Fan model high and low voltage ride through process parameter identification method and system based on Harlisia eagle algorithm and medium

Abstract

The invention relates to a fan model high and low voltage ride-through process parameter identification method and system based on a Harlisia eagle algorithm and a medium, and the method comprises the steps: firstly verifying a PSASP electromechanical transient simulation model of a direct-drive permanent magnet synchronous wind generating set, and researching the influence of high and low voltage ride-through faults of a direct-drive fan on a power grid; and meanwhile, the influence of key parameters of the direct-driven permanent magnet synchronous wind generating set model on high and low voltage ride-through characteristics is analyzed. According to a fan type test result, wind turbine generator fault transient state control parameter identification is realized by using a Harlisia eagle optimization algorithm, wind turbine generator controller key parameter identification based on PSASP and influence analysis on high and low voltage ride-through characteristics are completed, the deviation between a simulation result and an actual measurement result is within an allowable range, and the method has the advantages of high simulation accuracy and high reliability. Model verification requirements are met, and stable operation of the wind turbine generator is guaranteed.

Description

Technical Field

[0001] The present invention relates to a parameter identification of high and low voltage ride-through process of a wind turbine model based on Harris Eagle algorithm. The model is mainly used to study the influence of high and low voltage ride-through faults of direct-drive wind turbines on power grids, and provides a basis for the safe and stable operation of wind turbines connected to power grids. Background Art

[0002] With the advancement of the "dual carbon" goals and the continuous construction of new power systems, wind power has increasingly penetrated into the power grid, posing a huge challenge to the stable operation of the power grid. Stability issues have received more and more attention, especially transient stability. Therefore, power grid operators have formulated requirements and guidelines for wind turbines to access the power grid. One of the most important requirements is that wind turbines should have low / high voltage ride-through capabilities. When a grid fault occurs, wind turbines should maintain uninterrupted operation. In addition, wind turbines are also required to provide reactive power to support the restoration of the grid. In order to meet this challenge, it is very important to establish an electromechanical transient model that can accurately reflect the actual characteristics of wind turbines.

[0003] By optimizing the operation mode and control measures of the power grid, we can better cope with the impact of wind power on the power grid. For example, in response to the challenges of power grid peak regulation, flexible scheduling strategies can be adopted to coordinate the operation of renewable energy generation and traditional power sources to achieve a balance between supply and demand. For voltage and frequency issues, effective regulation and control measures can be taken to ensure the stable operation of the power grid.

[0004] At present, a large number of scholars have studied how to improve the low / high voltage ride-through capability of renewable energy power stations. Current existing research mostly considers the specific characteristics of wind turbines, and less research analyzes the modeling and parameter identification of wind turbines in PSASP. The existing models cannot perform power system stability calculations in PSASP.

[0005] In this context, a study on modeling and parameter identification of wind turbines in PSASP is conducted. Taking direct-drive wind turbines as an example, the measured data of the wind turbine is first obtained based on the type test report of the wind turbine. Then, the measured data of the wind turbine under various working conditions of low voltage ride-through and voltage drop are used as the initial value and input data for parameter identification. Secondly, the absolute value of the difference between the measured data of the wind turbine under low voltage ride-through and voltage drop working conditions and the parameter identification result is defined as the objective function.

[0006] minf=|f sim (x)-f true (x) (1)

[0007] Calculate the objective function and describe the HHO algorithm parameter identification result with f sim (x) and the measured result f trueThe smaller the f, the closer the data is to the measured result. Finally, the identification result is output.

[0008] According to the test results of wind turbine type test, the Harris Eagle optimization algorithm was used to realize the identification of transient control parameters of wind turbine faults, and the key parameters of wind turbine controller based on PSASP and the analysis of their impact on high and low voltage ride-through characteristics were completed, providing a basis for the safe and stable operation of wind turbines connected to the power grid. Summary of the invention

[0009] The present invention first verifies the PSASP electromechanical transient simulation model of direct-drive permanent magnet synchronous wind turbine generator sets, studies the impact of high and low voltage ride-through faults of direct-drive wind turbines on the power grid, and analyzes the influence of key parameters of the direct-drive permanent magnet synchronous wind turbine model on high and low voltage ride-through characteristics. Based on the test results of the wind turbine type test, the Harris Eagle optimization algorithm is used to realize the identification of transient control parameters of wind turbine faults, completes the identification of key parameters of wind turbine controllers based on PSASP and the analysis of the impact on high and low voltage ride-through characteristics, and provides a basis for the safe and stable operation of wind turbines connected to the power grid.

[0010] The above technical problems of the present invention are mainly achieved through the following technical solutions:

[0011] A method for identifying high and low voltage ride through control parameters of a wind turbine based on Harris Eagle algorithm comprises the following steps:

[0012] Obtain the measured data of the fan and use it as the initial value and input data for parameter identification;

[0013] The absolute value of the difference between the measured data of the wind turbine low voltage ride-through voltage drop working condition and the parameter identification result is set as the objective function, and the parameter identification result is the Harris Eagle algorithm re-fitness function f;

[0014] Get the parameter identification result corresponding to the minimum value of the re-fitness function f.

[0015] In the above-mentioned wind turbine high and low voltage ride through control parameter identification method based on Harris Eagle algorithm,

[0016] Obtain the actual measured data of the fan according to the fan type test report;

[0017] The measured data under four working conditions of low voltage ride-through voltage drops of 0.35, 0.5, 0.75, and 0.9 (pu) are used as the initial values ​​and input data for parameter identification.

[0018] In the above-mentioned wind turbine high and low voltage ride through control parameter identification method based on Harris Hawk algorithm, the HHO population is initialized, and the fitness function f of the i-th generation population is calculated according to the Harris Hawk algorithm (HHO) 3 parts, including the exploration stage, the conversion stage between exploration and development, and the development stage.

[0019] In the above-mentioned wind turbine high and low voltage ride through control parameter identification method based on Harris Eagle algorithm, the formula in the exploration stage is as follows:

[0020]

[0021] Where: X(t) and X(t+1) are the positions of the current and next iteration individuals, respectively; t is the number of iterations; X r (t) is the randomly selected individual position; X rab (t) is the position of the individual with the best fitness; r1, r2, r3, r4 and q are all random numbers between [0,1]; X n (t) is the average position of individuals; ub and lb represent the upper and lower bounds of individuals; N is the population size.

[0022] In the above-mentioned wind turbine high and low voltage ride through control parameter identification method based on Harris Eagle algorithm, the conversion stage between exploration and development is determined by the escape energy, which is:

[0023]

[0024] Where: E0 is the initial escape energy; the value is a random number between [0,1]; T is the maximum number of iterations; when |E|≥1, it enters the exploration phase, and when |E|<1, it enters the development phase.

[0025] In the above-mentioned method for identifying control parameters of wind turbine high and low voltage ride-through based on Harris Eagle algorithm, the development stage includes four strategies, which determine different stages according to different values ​​of |E| and r;

[0026] Strategy 1: When 0.5≤|E|<1 and r≥0.5, adopt the soft siege strategy to update the position:

[0027]

[0028] Where: J is a random number between [0,2], ΔX(t) is the difference between the prey position and the individual's current position;

[0029] Strategy 2: |E|<0.5 and r≥0.5, adopt the hard siege strategy to update the position:

[0030] X(t+1)=X rab (t)-E|X(t)| Formula 5

[0031] Strategy 3: When 0.5≤|E|<1 and r<0.5, adopt the soft encirclement strategy of asymptotic rapid dive to update the position:

[0032]

[0033] Where: f() is the objective function value of the minimization problem; D is the dimension of the problem; S is a random vector on (0,1) of size 1×D; LF is the Levy flight function.

[0034] Strategy 4: |E|<0.5 and r<0.5, adopt the hard encirclement strategy of asymptotic fast dive to update the position:

[0035]

[0036] In the above-mentioned wind turbine high and low voltage ride through control parameter identification method based on Harris Eagle algorithm, the absolute value of the difference between the measured data of the wind turbine low voltage ride through voltage drop 0.2 (pu) working condition and the parameter identification result is defined as the objective function, and equation (26) is used as the objective function calculation, and f is used to describe the HHO algorithm parameter identification result f sim (x) and the measured result f true The smaller the f, the closer the data is to the measured results.

[0037] min f=|f sim (x)-f true (x)|Formula 10.

[0038] A wind turbine high and low voltage ride through control parameter identification system based on Harris Eagle algorithm, including

[0039] The first module: configures the fan to obtain measured data and uses it as the initial value and input data for parameter identification;

[0040] The second module: the absolute value of the difference between the measured data configured to set the wind turbine low voltage ride-through voltage drop working condition and the parameter identification result is the target function, and the parameter identification result is the Harris Eagle algorithm re-fitness function f;

[0041] The third module: configures the parameter identification result corresponding to the minimum value of the re-fitness function f.

[0042] A computer medium stores a computer program, wherein the computer program is capable of executing the method steps.

[0043] The deviation between the simulation results of the present invention and the measured results is within the allowable range and meets the model verification requirements. During the high / low voltage ride-through process of the wind turbine generator set, the recovery slope of the wind turbine generator set after the ride-through should be appropriately increased to improve the recovery capacity and ensure the stable operation of the wind turbine generator set. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a parameter identification process of high and low voltage ride-through of wind turbines based on HHO.

[0045] Figure 2 It is the three-phase fault setting position.

[0046] Figure 3a It is the verification ratio (positive sequence voltage) under high power output three-phase voltage drop to 20%Un condition.

[0047] Figure 3b It is the verification ratio (active power) under the condition of high-power output three-phase voltage dropping to 20%Un.

[0048] Figure 3c It is the verification ratio (reactive power) under the condition of high-power output three-phase voltage dropping to 20%Un.

[0049] Figure 3d It is the verification ratio (reactive current) under the condition of high-power output three-phase voltage dropping to 20%Un.

[0050] Figure 4a It is the verification ratio (positive sequence voltage) under high power output three-phase voltage increased to 125%Un condition.

[0051] Figure 4b It is the verification ratio (active power) under the condition that the high-power output three-phase voltage increases to 125%Un.

[0052] Figure 4c It is the verification ratio (reactive power) under the condition that the high-power output three-phase voltage increases to 125%Un.

[0053] Figure 4d It is the verification ratio (reactive current) under the condition that the high-power output three-phase voltage increases to 125%Un.

[0054] Figure 5 This paper analyzes the influence of low voltage ride through active power control parameter 2 on low voltage ride through characteristics.

[0055] Figure 6 It is an analysis of the impact of recovery slope on low voltage ride-through characteristics. DETAILED DESCRIPTION

[0056] The following examples are combined with the accompanying drawings. Taking a direct-drive wind turbine as an example, based on the test results of the wind turbine type test, the Harris Eagle optimization algorithm is used to realize the identification of transient control parameters of wind turbine faults, and the key parameters of the wind turbine controller based on PSASP and the analysis of the impact on high and low voltage ride-through characteristics are completed, providing a basis for the safe and stable operation of wind turbines connected to the power grid.

[0057] The technical solution of the present invention is further described.

[0058] Step 1: Obtain the actual measured data of the fan according to the fan type test report.

[0059] Step 2: The measured data under the four working conditions of low voltage ride-through voltage drops of 0.35, 0.5, 0.75, and 0.9 (pu) of the wind turbine are used as the initial value and input data for parameter identification.

[0060] Step 3: Initialize the HHO population and calculate the fitness function f of the i-th generation population according to the Harris Hawk Optimization (HHO) algorithm (Part 3).

[0061] The formula for the first part of the exploration phase is as follows:

[0062]

[0063] Where: X(t) and X(t+1) are the positions of the current and next iteration individuals, respectively; t is the number of iterations; X r (t) is the randomly selected individual position; X rab (t) is the position of the individual with the best fitness; r1, r2, r3, r4 and q are all random numbers between [0,1]; X n (t) is the average position of individuals; ub and lb represent the upper and lower bounds of individuals; N is the population size.

[0064] Part II: Conversion phase between exploration and development:

[0065] The conversion between exploration and exploitation of the HHO algorithm is determined by the escape energy, which is:

[0066]

[0067] Where: E0 is the initial escape energy; the value is a random number between [0,1]; T is the maximum number of iterations; when |E|≥1, it enters the exploration phase, and when |E|<1, it enters the development phase.

[0068] Part III Development Phase

[0069] The development phase of HHO includes four strategies, and different phases are determined according to different values ​​of |E| and r.

[0070] Strategy 1: When 0.5≤|E|<1 and r≥0.5, adopt the soft siege strategy to update the position:

[0071]

[0072] Where: J is a random number between [0,2]. ΔX(t) is the difference between the prey position and the individual's current position.

[0073] Strategy 2: |E|<0.5 and r≥0.5, adopt the hard siege strategy to update the position:

[0074] X(t+1)=X rab (t)-E|X(t)| (6)

[0075] Strategy 3: When 0.5≤|E|<1 and r<0.5, adopt the soft encirclement strategy of asymptotic rapid dive to update the position:

[0076]

[0077] Where: f() is the objective function value of the minimization problem; D is the dimension of the problem; S is a random vector on (0,1) of size 1×D; LF is the Levy flight function.

[0078] Strategy 4: |E|<0.5 and r<0.5, adopt the hard encirclement strategy of asymptotic fast dive to update the position:

[0079]

[0080] Step 4: Define the absolute value of the difference between the measured data of the wind turbine under low voltage ride-through voltage drop of 0.2 (pu) and the parameter identification result as the objective function. Formula (26) is used as the objective function calculation, and f is used to describe the HHO algorithm parameter identification result f sim (x) and the measured result f true The smaller f is, the closer the data is to the measured results.

[0081] minf=|f sim (x)-f true (x) (11)

[0082] Step 5: Output parameter identification results.

[0083] 2. Take a wind farm in Hubei Province as an example to conduct modeling analysis. The wind farm has an installed capacity of 24MW and is equipped with 16 1.5MW direct-drive wind turbines of a certain model. By collecting the high and low voltage ride-through type test report data of the wind turbine, according to the high and low voltage ride-through fault ride-through parameter identification process of the wind turbine based on HHO, using PSASP to repeatedly simulate and verify the comparison and fitting under various working conditions, the simulation parameters are obtained as shown in Table 1 below, and the active current recovery slope is set to 0.5 (pu / s).

[0084] PSASP verifies the location information of the wind turbine low voltage ride-through fault setting as shown in the attached Figure 2 As shown, a fault is set at the wind turbine port. The reference value SB = 1.5MW, UB = 37kV.

[0085] The high and low voltage ride-through characteristics are analyzed by taking the two working conditions of three-phase short circuit fault voltage dropping to 20%Un (P≥0.9Pn) and three-phase short circuit fault voltage rising to 125%Un (P≥0.9Pn) as examples.

[0086] A three-phase short circuit fault is set at the generator port. The voltage drop level of the fan port is adjusted by setting the grounding resistance. The generator port voltage drops to 20%Un for a duration of 0.625S. The simulation results are attached. Figures 3a to 3d As shown:

[0087] When the voltage drops to 20%Un, the positive sequence voltage, reactive power and reactive current can all recover to the working state before the drop in a relatively short time, while the active power recovers more slowly, affected by the active current recovery slope.

[0088] A three-phase short circuit fault is set at the generator port. The voltage drop level of the fan port is adjusted by setting the grounding resistance to increase the generator port voltage to 125%Un for 1S. The simulation results are shown in the attached figure. Figures 4a to 4d As shown:

[0089] When the voltage rises to 125%Un, the positive sequence voltage, reactive power, reactive current and active power can be restored in a relatively short time. This is because the depth of high voltage ride-through is low and the fault recovery is fast.

[0090] Table 1 Model parameters of wind turbine generator set during voltage ride-through

[0091]

[0092]

[0093] The active power, reactive power, active current, reactive current, etc. of wind turbines are greatly affected by several key parameters during voltage ride-through. Taking low voltage ride-through active power control as an example, the active power control parameter 2P of low voltage ride-through is studied. set_LV The influence of the recovery slope μ on the high and low pressure ride-through characteristics is analyzed. Figure 5-6 As shown, this is the fixed active power control mode, K P_LVRT is 0.

[0094] Attached Figure 5 Demonstrates low voltage ride through active power control parameter 2P set_LV The low voltage ride-through characteristics are 0, 0.05, 0.1, 0.15, and 0.2 respectively. Figure 5 It can be seen that when K P_LVRT When P is 0, set_LV The value is the lower limit of the low voltage ride through active power. The low voltage ride through active power control parameter 2P set_LV It affects the lower limit of low voltage ride-through. This parameter controls the lower limit of active power during low voltage ride-through. It can be seen that in the fixed active power control mode, if you want to increase the lower limit of active power, you should appropriately increase the low voltage ride-through active power control parameter 2P. set_LV The numerical value of .

[0095] Attached Figure 6 The low voltage ride-through characteristics analysis is shown for nine cases where the recovery slope μ is 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1, and 3. Figure 6 It can be seen that with the increase of the recovery slope, the time for the wind turbine to recover from the transient state to the steady state is gradually decreasing, the recovery speed from the low voltage ride-through state is increasing, and the low voltage ride-through capability is gradually improving. At the same time, when μ is 0.1, 0.2, 0.3, 0.4, 0.5 and other small recovery slopes, the recovery time is too long, resulting in power loss of the wind turbine. If the recovery slope is too small, it is easy to be disturbed by external faults again during the recovery process, causing the wind turbine to run offline and affect the stability of the power grid. During the grid-connected operation of the wind turbine, its recovery slope should be appropriately adjusted.

[0096] The present invention verifies the PSASP electromechanical transient simulation model of direct-drive permanent magnet synchronous wind turbine generator set, studies the impact of high and low voltage ride-through faults of direct-drive wind turbines on the power grid, and analyzes the influence of key parameters of the direct-drive permanent magnet synchronous wind turbine generator set model on high and low voltage ride-through characteristics. The following conclusions are drawn:

[0097] 1) The identification of high and low voltage ride-through control parameters of wind turbines based on the HHO algorithm was completed, and the comparison between the simulation results of high and low voltage ride-through of the PSASP model of direct-drive wind turbines and the type test report was given. The deviation between the simulation results and the measured results was within the allowable range, meeting the model verification requirements.

[0098] 2) During the low voltage ride-through process, the low voltage ride-through active power control parameter 2P set_LV It affects the lower limit of active power. Appropriate adjustment of low voltage ride-through active power control parameter 2 can ensure that the wind turbine maintains a certain active power output during the low voltage ride-through process.

[0099] 3) During the high / low voltage ride-through process of the wind turbine, the recovery slope of the wind turbine after the ride-through should be appropriately increased to improve the recovery capacity and ensure the stable operation of the wind turbine.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Those skilled in the art may make various modifications or compensations to the specific examples described, but they will not deviate from the scope defined by the appended claims of the present invention.

Claims

1. A method for identifying high and low voltage ride through control parameters of a wind turbine based on Harris Eagle algorithm, characterized in that: The following steps are included Obtain the measured data of the fan and use it as the initial value and input data for parameter identification; The absolute value of the difference between the measured data of the wind turbine low voltage ride-through voltage drop working condition and the parameter identification result is set as the objective function, and the parameter identification result is the Harris Eagle algorithm re-fitness function f; Get the parameter identification result corresponding to the minimum value of the re-fitness function f.

2. According to the Harris Eagle algorithm-based method for identifying high and low voltage ride-through control parameters of a wind turbine, the method is characterized in that: Obtain the actual measured data of the fan according to the fan type test report; The measured data under four working conditions of low voltage ride-through voltage drops of 0.35, 0.5, 0.75, and 0.9 (pu) are used as the initial values ​​and input data for parameter identification.

3. According to claim 1, a method for identifying high and low voltage ride through control parameters of a wind turbine based on Harris Eagle algorithm is characterized in that: Initialize the HHO population and calculate the fitness function f of the i-th generation population according to the Harris Hawk Algorithm (HHO) Part 3, including the exploration phase, the conversion phase between exploration and development, and the development phase.

4. According to the Harris Eagle algorithm-based method for identifying high and low voltage ride-through control parameters of a wind turbine, the method is characterized in that: The exploration phase formula is as follows: Where: X(t) and X(t+1) are the positions of the current and next iteration individuals, respectively; t is the number of iterations; X r (t) is the randomly selected individual position; X rab (t) is the position of the individual with the best fitness; r1, r2, r3, r4 and q are all random numbers between [0,1]; X n (t) is the average position of individuals; ub and lb represent the upper and lower bounds of individuals; N is the population size.

5. According to the Harris Eagle algorithm-based method for identifying high and low voltage ride-through control parameters of a wind turbine, the method is characterized in that: The transition phase between exploration and exploitation is determined by the escape energy, which is: Where: E0 is the initial escape energy; the value is a random number between [0,1]; T is the maximum number of iterations; when |E|≥1, it enters the exploration phase, and when |E|<1, it enters the development phase.

6. The method for identifying high and low voltage ride through control parameters of a wind turbine based on Harris Eagle algorithm according to claim 1 is characterized in that: The development phase includes four strategies, which are determined by the different values ​​of |E| and r; Strategy 1: When 0.5≤|E|<1 and r≥0.5, adopt the soft siege strategy to update the position: Where: J is a random number between [0,2], ΔX(t) is the difference between the prey position and the individual's current position; Strategy 2: |E|<0.5 and r≥0.5, adopt the hard siege strategy to update the position: X(t+1)=X rab (t)-E | X(t) Formula 5 Strategy 3: When 0.5≤|E|<1 and r<0.5, adopt the soft encirclement strategy of asymptotic rapid dive to update the position: Where: f() is the objective function value of the minimization problem; D is the dimension of the problem; S is a random vector on (0,1) of size 1×D; LF is the Levy flight function. Strategy 4: |E|<0.5 and r<0.5, adopt the hard encirclement strategy of asymptotic fast dive to update the position:

7. The method for identifying high and low voltage ride through control parameters of a wind turbine based on Harris Eagle algorithm according to claim 1, characterized in that: The absolute value of the difference between the measured data of the wind turbine under the condition of voltage drop of 0.2 (pu) and the parameter identification result is defined as the objective function. Formula (26) is used as the objective function calculation, and f is used to describe the HHO algorithm parameter identification result f sim (x) and the measured result f true The smaller the f, the closer the data is to the measured results. minf= | f sim (x)-f true (x) Formula 10.

8. A wind turbine high and low voltage ride through control parameter identification system based on Harris Eagle algorithm, characterized by: include The first module: configures the fan to obtain measured data and uses it as the initial value and input data for parameter identification; The second module: the absolute value of the difference between the measured data configured to set the wind turbine low voltage ride-through voltage drop working condition and the parameter identification result is the target function, and the parameter identification result is the Harris Eagle algorithm re-fitness function f; The third module: configures the parameter identification result corresponding to the minimum value of the re-fitness function f.

9. A computer medium storing a computer program, characterized in that: The computer program can execute the method steps described in any one of claims 1 to 7.

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