Optimization method of fan output curve

Abstract

The invention discloses a wind power output curve optimization method which comprises the following steps: designing an envelope model based on a generated theoretical wind power output curve, performing piecewise function change on the envelope model according to power change to obtain a minimum value envelope function and a maximum value envelope function, and setting three random working conditions according to the actual operation condition of a fan, different times and time are set for the three working conditions, and different time periods are intercepted through a random algorithm to obtain a final output curve. The method mainly pays attention to increase of medium-low wind speed width and abnormal value scatter simulation, so that a theoretical processing curve is close to the actual value.

Description

Technical Field

[0001] This invention relates to an optimization method, specifically a method for optimizing the output curve of a wind turbine, belonging to the field of wind power intelligent control technology. Background Technology

[0002] In the early stages of wind power projects, especially integrated projects with their own grid integration capabilities, grid companies need wind turbine output curves when conducting grid integration analysis or engineers calculate capacity. The industry generally uses WT software to simulate theoretical output curve sequences. However, in actual operation, wind turbines are affected by environmental factors (terrain, wind direction changes, extreme weather, etc.), turbine-related factors (pitch rate, yaw action, blade turbulence, malfunctions, etc.), and external factors (line maintenance, power-limited operation, etc.). This results in a wide range of actual power output scatter points in the low-to-medium wind speed range, with many abnormal scatter points, leading to significant deviations from the theoretical output curves calculated by WT software. Therefore, to make the theoretical output scatter points approximate the actual output, further optimization of the theoretical output curves is necessary. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for optimizing the output curve of a wind turbine so that it is closer to the actual output curve.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An optimization method for wind power output curves includes the following steps: S1. Design an envelope model based on the generated theoretical wind power output curve; S2. Based on the power change, the envelope model is segmented into a piecewise function to obtain the minimum value envelope function and the maximum value envelope function; S3. Based on the actual operation of the wind turbine, three random operating conditions are set, with different frequencies and durations for each condition. The final output curve is obtained by extracting different time periods through a random algorithm.

[0005] Furthermore, the envelope model in step S1 is as follows:

[0006] Where Cp is the wind energy utilization factor and P is the wind turbine power. V is air density, V is wind speed, and S is the area swept by the wind turbine. From the above formula, we can obtain: .

[0007] Furthermore, the minimum value envelope function in step S2 is:

[0008] in, The minimum envelope coefficient, The wind energy utilization coefficient, The minimum envelope amplitude coefficient, The minimum envelope contraction coefficient, Let i be the wind speed at time i.

[0009] Furthermore, the minimum value envelope amplitude coefficient The value ranges from 1.8 to 2.6, with the lowest value being the envelope contraction coefficient. The value ranges from 1.7 to 2.0.

[0010] Furthermore, the highest value envelope function in step S2 is:

[0011] in, The highest value envelope coefficient, Let i be the wind energy utilization coefficient. The maximum value envelope amplitude coefficient, The maximum value is the envelope contraction coefficient. Let i be the wind speed at time i.

[0012] Furthermore, the maximum value envelope amplitude coefficient The value ranges from 0.3 to 0.8, with the highest value being the envelope contraction coefficient. The value ranges from 1.0 to 1.6.

[0013] Further, in step S2, the minimum and maximum envelope functions are randomly generated based on the minimum and maximum values ​​of the theoretical wind power output curve to form a new output curve. The new output curve is then checked to ensure it meets the following criteria: First, the output curve meets 90%–95% of the criteria; second, the wind energy utilization coefficient Cp is between 0.38 and 0.43. If both criteria are met, the process proceeds to the next step of adjusting the random operating conditions. Otherwise, the minimum envelope amplitude coefficient is readjusted based on the magnitude of the criterion results. Minimum value envelope contraction coefficient Maximum value envelope amplitude coefficient and the highest value envelope contraction coefficient And a new output curve is generated.

[0014] Furthermore, in step S3, the three random operating conditions include random shutdown maintenance, random power-limited debugging, and random self-start.

[0015] Further, step S3 specifically includes: 3.1. Using the output curve sequence list0 generated by the envelope model, set the number of shutdowns for maintenance n1 and the time t1, and generate n1 segments of different start times, intervals of t1, and output of 0 through a random function. Replace the corresponding time and value sequences in the original output curve to generate a new output curve sequence list1. 3.2 Set the number of power limit debugging times n2 and the time period t2, as well as the power limit percentage c1. Generate n2 sequences with different start times, intervals of t2, and maximum output values ​​of c1*total capacity using a random function. Replace the corresponding time and value sequences in the output curve sequence list1 to generate a new output curve sequence list2. 3.3 Set the number of times the fan starts automatically (n3) and the adjustment time (t3). Generate n3 sequences with different start times, intervals of t3, and output of 0 using a random function. Replace the corresponding time and value sequences in the output curve sequence list2 to form the output curve list3. 3.4 When the reduction factor corresponding to the equivalent full-load hours calculated by the output curve list3 is between 83% and 85%, the output curve list3 is the final output curve; otherwise, repeat steps 3.1 to 3.2.

[0016] Compared with the prior art, the present invention has the following advantages and effects: The present invention provides an optimization method for the power output curve of a wind turbine, making the theoretically processed curve closer to the actual one; The present invention mainly focuses on increasing the range of medium and low wind speeds and simulating outlier scatter points. An envelope model has been developed. Through envelope control, a random algorithm is used to process the power point sequence to increase the range of medium and low wind speeds. A random operating condition model has been developed. Based on the three common types of outlier points in the power output curve, the present invention mainly considers three types of operating conditions: shutdown for maintenance, power-limited operation, and wind turbine fault self-start, thus solving the problem of outlier scatter point simulation. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for optimizing the output curve of a wind turbine according to the present invention.

[0018] Figure 2 This is the envelope diagram of the lowest and highest values ​​of the randomly distributed wind turbine output curve of the present invention. Detailed Implementation

[0019] To illustrate in detail the technical solutions adopted by the present invention to achieve the intended technical objectives, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Furthermore, the technical means or technical features in the embodiments of the present invention can be replaced without creative effort. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0020] like Figure 1 As shown, the method for optimizing the wind power output curve of the present invention includes the following steps: S1. Design an envelope model based on the generated theoretical wind power output curve.

[0021] The envelope model is as follows:

[0022] Where Cp is the wind energy utilization factor and P is the wind turbine power. V is air density, V is wind speed, and S is the area swept by the wind turbine. From the above formula, we can obtain: .

[0023] S2. The envelope model design approach is mainly based on existing theoretical power curves, setting relevant parameters, and adjusting the envelope sequence. For example... Figure 2 As shown, since the power change amplitude varies with wind speed, the envelope model is piecewise transformed according to the power change to obtain the minimum value envelope function and the maximum value envelope function.

[0024] The minimum envelope function is:

[0025] in, The minimum envelope coefficient, The wind energy utilization coefficient, The minimum envelope amplitude coefficient, The minimum envelope contraction coefficient, Let i be the wind speed at time i.

[0026] Minimum value envelope amplitude coefficient The value ranges from 1.8 to 2.6, with the lowest value being the envelope contraction coefficient. The value ranges from 1.7 to 2.0.

[0027] The highest value envelope function is:

[0028] in, The highest value envelope coefficient, Let i be the wind energy utilization coefficient. The maximum value envelope amplitude coefficient, The maximum value is the envelope contraction coefficient. Let i be the wind speed at time i.

[0029] Maximum value envelope amplitude coefficient The value ranges from 0.3 to 0.8, with the highest value being the envelope contraction coefficient. The value ranges from 1.0 to 1.6.

[0030] The larger the amplitude coefficient, the greater the adjustment range, meaning the easier the adjustment; the contraction coefficient is mainly based on the determined amplitude coefficient, and the envelope value is corrected and adjusted.

[0031] The minimum and maximum envelope functions are randomly generated based on the minimum and maximum values ​​of the theoretical wind power output curve, forming a new output curve. The new output curve is then checked to ensure it meets the following criteria: First, the output curve conformity reaches 90%–95%; second, the wind energy utilization coefficient Cp is between 0.38 and 0.43. If both criteria are met, the next step of adjusting the random operating conditions proceeds. Otherwise, the minimum envelope amplitude coefficient is readjusted based on the magnitude of the criterion results. Minimum value envelope contraction coefficient Maximum value envelope amplitude coefficient and the highest value envelope contraction coefficient And a new output curve is generated.

[0032] S3. Based on the actual operation of the wind turbine, three random operating conditions are set, with different frequencies and durations for each condition. The final output curve is obtained by extracting different time periods through a random algorithm.

[0033] The three random operating conditions include random shutdown for maintenance, random power-limited commissioning, and random self-starting. The random shutdown for maintenance primarily simulates the first type of scatter plot anomaly in the unit power scatter plot classification; the random power-limited commissioning primarily simulates the third type of anomaly; and the random self-starting primarily simulates the second type. Each operating condition has different number of occurrences and durations, and a random algorithm is used to extract different time periods to simulate random occurrences in reality.

[0034] 3.1. Using the output curve sequence list0 generated by the envelope model, set the number of shutdowns for maintenance n1 and the time t1 (generally recommended to be 100 times and 3 hours, which can be modified according to the actual situation of the project site). Generate n1 segments of different start times, intervals of t1, and output of 0 through a random function, and replace the corresponding time and value sequences in the original output curve to generate a new output curve sequence list1. 3.2 Set the number of power limit debugging times n2 and the time period t2 (generally recommended to be 6 times and 5 days, which can be modified according to the actual situation of the project site), as well as the power limit percentage c1. Generate n2 segments of different start times, intervals of t2, and maximum output values ​​of c1*total capacity (if the original value is not greater than c1*total capacity, the output value is the original value) through a random function. Replace the corresponding time and value sequences in the output curve sequence list1 to generate a new output curve sequence list2. 3.3 Set the number of times the fan starts automatically (n3) and the adjustment time (t3) (it is generally recommended to use 300 times and 0.5h, which can be modified according to the actual situation of the project site). Generate n3 sequences with different start times, intervals of t3, and output of 0 through a random function, and replace the corresponding time and value sequences in the output curve sequence list2 to form the output curve list3. 3.4 When the equivalent full-load hours (sum of power / total capacity) calculated from the output curve list3 (excluding line loss) corresponds to a reduction factor of 83% to 85%, the output curve list3 is the final output curve; otherwise, repeat steps 3.1 to 3.2.

[0035] This invention provides an optimization method for wind turbine output curves, making the theoretically processed curves closer to the actual ones. This invention mainly focuses on increasing the range of medium and low wind speeds and simulating outlier scatter points. An envelope model was developed, and through envelope control, a stochastic algorithm was used to process the power point sequence to increase the range of medium and low wind speeds. A stochastic operating condition model was also developed. Based on the three common types of outliers in the output curve, the model mainly considers three operating conditions: shutdown for maintenance, power-limited operation, and wind turbine self-starting due to faults, thus solving the problem of simulating outlier scatter points.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing wind power output curves, characterized in that... Includes the following steps: S1. Design an envelope model based on the generated theoretical wind power output curve; S2. Based on the power change, the envelope model is segmented into a piecewise function to obtain the minimum value envelope function and the maximum value envelope function; S3. Based on the actual operation of the wind turbine, three random operating conditions are set, with different frequencies and durations for each condition. The final output curve is obtained by extracting different time periods through a random algorithm.

2. The method for optimizing wind power output curves according to claim 1, characterized in that: The envelope model in step S1 is as follows: Where Cp is the wind energy utilization factor and P is the wind turbine power. V is air density, V is wind speed, and S is the area swept by the wind turbine. From the above formula, we can obtain: 。 3. The method for optimizing wind power output curves according to claim 1, characterized in that: The minimum envelope function in step S2 is: in, The minimum envelope coefficient, The wind energy utilization coefficient, The minimum envelope amplitude coefficient, The minimum envelope contraction coefficient, Let i be the wind speed at time i.

4. The method for optimizing wind power output curves according to claim 3, characterized in that: Minimum value envelope amplitude coefficient The value ranges from 1.8 to 2.6, with the lowest value being the envelope contraction coefficient. The value ranges from 1.7 to 2.

0.

5. The method for optimizing wind power output curves according to claim 1, characterized in that: The highest value envelope function in step S2 is: in, The highest value envelope coefficient, Let i be the wind energy utilization coefficient. The maximum value envelope amplitude coefficient, The maximum value is the envelope contraction coefficient. Let i be the wind speed at time i.

6. The method for optimizing wind power output curves according to claim 5, characterized in that: Maximum value envelope amplitude coefficient The value ranges from 0.3 to 0.8, with the highest value being the envelope contraction coefficient. The value ranges from 1.0 to 1.

6.

7. The method for optimizing wind power output curves according to claim 1, characterized in that: In step S2, the minimum and maximum envelope functions are randomly generated based on the minimum and maximum values ​​of the theoretical wind power output curve to form a new output curve. The new output curve is then checked to ensure it meets the following criteria: First, the output curve compliance rate reaches 90%–95%; second, the wind energy utilization coefficient Cp is between 0.38 and 0.

43. If both criteria are met, the process proceeds to the next step of adjusting the random operating conditions. Otherwise, the minimum envelope amplitude coefficient is readjusted based on the magnitude of the criterion results. Minimum value envelope contraction coefficient Maximum value envelope amplitude coefficient and the highest value envelope contraction coefficient And a new output curve is generated.

8. The method for optimizing wind power output curves according to claim 1, characterized in that: In step S3, the three random operating conditions include random shutdown and maintenance, random power-limited debugging, and random self-start.

9. The method for optimizing wind power output curves according to claim 1, characterized in that: Step S3 specifically involves: 3.

1. Using the output curve sequence list0 generated by the envelope model, set the number of shutdowns for maintenance n1 and the time t1, and generate n1 segments of different start times, intervals of t1, and output of 0 through a random function. Replace the corresponding time and value sequences in the original output curve to generate a new output curve sequence list1. 3.2 Set the number of power limit debugging times n2 and the time period t2, as well as the power limit percentage c1. Generate n2 sequences with different start times, intervals of t2, and maximum output values ​​of c1*total capacity using a random function. Replace the corresponding time and value sequences in the output curve sequence list1 to generate a new output curve sequence list2. 3.3 Set the number of times the fan starts automatically (n3) and the adjustment time (t3). Generate n3 sequences with different start times, intervals of t3, and output of 0 using a random function. Replace the corresponding time and value sequences in the output curve sequence list2 to form the output curve list3. 3.4 When the reduction factor corresponding to the equivalent full-load hours calculated by the output curve list3 is between 83% and 85%, the output curve list3 is the final output curve; otherwise, repeat steps 3.1 to 3.2.

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