An Equivalent Method for Wind Farms Considering Wake Effect and Connecting Branches

Abstract

The present invention discloses a wind farm equivalent method considering wake effect and connection branches, which comprises the following steps: S1: calculating the input wind speed of each wind turbine in the wind farm through the wake effect; S2: preliminarily dividing the wind turbines according to the original connection branches; S3: grouping the wind turbine clusters considering the wake effect on the basis of the original connection branches in the wind farm; S4: using the capacity weighted method to aggregate parameters for each group to obtain the equivalent wind turbine parameters. The present invention makes the equivalent modeling of the wind farm more targeted and effective by considering the wake effect and connection branches of the wind farm.

Description

Technical Field

[0001] The present invention relates to the field of wind power, and particularly to a method for equivalent modeling of a wind farm considering wake effect and connection branches. Background Art

[0002] Due to the cost - effectiveness and environmental friendliness of wind energy, the installed capacity of wind power has increased rapidly in recent years, and the modeling of wind farms has become an important research topic. At present, the methods for equivalent modeling of wind farms mainly include single - machine equivalent method, multi - machine equivalent method, and composite model method. The process of equivalence is divided into two parts: clustering and aggregation. The most commonly used method in aggregation is the capacity - weighted method. That is, first, the weight factors are obtained according to the capacity of each wind turbine, and then the weight factors are used to calculate the corresponding equivalent generator parameters. The capacity - weighted method is not only simple in calculation, but also has extremely high accuracy when the input wind speeds of the equivalent generators are equal or nearly equal.

[0003] However, when the wind direction changes, affected by the wake effect, the input wind speeds of the same group of wind turbines may vary greatly. Directly using the capacity - weighted method to obtain equivalent parameters in the aggregation analysis of wind farms may lead to large errors. In the current research considering the wake effect, fuzzy c - means and single - machine representation method are commonly used methods in addition to the capacity - weighted method.

[0004] Currently, there is little research on equivalent modeling considering connection branches. When a wind farm is designed and established, the connection method between each wind turbine is fixed. For a wind farm with a given wiring diagram, it is impossible to freely group all wind turbines according to wind speed using general traditional equivalent methods. The common method is to group the generators connected to the same busbar in the wiring diagram and equivalent them to one generator. However, considering the influence of the wake effect, the input wind speeds of the same group of wind turbines are different under different wind directions, and the equivalent accuracy of the same wind farm is different under different wind directions. Therefore, when grouping, the inherent connection method of wind turbines should be considered first, and then equivalence should be carried out according to other wind farm indexes. To solve this problem, the present invention proposes an equivalent method that can cope with different wind direction changes by analyzing the connection branches of the wind farm and combining the influence of the wake effect on wind turbines. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide a method for equivalent modeling of a wind farm based on connection branches and wake effect, which can solve the defects existing in the prior art and effectively improve the equivalent accuracy.

[0006] Technical Solution: The present invention proposes a method for equivalent modeling of a wind farm considering wake effect and connection branches, and the method includes the following steps:

[0007] S1: Calculate the input wind speeds of each wind turbine in the wind farm through the wake effect;

[0008] S2: Initially divide the wind turbines according to the original connection branches;

[0009] S3: Classify the wind turbine groups considering the wake effect on the basis of the original connection branches in the wind farm;

[0010] S4: Aggregate the parameters of each group using the capacity weighting method to obtain the equivalent wind turbine parameters.

[0011] Preferably, in step S1, the input wind speed of each wind turbine in the wind farm is calculated by the Jensen wake model.

[0012] Preferably, the method for initially dividing the wind turbines in step S2 is: divide the wind turbines on the same branch into one group according to the connection method, and determine the number of groups N.

[0013] Preferably, in step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connection branches in the wind farm includes:

[0014] When the types of wind turbines on the same connection branch in the N groups are the same, while the types of wind turbines between different groups are different, arrange the input wind speeds of the wind turbines in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows:

[0015] Wind turbines that satisfy v r -v i <0.1 are classified as type a, where v r is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ;

[0016] Wind turbines that satisfy 0.1 < v r -v i ≤1 are classified as type b;

[0017] Wind turbines that satisfy v r -v i >1 are classified into type c.

[0018] Preferably, in step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connection branches in the wind farm includes:

[0019] When the types of wind turbines on the same connection branch among N groups are different, analyze the physical parameters of the wind turbines. If the differences in the same physical parameters between any two different types of wind turbines in the same group are all within 5%-10%, arrange the input wind speeds of each wind turbine in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows:

[0020] Classify the wind turbines that satisfy v r -v i <0.1 into category a, where v r is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ;

[0021] Classify the wind turbines that satisfy 0.1 < v r -v i ≤1 into category b;

[0022] Classify the wind turbines that satisfy v r -v i >1 into category c.

[0023] The physical parameters include the impeller radius, rated wind speed, rated power, resistance, inductance, moment of inertia, rated speed, and number of pole pairs of the wind turbine.

[0024] Preferably, in step S3, when classifying the wind turbine group considering the wake effect on the basis of the original connection branch of the wind farm, the method includes:

[0025] When the types of wind turbines on the same connection branch among N groups are different, analyze the physical parameters of the wind turbines. If the differences in the same physical parameters between any two different types of wind turbines in the same group are all more than 10%, in the case that the wind turbines on this connection branch have been classified into the same group, within the same group, classify the wind turbines with the differences in the same physical parameters between different types of wind turbines within the preset range into the same group. For example, assume that there are three different models of wind turbines in the x-th (x ≤ N) group. Among them, the parameters of the first two wind turbines are relatively close (the differences are all within 5%-10%), and the parameters of the third wind turbine are very different from the first two. Then, on the basis of group x, groups x 1 and x 2 can be divided, and the first two types of wind turbines are respectively placed into groups x 1 and the third type of wind turbine is placed into group x 2In the group, this can ensure that the error caused by the grouping of wind turbines is small. After all wind turbines are divided, the input wind speeds of each wind turbine in each group are arranged from large to small, and the wind turbines in each group are classified again. The specific method is as follows:

[0026] Wind turbines that satisfy v r -v i < 0.1 are classified as category a, where v r is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ;

[0027] Wind turbines that satisfy 0.1 < v r -v i ≤ 1 are classified as category b;

[0028] Wind turbines that satisfy v r -v i > 1 are classified into category c.

[0029] Preferably, in step S3, when there are wind turbines in category c but no wind turbines in category b, the wind turbine with the highest input wind speed in category c will be automatically extracted into the wind turbines in category b. If the difference in the input wind speed between the wind turbines in category c and the extracted wind turbine is within 0.1 m / s, all wind turbines within this wind speed range will be extracted into category b; otherwise, only the wind turbine with the highest input wind speed will be extracted into the wind turbines in category b.

[0030] Preferably, in step S4, in combination with formula (1), the wind turbines in categories a, b, and c are respectively equivalent to one wind turbine. The calculation formula for the parameters of the equivalent wind turbines is as follows:

[0031]

[0032] In the formula, S i represents the capacity of the i-th wind turbine, P eq , R eq , X eq , A eq , C p_eq , H eq , V eq respectively represent the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant, and wind speed of the equivalent wind turbine. P i , R i , X i , A i , C p_i , H i , V iThey are the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant, and wind speed of the i-th wind turbine before equivalence respectively, and n is the number of wind turbines in each category.

[0033] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0034] Considering the problem that the existing equivalent technology does not consider the connecting branch during equivalence, and on this basis, comprehensively considering the influence of the wake effect, the present invention proposes an equivalent method that can handle different connecting branches, wind speeds, and different wind turbine parameters. Description of the drawings

[0035] Figure 1 It is a flowchart of the equivalent method based on the connection method and wake effect in the specific embodiment of the present invention. Specific embodiments

[0036] The present invention proposes a wind farm equivalent method considering the wake effect and connecting branches, and the method includes the following steps:

[0037] S1: Calculate the input wind speed of each wind turbine in the wind farm through the wake effect;

[0038] In the step S1, the Jensen wake model is used to calculate the input wind speed of each wind turbine in the wind farm.

[0039] S2: Make a preliminary division of the wind turbines according to the original connecting branches;

[0040] The method for making a preliminary division of the wind turbines in the step S2 is: group the wind turbines on the same branch according to the connection method respectively, and determine the number of groups N.

[0041] S3: Classify the wind turbine groups considering the wake effect on the basis of the original connecting branches of the wind farm;

[0042] In the step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connecting branches of the wind farm includes:

[0043] When the types of wind turbines on the same connecting branch in the N groups are the same, while the types of wind turbines between different groups are different, arrange the input wind speeds of each wind turbine in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows:

[0044] Classify the wind turbines that satisfy v r -v i <0.1 as type a, where v r is the initial input wind speed of the wind farm, and this wind speed is not affected by the wake effect; vi is the input wind speed of the i-th wind turbine after wake effect, and its value is not greater than v r ;

[0045] Wind turbines that satisfy 0.1 < v r - v i ≤ 1 are classified as class b;

[0046] Wind turbines that satisfy v r - v i > 1 are classified into class c.

[0047] In step S3, based on the original connection branches of the wind farm, the classification of the wind turbine group considering wake effect includes:

[0048] When the types of wind turbines on the same connection branch in N groups are different, analyze the physical parameters of the wind turbines. If the differences in the same physical parameters between two different types of wind turbines in the same group are all within 5% - 10%, arrange the input wind speeds of the wind turbines in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows:

[0049] Wind turbines that satisfy v r - v i < 0.1 are classified as class a, where v r is the initial input wind speed of the wind farm, which is not affected by wake effect; v i is the input wind speed of the i-th wind turbine after wake effect, and its value is not greater than v r ;

[0050] Wind turbines that satisfy 0.1 < v r - v i ≤ 1 are classified as class b;

[0051] Wind turbines that satisfy v r - v i > 1 are classified into class c.

[0052] In step S3, based on the original connection branches of the wind farm, the classification of the wind turbine group considering wake effect includes:

[0053] When the types of wind turbines on the same connection branch among N groups are different, analyze the physical parameters of the wind turbines. If the differences in the same physical parameters between any two different types of wind turbines in the same group are all more than 10%, when the wind turbines on this connection branch have been divided into the same group, among the wind turbines in the same group, divide the wind turbines with the differences in the same physical parameters between different types of wind turbines within a preset range into the same group. For example, assume that there are three types of wind turbines with different models in the x (x ≤ N)th group. Among them, the parameters of the first two types of wind turbines are relatively close (the differences are all within 5% - 10%), and the parameters of the third type of wind turbine are quite different from those of the first two. Based on the xth group, x 1 and x 2 groups can be divided, and the first two types of wind turbines are respectively placed into x 1 groups respectively, and the third type of wind turbine is placed into x 2 group. In this way, it can ensure that the error caused by the grouping of wind turbines is relatively small. After all the wind turbines are divided, arrange the input wind speeds of the wind turbines in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows:

[0054] Classify the wind turbines that satisfy v r -v i <0.1 as type a. v r is the initial input wind speed of the wind farm, and this wind speed is not affected by the wake effect; v i is the input wind speed of the ith wind turbine after the wake effect, and its value is not greater than v r ;

[0055] Classify the wind turbines that satisfy 0.1 < v r -v i ≤1 as type b;

[0056] Classify the wind turbines that satisfy v r -v i >1 into type c.

[0057] Preferably, in step S3, when there are wind turbines in type c but no wind turbines in type b, automatically extract the wind turbine with the maximum input wind speed in type c to the wind turbines in type b. If the difference in the input wind speed between the wind turbines in type c and the extracted wind turbine is within 0.1 m / s, extract all the wind turbines within this wind speed range to type b; if not, only extract the wind turbine with the maximum input wind speed to the wind turbines in type b.

[0058] S4: Use the capacity weighting method to perform parameter aggregation on each group to obtain the equivalent wind turbine parameters.

[0059] In the step S4, the wind turbines in the three categories of a, b, and c are respectively equivalent to one wind turbine in combination with the formula (1). The calculation formula for the parameters of the equivalent wind turbine is as follows:

[0060]

[0061] In the formula, S i represents the capacity of the i-th wind turbine, P eq , R eq , X eq , A eq , C p_eq , H eq , V eq respectively represent the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant, and wind speed of the equivalent wind turbine. P i , R i , X i , A i , C p_i , H i , V i are respectively the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant, and wind speed of the i-th wind turbine before equivalence. n is the number of wind turbines in each category.

Claims

1. A wind farm equivalent method considering wake effect and connecting branches, characterized in that, the method comprises the following steps: S1: Calculate the input wind speed of each wind turbine in the wind farm through the wake effect; S2: Conduct a preliminary division of the wind turbines according to the original connecting branches; S3: Classify the wind turbine groups considering the wake effect on the basis of the original connecting branches in the wind farm; S4: Use the capacity weighted method to aggregate parameters for each group to obtain the equivalent wind turbine parameters; The method for the preliminary division of the wind turbines in step S2 is: Divide the wind turbines on the same branch into one group according to the connection method, and determine the number of groups N; In step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connecting branches in the wind farm includes: When the wind turbine types on the same connecting branch in the N groups are the same, while the wind turbine types between different groups are different, arrange the input wind speeds of the wind turbines in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows: Meet v r -v i Wind turbines with < 0.1 are classified as class a, v r Is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i Is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ; Wind turbines that satisfy 0.1 < v r - v i ≤ 1 are classified as Class B; Wind turbine units that satisfy v r -v i > 1 are classified into category c; In step S3, when there are wind turbines in class c and no wind turbines in class b, automatically extract the wind turbine with the maximum input wind speed in class c into class b. If the difference in the input wind speed between the wind turbines in class c and the extracted wind turbine is within 0.1 m / s, extract all the wind turbines within this wind speed range into class b; if not, only extract the wind turbine with the maximum input wind speed into class b.

2. The wind farm equivalent method considering wake effect and connecting branches according to claim 1, characterized in that: In step S1, the input wind speed of each wind turbine in the wind farm is calculated through the Jensen wake model.

3. The wind farm equivalent method considering wake effect and connecting branches according to claim 1, characterized in that: In step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connecting branches in the wind farm includes: When the wind turbine types on the same connecting branch in the N groups are different, analyze the physical parameters of the wind turbines. If the difference in the same physical parameters between any two different types of wind turbines in the same group is within 5%-10%, arrange the input wind speeds of the wind turbines in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows: Meet v r -v i Wind turbines with < 0.1 are classified as type a, v r Is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i Is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ; Wind turbines that satisfy 0.1 < v r - v i ≤ 1 are classified as Class B; Wind turbine units that satisfy v r -v i > 1 are classified into category c.

4. The wind farm equivalent method considering wake effect and connecting branches according to claim 1, characterized in that: In step S3, the method for classifying the wind turbine groups considering the wake effect on the basis of the original connecting branches in the wind farm includes: When the types of wind turbines on the same connection branch in N groups are different, analyze the physical parameters of the wind turbines. If the difference in the same physical parameters between any two different types of wind turbines in the same group is more than 10%, in the case where the wind turbines on the connection branch have been divided into the same group, within the same group, divide the wind turbines with the difference in the same physical parameters between different types of wind turbines within the preset range into the same group. After all the wind turbines are divided, arrange the input wind speeds of each wind turbine in each group from large to small, and classify the wind turbines in each group again. The specific method is as follows: Wind turbines that satisfy v r -v i < 0.1 are classified as type a, where v r is the initial input wind speed of the wind farm, which is not affected by the wake effect; v i is the input wind speed of the i-th wind turbine after the wake effect, and its value is not greater than v r ; Wind turbine units that satisfy 0.1 < v r - v i ≤ 1 are classified as Class B; Wind turbines that satisfy v r -v i > 1 are classified into category c.

5. The wind farm equivalent method considering wake effect and connection branch according to claim 1, characterized in that: In step S4, combine formula (1) to equivalently convert the wind turbines in categories a, b, and c into one wind turbine respectively. The calculation formula for the parameters of the equivalent wind turbine is as follows: Wherein, S i represents the capacity of the i-th wind power generator set, P eq , R eq , X eq , A eq , C p_eq , H eq , V eq respectively represent the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant and wind speed of the wind power generator set after equivalence. P i , R i , X i , A i , C p_i , H i , V i are respectively the power, resistance, reactance, swept area, wind energy utilization rate, inertia time constant and wind speed of the i-th wind power generator set before equivalence, and n is the number of wind power generator sets in each category.

Images