polynomial is fitted. The calculation formula is as follows: in: The normalized expected value CP for the deviation interval; These are the fitting coefficients for the quadratic term; B is the fitting coefficient for the linear term; C is the fitting constant term; x is the windward deviation angle.

7. The method for quantitatively assessing wind deviation according to claim 6, characterized in that, In step S4, the deviation is quantitatively evaluated based on the polynomial fitting curve, and a graded early warning is issued for the wind deviation angle. This specifically includes: Based on the obtained deviation angle-expected CP fitting curve, the windward deviation angle is quantitatively evaluated. The derivative of the quadratic fitting curve is calculated, and the inflection point is determined. The calculation formula is as follows: in: The normalized expected value CP for the deviation interval; These are the fitting coefficients for the quadratic term; B is the fitting coefficient for the linear term; x is the windward deviation angle; The inflection point or deviation quantification value is: in: This is the inflection point, and also the quantified value of the deviation; These are the fitting coefficients for the quadratic term; B is the fitting coefficient for the linear term; Set early warning thresholds and issue graded early warnings based on the wind deviation angle; Based on operational data and the unit's yaw characteristics in response to wind, the warning threshold is set at three levels. Simply provide a prompt when To issue an early warning, when An alarm will be triggered.

8. A system for quantitatively assessing wind deviation, used to implement the method for quantitatively assessing wind deviation as described in claim 1, characterized in that, include: The sampling data acquisition module is used to acquire sampling data; The standardized calculation and evaluation wind speed range selection module is used to calculate the average air density based on the acquired sampling data, perform standardized calculations on the measured wind speed, clean the unit operation data, analyze the consistency of unit control parameters, unit turbulence intensity and blade inspection data, and evaluate the selection of wind speed ranges. The expected CP normalization module is used to divide the evaluation interval into partitions according to the wind deviation based on the obtained evaluation wind speed interval, calculate the expected CP for each wind deviation interval, and normalize the expected CP based on the expected CP for each wind deviation interval to obtain the normalized expected CP. The deviation quantitative assessment and wind deviation angle classification early warning module is used to perform polynomial fitting based on the obtained normalized expected value CP to obtain a polynomial fitting curve, and to perform quantitative assessment of the deviation based on the polynomial fitting curve and classify and issue early warnings for wind deviation angle.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for quantitatively assessing wind deviation according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for quantitatively assessing wind deviation as described in any one of claims 1-7.

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