Online assessment method and device for reactive power supporting capability of wind power plant

Abstract

The invention discloses an online assessment method and device for reactive power supporting capacity of a wind power plant, and belongs to the technical field of wind power plant control, and the method comprises the steps: inputting the collected current operation state data of the wind power plant into a planning model based on a Koopman operator for solving, and obtaining a multi-level quantitative assessment index, the reactive power supporting capability of the wind power plant can be quickly and accurately quantified, and finally online evaluation of the reactive power supporting capability of the wind power plant is realized; the planning model calculates the minimum reactive power output under the condition of small disturbance and sufficient reactive power so as to meet the condition that the voltage is just not out of limit, reactive power resources are saved, and the residual capacity can be used for active power output so as to improve the economical efficiency; and under the condition of insufficient large-disturbance reactive power, the maximum reactive power output is calculated, so that the voltage offset is minimum, and the safety of the system is improved. The planning model improves pertinence, credibility and accuracy of analysis. The method shows an extremely high effect in the assessment of the reactive power supporting capability of the wind power plant, and ensures the high reliability and practicability of the assessment result.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind farm control, and more specifically, relates to an online evaluation method and device for reactive power support capability of a wind farm. Background Art

[0002] Voltage safety is one of the important issues for the safety and stability of the power system. The penetration rate of new energy represented by wind power is increasing rapidly. The power grid stipulates that wind farms should have a certain reactive voltage support capacity. However, the volatility and uneven distribution of wind resources and the complex operating conditions of wind turbines make it difficult to accurately quantify and evaluate the reactive support capacity of wind farms.

[0003] In recent years, the evaluation methods of reactive support capacity of wind farms can be mainly divided into two categories: model-driven and data-driven. Model-driven methods simplify the voltage regulation model of wind farms, derive expressions or perform complex nonlinear programming calculations to obtain evaluation indicators of reactive support capacity. However, this type of method requires a large number of wind farm model parameters, and there is a certain deviation between the existing parameter estimation methods and the actual parameters. In addition, the simplification of some mechanisms in the model and the complex nonlinear calculation process reduce the evaluation efficiency and make it difficult to meet the requirements of online evaluation. In contrast, data-driven methods use technologies such as neural networks to fit historical data and generate data models to quickly calculate evaluation indicators. However, this method is separated from the mathematical model and it is difficult to reveal the internal relationship of the system. The interpretability of its evaluation results is still insufficient. At the same time, since the reactive voltage regulation data under extreme scenarios in the actual operation of wind farms is difficult to obtain, the accuracy of the training model in the global range is difficult to guarantee. Therefore, the existing research on the reactive support capacity of wind farms has not proposed a practical online evaluation method.

[0004] In summary, the existing quantitative methods for reactive power support capacity of wind farms are not accurate enough and the evaluation speed is not fast enough, which makes it difficult to meet the needs of online evaluation. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and device for online evaluation of reactive support capacity of a wind farm, which aims to solve the technical problems of low evaluation result accuracy and low evaluation speed of the existing wind farm reactive support capacity quantification method.

[0006] To achieve the above object, according to one aspect of the present invention, a method for online evaluation of reactive power support capability of a wind farm is provided, comprising:

[0007] S1: inputting the collected current operation status data of the wind farm into the planning model based on the Koopman operator for solving, and obtaining a multi-level quantitative evaluation index; the multi-level quantitative evaluation index includes a theoretical reactive power support capacity index and an actual reactive power support demand index;

[0008] Wherein, the objective function of the planning model based on the Koopman operator includes: minf(x)=(U PCC -U min ) 2 、minf(x)=(Q N -Q Nmin ) 2 、minf(x)=(U PCC -U max ) 2 and minf(x)=(Q N -Q Nmax ) 2 ; The four objective functions are respectively used for the voltage drop small disturbance condition with sufficient reactive power, the voltage drop large disturbance condition with insufficient reactive power, the voltage swell small disturbance condition with sufficient reactive power, and the voltage swell large disturbance condition with insufficient reactive power; U PCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum supporting capacity indicators of wind farms, respectively;

[0009] S2: Evaluate the reactive power support capability of the wind farm using the multi-level quantitative evaluation index.

[0010] In one embodiment, the constraints of the Koopman operator-based planning model include equality constraints: in, refers to the Koopman dictionary function, K is the global linearized Koopman operator, ΔQ i is the reactive output offset of the i-th wind turbine in the wind farm, P i is the active output of the i-th wind turbine in the wind farm, ΔU g is the voltage drop at the disturbance point, k Qi is the droop control coefficient of the i-th wind turbine in the wind farm.

[0011] In one embodiment, the constraints of the planning model based on the Koopman operator include inequality constraints: reactive power size constraint Q imin ≤Q i ≤Q imax and grid voltage constraint U min ≤U PCC ≤U max ; Among them, Q i is the reactive power output of the i-th fan, Q imax, Q imin is the upper and lower limits of the theoretical support capacity of the i-th wind turbine, U max , U min The upper and lower limits of the grid connection point voltage.

[0012] In one embodiment, the theoretical reactive power support capability index refers to the maximum reactive power output that a wind power unit and a wind farm station theoretically have.

[0013] In one embodiment,

[0014] Using the formula ΔQ max ∈(o1∩o2)+o3Calculate the theoretical reactive power support capacity index ΔQ of a single wind turbine max ; o1 is the first reactive capacity on the stator side, o2 is the second reactive capacity on the stator side, and o3 is the reactive capacity on the rotor side;

[0015] Using the formula Calculate the theoretical reactive power support capacity index of the wind farm;

[0016] Among them, ΔQ Nmax is the theoretical reactive power support capacity of the wind farm, l is the number of wind turbines in the wind farm, S is the capacity of the wind turbine, and the subscript i represents the i-th wind turbine.

[0017] In one of the embodiments, the actual reactive power support demand index refers to the reactive power that actually needs to be output while maintaining the voltage safety at the PCC point under the premise of considering the current, capacity, and flow-related constraints of each power generation unit.

[0018] In one embodiment, before S1, the method further includes:

[0019] Using the Koopman dictionary function to perform dimensionality-upgrading transformation on the historical voltage regulation data of the wind farm to obtain a high-dimensional data set, and fitting the high-dimensional data set to obtain a global linearized Koopman operator;

[0020] Linear programming equality constraints are listed based on the Koopman operator, inequality constraints are listed based on the safe operation conditions of the wind farm, and objective functions are listed based on quantitative evaluation indicators to construct the planning model based on the Koopman operator.

[0021] According to another aspect of the present invention, there is provided a device for online evaluation of reactive power support capability of a wind farm, comprising:

[0022] A solution module is used to input the collected current operating status data of the wind farm into a planning model based on the Koopman operator for solution to obtain multi-level quantitative evaluation indicators; the multi-level quantitative evaluation indicators include a theoretical reactive power support capacity indicator and an actual reactive power support demand indicator;

[0023] Wherein, the objective function of the planning model based on the Koopman operator includes: minf(x)=(U PCC -U min ) 2 、minf(x)=(Q N -Q Nmin ) 2 、minf(x)=(U PCC -U max ) 2 and minf(x)=(Q N -Q Nmax ) 2 ; The four objective functions are respectively used for the voltage drop small disturbance condition with sufficient reactive power, the voltage drop large disturbance condition with insufficient reactive power, the voltage swell small disturbance condition with sufficient reactive power, and the voltage swell large disturbance condition with insufficient reactive power; U PCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum supporting capacity indicators of wind farms, respectively;

[0024] The evaluation module is used to evaluate the reactive power support capability of the wind farm using the multi-level quantitative evaluation index.

[0025] According to another aspect of the present invention, a control system for a wind farm is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0028] (1) The present invention provides an online evaluation method for reactive power support capability of a wind farm, which inputs the collected current operating status data of the wind farm into a planning model based on the Koopman operator for solution, and obtains a multi-level quantitative evaluation index, which can quickly and accurately quantify the reactive power support capability of the wind farm, and finally realizes the online evaluation of the reactive power support capability of the wind farm; wherein, the formula min f(x)=(U PCC -U min ) 2、min f(x)=(Q N -Q Nmin ) 2 、min f(x)=(U PCC -U max ) 2 、min f(x)=(Q N -Q Nmax ) 2 It represents the objective function of the planning model. When there is sufficient reactive power in a small disturbance, the minimum reactive output is calculated to ensure that the voltage does not exceed the limit, which saves reactive resources. The remaining capacity can be used for active output to improve economic efficiency. When there is insufficient reactive power in a large disturbance, the maximum reactive output is calculated to minimize the voltage offset, which improves the safety of the system. That is, the objective function corresponding to the planning model fully considers the actual operation mechanism of the wind farm, improves the pertinence, credibility and accuracy of the analysis. It shows extremely high effect in the evaluation of the reactive support capacity of the wind farm, ensuring the high reliability and practicality of the evaluation results.

[0029] (2) This scheme uses Koopman to describe non-initial value equation constraints of variables to characterize linear programming equation constraints, linearize the system in high dimensions, and improve the efficiency of planning calculations. In addition, the Koopman operator is used to replace complex system parameters, and there is no need to measure line impedance information, which reduces the impact of impedance parameter measurement errors on the evaluation results.

[0030] (3) The inequality constraints listed in this scheme based on the safe operation conditions of the wind farm include: reactive power size constraints and grid connection point voltage constraints. Not only the capacity of the wind turbine under different operating conditions is considered, but also the limitations of safe operation constraints are considered. This comprehensive consideration makes the evaluation results more accurate and reliable.

[0031] (4) The quantitative evaluation indicators in this scheme include: theoretical support capacity and actual support demand. The former quantifies the maximum reactive power support capacity of the wind farm, and the latter quantifies the reactive power that the wind farm needs to output under specific working conditions. It more comprehensively describes the reactive power support capacity of the wind farm and enriches the evaluation level.

[0032] (5) This solution uses the Koopman dimensionality-raising function to fit the historical operating status data, converts the complex low-dimensional nonlinear data into a high-dimensional linear data set to obtain the corresponding Koopman operator, and constructs a linear model for evaluating the reactive support capacity of the wind farm based on the Koopman operator. The linear model is solved to obtain multi-level quantitative evaluation indicators, which can quickly and accurately quantify the reactive support capacity of the wind farm. The present invention can quickly and accurately calculate the reactive support capacity of the wind farm online. The unit can use the evaluation results to reasonably adjust the control coefficient. When the station adjusts the voltage, each wind turbine can allocate reactive power according to its energy. The power grid can also reasonably allocate reactive resources according to the evaluation results to avoid waste of resources, further improving the voltage safety of the new power system and having strong engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of an online quantitative evaluation method for reactive power support capability of a wind farm provided in Example 1 of the present invention;

[0034] Figure 2 A schematic diagram of a wind farm access grid-connected system provided by an embodiment of the present invention;

[0035] Figure 3a Comparison of evaluation errors of different evaluation methods provided in Example 1 of the present invention;

[0036] Figure 3b Comparison of evaluation errors of different training sets provided in Example 1 of the present invention;

[0037] Figure 4a The evaluation results of the theoretical support capability provided in Example 1 of the present invention as the active power and slip change.

[0038] Figure 4b The evaluation results of the theoretical support capability provided in Example 1 of the present invention as the terminal voltage changes.

[0039] Figure 5 This is the evaluation result of the actual support demand at the unit level provided in Example 1 of the present invention as the active power and disturbance change.

[0040] Figure 6 This is the evaluation result of the actual support demand at the station level under the wake effect provided in Example 1 of the present invention.

[0041] Figure 7a The droop coefficient is adjusted according to the application unit level evaluation result provided in Example 1 of the present invention.

[0042] Figure 7b The reactive power curve after the droop coefficient is adjusted according to the embodiment 1 of the present invention.

[0043] Figure 7cThis is a voltage curve after the droop coefficient is adjusted according to the first embodiment of the present invention.

[0044] Figure 8a The current boundary of the application site-level assessment result allocation instruction provided in Example 1 of the present invention.

[0045] Figure 8b The reactive power curve of the station-level evaluation result allocation instruction provided in Example 1 of the present invention.

[0046] Figure 8c This is the reactive power curve of the average distribution instruction provided in Example 1 of the present invention.

[0047] Fig. 9 This is a flow chart of an online quantitative evaluation method for reactive power support capability of a wind farm based on the Koopman operator provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0049] Example 1

[0050] This embodiment provides a method for online evaluation of reactive power support capability of a wind farm. Figure 1 As shown, it includes: S1-S2. Among them, S1: input the collected current operating status data of the wind farm into the planning model based on the Koopman operator for solution to obtain multi-level quantitative evaluation indicators; the multi-level quantitative evaluation indicators include theoretical reactive power support capacity indicators and actual reactive power support demand indicators; the objective function of the planning model based on the Koopman operator includes: min f(x) = (U PCC -U min ) 2 、min f(x)=(Q N -Q Nmin ) 2 、min f(x)=(U PCC -U max ) 2 and min f(x)=(Q N -Q Nmax ) 2 ; The four objective functions are respectively used for the voltage drop small disturbance condition with sufficient reactive power, the voltage drop large disturbance condition with insufficient reactive power, the voltage swell small disturbance condition with sufficient reactive power, and the voltage swell large disturbance condition with insufficient reactive power; UPCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum support capacity indicators of wind farms, respectively. S2: Use multi-level quantitative evaluation indicators to evaluate the reactive support capacity of wind farms.

[0051] The evaluation results are reported to the station and the power grid and can be used to adjust the control coefficient, improve the voltage support effect, or distribute reactive power instructions so that the reactive capacity can be allocated according to energy and fully utilized.

[0052] The required historical operating status data include: grid connection point voltage U PCC , the zero output offset of each fan ΔQ i , the active output P of each wind turbine i , the coefficient k of droop control adopted by each wind turbine Qi , the voltage disturbance magnitude ΔU at the fault point g .

[0053] As an optional implementation manner, the theoretical reactive power support capability index refers to the maximum reactive power output that a wind power unit and a wind farm station theoretically have.

[0054] As an optional implementation, using the formula ΔQ max ∈(o1∩o2)+o3Calculate the theoretical reactive power support capacity index ΔQ of a single wind turbine max ; o1 is the first reactive capacity on the stator side, o2 is the second reactive capacity on the stator side, and o3 is the reactive capacity on the rotor side; using the formula Calculate the theoretical reactive power support capacity index of the wind farm; where ΔQ Nmax is the theoretical reactive power support capacity of the wind farm, l is the number of wind turbines in the wind farm, S is the capacity of the wind turbine, and the subscript i represents the i-th wind turbine.

[0055] It should be noted that using the formula Calculate the reactive capacity o1 on the stator side. Where U s is the amplitude of the stator voltage, I smax Indicates the maximum stator current, P s , Q s are the active and reactive power output by the stator respectively. Calculate the reactive capacity o2 on the stator side. Where, L s is the total self-inductance of the stator winding, L m is the mutual inductance, L r为 The rotor winding is fully self-inducted, ωs is the stator rotation electrical angular velocity, I rscmax is the maximum operating current of the machine-side converter. Further, using the formula Calculate the reactive capacity o3 on the rotor side. Where, P r , Q r are respectively the active and reactive power output from the wind turbine rotor to the grid, I gscmax It is the maximum operating current of the grid-side converter on the rotor side.

[0056] As an optional implementation method, the actual reactive power support demand index refers to the reactive power that actually needs to be output while maintaining the voltage safety of the PCC point under the premise of considering the current, capacity, and flow-related constraints of each power generation unit.

[0057] As an optional implementation, before S1, the method further includes: S01 and S02. S01: Using the Koopman dictionary function to perform dimensionality-up transformation on the historical voltage regulation data of the wind farm to obtain a high-dimensional data set, and fitting the high-dimensional data set to obtain a global linearized Koopman operator.

[0058] Control variable v t and the state variable u t Together they form the training fitting data set:

[0059]

[0060] Among them, the state variables evolve forward in time series under the action of the control variables, and the control variables remain approximately unchanged in a single reactive support. Due to the global linearization effect of Koopman, the training fitting data set does not need to include data under extreme operation scenarios.

[0061] Furthermore, the results of the data dimension-up transformation using the Koopman dictionary function are as follows:

[0062] V Nlift =[g(v1),g(v2)...g(v m )]

[0063] in, q is the number of variables in the high-dimensional space. The approximate fitting matrix K of the K operator can be obtained by performing least squares fitting on the dimensional samples:

[0064]

[0065] in, represents the pseudo-inverse matrix, and

[0066] Further, S02: write linear programming equality constraints based on Koopman operator, write inequality constraints based on wind farm safe operation conditions, and write objective functions based on quantitative evaluation indicators to build a planning model based on Koopman operator. The initial value constraints of variables in the planning model are provided by the measured operation data: [P i ,ΔU g ] T ; Treat the remaining parameters as variables to be solved and do not set their initial values. Then perform a dimensionality-increasing transformation on the expression.

[0067] As an optional implementation, the constraints of the planning model based on the Koopman operator include equality constraints: in, refers to the Koopman dictionary function, K is the global linearized Koopman operator, ΔQ i is the reactive output offset of the i-th wind turbine in the wind farm, P i is the active output of the i-th wind turbine in the wind farm, ΔU g is the voltage drop at the disturbance point, k Qi is the droop control coefficient of the i-th wind turbine in the wind farm.

[0068] As an optional implementation, the constraints of the planning model based on the Koopman operator include inequality constraints: reactive power size constraint Q imin ≤Q i ≤Q imax and grid voltage constraint U min ≤U PCC ≤U max ; Among them, Q i is the reactive power output of the i-th fan, Q imax , Q imin is the upper and lower limits of the theoretical support capacity of the i-th wind turbine, U max , U min The upper and lower limits of the grid connection point voltage.

[0069] A simulation experiment process is given below:

[0070] like Fig. 9 As shown, the online quantitative evaluation method of wind farm reactive support capacity based on Koopman operator includes the following steps.

[0071] First: define a quantitative index of reactive support capacity according to the reactive voltage characteristics of the wind farm. The quantitative evaluation index is used to characterize the theoretical support capacity and actual support demand of the wind turbines in the site.

[0072] The theoretical support capacity is calculated as follows:

[0073] Using the formula Calculate the reactive capacity o1 on the stator side. Where U s is the amplitude of the stator voltage, I smax Indicates the maximum stator current, P s , Q s They are the active and reactive power output by the stator respectively.

[0074] Using the formula Calculate the reactive capacity o2 on the stator side. Where, L s is the total self-inductance of the stator winding, L m is the mutual inductance, L r为 The rotor winding is fully self-inducted, ω s is the stator rotation electrical angular velocity, I rscmax is the maximum operating current of the machine-side converter.

[0075] Using the formula Calculate the reactive capacity o3 on the rotor side. Where, P r , Q r are respectively the active and reactive power output from the wind turbine rotor to the grid, I gscmax It is the maximum operating current of the grid-side converter on the rotor side.

[0076] Using the formula ΔQ max ∈(o1∩o2)+o3 calculates the theoretical reactive power support capacity of a single wind turbine, where ΔQ max It is the theoretical reactive power support capacity.

[0077] Using the formula Calculate the theoretical reactive power support capacity of the wind farm. N max is the theoretical reactive power support capacity of the wind farm, l is the number of wind turbines in the wind farm, S is the capacity of the wind turbine, and the subscript i represents the i-th wind turbine.

[0078] The calculation method of the actual support demand is described as follows.

[0079] Second: Use the Koopman dictionary function to perform dimensional transformation on the historical voltage regulation data of the wind farm to obtain a high-dimensional data set, and fit the high-dimensional data set to obtain a global linearized Koopman operator. The historical data is artificially generated by simulation. The parameter setting range is as follows: Set the wind speed V w The system voltage disturbance ΔP changes from 9.4m / s to 11.4m / s. L It varies from -0.2pu to +0.2pu, and the active output of the wind turbine operates at the maximum power point according to the maximum power curve, which varies with the wind speed range, that is, 0.35pu to 1.0pu.

[0080] Third: Write linear programming equality constraints based on Koopman operator, write inequality constraints based on wind farm safe operation conditions, and write objective functions based on quantitative evaluation indicators to build a planning model. Specifically, set the voltage safety lower limit U min is 0.97pu, the voltage safety lower limit U max is 1.07pu, the maximum operating current of the machine-side converter is rsc max is 1.2pu, the maximum operating current of the grid-side converter is I gsc max is 0.36pu, the maximum stator current I s max The maximum reactive power output of the fan is Q imax , Q imin Obtained from the first step.

[0081] Fourth: Collect the current operating status data of the wind farm, including: the wind speed V of each wind turbine w , Active output of each wind turbine P i , the voltage disturbance magnitude ΔU at the fault point g The wind farm contains 250 2MW double-fed units, each with the same internal parameters, and all use droop control. The wind farm and test system models are as follows: Figure 2 As shown in the figure, the wind speed and the disturbance voltage are changed to obtain the voltage regulation data.

[0082] Fifth: input the current operating status data of the wind farm as the initial value into the planning model for solving, and obtain the multi-level quantitative evaluation index; use the quantitative evaluation index to evaluate the reactive support capacity of the wind farm. After sorting out the results, report to the power grid.

[0083] Among them, the results of the theoretical support capacity index changing with active power, slip, and terminal voltage are as follows: Figure 4a and Figure 4b shown.

[0084] Among them, the actual support demand index changes with the active power and disturbance conditions as follows: Figure 5 shown.

[0085] like Figure 2 As shown, the test system is a doubly-fed wind farm connected to the grid, the synchronous generator adopts a third-order model, the doubly-fed wind farm adopts 250 DFIGs with a rated power of 2MW, and the load adopts a constant impedance model.

[0086] like Figure 3a and Figure 3bAs shown in the figure, the evaluation errors under different methods. The nonlinear optimization method uses a nonlinear solver for solution, and sets the model parameters to deviate by 10% to verify whether the evaluation results will be affected by the accuracy of the parameters. The proposed Koopman method uses a universal function as a dictionary function, and the number of dimensions is 50. The evaluation error of the proposed Koopman method is within 2%, which is the smallest among the three methods. Due to the linear nature of its calculation method, the calculation time is also fast, which meets the needs of online calculation. Compared with the traditional nonlinear optimization method, the evaluation time of the proposed Koopman method is shortened by nearly 10 times; compared with the ANN method, the proposed Koopman method also has obvious advantages in accuracy and speed. After the data of some working conditions are processed by Koopman global linearization, the working conditions of the global range can be estimated, thereby avoiding data collection under extreme working conditions and reducing the difficulty of obtaining evaluation data. Appropriately expanding the range of the training sample training set can effectively reduce the evaluation error. Therefore, it is necessary to comprehensively consider the two factors of accuracy and data acquisition. While avoiding the occurrence of extremely large errors, the range of the sample training set should be reasonably selected in combination with the actual operation of the wind farm.

[0087] like Figure 4a and Figure 4b As shown in the figure, the theoretical support capacity changes with the physical quantities such as active power, terminal voltage, and slip rate. Under typical parameters, the upper limit of wind power reactive power is dominated by the DFIG capacity constraint, and the lower limit of wind power reactive power is dominated by the maximum operating current constraint of the machine-side converter. As the slip rate increases, the theoretical reactive support capacity of the wind turbine decreases; as the terminal voltage decreases, the theoretical support capacity decreases; as the active output increases, the theoretical support capacity decreases. Therefore, the theoretical reactive support capacity of a wind turbine in a high wind speed state is smaller.

[0088] like Figure 5 As shown in the figure, the actual support demand indicators under different active working conditions and disturbance working conditions. Area 1 belongs to the dead zone; in area 2, the voltage disturbance is small and the theoretical capacity is sufficient, so the reactive output only needs to meet the normal range of the grid voltage, that is, 0.97pu~1.07pu, and the remaining capacity can be used for active output to improve the operation economy; in area 3, the voltage disturbance is large and the theoretical capacity is insufficient. Since the grid voltage has not entered the high and low penetration range, the active output can still be regarded as a constant, and the reactive demand is the maximum reactive capacity.

[0089] like Figure 6 As shown in the figure, the actual support demand index evaluation results of the wind farm considering the wake effect. Due to the wake effect, the wind speeds of the wind turbines in the wind farm are not exactly the same, which will further increase the dimension of the variable in the solution. The proposed Koopman method can still accurately evaluate the wind turbines under multiple wind speed conditions in the wind farm.

[0090] like Figure 7a As shown in , the droop coefficient is adjusted using the evaluation results. Figure 7b , Figure 7c As shown in the figure, the voltage supported by the configured coefficient has a better supporting effect, the actual reactive power release of the wind turbine is close to the theoretical boundary, and the reactive power capacity is fully utilized. However, under the droop control of the traditional configuration method, the actual reactive power release of the wind turbine exceeds the boundary, and the reactive power capacity is over-utilized.

[0091] like Figure 8a , Figure 8b , Figure 8c As shown in the figure, the reactive power command of the station is allocated by applying the evaluation results. The reactive power command allocated by applying the evaluation results ensures that the current of each wind turbine is within the safety constraint limit, while the average allocation by capacity leads to the current exceeding the limit of the wind turbine with weak support capacity and the waste of resources of the wind turbine with strong capacity.

[0092] The present invention discloses an online evaluation method for the reactive support capacity of a wind farm based on the Koopman operator and multi-level evaluation indicators: two levels of evaluation indicators, namely theoretical support capacity and actual support demand, are defined based on the reactive characteristics of wind power, and a data-driven online evaluation method based on the Koopman operator is proposed to accurately calculate the defined indicators. Different from the existing methods, the proposed indicators further release the active output capacity on the basis of ensuring reactive voltage safety, thus avoiding waste of resources; the proposed method does not rely on the accuracy of the model, thus reducing the difficulty of data acquisition and improving the calculation speed. Finally, the example system test verifies the rationality of the defined indicators, the accuracy of the proposed method, and the practicality of the evaluation results.

[0093] Example 2

[0094] This embodiment provides an online evaluation device for reactive support capacity of a wind farm, including: a solution module and an evaluation module. The solution module is used to input the collected current operating status data of the wind farm into a planning model based on the Koopman operator for solution to obtain multi-level quantitative evaluation indicators; the multi-level quantitative evaluation indicators include theoretical reactive support capacity indicators and actual reactive support demand indicators; the objective function of the planning model based on the Koopman operator includes: minf(x) = (U PCC -U min ) 2 、minf(x)=(Q N -Q Nmin ) 2 、minf(x)=(U PCC -U max ) 2 and minf(x)=(Q N -Q Nmax )2 ; U PCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum support capacity indicators of wind farms, respectively. The evaluation module is used to evaluate the reactive support capacity of wind farms using multi-level quantitative evaluation indicators.

[0095] Example 3

[0096] This embodiment provides a control system for a wind farm, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0097] Example 4

[0098] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0099] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for online evaluation of reactive power support capability of a wind farm, characterized in that: include: S1: Input the collected current operating status data of the wind farm into the planning model based on the Koopman operator to solve and obtain multi-level quantitative evaluation indicators; The multi-level quantitative evaluation index includes a theoretical reactive power support capacity index and an actual reactive power support demand index; Wherein, the objective function of the planning model based on the Koopman operator includes: minf(x)=(U PCC -U min ) 2 、minf(x)=(Q N -Q Nmin ) 2 、minf(x)=(U PCC -U max ) 2 and minf(x)=(Q N -Q Nmax ) 2 ; The four objective functions are respectively used for the voltage drop small disturbance condition with sufficient reactive power, the voltage drop large disturbance condition with insufficient reactive power, the voltage swell small disturbance condition with sufficient reactive power, and the voltage swell large disturbance condition with insufficient reactive power; U PCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum supporting capacity indicators of wind farms, respectively; S2: Evaluate the reactive power support capability of the wind farm using the multi-level quantitative evaluation index.

2. The online evaluation method for reactive power support capability of a wind farm according to claim 1, characterized in that: The constraints of the Koopman operator-based planning model include equality constraints: in, refers to the Koopman dictionary function, K is the global linearized Koopman operator, ΔQ i is the reactive output offset of the i-th wind turbine in the wind farm, P i is the active output of the i-th wind turbine in the wind farm, ΔU g is the voltage drop at the disturbance point, k Qi is the droop control coefficient of the i-th wind turbine in the wind farm.

3. The online evaluation method for reactive power support capability of a wind farm according to claim 1 or 2, characterized in that: The constraints of the Koopman operator-based planning model include inequality constraints: reactive power size constraint Q imin ≤Q i ≤Q imax and grid voltage constraint U min ≤U PCC ≤U max ; Among them, Q i is the reactive power output of the i-th fan, Q imax , Q imin is the upper and lower limits of the theoretical support capacity of the i-th wind turbine, U max , U min The upper and lower limits of the grid connection point voltage.

4. The online evaluation method for reactive power support capability of a wind farm according to claim 1, characterized in that: The theoretical reactive power support capability index refers to the maximum reactive power output that a wind power unit and a wind farm station theoretically have.

5. The online evaluation method for reactive power support capability of a wind farm according to claim 4, characterized in that: Using the formula ΔQ max ∈(o1∩o2)+o3Calculate the theoretical reactive power support capacity index ΔQ of a single wind turbine max ; o1 is the first reactive capacity on the stator side, o2 is the second reactive capacity on the stator side, and o3 is the reactive capacity on the rotor side; Using the formula Calculate the theoretical reactive power support capacity index of the wind farm; Among them, ΔQ Nmax is the theoretical reactive power support capacity of the wind farm, l is the number of wind turbines in the wind farm, S is the capacity of the wind turbine, and the subscript i represents the i-th wind turbine.

6. The online evaluation method for reactive power support capability of a wind farm according to claim 1 or 4, characterized in that: The actual reactive power support demand index refers to the reactive power that actually needs to be output while maintaining the voltage safety of the PCC point under the premise of considering the current, capacity, and flow-related constraints of each power generation unit.

7. The online evaluation method for reactive power support capability of a wind farm according to claim 1, characterized in that: Before S1, the method further includes: Using the Koopman dictionary function to perform dimensionality-upgrading transformation on the historical voltage regulation data of the wind farm to obtain a high-dimensional data set, and fitting the high-dimensional data set to obtain a global linearized Koopman operator; Linear programming equality constraints are listed based on the Koopman operator, inequality constraints are listed based on the safe operation conditions of the wind farm, and objective functions are listed based on quantitative evaluation indicators to construct the planning model based on the Koopman operator.

8. An online evaluation device for reactive power support capability of a wind farm, characterized in that: include: A solution module is used to input the collected current operating status data of the wind farm into the planning model based on the Koopman operator for solution to obtain multi-level quantitative evaluation indicators; The multi-level quantitative evaluation index includes a theoretical reactive power support capability index and an actual reactive power support demand index; Wherein, the objective function of the planning model based on the Koopman operator includes: minf(x)=(U PCC -U min ) 2 、minf(x)=(Q N -Q Nmin ) 2 、minf(x)=(U PCC -U max ) 2 and minf(x)=(Q N -Q Nmax ) 2 ; The four objective functions are respectively used for the voltage drop small disturbance condition with sufficient reactive power, the voltage drop large disturbance condition with insufficient reactive power, the voltage swell small disturbance condition with sufficient reactive power, and the voltage swell large disturbance condition with insufficient reactive power; U PCC is the actual voltage at the wind farm grid connection point, U min Set the minimum value for the grid connection point voltage, U max Set the maximum value for the grid connection point voltage, Q N is the actual reactive power demand index of the wind farm, Q Nmax , Q Nmin They are the theoretical maximum and minimum supporting capacity indicators of wind farms, respectively; The evaluation module is used to evaluate the reactive power support capability of the wind farm using the multi-level quantitative evaluation index.

9. A control system for a wind farm, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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