Low-pass adaptive control method and device of wind turbine generator, electronic equipment and medium

Abstract

The invention discloses a low-voltage-crossing adaptive control method and device of a wind turbine generator, electronic equipment and a medium, and aims to solve the problems of slow system recovery, voltage overshoot and current out-of-limit in the current low-voltage-crossing control technology. The method comprises the following steps: acquiring running state data of a wind turbine generator at a grid-connected point in a low-voltage period in real time, and calculating a system short-circuit ratio according to the running state data; considering a power adjustment coefficient, and constructing a current feedback controller based on the operation state data; introducing an adaptive weighting strategy of a system short-circuit ratio, and combining a power adjustment coefficient to construct a multi-objective optimization function; the multi-objective optimization function is subjected to parameter optimization solution combined with system short-circuit ratio self-adaptive correction, and an optimized power adjustment coefficient is obtained; and substituting the optimized power adjustment coefficient into a current feedback controller, solving a control current value, and performing current control on the wind turbine generator based on the control current value.

Description

Technical Field

[0001] This invention relates to the field of wind power grid connection control technology, and in particular to a low-voltage adaptive control method, device, electronic equipment and medium for wind turbine generators. Background Technology

[0002] With the rapid growth of installed wind power capacity, wind power has become an important component of the new power system. However, the intermittency and volatility of wind power significantly increase the risk to grid stability, especially during grid failures. To ensure system safety and stability, wind turbines must possess good low voltage ride-through (LVRT) capability. This means that they should not disconnect from the grid during short-term voltage drops and should maintain voltage levels through reactive power support and quickly restore active power support to the system frequency.

[0003] In traditional technologies, low-voltage control often employs a fixed-parameter "reactive power priority" or "active power priority" strategy. This approach can easily lead to problems such as slow system recovery, voltage overshoot, and current exceeding limits, and in severe cases, it can even cause wind turbines to disconnect from the grid.

[0004] Therefore, there is an urgent need for a control method with adaptive capabilities to improve the anti-disturbance capability and recovery performance of wind turbine grid-connected systems. Summary of the Invention

[0005] This invention provides a low-voltage adaptive control method, device, electronic equipment, and medium for wind turbine generators, which solves or partially solves the problems of slow system recovery, voltage overshoot, and current overrun in current low-voltage control technologies.

[0006] This invention provides a low-penetration adaptive control method for wind turbine generators, the method comprising:

[0007] Real-time data on the operating status of wind turbines located at the grid connection point during low-voltage operation is collected, and the system short-circuit ratio is calculated based on the operating status data.

[0008] Considering the power adjustment coefficient, a current feedback controller is constructed based on the aforementioned operating status data;

[0009] An adaptive weighted strategy for the system short-circuit ratio is introduced, and a multi-objective optimization function is constructed by combining the power adjustment coefficient.

[0010] The multi-objective optimization function is solved by combining the system short-circuit ratio adaptive correction with parameter optimization to obtain the optimized power adjustment coefficient;

[0011] The optimized power adjustment coefficient is substituted into the current feedback controller to solve for the control current value, and the wind turbine is controlled based on the control current value.

[0012] Optionally, the operating status data includes grid connection point voltage and rated current; the power adjustment coefficient includes active power adjustment coefficient and reactive power adjustment coefficient; considering the power adjustment coefficient, based on the operating status data, constructing a current feedback controller includes:

[0013] Based on the active power adjustment coefficient, the grid connection point voltage, and the rated current, a d-axis current controller is constructed.

[0014] Based on the reactive power adjustment coefficient, the grid connection point voltage, and the rated current, a q-axis current controller is constructed.

[0015] The d-axis current controller and the q-axis current controller are integrated to form a current feedback controller.

[0016] Optionally, the adaptive weighting strategy for the system short-circuit ratio, combined with the power adjustment coefficient, constructs a multi-objective optimization function, including:

[0017] Based on the system short-circuit ratio, a weighting factor is generated according to a preset short-circuit ratio adaptive function;

[0018] Based on the weighting factor, the power adjustment coefficient, and the system short-circuit ratio, and taking into account grid connection point voltage deviation, system frequency deviation, current over-limit, and short-circuit ratio sensitivity adjustment, a multi-objective optimization function is constructed.

[0019] Optionally, the weighting factors include reactive power weighting factors, active power weighting factors, and current over-limit penalty weighting factors; the step of generating weighting factors based on the system short-circuit ratio according to a preset short-circuit ratio adaptive function includes:

[0020] The power grid state level is determined based on the system short-circuit ratio.

[0021] When the power grid state level is the first state level, according to the preset short-circuit ratio adaptive function, with the adaptive adjustment target of increasing the reactive power weighting factor, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the first state level are output.

[0022] When the power grid state level is the second state level, according to the preset short-circuit ratio adaptive function, with the goal of balancing the weighting factors of each weight, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the second state level are output.

[0023] When the power grid state level is the third state level, according to the preset short-circuit ratio adaptive function, with the goal of increasing the active power weighting factor, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the third state level are output.

[0024] The sum of the reactive power weighting factor, the active power weighting factor, and the current over-limit penalty weighting factor is always 1.

[0025] Optionally, the step of performing parameter optimization on the multi-objective optimization function in conjunction with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient includes:

[0026] Under preset constraints, the power adjustment coefficient is used as the parameter to be optimized, and the particle swarm optimization algorithm is used to optimize the parameters of the multi-objective optimization function to obtain candidate power adjustment coefficients.

[0027] The candidate power adjustment coefficients are adaptively corrected based on the system short-circuit ratio to obtain the optimized power adjustment coefficients.

[0028] Optionally, the candidate power adjustment coefficient includes candidate active power adjustment coefficient and candidate reactive power adjustment coefficient; the adaptive correction of the candidate power adjustment coefficient based on the system short-circuit ratio to obtain the optimized power adjustment coefficient includes:

[0029] For the candidate active power adjustment coefficient, determine whether the value of the system short-circuit ratio is greater than a first preset threshold.

[0030] If yes, then the candidate active power adjustment coefficient is used as the optimized active power adjustment coefficient; otherwise, 0 is taken as the optimized active power adjustment coefficient.

[0031] For the candidate reactive power adjustment coefficient, determine whether the value of the system short-circuit ratio is less than a second preset threshold; wherein, the second preset threshold is greater than the first preset threshold;

[0032] If yes, then 0 is taken as the optimized reactive power adjustment coefficient; otherwise, the candidate reactive power adjustment coefficient is taken as the optimized reactive power adjustment coefficient.

[0033] The optimized active power adjustment coefficient and the optimized reactive power adjustment coefficient are combined to form the optimized power adjustment coefficient.

[0034] Optionally, the operating status data includes grid connection point voltage, grid connection point equivalent impedance, and wind turbine rated capacity; the step of calculating the system short-circuit ratio based on the operating status data includes:

[0035] The system short-circuit ratio is calculated based on the grid connection point voltage, the grid connection point equivalent impedance, and the rated capacity of the wind turbine.

[0036] The present invention also provides a low-penetration adaptive control device for wind turbine generators, comprising:

[0037] The system short-circuit ratio calculation unit is used to collect the operating status data of the wind turbine located at the grid connection point during the low-voltage period in real time, and calculate the system short-circuit ratio based on the operating status data;

[0038] A current feedback controller construction unit is used to construct a current feedback controller based on the operating state data, taking into account the power adjustment coefficient.

[0039] A multi-objective optimization function construction unit is used to introduce an adaptive weighting strategy for the system short-circuit ratio and, in conjunction with the power adjustment coefficient, construct a multi-objective optimization function.

[0040] The parameter optimization solution unit is used to perform parameter optimization solution on the multi-objective optimization function in combination with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient;

[0041] The current control unit is used to substitute the optimized power adjustment coefficient into the current feedback controller, solve for the control current value, and perform current control on the wind turbine based on the control current value.

[0042] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0043] The memory is used to store program code and transmit the program code to the processor;

[0044] The processor is used to execute the low-voltage adaptive control method for wind turbines as described above, according to the instructions in the program code.

[0045] The present invention also provides a computer-readable storage medium for storing program code for executing the low-voltage adaptive control method for wind turbines as described in any of the preceding claims.

[0046] As can be seen from the above technical solutions, the present invention has the following advantages:

[0047] This paper presents an adaptive control method for wind turbines during low-voltage periods. Real-time operational status data of the wind turbines located at the grid connection point during low-voltage periods is collected, and the system short-circuit ratio is calculated based on this data. This dynamic sensing of changes in the grid short-circuit ratio serves as a basis for identifying grid strength and weakness, and for implementing adaptive adjustments to the wind turbine control strategy. Considering the power adjustment coefficient, a current feedback controller is constructed based on the operational status data. An adaptive weighting strategy for the system short-circuit ratio is introduced, and a multi-objective optimization function is constructed in conjunction with the power adjustment coefficient. This multi-objective optimization function is then built based on the power adjustment coefficient, and a short-circuit ratio weighting mechanism is introduced to enhance grid sensing capabilities. The multi-objective optimization function is then optimized by combining adaptive correction with the system short-circuit ratio to obtain the optimized power adjustment coefficient. The current feedback gain parameter is then co-optimized using an optimization algorithm to achieve dynamic coordinated control of the active and reactive power output of the wind turbines during faults, improving system stability and response speed under various grid conditions. The optimized power adjustment coefficient is substituted into the current feedback controller to solve for the control current value, and the wind turbines are then controlled based on this control current value. This enables adaptive control of active and reactive power during the low-voltage operation of wind turbine units. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating the steps of a low-penetration adaptive control method for wind turbine units;

[0050] Figure 2 A simplified flowchart of a particle swarm optimization algorithm;

[0051] Figure 3 This is a flowchart illustrating a method for determining adaptive correction targets based on the system short-circuit ratio (SCR).

[0052] Figure 4 A schematic diagram of the overall process of a low-penetration adaptive control method for wind turbine units;

[0053] Figure 5 This is a structural block diagram of a low-penetration adaptive control device for a wind turbine. Detailed Implementation

[0054] This invention provides a low-voltage adaptive control method, device, electronic equipment, and medium for wind turbines, which solves or partially solves the problems of slow system recovery, voltage overshoot, and current overshoot in current low-voltage control technologies.

[0055] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0056] As an example, for wind turbine grid-connected systems, low-voltage control refers to preventing grid disconnection during short-term voltage drops and maintaining voltage levels through reactive power support while quickly restoring the active power support system frequency.

[0057] In traditional technologies, low-voltage control often employs fixed-parameter "reactive power priority" or "active power priority" strategies. This approach easily leads to problems such as slow system recovery, voltage overshoot, and current exceeding limits, and in severe cases, even causes wind turbines to disconnect from the grid. There is an urgent need for a control method with adaptive capabilities to improve the disturbance rejection and recovery performance of wind turbine grid-connected systems.

[0058] Therefore, one of the core inventive points of this invention is: for grid-connected direct-drive wind turbines with a full-power converter structure, a method for adaptive coordinated control of active and reactive power output based on short-circuit ratio sensing during low-voltage periods is proposed to achieve flexible coordination of active and reactive power output under different short-circuit ratio conditions. On one hand, by calculating the system short-circuit ratio in real time, the strength and weakness of the power grid are identified, and a multi-objective optimization function including voltage deviation, active power recovery, frequency response, and current limit exceedance is constructed. A short-circuit ratio weighting mechanism is introduced into the optimization function to enhance the power grid sensing capability. On the other hand, a particle swarm optimization algorithm is used to collaboratively optimize the d-axis and q-axis current feedback gain parameters to achieve dynamic coordinated control of active and reactive power output of the wind turbine during faults. Using the method provided by this invention, not only can changes in the power grid short-circuit ratio be dynamically sensed to achieve adaptive adjustment of the wind turbine control strategy, but also the optimal coordination of active and reactive power output is achieved by adaptively adjusting the controller gain coefficient using an optimization algorithm, thereby improving the stability and response speed of the system under various power grid conditions. It can also significantly improve the voltage support capability, active power recovery speed, and control robustness of wind turbines under different grid conditions, exhibiting good adaptability and being suitable for the grid connection control requirements of wind farms with a high proportion of new energy access. It is also applicable to various operating conditions, including strong, medium, and weak grids. Furthermore, the method provided in this embodiment can meet current limit constraints while avoiding problems such as system voltage overshoot, frequency fluctuations, and wind turbine disconnection caused by rigid control strategies.

[0059] Reference Figure 1 The diagram illustrates a flowchart of a low-voltage adaptive control method for wind turbines according to an embodiment of the present invention, which may specifically include the following steps:

[0060] Step 101: Collect real-time operating status data of the wind turbine located at the grid connection point during the low-voltage period, and calculate the system short-circuit ratio based on the operating status data;

[0061] In practical implementation, the operating status data of the wind turbine located at the grid connection point during the low-voltage period can be collected in real time, and the system short-circuit ratio can be calculated based on the collected operating status data.

[0062] Furthermore, the operating status data mainly includes the grid connection point voltage, the grid connection point equivalent impedance, and the rated capacity of the wind turbine. Based on this operating status data, the system short-circuit ratio is calculated, specifically: the system short-circuit ratio is calculated based on the grid connection point voltage, the grid connection point equivalent impedance, and the rated capacity of the wind turbine.

[0063] Specifically, this step actually identifies the grid status. It can collect real-time data on the wind turbine grid connection point voltage, current (current used for calculations in subsequent steps), and the grid's equivalent impedance, and calculate the system short-circuit ratio (SCR) using the following formula:

[0064]

[0065] in, Short-circuit capacity; This refers to the voltage at the grid connection point. The equivalent impedance at the grid connection point; This refers to the rated capacity of the wind turbine unit.

[0066] The strength or weakness of the power grid can be determined based on the calculated system short-circuit ratio. Specifically, the power grid strength or weakness can be divided into three power grid state levels according to the system short-circuit ratio. For ease of distinction, these are designated as the first state level, the second state level, and the third state level, based on the numerical value of the system short-circuit ratio. The first state level corresponds to a weak power grid (SCR < 3); the second state level corresponds to a medium power grid (3 ≤ SCR < 6); and the third state level corresponds to a strong power grid (SCR ≥ 6).

[0067] Step 102: Considering the power adjustment coefficient, construct a current feedback controller based on the operating status data;

[0068] In some embodiments, the operating status data may further include grid connection point voltage and rated current. The power adjustment factor may further include active power adjustment factor and reactive power adjustment factor.

[0069] Considering the power adjustment coefficient, a current feedback controller is constructed based on the operating status data. Specifically, it can be done as follows: First, based on the active power adjustment coefficient, grid connection point voltage, and rated current, a d-axis current controller is constructed; simultaneously, based on the reactive power adjustment coefficient, grid connection point voltage, and rated current, a q-axis current controller is constructed; then, the d-axis current controller and the q-axis current controller are integrated as a current feedback controller.

[0070] Constructing a fast current feedback controller for the dq axis is essentially about developing an active-reactive current control strategy for the converter during low-voltage ride-through. Based on the dq axis current control structure of a direct-drive wind turbine, the active current command for the d-axis during low-voltage ride-through is... and q-axis reactive current command They can be represented as:

[0071]

[0072] in, This is the grid connection point voltage (using per-unit values). , These are the d-axis active power adjustment coefficient and the q-axis reactive power adjustment coefficient of the direct-drive wind turbine; This is the rated current.

[0073] Step 103: Introduce the adaptive weighting strategy for the system short-circuit ratio, and combine it with the power adjustment coefficient to construct a multi-objective optimization function;

[0074] In this step, an adaptive weighted strategy for the system short-circuit ratio is introduced, and a multi-objective optimization function is constructed by combining it with the power adjustment coefficient.

[0075] In some embodiments, an adaptive weighting strategy for the system short-circuit ratio is introduced, and combined with the power adjustment coefficient, a multi-objective optimization function is constructed. The implementation process may specifically include the following steps S01 to S02:

[0076] Step S01: Based on the system short-circuit ratio, generate a weighting factor according to a preset short-circuit ratio adaptive function;

[0077] First, based on the calculated system short-circuit ratio, weighting factors for constructing a multi-objective optimization function need to be generated according to the short-circuit ratio adaptive function. These weighting factors can further include reactive power weighting factors, active power weighting factors, and current limit violation penalty weighting factors. In specific implementations, generating weighting factors based on the system short-circuit ratio and a preset short-circuit ratio adaptive function can be achieved by: determining the grid state level based on the system short-circuit ratio; determining the adaptive adjustment target based on the grid state level and the preset short-circuit ratio adaptive function; and outputting the various weighting factors adjusted according to the adaptive adjustment target under that grid state level.

[0078] As mentioned above, the power grid's strength and weakness can be divided into three power grid state levels based on the system short-circuit ratio (SCR). These are the first state level, the second state level, and the third state level. The first state level corresponds to a weak power grid (SCR < 3); the second state level corresponds to a medium power grid (3 ≤ SCR < 6); and the third state level corresponds to a strong power grid (SCR ≥ 6).

[0079] The power grid state level is mainly used to determine the weighting factors of each deviation term in the multi-objective optimization function, thereby realizing the weighted construction of the multi-objective optimization function. These are the reactive power weighting factors. Active weighting factor Current over-limit penalty weighting factor Furthermore, the weighting factors in the multi-objective optimization function... , , It can be automatically determined based on the system short-circuit ratio, satisfying the following:

[0080]

[0081] in, The result is a preset adaptive function relationship or a lookup table interpolation result; when the system short-circuit ratio (SCR) is low (corresponding to a weak grid in the first state level), the function output tends to increase the reactive power weighting factor. When the system short-circuit ratio (SCR) is high (corresponding to a strong power grid in the third state level), the function output tends to increase the active power weighting factor. When the system short-circuit ratio (SCR) is in the medium range (corresponding to the medium power grid of the second state level), the weights are balanced between voltage support and active power recovery (i.e., the weighting factors of each weight are balanced). , , The sum of these values ​​is always 1 to ensure the weights of the multi-objective optimization function are normalized.

[0082] function This can be constructed using a lookup table or a fitting function. When the system short-circuit ratio (SCR) is small, the weight of the reactive power index (reactive power weighting factor) should be increased. When the system short-circuit ratio (SCR) is large, the weight of the active power index (active power weighting factor) is increased. When the system short-circuit ratio (SCR) is in the middle range, the weighting factor terms corresponding to each sub-objective are balanced.

[0083] In the specific implementation, when the power grid state level is the first state level, based on the preset short-circuit ratio adaptive function, the reactive power weighting factor is increased as the adaptive adjustment target, and the reactive power weighting factor, active power weighting factor, and current limit violation penalty weighting factor under the first state level are output. When the power grid state level is the second state level, based on the preset short-circuit ratio adaptive function, the reactive power weighting factor, active power weighting factor, and current limit violation penalty weighting factor under the second state level are output with the goal of balancing the weighting factors. When the power grid state level is the third state level, based on the preset short-circuit ratio adaptive function, the reactive power weighting factor, active power weighting factor, and current limit violation penalty weighting factor under the third state level are output with the goal of increasing the active power weighting factor.

[0084] Among them, the sum of the reactive power weighting factor, the active power weighting factor, and the current over-limit penalty weighting factor is always 1.

[0085] Step S02: Based on the weighting factor, power adjustment coefficient, and system short-circuit ratio, while also considering grid connection point voltage deviation, system frequency deviation, current over-limit, and short-circuit ratio sensitivity adjustment, construct a multi-objective optimization function.

[0086] Construct a comprehensive evaluation function for low-penetration recovery characteristics. Specifically, this is a multi-objective optimization function as shown below:

[0087] in, The voltage deviation at the grid connection point of the wind turbine is affected by reactive power control. The frequency deviation of the wind turbine grid connection system is affected by active power control. This is a current over-limit penalty term to ensure that the optimized solution meets the current constraint; This represents the maximum output current of the inverter. This is the short-circuit ratio sensitivity adjustment coefficient, i.e., the weighting coefficient of the system short-circuit ratio (SCR) (recommended empirical value of 1 to 3). This is the rated voltage at the grid connection point.

[0088] The aforementioned multi-objective optimization function comprehensively evaluates voltage support effect, active power recovery capability, and current safety. By introducing a short-circuit ratio weighting mechanism, the control strategy is made adaptive to grid conditions.

[0089] Step 104: Perform parameter optimization on the multi-objective optimization function in conjunction with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient;

[0090] Based on the multi-objective optimization function constructed through the aforementioned steps, the multi-objective optimization function is solved by parameter optimization combined with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient.

[0091] In some embodiments, the implementation process of performing parameter optimization on the multi-objective optimization function in conjunction with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient may specifically include the following steps S11 to S12:

[0092] Step S11: Under preset constraints, the power adjustment coefficient is used as the parameter to be optimized, and the particle swarm optimization algorithm is used to optimize the parameters of the multi-objective optimization function to obtain the candidate power adjustment coefficient.

[0093] The active power adjustment coefficient of the wind turbine was adjusted using the Particle Swarm Optimization (PSO) algorithm. Reactive power adjustment coefficient Optimization is required. The constraints to be followed during the optimization process include:

[0094]

[0095] Among the constraints mentioned above, the following equation represents the gain boundary constraint:

[0096]

[0097] in, , These are the active power adjustment coefficients. The minimum and maximum values; and These are the reactive power adjustment coefficients. The minimum and maximum values.

[0098] The following formula represents the current capacity constraint (maximum combined current of the wind turbine):

[0099]

[0100] Furthermore, the particle swarm optimization algorithm updates the particle position and velocity using the following iterative formula:

[0101]

[0102] in, express time; express time; The particle position; The particle velocity; For the optimal position of an individual, The optimal position for the group; , , For algorithm parameters, Inertia factor , For learning factors; , It is a random number.

[0103] The iterative convergence stopping condition of the particle swarm optimization algorithm is reaching the maximum number of iterations, or the change in the optimal solution of the population is less than a set threshold for several consecutive generations.

[0104] In the specific implementation, refer to Figure 2 The particle swarm optimization algorithm is used to solve the multi-objective optimization function, where the optimization variables (i.e., the parameters to be optimized) are: and Each particle represents a set of control gains. The algorithm flow is as follows:

[0105] 1) Initialize the particle swarm, each particle containing a position. With speed ;

[0106] 2) Calculate the current objective function value, i.e., the fitness value, for each particle. ;

[0107] 3) Update the individual's optimal position and global optimal position ;

[0108] Iterate using the following update formula:

[0109]

[0110]

[0111] 4) Output the optimal solution when the maximum number of iterations is reached, or when the objective function converges. As a candidate power adjustment coefficient.

[0112] Step S12: Adaptively correct the candidate power adjustment coefficients based on the system short-circuit ratio to obtain the optimized power adjustment coefficients.

[0113] , These are the active and reactive power adjustment parameters ultimately used for current command calculations, i.e., the optimized power adjustment coefficients (including candidate active power adjustment coefficients and candidate reactive power adjustment coefficients) after adaptive correction. Their values ​​can be determined by the following relationship:

[0114]

[0115]

[0116] in, and These are the active and reactive power correction functions for short-circuit ratio adaptation, respectively, and is the adaptive function driven by the short-circuit ratio or a lookup table interpolation relationship.

[0117] Combination Figure 3 In some embodiments, the candidate power adjustment coefficient is adaptively corrected based on the system short-circuit ratio to obtain the optimized power adjustment coefficient. Specifically, this can be as follows:

[0118] For candidate active power adjustment coefficients, determine whether the system short-circuit ratio is greater than the first preset threshold (e.g., SCR > 3).

[0119] If so, the candidate active power adjustment coefficient will be used as the optimized active power adjustment coefficient. If not, then take 0 as the optimized active power adjustment coefficient. );

[0120] For candidate reactive power adjustment coefficients, determine whether the system short-circuit ratio is less than the second preset threshold (e.g., SCR < 6); wherein the second preset threshold is greater than the first preset threshold.

[0121] If so, then take 0 as the optimized reactive power adjustment coefficient. If not, then the candidate reactive power adjustment coefficient will be used as the optimized reactive power adjustment coefficient. );

[0122] The active power adjustment coefficient and the reactive power adjustment coefficient are integrated and optimized to form the optimized power adjustment coefficient.

[0123] Step 105: Substitute the optimized power adjustment coefficient into the current feedback controller to solve for the control current value, and perform current control on the wind turbine based on the control current value.

[0124] The optimized result , As parameters for the real-time controller, these are substituted into the current feedback controller shown in the following formula to generate a control current value, which is then used to control the current of the wind turbine.

[0125]

[0126] By applying the optimized active and reactive power adjustment coefficients of the wind turbine to the current command calculation, the active and reactive power coordinated control of the wind turbine during the low-voltage process is realized.

[0127] The above technical solution achieves the following collaborative optimization strategy:

[0128] Firstly, during the low-voltage ride-through period, the active current command of the wind turbine is dynamically superimposed by the voltage support component and the DC voltage regulation component according to weights. The weights are set based on the system short-circuit ratio (SCR), voltage sag depth, and DC voltage deviation at the grid connection point.

[0129] Secondly, the phase-locked loop (PLL) control parameters are adaptively tuned based on the system short-circuit ratio (SCR) and voltage sag, including adjustments to bandwidth and damping factor. In the early stages of a fault, a phase-holding or phase-locked loop soft-start strategy is employed to limit the frequency / phase change rate, thereby enhancing system synchronization stability and disturbance rejection capability.

[0130] In this embodiment of the invention, an active-reactive adaptive coordinated control method based on short-circuit ratio sensing is proposed for grid-connected direct-drive wind turbines during low-fault periods. On one hand, by calculating the system short-circuit ratio in real time, the strength and weakness of the power grid are identified, and a multi-objective optimization function is constructed, including voltage deviation, active power recovery, frequency response, and current over-limit. A short-circuit ratio weighting mechanism is introduced into the optimization function to enhance the power grid sensing capability. On the other hand, a particle swarm optimization algorithm is used to collaboratively optimize the d-axis and q-axis current feedback gain parameters to achieve dynamic coordinated control of the active and reactive power output of the wind turbine during fault periods. Using the method provided in this embodiment of the invention, not only can changes in the power grid short-circuit ratio be dynamically sensed to achieve adaptive adjustment of the wind turbine control strategy, but also the optimal coordination of active and reactive power output can be achieved by adaptively adjusting the controller gain coefficient using an optimization algorithm, thereby improving the system's stability and response speed under various power grid conditions. It can also significantly improve the voltage support capability, active power recovery speed, and control robustness of wind turbines under different grid conditions, exhibiting good adaptability and being suitable for the grid connection control requirements of wind farms with a high proportion of new energy access. It is also applicable to various operating conditions, including strong, medium, and weak grids. Furthermore, the method provided in this embodiment can meet current limit constraints while avoiding problems such as system voltage overshoot, frequency fluctuations, and wind turbine disconnection caused by rigid control strategies.

[0131] For better illustration, refer to Figure 4 This diagram illustrates the overall flow of a low-voltage adaptive control method for wind turbines according to an embodiment of the present invention. It should be noted that this embodiment only provides a brief description of the general flow of the low-voltage adaptive control method for wind turbines. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments, and will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0132] Step 401: Collect real-time operating status data of the wind turbine units located at the grid connection point during the low-voltage period, and calculate the system short-circuit ratio based on the operating status data;

[0133] Step 402: Considering the active power adjustment coefficient and the reactive power adjustment coefficient, construct a dq-axis current feedback controller based on the operating status data;

[0134] Step 403: Based on the system short-circuit ratio, generate a weighting factor according to a preset short-circuit ratio adaptive function;

[0135] Step 404: Based on the weighting factor, power adjustment coefficient, and system short-circuit ratio, while also considering grid connection point voltage deviation, system frequency deviation, current over-limit, and short-circuit ratio sensitivity adjustment, construct a multi-objective optimization function;

[0136] Step 405: Combine the grid state levels to perform parameter optimization of the multi-objective optimization function with adaptive correction of the system short-circuit ratio, and obtain the optimized power adjustment coefficient;

[0137] Step 406: Substitute the optimized power adjustment coefficient into the current feedback controller, solve for the control current value, and perform current control on the wind turbine based on the control current value.

[0138] Reference Figure 5 The diagram illustrates a structural block diagram of a low-penetration adaptive control device for a wind turbine provided in an embodiment of the present invention, which may specifically include:

[0139] The system short-circuit ratio calculation unit 501 is used to collect the operating status data of the wind turbine located at the grid connection point during the low-voltage period in real time, and calculate the system short-circuit ratio based on the operating status data.

[0140] The current feedback controller construction unit 502 is used to construct a current feedback controller based on the operating state data, taking into account the power adjustment coefficient.

[0141] The multi-objective optimization function construction unit 503 is used to introduce the adaptive weighting strategy of the system short-circuit ratio and, in conjunction with the power adjustment coefficient, construct a multi-objective optimization function.

[0142] The parameter optimization solution unit 504 is used to perform parameter optimization solution on the multi-objective optimization function in combination with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient;

[0143] The current control unit 505 is used to substitute the optimized power adjustment coefficient into the current feedback controller, solve for the control current value, and perform current control on the wind turbine based on the control current value.

[0144] In one optional embodiment, the operating status data includes grid connection point voltage and rated current; the power adjustment coefficient includes active power adjustment coefficient and reactive power adjustment coefficient; the current feedback controller construction unit 502 includes:

[0145] A d-axis current controller construction unit is used to construct a d-axis current controller based on the active power adjustment coefficient, the grid connection point voltage, and the rated current.

[0146] A q-axis current controller construction unit is used to construct a q-axis current controller based on the reactive power adjustment coefficient, the grid connection point voltage, and the rated current.

[0147] A current feedback controller integration unit is used to integrate the d-axis current controller and the q-axis current controller as a current feedback controller.

[0148] In one optional embodiment, the multi-objective optimization function construction unit 503 includes:

[0149] The weighting factor generation unit is used to generate a weighting factor based on the short-circuit ratio of the system and according to a preset short-circuit ratio adaptive function.

[0150] A multi-objective optimization function construction sub-unit is used to construct a multi-objective optimization function based on the weighting factor, the power adjustment coefficient, the system short-circuit ratio, and simultaneously considering the grid connection point voltage deviation, system frequency deviation, current over-limit, and short-circuit ratio sensitivity adjustment.

[0151] In one optional embodiment, the weighting factor includes a reactive power weighting factor, an active power weighting factor, and a current over-limit penalty weighting factor; the weighting factor generation unit includes:

[0152] A power grid state level determination unit is used to determine the power grid state level based on the system short-circuit ratio;

[0153] The first state level calculation unit is used to output the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the first state level according to the preset short-circuit ratio adaptive function, with the adaptive adjustment target of increasing the reactive power weighting factor.

[0154] The second state level calculation unit is used to output the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the second state level when the power grid state level is the second state level, according to the preset short-circuit ratio adaptive function and with the goal of balancing the weighting factors of each weight.

[0155] The third state level calculation unit is used to output the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the third state level, based on the preset short-circuit ratio adaptive function and with the goal of increasing the active power weighting factor.

[0156] The sum of the reactive power weighting factor, the active power weighting factor, and the current over-limit penalty weighting factor is always 1.

[0157] In one optional embodiment, the parameter optimization solution unit 504 includes:

[0158] The parameter optimization unit is used to optimize the power adjustment coefficient as a parameter to be optimized under preset constraints, and to use the particle swarm optimization algorithm to optimize the parameters of the multi-objective optimization function to obtain candidate power adjustment coefficients.

[0159] An adaptive correction unit is used to adaptively correct the candidate power adjustment coefficient based on the system short-circuit ratio to obtain an optimized power adjustment coefficient.

[0160] In one optional embodiment, the candidate power adjustment coefficient includes a candidate active power adjustment coefficient and a candidate reactive power adjustment coefficient; the adaptive correction unit includes:

[0161] The candidate active power adjustment coefficient correction unit is used to determine whether the value of the system short-circuit ratio is greater than a first preset threshold for the candidate active power adjustment coefficient; if yes, the candidate active power adjustment coefficient is used as the optimized active power adjustment coefficient; if no, 0 is taken as the optimized active power adjustment coefficient.

[0162] The candidate reactive power adjustment coefficient correction unit is used to determine whether the value of the system short-circuit ratio is less than a second preset threshold for the candidate reactive power adjustment coefficient; wherein the second preset threshold is greater than the first preset threshold; if yes, then 0 is taken as the optimized reactive power adjustment coefficient; if no, then the candidate reactive power adjustment coefficient is taken as the optimized reactive power adjustment coefficient.

[0163] An optimized power adjustment coefficient integration unit is used to integrate the optimized active power adjustment coefficient and the optimized reactive power adjustment coefficient as the optimized power adjustment coefficient.

[0164] In one optional embodiment, the operating status data includes grid connection point voltage, grid connection point equivalent impedance, and wind turbine rated capacity; the system short-circuit ratio calculation unit 501 is specifically used for:

[0165] The system short-circuit ratio is calculated based on the grid connection point voltage, the grid connection point equivalent impedance, and the rated capacity of the wind turbine.

[0166] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0167] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, the embodiments of the present invention use terms such as "first" and "second" to distinguish and describe some technical features. The terms "first" and "second" are used only for data differentiation and have no other special meanings. It is understood that the present invention does not impose any limitations on them.

[0168] This invention also provides an electronic device, which includes a processor and a memory:

[0169] The memory is used to store program code and transfer the program code to the processor;

[0170] The processor is used to execute the low-voltage adaptive control method for wind turbines according to instructions in the program code of any embodiment of the present invention.

[0171] This invention also provides a computer-readable storage medium for storing program code for executing the low-voltage adaptive control method for wind turbines according to any embodiment of this invention.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0173] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-penetration adaptive control method for wind turbine generators, characterized in that, include: Real-time data on the operating status of wind turbines located at the grid connection point during low-voltage operation is collected, and the system short-circuit ratio is calculated based on the operating status data. Considering the power adjustment coefficient, a current feedback controller is constructed based on the aforementioned operating status data; An adaptive weighted strategy for the system short-circuit ratio is introduced, and a multi-objective optimization function is constructed by combining the power adjustment coefficient. The multi-objective optimization function is solved by combining the system short-circuit ratio adaptive correction with parameter optimization to obtain the optimized power adjustment coefficient; The optimized power adjustment coefficient is substituted into the current feedback controller to solve for the control current value, and the wind turbine is controlled based on the control current value.

2. The low-penetration adaptive control method for wind turbine generators according to claim 1, characterized in that, The operating status data includes grid connection point voltage and rated current; the power adjustment coefficient includes active power adjustment coefficient and reactive power adjustment coefficient. The current feedback controller, which considers the power adjustment coefficient and is constructed based on the operating state data, includes: Based on the active power adjustment coefficient, the grid connection point voltage, and the rated current, a d-axis current controller is constructed. Based on the reactive power adjustment coefficient, the grid connection point voltage, and the rated current, a q-axis current controller is constructed. The d-axis current controller and the q-axis current controller are integrated to form a current feedback controller.

3. The low-penetration adaptive control method for wind turbine generators according to claim 1, characterized in that, The adaptive weighted strategy that introduces the system short-circuit ratio, combined with the power adjustment coefficient, constructs a multi-objective optimization function, including: Based on the system short-circuit ratio, a weighting factor is generated according to a preset short-circuit ratio adaptive function; Based on the weighting factor, the power adjustment coefficient, and the system short-circuit ratio, and taking into account grid connection point voltage deviation, system frequency deviation, current over-limit, and short-circuit ratio sensitivity adjustment, a multi-objective optimization function is constructed.

4. The low-penetration adaptive control method for wind turbine generators according to claim 3, characterized in that, The weighting factors include reactive power weighting factors, active power weighting factors, and current over-limit penalty weighting factors; the generation of weighting factors based on the system short-circuit ratio and according to a preset short-circuit ratio adaptive function includes: The power grid state level is determined based on the system short-circuit ratio. When the power grid state level is the first state level, according to the preset short-circuit ratio adaptive function, with the adaptive adjustment target of increasing the reactive power weighting factor, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the first state level are output. When the power grid state level is the second state level, according to the preset short-circuit ratio adaptive function, with the goal of balancing the weighting factors of each weight, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the second state level are output. When the power grid state level is the third state level, according to the preset short-circuit ratio adaptive function, with the goal of increasing the active power weighting factor, the reactive power weighting factor, active power weighting factor and current over-limit penalty weighting factor under the third state level are output. The sum of the reactive power weighting factor, the active power weighting factor, and the current over-limit penalty weighting factor is always 1.

5. The low-penetration adaptive control method for wind turbine generators according to claim 1, characterized in that, The step of performing parameter optimization on the multi-objective optimization function in conjunction with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient includes: Under preset constraints, the power adjustment coefficient is used as the parameter to be optimized, and the particle swarm optimization algorithm is used to optimize the parameters of the multi-objective optimization function to obtain candidate power adjustment coefficients. The candidate power adjustment coefficients are adaptively corrected based on the system short-circuit ratio to obtain the optimized power adjustment coefficients.

6. The low-penetration adaptive control method for wind turbine generators according to claim 5, characterized in that, The candidate power adjustment coefficients include candidate active power adjustment coefficients and candidate reactive power adjustment coefficients; the adaptive correction of the candidate power adjustment coefficients based on the system short-circuit ratio to obtain optimized power adjustment coefficients includes: For the candidate active power adjustment coefficient, determine whether the value of the system short-circuit ratio is greater than a first preset threshold. If yes, then the candidate active power adjustment coefficient is used as the optimized active power adjustment coefficient; otherwise, 0 is taken as the optimized active power adjustment coefficient. For the candidate reactive power adjustment coefficient, determine whether the value of the system short-circuit ratio is less than a second preset threshold; wherein, the second preset threshold is greater than the first preset threshold; If yes, then 0 is taken as the optimized reactive power adjustment coefficient; otherwise, the candidate reactive power adjustment coefficient is taken as the optimized reactive power adjustment coefficient. The optimized active power adjustment coefficient and the optimized reactive power adjustment coefficient are combined to form the optimized power adjustment coefficient.

7. The low-penetration adaptive control method for wind turbine generators according to any one of claims 1 to 6, characterized in that, The operating status data includes grid connection point voltage, grid connection point equivalent impedance, and wind turbine rated capacity. The step of calculating the system short-circuit ratio based on the operating status data includes: The system short-circuit ratio is calculated based on the grid connection point voltage, the grid connection point equivalent impedance, and the rated capacity of the wind turbine.

8. A low-penetration adaptive control device for wind turbine generators, characterized in that, include: The system short-circuit ratio calculation unit is used to collect the operating status data of the wind turbine located at the grid connection point during the low-voltage period in real time, and calculate the system short-circuit ratio based on the operating status data; A current feedback controller construction unit is used to construct a current feedback controller based on the operating state data, taking into account the power adjustment coefficient. A multi-objective optimization function construction unit is used to introduce an adaptive weighting strategy for the system short-circuit ratio and, in conjunction with the power adjustment coefficient, construct a multi-objective optimization function. The parameter optimization solution unit is used to perform parameter optimization solution on the multi-objective optimization function in combination with adaptive correction of the system short-circuit ratio to obtain the optimized power adjustment coefficient; The current control unit is used to substitute the optimized power adjustment coefficient into the current feedback controller, solve for the control current value, and perform current control on the wind turbine based on the control current value.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the low-voltage adaptive control method for wind turbines according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the low-voltage adaptive control method for wind turbines according to any one of claims 1-7.

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