Wind power plant system parameter adjusting method, device and equipment and storage medium

Abstract

The invention discloses a wind power plant system parameter adjusting method, device and equipment and a storage medium, which are applied to the field of wind power plant control, and comprise the following steps: acquiring test curves of each test condition of a wind power plant system under initial parameters, determining indexes of each test curve, and determining a full-condition curve qualification rate of the test curves based on the indexes; when the qualified rate of the full working condition curve is smaller than a qualified rate threshold value, determining the qualified rate of the frequency upper disturbance working condition curve and the qualified rate of the frequency lower disturbance working condition curve; determining positive and negative correlation of each parameter and index in the wind power plant system and index optimization preference setting; and when the qualified rate of the frequency upper disturbance working condition curve and / or the qualified rate of the frequency lower disturbance working condition curve are / is smaller than a qualified rate threshold value, setting optimization parameters based on positive and negative correlation and index optimization preference. The problem that accurate modeling cannot be achieved due to the fact that environmental influence factors are complex is solved, a large amount of historical data does not need to be fitted, and the problem that when parameter adjustment is conducted through data modeling, the accuracy of optimal parameters is low due to insufficient data is solved.

Description

Technical Field

[0001] The present invention relates to the field of wind farm control, and in particular to a wind farm system parameter adjustment method, a wind farm system parameter adjustment device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Wind power generation is influenced by environmental factors such as wind speed, direction, and pressure, as well as various mechanical components, and thus exhibits significant uncertainty. Consequently, major OEMs are adopting closed-loop control strategies, utilizing multiple adjustment parameters to adapt to changing control scenarios. Since each wind farm's environment and mechanical components vary, a single set of parameters cannot meet the needs of all wind farms. Parameter tuning requires on-site operators, placing high technical demands on these operators and resulting in low parameter tuning efficiency. Numerous relevant literature proposes using big data modeling to fit wind farm models and determine optimal parameters. However, since parameter adjustment is significantly affected by the wind farm environment and big data modeling methods require a large amount of historical data for fitting, the accuracy of the optimal parameters output by the model is low when the data volume is small. Furthermore, unpredictable seasonal variations in environmental factors increase the difficulty of data model prediction and reduce the reliability of the fitted model. Summary of the Invention

[0003] The purpose of the present invention is to provide a wind farm system parameter adjustment method, device, equipment and storage medium, which are applied to the field of wind farm control. The method adjusts parameters based on indicators under real-time working conditions, avoiding the problem of being unable to accurately model due to complex influencing factors, and does not require fitting a large amount of historical data, avoiding the problem of low accuracy of optimal parameters due to insufficient data when adjusting parameters through data modeling.

[0004] To solve the above technical problems, the present invention provides a method for adjusting wind farm system parameters, comprising:

[0005] Obtaining test curves of various test operating conditions of the wind farm system under initial parameters, determining an index of each test curve, and determining a full-operating condition curve qualification rate of the test curve based on the index;

[0006] When the qualified rate of the full operating condition curve is less than the qualified rate threshold, determining the qualified rate of the frequency upper disturbance operating condition curve and the qualified rate of the frequency lower disturbance operating condition curve;

[0007] Determining the positive and negative correlations between various parameters in the wind farm system and the indicators and the indicator optimization preference settings;

[0008] When the qualified rate of the upper frequency disturbance working condition curve and / or the qualified rate of the indicator under the lower frequency disturbance working condition is less than the qualified rate threshold, the parameter is optimized based on the positive and negative correlation and the indicator optimization preference setting.

[0009] Optionally, when the qualified rate of the upper frequency disturbance operating condition curve and / or the qualified rate of the lower frequency disturbance operating condition index is less than the qualified rate threshold, optimizing the parameters based on the positive and negative correlation and the index optimization preference setting includes:

[0010] When the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, determining the qualified rate of the frequency disturbance working condition individual indicator;

[0011] When the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, determining the qualified rate of the individual indicator of the frequency disturbance working condition;

[0012] Determine the indicator whose qualified rate of the single indicator of the upper frequency interference working condition and / or the qualified rate of the single indicator of the lower frequency interference working condition is less than the qualified rate threshold as the indicator to be optimized;

[0013] The parameters are optimized based on the positive and negative correlation and the indicator optimization preference setting so that the pass rate of the individual indicator of the frequency disturbance working condition and / or the pass rate of the individual indicator of the frequency disturbance working condition is greater than the pass rate threshold.

[0014] Optionally, optimizing the parameter based on the positive and negative correlation and the indicator optimization preference setting includes:

[0015] Determining a parameter adjustment amount for the parameter based on the positive and negative correlation and the optimization preference setting, and adjusting the parameter based on the parameter adjustment amount;

[0016] After the parameter adjustment, re-determining the qualified rate of the single indicator of the upper frequency disturbance working condition and / or the qualified rate of the single indicator of the lower frequency disturbance working condition;

[0017] If the qualified rate of the single indicator of the upper frequency disturbance working condition and / or the qualified rate of the single indicator of the lower frequency disturbance working condition are both greater than the qualified rate threshold, the parameter adjustment is stopped, and the adjusted parameters are determined as the optimal parameters;

[0018] If the pass rate of the single indicator of the frequency disturbance condition and / or the pass rate of the single indicator of the frequency disturbance condition is less than the pass rate threshold, the step of determining the parameter adjustment amount based on the positive and negative correlation and the optimization preference setting is restarted.

[0019] Optionally, the adjustment parameter is greater than a minimum adjustment parameter threshold and less than a maximum adjustment parameter threshold; the parameter after adjustment is greater than a minimum parameter threshold and less than a maximum parameter threshold.

[0020] Optionally, also include:

[0021] When the number of times the parameter is optimized is greater than the preset number of parameter adjustment times, the parameter optimization is stopped, and the optimal parameter is determined from the parameters optimized each time.

[0022] Optionally, the indicators include: lag time, response time, regulation time and overshoot of wind power regulation.

[0023] Optionally, the indicator optimization preference setting includes: determining the response time as the primary optimization target, determining the overshoot as the secondary optimization target, and determining the adjustment time as the final optimization target.

[0024] To solve the above technical problems, the present invention provides a wind farm system parameter adjustment device, comprising:

[0025] The first module is used to obtain test curves of various test operating conditions of the wind farm system under initial parameters, determine indicators of each test curve, and determine the full-operating condition curve qualification rate of the test curve based on the indicators;

[0026] The second module is used to determine the qualified rate of the frequency upper disturbance working condition curve and the qualified rate of the frequency lower disturbance working condition curve when the qualified rate of the full working condition curve is less than the qualified rate threshold;

[0027] The third module is used to determine the positive and negative correlation between each parameter in the wind farm system and the indicator and the indicator optimization preference setting;

[0028] The fourth module is used to optimize the parameters based on the positive and negative correlation and the indicator optimization preference setting when the qualified rate of the frequency disturbance working condition curve and / or the qualified rate of the indicator under the frequency disturbance working condition is less than the qualified rate threshold.

[0029] To solve the above technical problems, the present invention provides an electronic device, comprising:

[0030] Memory for storing computer programs;

[0031] A processor is configured to implement the above-mentioned method for adjusting wind farm system parameters when executing the computer program.

[0032] To solve the above technical problems, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the above-mentioned wind farm system parameter adjustment method is implemented.

[0033] It can be seen that the present invention obtains the test curves of each test condition of the wind farm system under the initial parameters, determines the index of each test curve, and determines the full working condition curve qualification rate of the test curve based on the index; when the full working condition curve qualification rate is less than the qualification rate threshold, determines the frequency disturbance working condition curve qualification rate and the frequency disturbance working condition curve qualification rate; determines the positive and negative correlations between each parameter and the index in the wind farm system and the index optimization preference setting; when the frequency disturbance working condition curve qualification rate and / or the frequency disturbance working condition curve qualification rate are less than the qualification rate threshold, sets the optimization parameters based on the positive and negative correlations and the index optimization preference setting. Parameter adjustment based on the index under the real-time working condition avoids the problem of being unable to accurately model due to the complexity of the influencing factors, and does not require fitting a large amount of historical data, thus avoiding the problem of low accuracy of the optimal parameters due to insufficient data when adjusting parameters through data modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0035] Figure 1 A flow chart of a method for adjusting wind farm system parameters provided by an embodiment of the present invention;

[0036] Figure 2 This is a structural block diagram of a wind farm system parameter adjustment device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] As renewable energy penetration continues to rise, stricter requirements are being placed on the power control accuracy of grid-connected renewable energy stations. Wind power generation is subject to significant uncertainty, influenced by environmental factors such as wind speed, direction, and pressure, as well as various mechanical components. A single control parameter simply cannot meet these requirements. Consequently, major OEMs are adopting closed-loop control strategies, utilizing multiple adjustment parameters to adapt to diverse control scenarios.

[0039] Because each wind farm has varying environments and mechanical components, a single set of parameters cannot meet the needs of all wind farms. Parameter tuning requires on-site operators, which places high demands on their technical skills and results in inefficient tuning. Numerous literature proposes using big data modeling to fit wind farm models and determine optimal parameters. However, this approach increases the complexity. First, it is significantly affected by environmental factors, making model accuracy difficult to assess. Second, in actual engineering applications, achieving a global optimal solution is not necessary; simply meeting the required performance indicators is sufficient.

[0040] Therefore, the present invention proposes an automated wind farm parameter adjustment control strategy, which performs parameter adjustment based on indicators under real-time working conditions, avoiding the problem of being unable to accurately model due to complex influencing factors, and does not require fitting a large amount of historical data, thus avoiding the problem of low accuracy of optimal parameters due to insufficient data when adjusting parameters through data modeling.

[0041] The following combination Figure 1 , Figure 1 A flow chart of a method for adjusting wind farm system parameters provided by an embodiment of the present invention may include:

[0042] S101: Obtain test curves of various test operating conditions of the wind farm system under initial parameters, determine indicators of each test curve, and determine a full-operating condition curve qualification rate of the test curve based on the indicators.

[0043] This embodiment may be a test curve of each test condition of the wind farm system under initial parameters. This embodiment does not limit the type of parameters and the method of selecting the parameters, which can generally be set based on actual applications.

[0044] This embodiment can perform frequency modulation control under various test conditions and obtain test curves for each test condition. Grid-connected wind farms must implement primary frequency modulation control according to a specific droop curve. The primary frequency modulation droop curve specifies the primary frequency modulation dead zone, frequency overshoot, frequency undershoot droop coefficient, and power support capacity.

[0045] This embodiment does not limit the setting method of the test conditions, which can generally be as shown in Table 1.

[0046] Table 1: Test conditions examples

[0047]

[0048] To adapt to all scenarios of actual operation, six test conditions can be set, namely: low load-reserved-frequency disturbance condition, low load-reserved-frequency disturbance condition, low load-no reserve-frequency disturbance condition, high load-reserved-frequency disturbance condition, high load-reserved-frequency disturbance condition, high load-no reserve-frequency disturbance condition, where reserved means controlling the power below the available power, no reserve means that the power is equal to the available power, the available power is the maximum power that the wind turbine can generate at the current wind speed, and Pn is the rated power.

[0049] The primary frequency control strategy includes multiple control parameters to adapt to changing operating conditions. Therefore, before optimizing the parameters, it is necessary to summarize the test indicators under different operating conditions.

[0050] This embodiment does not limit the specific types of indicators in the test curve, which can generally include the lag time td, response time tp, adjustment time ts, and overshoot of wind power regulation. Lag time: the time it takes for the absolute value of the actual power adjustment to exceed 10% of the absolute value of the primary frequency regulation target change; response time: the time it takes for the actual power to exceed the current frequency regulation target value plus 90% of the primary frequency regulation target change; adjustment time: the time it takes for the actual power to stabilize at the frequency regulation target value plus plus or minus 1% of the installed capacity; overshoot: the maximum error between the actual power exceeding the given frequency regulation target value and the given frequency regulation target value. To quantify the overshoot, this embodiment can standardize the overshoot.

[0051] In this embodiment, the standard value Td of the lag time may be 2s, the standard value Tp of the response time may be 9s, the standard value Ts of the adjustment time may be 15s, and the standard value Overshot of the overshoot may be ±1% of the installed capacity.

[0052] In this embodiment, if the lag time in a test curve is no greater than 2 seconds, the lag time index of the test curve can be determined to be qualified; if the response time is no greater than 9 seconds, the response time index of the test curve can be determined to be qualified; if the adjustment time is no greater than 15 seconds, the adjustment time index of the test curve can be determined to be qualified; if the overshoot does not exceed ±1% of the installed capacity, the overshoot index of the test curve can be determined to be qualified. If the lag time index, response time index, adjustment time index, and overshoot index of a test curve are all qualified, the curve can be determined to be qualified.

[0053] That is, when the j-th test curve under the i-th test condition satisfies td≤Td, tp≤Tp, ts≤Ts and |overshot|≤|Overshot| at the same time, the qualified flag of the test curve can be set. i,jIf it is 1, it indicates that the test curve is qualified. If any of the above conditions is not met, the qualified flag of the test curve is set. i,j A value of 0 indicates that the test curve is unqualified.

[0054] Furthermore, this embodiment can determine the pass rate of the test curve under all working conditions:

[0055] ;

[0056] Where, Rate is the qualified rate of all working condition curves, M is the number of test conditions, N is the number of test curves under each test condition, flag i,j It is the qualified mark of the jth test curve under the i-th test condition.

[0057] S102: When the qualified rate of the full operating condition curve is less than the qualified rate threshold, determining the qualified rate of the frequency upper disturbance operating condition curve and the qualified rate of the frequency lower disturbance operating condition curve.

[0058] In this embodiment, when the qualified rate of the full operating condition curve is less than the qualified rate threshold, the qualified rate of the frequency upper disturbance operating condition curve and the qualified rate of the frequency lower disturbance operating condition curve may be determined respectively.

[0059] In this embodiment, the test conditions can be divided into two categories: frequency up-disturbance conditions and frequency down-disturbance conditions. Frequency up-disturbance conditions can include: low load - reserved - frequency up-disturbance conditions, low load - no reserved - frequency up-disturbance conditions, and high load - reserved - frequency up-disturbance conditions; frequency down-disturbance conditions can include: low load - reserved - frequency down-disturbance conditions, high load - reserved - frequency down-disturbance conditions, and high load - no reserved - frequency up-disturbance conditions.

[0060] The mechanical power generation characteristics of wind turbines determine significant differences in their response strategies under tailwind and headwind conditions, based on the statistical analysis of different operating conditions (frequency up-disturbance and frequency down-disturbance). To ensure that control indicators are met under both frequency up-disturbance and frequency down-disturbance, the primary frequency regulation control strategy utilizes specific control parameters in addition to common control parameters. Therefore, statistical analysis of frequency up-disturbance and frequency down-disturbance indicators is required before optimizing these parameters.

[0061] In this embodiment, the calculation formula of the frequency disturbance working condition curve qualification rate can be shown as follows:

[0062] ;

[0063] Where, Rate p is the qualified rate of frequency interference working condition curve, M is the number of test conditions, P is the number of test curves of frequency interference under each test condition, flag i,p It is the qualified mark of the test curve of the pth frequency interference under the i-th test condition.

[0064] In this embodiment, the calculation formula of the qualified rate of the frequency disturbance working condition curve can be shown as follows:

[0065] ;

[0066] Where, Rate q is the qualified rate of the frequency disturbance working condition curve, M is the number of test conditions, Q is the number of test curves of frequency disturbance under each test condition, flag i,q It is the qualified mark of the test curve of the qth frequency interference under the i-th test condition.

[0067] S103: Determine the positive and negative correlations between various parameters and indicators in the wind farm system and the indicator optimization preference settings.

[0068] This embodiment can determine the positive and negative correlations between various parameters and indicators in a wind farm system, as well as indicator optimization preferences. Because the primary frequency regulation control strategy employs multiple control parameters to accommodate different operating conditions, this embodiment can determine the positive and negative correlations between parameters and indicators under both up- and down-frequency perturbation conditions. This correlation can be used to determine parameter adjustment strategies, such as parameter increase or decrease.

[0069] Table 2: Example table of positive and negative correlation of parameter indicators

[0070]

[0071] This embodiment does not limit the parameter selection method during the parameter adjustment and control process, and can generally be set based on actual applications. In a certain example, four parameters can be adjusted and controlled, specifically Para1, Para2, Para3, and Para4. The positive and negative correlations between the parameters and the indicators can be shown in Table 2, where √ represents that there is a correlation between the parameter and the indicator, (-) represents that there is a negative correlation between the parameter and the indicator, (+) represents that there is a negative correlation between the parameter and the indicator, and / represents that there is no correlation between the parameter and the indicator.

[0072] Furthermore, this embodiment allows for setting an indicator optimization preference, which represents the priority of the indicator pass rate optimization process. This embodiment does not limit the specific method for setting the indicator optimization preference. Generally, response time can be determined as the primary optimization objective, overshoot as the secondary optimization objective, and adjustment time as the final optimization objective.

[0073] That is, during the parameter optimization process, if the response time, overshoot, and adjustment time all fail to meet the pass rate threshold, a parameter that has a positive or negative correlation with all three parameters can be identified and preferentially adjusted to ensure that the response time meets the pass rate threshold. This parameter can then be further adjusted to ensure that the overshoot meets the pass rate threshold, provided that the response time meets the pass rate threshold. Finally, this parameter can be adjusted to ensure that the adjustment time also meets the pass rate threshold, provided that both the response time and overshoot meet the pass rate threshold.

[0074] The current primary frequency modulation control strategy has a very fast response speed in the initial response period. Therefore, in a certain embodiment, the lag time indicator may not be considered temporarily.

[0075] S104: When the qualified rate of the frequency up-disturbance operating condition curve and / or the qualified rate of the frequency down-disturbance operating condition curve is less than the qualified rate threshold, setting optimization parameters based on positive and negative correlation and indicator optimization preference.

[0076] In this embodiment, when the qualified rate of the frequency up-disturbance operating condition curve and / or the qualified rate of the frequency down-disturbance operating condition curve is less than the qualified rate threshold, the optimization parameters can be set based on the positive and negative correlation and the indicator optimization preference.

[0077] Specifically, when the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, the qualified rate of the frequency disturbance working condition individual indicator is determined; when the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, the qualified rate of the frequency disturbance working condition individual indicator is determined.

[0078] The indicators whose pass rate of the individual indicators of the frequency disturbance working condition and / or the pass rate of the individual indicators of the frequency disturbance working condition are less than the pass rate threshold are determined as the indicators to be optimized; the parameters are optimized based on the positive and negative correlation and the indicator optimization preference so that the pass rate of the individual indicators of the frequency disturbance working condition and / or the pass rate of the individual indicators of the frequency disturbance working condition are greater than the pass rate threshold.

[0079] Specifically, in this embodiment, when the pass rate of the frequency disturbance working condition curve is less than the pass rate threshold, and / or when the pass rate of the frequency disturbance working condition curve is less than the pass rate threshold, the pass rate of the individual indicators of the corresponding working condition can be determined, that is, the proportion of the pass index of the test curve under the frequency disturbance working condition in the total number of test curves under the frequency disturbance working condition is determined, and the proportion of the pass index of the test curve under the frequency disturbance working condition in the total number of test curves under the frequency disturbance working condition is determined to determine the indicator that needs to be optimized.

[0080] This embodiment does not limit the method for determining the pass rate of each individual indicator under each working condition, which can generally be shown as follows:

[0081] ;

[0082] Where, Rate p,tdis the qualified rate of lag time under frequency disturbance condition, flag i,p |(td≤Td) is the qualified flag of the lag time index in the test curve of the pth frequency interference under the i-th test condition. When td≤Td, flag i,p |(td≤Td) is 1, otherwise it is 0.

[0083] ;

[0084] Where, Rate p,tp is the qualified rate of response time under frequency disturbance condition, flag i,p |(tp≤Tp) is the qualified flag of the response time indicator in the test curve of the pth frequency interference under the i-th test condition. When tp≤Tp, flag i,p |(tp≤Tp) is 1, otherwise it is 0.

[0085] ;

[0086] Where, Rate p,ts is the qualified rate of adjustment time under frequency disturbance condition, flag i,p |(ts≤Ts) is the qualified flag of the adjustment time index in the test curve of the pth frequency interference under the i-th test condition. When ts≤Ts, flag i,p |(ts≤Ts) is 1, otherwise it is 0.

[0087] ;

[0088] Where, Rate p,overshot is the qualified rate of overshoot under frequency disturbance condition, flag i,p |(|overshot|≤|Overshot|) is the qualified flag of the overshoot index in the test curve of the pth frequency overshoot under the i-th test condition. When |overshot|≤|Overshot|, flag i,p |(|overshot|≤|Overshot|) is 1, otherwise it is 0.

[0089] ;

[0090] Where, Rate q,td is the qualified rate of lag time under frequency disturbance condition, flag i,q |(td≤Td) is the qualified flag of the lag time index in the test curve of the qth frequency disturbance under the i-th test condition. When td≤Td, flag i,q |(td≤Td) is 1, otherwise it is 0.

[0091] ;

[0092] Where, Rate q,tp is the qualified rate of response time under frequency disturbance conditions, flag i,q |(tp≤Tp) is the qualified flag of the response time index in the test curve of the qth frequency disturbance under the i-th test condition. When tp≤Tp, flag i,q |(tp≤Tp) is 1, otherwise it is 0.

[0093] ;

[0094] Where, Rate q,ts is the qualified rate of adjustment time under frequency disturbance condition, flag i,q |(ts≤Ts) is the qualified flag of the adjustment time index in the test curve of the qth frequency interference under the i-th test condition. When ts≤Ts, flag i,q |(ts≤Ts) is 1, otherwise it is 0.

[0095] ;

[0096] Where, Rate q,overshot is the qualified rate of overshoot under frequency disturbance condition, flag i,q |(|overshot|≤|Overshot|) is the qualified flag of the overshoot index in the qth frequency disturbance test curve under the i-th test condition. When |overshot|≤|Overshot|, flag i,q |(|overshot|≤|Overshot|) is 1, otherwise it is 0.

[0097] In this implementation, when the full operating condition curve qualification rate does not meet the requirements, it is necessary to optimize and adjust the parameters based on the qualification rate of the frequency down-disturbance curve, the qualification rate of the frequency up-disturbance curve and the qualification rate of each indicator.

[0098] This embodiment can set optimization parameters based on positive and negative correlation and indicator optimization preferences. Specifically, the parameter adjustment amount is determined based on the positive and negative correlation and optimization preference settings, and the parameter is adjusted based on the parameter adjustment amount; after the parameter adjustment, the pass rate of the individual indicator of the frequency disturbance working condition and / or the pass rate of the individual indicator of the frequency disturbance working condition are re-determined.

[0099] If the pass rate of the individual indicators in the frequency upward interference condition and / or the pass rate of the individual indicators in the frequency downward interference condition is greater than the pass rate threshold, stop adjusting the parameters and determine the adjusted parameters as the optimal parameters; if the pass rate of the individual indicators in the frequency upward interference condition and / or the pass rate of the individual indicators in the frequency downward interference condition is less than the pass rate threshold, start over from the step of determining the parameter adjustment amount based on positive and negative correlations and optimization preferences.

[0100] Specifically, since there is a positive or negative correlation between the parameters and the indicators, the increase or decrease of the parameters can be controlled through the positive and negative correlations to synchronously control the increase and decrease of the indicators. For example, when there is a positive correlation between Indicator 1 and Parameter 1, if the pass rate of Indicator 1 is lower than the pass rate threshold, the value of Parameter 1 can be decreased to increase the pass rate of Indicator 1.

[0101] And in this embodiment, the adjustment of the parameters needs to be performed according to the optimization preference settings for the optimization of the indicators. That is, if the optimization priority of Indicator 1 is higher than the optimization priority of Indicator 2, then it can be determined that for the parameter that affects both Indicator 1 and Indicator 2, the parameter needs to be adjusted first to make the pass rate of Indicator 1 greater than the pass rate threshold, and then the parameter is adjusted to make the pass rate of Indicator 2 greater than the pass rate threshold.

[0102] In this embodiment, to avoid abnormal parameter optimization, constraint conditions in the parameter optimization process can be set. For example, the adjustment amount is greater than the minimum adjustment amount threshold and less than the maximum adjustment amount threshold; the adjusted parameter is greater than the minimum parameter threshold and less than the maximum parameter threshold.

[0103] Specifically, Para2min < Para2 < Para2max, Para3min < Para3 < Para3max, |ΔPara1min| < |ΔPara1| < |ΔPara1_max|, |ΔPara4min| < |ΔPara4| < |ΔPara4max| can be set, where Para2min and Para3min are the minimum parameter thresholds of Para2 and Para3 respectively, Para2max and Para3max are the maximum parameter thresholds of Para2 and Para3 respectively, ΔPara1min and ΔPara4min are the minimum adjustment amount thresholds of Para1 and Para4 respectively, and ΔPara1max and ΔPara4max are the maximum parameter thresholds of Para1 and Para4 respectively.

[0104] In this embodiment, after each parameter adjustment and re - testing, the pass rates of each individual indicator under the frequency upward interference and frequency downward interference conditions can be recalculated until the pass rates of all individual indicators are greater than the pass rate threshold. The pass rate thresholds for each step in this embodiment can be different and can be set based on actual applications.

[0105] Furthermore, to avoid wasting computing resources, if there is no mutual influence between the parameters and the indicators, the qualified rate of the corresponding indicators after the parameter adjustment does not need to be retested. The specific retest conditions can be shown in Table 3. √ indicates that when a parameter is modified, the qualified rate of the corresponding indicator needs to be retested.

[0106] Table 3: Examples of retest conditions

[0107]

[0108] In this embodiment, to avoid the situation where multiple parameter adjustments still fail to achieve a pass rate for each indicator greater than the pass rate threshold, parameter adjustment needs to be stopped using a termination condition. This embodiment does not limit the setting method of the termination condition. Generally, when the number of parameter optimization cycles exceeds a preset number of parameter adjustment cycles, parameter optimization is stopped and the optimal parameters are determined from the parameters optimized in each cycle.

[0109] The following is an example of optimized parameter adjustment under a frequency disturbance condition provided by this embodiment. The parameters in this embodiment may have negative values:

[0110] If Rate p,tp 、Rate p,ts and Rate p,overshot If all values are less than the qualified rate threshold, you can reduce Para1, Para2, and Para3 and retest.

[0111] If Rate p,tp and Rate p,ts If all values are less than the qualified rate threshold, you can reduce Para1, reduce Para2, increase Para3, and retest;

[0112] If Rate p,tp and Rate p,overshot If all values are less than the qualified rate threshold, you can reduce Para1, reduce Para2, increase Para3, and retest;

[0113] If Rate p,tp If the value is less than the qualified rate threshold, Para1 can be reduced and retested;

[0114] If Rate p,ts and Rate p,overshot If both are less than the qualified rate threshold, you can reduce Para2 and Para3 and retest;

[0115] If Rate p,ts If the value is less than the qualified rate threshold, Para2 can be reduced and retested;

[0116] If Rate p,overshotIf the value is less than the qualified rate threshold, you can increase Para1 and retest.

[0117] Based on the above embodiments, the present invention performs parameter adjustment based on indicators under real-time working conditions, avoiding the problem of being unable to accurately model due to complex influencing factors, and does not require fitting a large amount of historical data, thereby avoiding the problem of low accuracy of optimal parameters due to insufficient data when adjusting parameters through data modeling.

[0118] The following combination Figure 2 , Figure 2 This is a structural block diagram of a wind farm system parameter adjustment device provided by an embodiment of the present invention. The device may include:

[0119] The first module 100 is configured to obtain test curves of various test operating conditions of the wind farm system under initial parameters, determine an index of each test curve, and determine a full-operating condition curve qualification rate of the test curve based on the index;

[0120] The second module 200 is configured to determine the pass rate of the frequency upper disturbance working condition curve and the pass rate of the frequency lower disturbance working condition curve when the pass rate of the full working condition curve is less than the pass rate threshold;

[0121] The third module 300 is used to determine the positive and negative correlation between each parameter in the wind farm system and the indicator and the indicator optimization preference setting;

[0122] The fourth module 400 is used to optimize the parameters based on the positive and negative correlation and the indicator optimization preference setting when the qualified rate of the frequency disturbance working condition curve and / or the qualified rate of the indicator under the frequency disturbance working condition is less than the qualified rate threshold.

[0123] Based on the above embodiments, the present invention performs parameter adjustment based on indicators under real-time working conditions, avoiding the problem of being unable to accurately model due to complex influencing factors, and does not require fitting a large amount of historical data, thereby avoiding the problem of low accuracy of optimal parameters due to insufficient data when adjusting parameters through data modeling.

[0124] Based on the above embodiment, the fourth module 400 may include:

[0125] The first unit is configured to determine a pass rate of a separate indicator of a frequency disturbance working condition when the pass rate of the frequency disturbance working condition curve is less than the pass rate threshold;

[0126] The second unit is configured to determine the pass rate of a separate indicator of the frequency disturbance working condition when the pass rate of the frequency disturbance working condition curve is less than the pass rate threshold;

[0127] The third unit is configured to determine the indicator whose qualified rate of the single indicator of the upper frequency interference working condition and / or the qualified rate of the single indicator of the lower frequency interference working condition is less than the qualified rate threshold as the indicator to be optimized;

[0128] The fourth unit is used to optimize the parameters based on the positive and negative correlation and the indicator optimization preference so that the pass rate of the single indicator of the frequency disturbance condition and / or the pass rate of the single indicator of the frequency disturbance condition is greater than the pass rate threshold.

[0129] Based on the above embodiments, the fourth module 400 may include:

[0130] A fifth unit is configured to determine a parameter adjustment amount of the parameter based on the positive and negative correlation and the optimization preference setting, and adjust the parameter based on the parameter adjustment amount;

[0131] A sixth unit is configured to re-determine the qualified rate of the single indicator of the upper frequency interference working condition and / or the qualified rate of the single indicator of the lower frequency interference working condition after the parameter adjustment;

[0132] The seventh unit is configured to stop adjusting the parameters if the qualified rate of the individual indicator of the upper frequency interference condition and / or the qualified rate of the individual indicator of the lower frequency interference condition are both greater than the qualified rate threshold, and determine the adjusted parameters as the optimal parameters;

[0133] The eighth unit is used to restart the step of determining the parameter adjustment amount based on the positive and negative correlation and the optimization preference setting if the pass rate of the individual indicator of the frequency disturbance condition and / or the pass rate of the individual indicator of the frequency disturbance condition is less than the pass rate threshold.

[0134] Based on the above embodiments, the adjustment parameter is greater than the minimum adjustment parameter threshold and less than the maximum adjustment parameter threshold; the parameter after adjustment is greater than the minimum parameter threshold and less than the maximum parameter threshold.

[0135] Based on the above embodiments, the device may further include:

[0136] The fifth module is used to stop optimizing the parameters when the number of times the parameters are optimized is greater than the preset number of parameter adjustments, and determine the optimal parameters from the parameters optimized each time.

[0137] Based on the above embodiments, the indicators include: lag time, response time, regulation time and overshoot of wind power regulation.

[0138] Based on the above embodiments, the indicator optimization preference setting includes: determining the response time as the primary optimization target, determining the overshoot as the secondary optimization target, and determining the adjustment time as the final optimization target.

[0139] Based on the above embodiments, the present invention further provides an electronic device, which may include a memory and a processor. The memory stores a computer program, and the processor, when invoking the computer program in the memory, can implement the steps provided in the above embodiments. Of course, the device may also include various necessary network interfaces, a power supply, and other components.

[0140] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by an execution terminal or a processor, the method provided in the embodiment of the present invention can be implemented. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program codes.

[0141] Relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0142] The above is a detailed introduction to a method, device, equipment and storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for adjusting wind farm system parameters, characterized in that: include: Obtaining test curves of various test operating conditions of the wind farm system under initial parameters, determining an index of each test curve, and determining a full-operating condition curve qualification rate of the test curve based on the index; When the qualified rate of the full operating condition curve is less than the qualified rate threshold, determining the qualified rate of the frequency upper disturbance operating condition curve and the qualified rate of the frequency lower disturbance operating condition curve; Determining the positive and negative correlations between various parameters in the wind farm system and the indicators and the indicator optimization preference settings; When the qualified rate of the upper frequency disturbance working condition curve and / or the qualified rate of the indicator under the lower frequency disturbance working condition is less than the qualified rate threshold, the parameter is optimized based on the positive and negative correlation and the indicator optimization preference setting.

2. The wind farm system parameter adjustment method according to claim 1, characterized in that: When the qualified rate of the upper frequency disturbance working condition curve and / or the qualified rate of the lower frequency disturbance working condition index is less than the qualified rate threshold, optimizing the parameters based on the positive and negative correlation and the index optimization preference setting includes: When the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, determining the qualified rate of the frequency disturbance working condition individual indicator; When the qualified rate of the frequency disturbance working condition curve is less than the qualified rate threshold, determining the qualified rate of the individual indicator of the frequency disturbance working condition; Determine the indicator whose qualified rate of the single indicator of the upper frequency interference working condition and / or the qualified rate of the single indicator of the lower frequency interference working condition is less than the qualified rate threshold as the indicator to be optimized; The parameters are optimized based on the positive and negative correlation and the indicator optimization preference setting so that the pass rate of the individual indicator of the frequency disturbance working condition and / or the pass rate of the individual indicator of the frequency disturbance working condition is greater than the pass rate threshold.

3. The wind farm system parameter adjustment method according to claim 2, characterized in that: Optimizing the parameters based on the positive and negative correlations and the indicator optimization preference settings includes: Determining a parameter adjustment amount for the parameter based on the positive and negative correlation and the optimization preference setting, and adjusting the parameter based on the parameter adjustment amount; After the parameter adjustment, re-determining the qualified rate of the single indicator of the upper frequency disturbance working condition and / or the qualified rate of the single indicator of the lower frequency disturbance working condition; If the qualified rate of the single indicator of the upper frequency disturbance working condition and / or the qualified rate of the single indicator of the lower frequency disturbance working condition are both greater than the qualified rate threshold, the parameter adjustment is stopped, and the adjusted parameters are determined as the optimal parameters; If the pass rate of the single indicator of the frequency disturbance condition and / or the pass rate of the single indicator of the frequency disturbance condition is less than the pass rate threshold, the step of determining the parameter adjustment amount based on the positive and negative correlation and the optimization preference setting is restarted.

4. The method for adjusting wind farm system parameters according to claim 3, characterized in that: The adjustment parameter is greater than the minimum adjustment parameter threshold and less than the maximum adjustment parameter threshold; the parameter after adjustment is greater than the minimum parameter threshold and less than the maximum parameter threshold.

5. The method for adjusting wind farm system parameters according to claim 1, characterized in that: Also includes: When the number of times the parameter is optimized is greater than the preset number of parameter adjustment times, the parameter optimization is stopped, and the optimal parameter is determined from the parameters optimized each time.

6. The method for adjusting wind farm system parameters according to claim 1, characterized in that: The indicators include: lag time, response time, regulation time and overshoot of wind power regulation.

7. The method for adjusting wind farm system parameters according to claim 1, characterized in that: The indicator optimization preference setting includes: determining the response time as the primary optimization target, determining the overshoot as the secondary optimization target, and determining the adjustment time as the final optimization target.

8. A wind farm system parameter adjustment device, characterized in that: include: The first module is used to obtain test curves of various test operating conditions of the wind farm system under initial parameters, determine indicators of each test curve, and determine the full-operating condition curve qualification rate of the test curve based on the indicators; The second module is used to determine the qualified rate of the frequency upper disturbance working condition curve and the qualified rate of the frequency lower disturbance working condition curve when the qualified rate of the full working condition curve is less than the qualified rate threshold; The third module is used to determine the positive and negative correlation between each parameter in the wind farm system and the indicator and the indicator optimization preference setting; The fourth module is used to optimize the parameters based on the positive and negative correlation and the indicator optimization preference setting when the qualified rate of the frequency disturbance working condition curve and / or the qualified rate of the indicator under the frequency disturbance working condition is less than the qualified rate threshold.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is configured to implement the wind farm system parameter adjustment method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for adjusting wind farm system parameters according to any one of claims 1 to 7 is implemented.

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