Cooperative low-voltage ride-through method, device and equipment for fan and static var generator, and storage medium

Abstract

The invention discloses a cooperative low-voltage ride-through method, device and equipment for a fan and a static var generator, and a storage medium, and the method comprises the steps: obtaining low-voltage ride-through to-be-optimized cooperative control parameters of the fan and the static var generator in a power distribution network, and carrying out the optimization of the low-voltage ride-through to-be-optimized cooperative control parameters of the fan and the static var generator according to the voltage supporting capability, the active recovery performance and a current out-of-limit punishment mechanism of the power distribution network; constructing a low-voltage ride-through optimization objective function for optimizing low-voltage ride-through to-be-optimized cooperative control parameters, optimizing the low-voltage ride-through optimization objective function through a particle swarm optimization algorithm to obtain low-voltage ride-through optimization parameters, and controlling current components of a converter of a fan based on the low-voltage ride-through optimization parameters so as to optimize the low-voltage ride-through to-be-optimized cooperative control parameters. And a reactive current component of the static var generator. Therefore, the cooperative control parameters of the SVG and the fan are optimized according to the voltage support capability and the active power recovery performance, and the optimization result is applied to the current component control, so that the response coordination of the fan and the SVG is realized, and the voltage recovery efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of power system automatic control technology, and more specifically, to a method, apparatus, equipment and storage medium for the coordinated low-voltage ride-through of a wind turbine and a static var generator. Background Technology

[0002] With the large-scale grid connection of wind power in new energy power systems, the low voltage ride-through (LVRT) capability of wind farms during grid faults has become a key technical requirement for ensuring the stable operation of the power grid. Currently, wind turbines mainly rely on local reactive power response to achieve voltage support. However, in scenarios with voltage drops or weak grids, such as those with low short-circuit ratios (SCR), their response speed and reactive power output capabilities are insufficient to meet grid demands. Static Var Generators (SVG) have millisecond-level reactive power regulation capabilities and can quickly inject reactive power to suppress voltage drops, making them an important auxiliary device for LVRT.

[0003] However, in existing technologies, wind turbines and SVG often adopt independent control strategies, without forming a unified and coordinated response mechanism based on grid status awareness. This often leads to control conflicts, waste of reactive power resources, and low voltage recovery efficiency.

[0004] In addition, how to coordinate the responses of wind turbines and SVG during low-voltage ride-through in the distribution network system to improve voltage recovery efficiency is an issue that needs attention. Summary of the Invention

[0005] In view of the above problems, this application provides a method, apparatus, equipment and storage medium for coordinated low-voltage ride-through of wind turbines and static var generators, so as to achieve response coordination between wind turbines and SVG during low-voltage ride-through of power distribution network systems and improve voltage recovery efficiency.

[0006] To achieve the above objectives, the following specific solutions are proposed:

[0007] A method for coordinated low-voltage ride-through of a wind turbine and a static var generator, comprising:

[0008] Obtain the low-voltage ride-through parameters of wind turbines and static var generators in the power distribution network to be optimized for coordinated control.

[0009] Based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network, a low-voltage ride-through optimization objective function is constructed to optimize the low-voltage ride-through collaborative control parameters to be optimized.

[0010] The low-pressure crossing optimization objective function is optimized using the particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters;

[0011] Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled.

[0012] Optionally, obtaining the low-voltage ride-through coordinated control parameters of wind turbines and static var generators within the distribution network includes:

[0013] Monitor the voltage amplitude at the wind farm connection point of the power distribution network;

[0014] When the voltage amplitude is greater than the low-voltage ride-through trigger threshold of the wind farm and the rate of change of the voltage amplitude is negative, the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the distribution network are obtained.

[0015] Optionally, the low-voltage ride-through optimized collaborative control parameters include the active current recovery ratio coefficient of the wind turbine and the voltage regulation gain of the static var generator.

[0016] The objective function for low-pressure ride-through optimization is:

[0017]

[0018] in, This refers to the active current recovery ratio coefficient. The voltage regulation gain, The objective function for the low-pressure ride-through is optimized. Let be the objective function for the voltage support capability of the distribution network. This represents the target value for the active power recovery performance of the distribution network. This is a penalty item for exceeding the current limit. As the first weighting factor, As the second weighting factor, It is the third weighting factor.

[0019] Optionally, the objective function for the voltage support capability is:

[0020]

[0021] in, The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The time when a low-voltage ride-through fault occurs in the aforementioned distribution network. This refers to the time when the low-voltage ride-through fault is cleared;

[0022] The target value for active power recovery performance is:

[0023]

[0024] in, The moment when the voltage at the grid connection point of the wind farm recovers to 90% of its rated value;

[0025] The current over-limit penalty item is:

[0026]

[0027] in, The output current of the fan is [value]. The output current of the static var generator is... This is the maximum allowable current value for the fan. This is the maximum permissible current value of the static var generator. This represents the initial moment of the current constraint. This is the time when the current constraint ends.

[0028] Optionally, the low-voltage ride-through optimization parameters include the active current recovery ratio optimization coefficient of the wind turbine and the voltage regulation optimization gain of the static var generator.

[0029] Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled, including:

[0030] Based on the active current recovery ratio optimization coefficient, the active current component of the wind turbine converter is controlled as follows:

[0031]

[0032] in, The active current component of the converter of the wind turbine. This is the optimization coefficient for the active current recovery ratio. The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The rated current of the fan;

[0033] Based on the voltage regulation optimization gain of the static var generator, the reactive current component of the static var generator is controlled as follows:

[0034]

[0035] in, The reactive circuit component output by the static var generator. Optimize the gain for the voltage regulation. The rated current of the static var generator is given.

[0036] Optionally, the method further includes:

[0037] Real-time monitoring of the first output current of the wind turbine and the second output current of the static var generator;

[0038] If the first output current is greater than the first preset rated current, the active current recovery ratio optimization coefficient is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm.

[0039] If the second output current is greater than the second preset rated current, the voltage regulation optimization gain is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm.

[0040] Optionally, the step of optimizing the low-pressure ride-through objective function using a particle swarm optimization algorithm to obtain low-pressure ride-through optimization parameters includes:

[0041] Initialize the particle population for the particle swarm optimization algorithm and determine the search boundary for each particle, wherein each particle represents the low-pressure crossing cooperative control parameters to be optimized.

[0042] Calculate the fitness of each particle;

[0043] Update the extreme values ​​and global optimal positions of each particle based on their fitness.

[0044] If the particle swarm optimization algorithm satisfies the convergence condition or reaches the maximum number of iterations, the low-pressure crossing cooperative control parameters represented by the optimal particle are output as the low-pressure crossing optimization parameters. Otherwise, each particle is updated according to the velocity update formula and the position update formula to perform iteration, and the algorithm returns to the step of calculating the fitness of each particle.

[0045] A low-voltage ride-through device combining a wind turbine and a static var generator includes:

[0046] The unit for acquiring the coordinated control parameters to be optimized is used to acquire the low-voltage ride-through coordinated control parameters of wind turbines and static var generators in the distribution network.

[0047] The objective function construction unit is used to construct a low-voltage ride-through optimization objective function for optimizing the low-voltage ride-through cooperative control parameters based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network.

[0048] The objective function optimization unit is used to optimize the low-pressure crossing optimization objective function using a particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters.

[0049] The current component control unit is used to control the converter current component of the wind turbine and the reactive current component of the static var generator based on the low-voltage ride-through optimization parameters.

[0050] Optionally, the unit for acquiring the cooperative control parameters to be optimized includes:

[0051] The voltage amplitude monitoring unit is used to monitor the voltage amplitude at the grid connection point of the wind farm in the power distribution network.

[0052] The low-voltage ride-through triggering unit is used to obtain the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the distribution network when the voltage amplitude is greater than the low-voltage ride-through triggering threshold of the wind farm and the rate of change of the voltage amplitude is negative.

[0053] Optionally, the low-voltage ride-through optimized collaborative control parameters include the active current recovery ratio coefficient of the wind turbine and the voltage regulation gain of the static var generator.

[0054] The objective function for low-pressure ride-through optimization is:

[0055]

[0056] in, This refers to the active current recovery ratio coefficient. The voltage regulation gain, The objective function for the low-pressure ride-through is optimized. Let be the objective function for the voltage support capability of the distribution network. This represents the target value for the active power recovery performance of the distribution network. This is a penalty item for exceeding the current limit. As the first weighting factor, As the second weighting factor, It is the third weighting factor.

[0057] Optionally, the objective function for the voltage support capability is:

[0058]

[0059] in, The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The time when a low-voltage ride-through fault occurs in the aforementioned distribution network. This refers to the time when the low-voltage ride-through fault is cleared;

[0060] The target value for active power recovery performance is:

[0061]

[0062] in, The moment when the voltage at the grid connection point of the wind farm recovers to 90% of its rated value;

[0063] The current over-limit penalty item is:

[0064]

[0065] in, The output current of the fan is [value]. The output current of the static var generator is... This is the maximum allowable current value for the fan. This is the maximum permissible current value of the static var generator. This represents the initial moment of the current constraint. This is the time when the current constraint ends.

[0066] Optionally, the low-voltage ride-through optimization parameters include the active current recovery ratio optimization coefficient of the wind turbine and the voltage regulation optimization gain of the static var generator.

[0067] The current component control unit is used for:

[0068] The active current component control unit for the wind turbine is used to control the active current component of the wind turbine converter to be as follows, based on the active current recovery ratio optimization coefficient:

[0069]

[0070] in, The active current component of the converter of the wind turbine. This is the optimization coefficient for the active current recovery ratio. The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The rated current of the fan;

[0071] A static var generator reactive current component control unit is used to control the reactive current component of the static var generator to be: based on the voltage regulation optimization gain of the static var generator.

[0072]

[0073] in, The reactive circuit component output by the static var generator. Optimize the gain for the voltage regulation. The rated current of the static var generator is given.

[0074] Optionally, the device may also include:

[0075] A real-time current monitoring unit is used to monitor the first output current of the wind turbine and the second output current of the static var generator in real time.

[0076] The active current recovery ratio optimization coefficient reduction unit is used to reduce the active current recovery ratio optimization coefficient according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm if the first output current is greater than the first preset rated current.

[0077] The voltage regulation optimization gain reduction unit is used to reduce the voltage regulation optimization gain according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm if the second output current is greater than the second preset rated current.

[0078] Optionally, the objective function optimization unit includes:

[0079] The first unit of the particle swarm optimization algorithm is used to initialize the particle population of the particle swarm algorithm and determine the search boundary of each particle, wherein each particle represents the low-pressure crossing cooperative control parameters to be optimized.

[0080] The second unit of the particle swarm optimization algorithm is used to calculate the fitness of each particle.

[0081] The third unit of the particle swarm optimization algorithm is used to update the extreme values ​​and global optimal positions of each particle based on their fitness.

[0082] The fourth unit of the particle swarm optimization algorithm is used to output the low-pressure crossing cooperative control parameters represented by the optimal particle as the low-pressure crossing optimization parameters if the particle swarm algorithm meets the convergence condition or reaches the maximum number of iterations. Otherwise, it updates each particle according to the velocity update formula and the position update formula to perform iteration and return to execute the second unit of the particle swarm optimization algorithm.

[0083] A low-voltage ride-through device that combines a wind turbine and a static var generator includes a memory and a processor;

[0084] The memory is used to store programs;

[0085] The processor is used to execute the program to implement the various steps of the coordinated low-voltage ride-through method of the wind turbine and static var generator as described above.

[0086] A storage medium storing a computer program, which, when executed by a processor, implements the steps of the coordinated low-voltage ride-through method of a wind turbine and a static var generator as described above.

[0087] By employing the aforementioned technical solution, this application obtains the low-voltage ride-through (LVRT) coordinated control parameters of the wind turbine and static var generator (SVG) within the distribution network. Based on the distribution network's voltage support capability, active power recovery performance, and current over-limit penalty mechanism, a low-voltage ride-through optimization objective function is constructed to optimize these parameters. The objective function is then optimized using a particle swarm optimization algorithm to obtain the optimized LVRT parameters. Based on these parameters, the converter current component of the wind turbine and the reactive current component of the SVG are controlled. Therefore, by optimizing the coordinated control parameters of the SVG and wind turbine according to voltage support capability and active power recovery performance, and applying the optimization results to current component control, the response coordination between the wind turbine and SVG is achieved, thereby improving voltage recovery efficiency. Attached Figure Description

[0088] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0089] Figure 1 This is a schematic diagram of a process for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator, provided in an embodiment of this application.

[0090] Figure 2 A schematic diagram of a device structure for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator, provided in an embodiment of this application;

[0091] Figure 3 This is a schematic diagram of a device for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator, provided as an embodiment of this application. Detailed Implementation

[0092] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0093] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a computer, cloud, or server.

[0094] Next, combined Figure 1 The low-voltage ride-through method for wind turbines and static var generators described in this application may include the following steps:

[0095] Step S110: Obtain the low-voltage ride-through coordinated control parameters of the wind turbines and static var generators in the distribution network.

[0096] Specifically, when a low-voltage ride-through fault occurs in the distribution network, the low-voltage ride-through parameters of the wind turbines and static var generators in the distribution network can be obtained for optimization and coordinated control.

[0097] Furthermore, by collecting the grid bus voltage and estimating the short-circuit ratio (SCR) in real time, the occurrence of a low-voltage ride-through fault can be determined. A higher SCR value indicates that the grid's short-circuit capacity is much greater than the wind farm's capacity, meaning the grid's voltage support for the wind farm is stronger, resulting in a shallower voltage drop and faster recovery during a fault. Conversely, a lower SCR indicates a weaker voltage support capacity from the grid for the wind farm, requiring reliance on SVG (Static Var Generator) and wind turbine coordinated compensation during a fault.

[0098] Understandably, SCR can help assess grid strength. For example, under the same voltage drop, weak grids with SCR < 3 are more prone to voltage collapse and should prioritize the activation of SVG for rapid reactive power support; strong grids with SCR ≥ 6 have strong voltage recovery capabilities and can appropriately accelerate the active power recovery of wind turbines.

[0099] The low-voltage ride-through (LVR) optimization parameters may include the active current recovery ratio of the wind turbine and the voltage regulation gain of the static var generator (SVG). The active current recovery ratio can be used to control the recovery speed of the active current component of the wind turbine after the fault is cleared. The voltage regulation gain can be used to control the sensitivity of the SVG's output reactive current when responding to voltage deviations.

[0100] Understandably, the active current recovery ratio coefficient and voltage regulation gain, as optimization parameters, can achieve synergy between SVG voltage support and wind turbine active power recovery, avoiding voltage overshoot caused by excessive reactive power injection due to the active current recovery ratio coefficient, and conflict caused by current overshoot due to excessive active power recovery caused by the voltage regulation gain.

[0101] Step S120: Based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network, construct a low-voltage ride-through optimization objective function to optimize the low-voltage ride-through collaborative control parameters.

[0102] Among these, the voltage support capability of the distribution network can be used as a sub-objective function of the low-voltage ride-through optimization objective function. Using the lowest voltage at the grid connection point during a fault as the core indicator, it can directly quantify the effect of wind turbines and SVG on suppressing grid voltage drops. This prioritizes ensuring grid voltage stability in the initial stage of a fault, avoiding the risk of grid collapse caused by sudden voltage drops.

[0103] Active power recovery performance can also be used as a sub-objective function of the low-voltage ride-through optimization objective function. It can accurately measure the recovery efficiency of wind power active power output, shorten the duration of active power deficit in the power grid after a fault, reduce wind power output loss, balance power grid frequency stability and wind power generation efficiency, and meet the needs of rapid power restoration in strong power grid scenarios.

[0104] The current over-limit penalty mechanism can also be used as a sub-objective function of the low-voltage ride-through optimization objective function. The current over-limit penalty mechanism can constrain the output current of the wind turbine and SCG to not exceed the maximum allowable value, avoid the risk of equipment overcurrent, protect the wind turbine converter and SVG power devices (such as IGBTs) from overheating damage, balance control response speed and equipment safety, and avoid hardware failures caused by pursuing voltage support or active power recovery.

[0105] Step S130: Optimize the low-pressure crossing objective function using the particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters.

[0106] Understandably, the particle swarm optimization (PSO) algorithm can simultaneously optimize the active current recovery ratio of the wind turbine and the voltage regulation gain of the SVG (Static Var Generator), thus ensuring rapid reactive power support from the SVG in the initial stage of a fault, shortening the active power recovery time, and avoiding current overruns. Furthermore, the PSO algorithm can quickly meet the millisecond-level response requirements for low-voltage ride-through, avoiding control lag caused by slow algorithm convergence.

[0107] Step S140: Based on the low-voltage ride-through optimization parameters, control the converter current component of the wind turbine and the reactive current component of the static var generator.

[0108] Specifically, the converter current components of a wind turbine can include both reactive and active current components. After the fault is cleared, the recovery slope of the active current component can be controlled according to the low-voltage ride-through optimization parameters, while the reactive current component gradually decreases to the steady-state reactive power output level.

[0109] More specifically, in the initial stage of a fault, the rapid reactive power support capability of the SVG can be activated first. Based on the optimized low-voltage ride-through parameters of the SVG, reactive current commands are generated, and the SVG can output reactive power in milliseconds, quickly suppressing voltage drops. During the voltage recovery period, after the voltage gradually stabilizes, active current can be smoothly restored based on the optimized low-voltage ride-through parameters of the wind turbine. At the same time, the reactive current component of the SVG can be withdrawn synchronously, avoiding voltage overshoot caused by excessive reactive power. Through the dynamic coordination of SVG supporting first and wind turbine recovering later, the conflict of objectives between independent controls is avoided.

[0110] The low-voltage ride-through (LVT) method for wind turbines and static var generators (SVG) provided in this embodiment obtains the LVT collaborative control parameters to be optimized for wind turbines and SVG within the distribution network. Based on the distribution network's voltage support capability, active power recovery performance, and current over-limit penalty mechanism, a low-voltage ride-through optimization objective function is constructed to optimize the LVT collaborative control parameters. This objective function is then optimized using a particle swarm optimization algorithm to obtain the optimized LVT parameters. Based on these optimized parameters, the converter current component of the wind turbine and the reactive current component of the SVG are controlled. Therefore, by optimizing the collaborative control parameters of the SVG and wind turbine according to voltage support capability and active power recovery performance, and applying the optimization results to current component control, the response coordination between the wind turbine and SVG is achieved, thereby improving voltage recovery efficiency.

[0111] In some embodiments of this application, the process of obtaining the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the power distribution network, as described above in step S110, is introduced. This process may include:

[0112] S1. Monitor the voltage amplitude at the wind farm connection point of the power distribution network.

[0113] Specifically, the three-phase voltage at the wind farm connection point can be collected, and then the voltage amplitude at the wind farm grid connection point can be measured.

[0114] S2. When the voltage amplitude is greater than the low-voltage ride-through trigger threshold of the wind farm and the rate of change of the voltage amplitude is negative, obtain the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the distribution network.

[0115] Specifically, if the voltage amplitude meets the following criteria, it indicates that a low-voltage ride-through fault has occurred in the distribution network:

[0116]

[0117] in, This refers to the voltage amplitude at the grid connection point of the wind farm. This is the trigger threshold for low-voltage ride-through faults (e.g., 0.9 pu). This indicates that the rate of change of voltage amplitude is negative, meaning that the voltage amplitude is in a downward trend.

[0118] Understandably, once a low-voltage ride-through fault is detected in the distribution network, the low-voltage ride-through coordinated control parameters of the wind turbines and static var generators in the distribution network need to be optimized in order to achieve low-voltage ride-through.

[0119] In some embodiments of this application, the low-voltage ride-through optimization objective function mentioned in the above embodiments is described. Specifically, the low-voltage ride-through optimization objective function can be:

[0120]

[0121] in, This is the active current recovery ratio coefficient. For voltage regulation gain, Optimize the objective function for low-pressure ride-through. Let be the objective function for the voltage support capability of the distribution network. This represents the target value for the active power recovery performance of the distribution network. This is a current over-limit penalty term, used to ensure that the output current of the wind turbine and SVG does not exceed their thermal limits throughout the entire process. As the first weighting factor, As the second weighting factor, It is the third weighting factor.

[0122] in, The three weighting factors reflect the respective impacts of the distribution network system on... , and The level of attention given to this can be adjusted based on the short-circuit ratio (SCR).

[0123] For example, when SCR < 3, the system voltage support capability is poor, and priority is given to ensuring voltage stability in the initial stage of a fault; therefore, the voltage should be increased. Weaken the active power recovery target, and set its weight as follows: =0.6, =0.2, ω3=0.2; when 3≤SCR≤6, the system can balance voltage and active power recovery, and the weights are set to =0.4, =0.4, =0.2; When SCR≥6, the voltage drop is relatively minor or the support capacity is strong, and the focus shifts to quickly restoring wind power output, therefore increasing Weaken the active recovery target and set its weight to [value]. =0.2, =0.6, =0.2.

[0124] Furthermore, the objective function for voltage support capability can be:

[0125]

[0126] in, The voltage amplitude at the wind farm's grid connection point in the power distribution network. The moment when a low-voltage ride-through fault occurs in the distribution network. This is the clearing time for a low-voltage ride-through fault, therefore Defined as the reciprocal of the lowest voltage during a fault, it reflects the ability of the distribution network system to suppress low voltage drops.

[0127] The target value for active power recovery performance can be:

[0128]

[0129] in, This is the moment when the voltage at the wind farm's grid connection point recovers to 90% of its rated value. Defined as the time required from fault clearance to the grid connection point voltage recovering to 90% of its rated value.

[0130] The current over-limit penalty item can be:

[0131]

[0132] in, This is the output current of the fan. This refers to the output current of the static var generator. This is the maximum allowable current value for the fan. This is the maximum permissible current value for the static var generator. This represents the initial moment of the current constraint. This is the time when the current constraint ends.

[0133] The low-voltage ride-through method for wind turbines and static var generators provided in this embodiment achieves optimal voltage support and active power recovery control of the wind farm during fault processes by constructing a multi-objective performance function and introducing an intelligent optimization algorithm to dynamically adjust the active current recovery ratio coefficient of the wind turbine and the voltage regulation gain of the SVG.

[0134] Considering the continuous optimization of the active current recovery ratio of the wind turbine and the voltage regulation gain of the SVG to actively prevent low-voltage ride-through faults, the coordinated low-voltage ride-through method of wind turbine and static var generator provided in this application may further include:

[0135] S1. Real-time monitoring of the first output current of the fan and the second output current of the static var generator.

[0136] S2. If the first output current is greater than the first preset rated current, the active current recovery ratio optimization coefficient is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm.

[0137] In addition, if the first output current is greater than the first preset rated current, the active current recovery ratio optimization coefficient can be automatically reduced according to the voltage deviation to ensure the safe operation of the equipment.

[0138] S3. If the second output current is greater than the second preset rated current, the voltage regulation optimization gain is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm.

[0139] In addition, if the second output current is greater than the second preset rated current, the voltage can be automatically reduced to optimize the gain based on the voltage deviation, so as to ensure the safe operation of the equipment.

[0140] The low-voltage ride-through method for wind turbines and static var generators provided in this embodiment continuously optimizes and controls the active current recovery ratio and voltage regulation gain by monitoring the output current of the wind turbine and the SVG, thereby avoiding wind power disconnection caused by hardware protection triggering and ensuring that the wind farm continues to operate in grid connection during faults, thus achieving proactive prevention of low-voltage ride-through faults.

[0141] In some embodiments of this application, the process of optimizing the low-voltage ride-through objective function using the particle swarm optimization algorithm to obtain the low-voltage ride-through optimization parameters is described. This process may include:

[0142] S1. Initialize the particle population for the particle swarm optimization algorithm and determine the search boundary for each particle.

[0143] Each particle can represent the low-pressure crossing cooperative control parameters to be optimized.

[0144] For example, if the low-voltage ride-through parameters to be optimized for coordinated control include the active current recovery ratio of the wind turbine... and the voltage regulation gain of the static var generator Therefore, the search boundary for the particle can be defined as 0.1 ≤ α ≤ 1.0, 0.01 ≤ k v ≤0.5.

[0145] S2. Calculate the fitness of each particle.

[0146] It is understandable that the fitness of each particle can be represented by the objective function.

[0147] S3. Update the extreme values ​​and global optimal positions of each particle based on their fitness.

[0148] S4. If the particle swarm optimization algorithm meets the convergence condition or reaches the maximum number of iterations, the low-pressure crossing cooperative control parameters represented by the optimal particle are output as the low-pressure crossing optimization parameters. Otherwise, each particle is updated according to the velocity update formula and the position update formula for iteration, and the process returns to the step of S2, which calculates the fitness of each particle.

[0149] The apparatus for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator provided in the embodiments of this application is described below. The apparatus for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator described below can be referred to in correspondence with the method for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator described above.

[0150] See Figure 2 , Figure 2 This is a schematic diagram of a device structure for achieving coordinated low-voltage ride-through of a wind turbine and a static var generator, as disclosed in an embodiment of this application.

[0151] like Figure 2 As shown, the device may include:

[0152] The optimization-optimized collaborative control parameter acquisition unit 11 is used to acquire the low-voltage ride-through optimization-optimized collaborative control parameters of wind turbines and static var generators in the power distribution network.

[0153] The objective function construction unit 12 is used to construct a low-voltage ride-through optimization objective function for optimizing the low-voltage ride-through cooperative control parameters based on the voltage support capability, active power recovery performance and current over-limit penalty mechanism of the distribution network.

[0154] The objective function optimization unit 13 is used to optimize the low-pressure crossing optimization objective function using a particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters;

[0155] The current component control unit 14 is used to control the converter current component of the wind turbine and the reactive current component of the static var generator based on the low-voltage ride-through optimization parameters.

[0156] Optionally, the unit for acquiring the cooperative control parameters to be optimized includes:

[0157] The voltage amplitude monitoring unit is used to monitor the voltage amplitude at the grid connection point of the wind farm in the power distribution network.

[0158] The low-voltage ride-through triggering unit is used to obtain the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the distribution network when the voltage amplitude is greater than the low-voltage ride-through triggering threshold of the wind farm and the rate of change of the voltage amplitude is negative.

[0159] Optionally, the low-voltage ride-through optimized collaborative control parameters include the active current recovery ratio coefficient of the wind turbine and the voltage regulation gain of the static var generator.

[0160] The objective function for low-pressure ride-through optimization is:

[0161]

[0162] in, This refers to the active current recovery ratio coefficient. The voltage regulation gain, The objective function for the low-pressure ride-through is optimized. Let be the objective function for the voltage support capability of the distribution network. This represents the target value for the active power recovery performance of the distribution network. This is a penalty item for exceeding the current limit. As the first weighting factor, As the second weighting factor, It is the third weighting factor.

[0163] Optionally, the objective function for the voltage support capability is:

[0164]

[0165] in, The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The time when a low-voltage ride-through fault occurs in the aforementioned distribution network. This refers to the time when the low-voltage ride-through fault is cleared;

[0166] The target value for active power recovery performance is:

[0167]

[0168] in, The moment when the voltage at the grid connection point of the wind farm recovers to 90% of its rated value;

[0169] The current over-limit penalty item is:

[0170]

[0171] in, The output current of the fan is [value]. The output current of the static var generator is... This is the maximum allowable current value for the fan. This is the maximum permissible current value of the static var generator. This represents the initial moment of the current constraint. This is the time when the current constraint ends.

[0172] Optionally, the low-voltage ride-through optimization parameters include the active current recovery ratio optimization coefficient of the wind turbine and the voltage regulation optimization gain of the static var generator.

[0173] The current component control unit is used for:

[0174] The active current component control unit for the wind turbine is used to control the active current component of the wind turbine converter to be as follows, based on the active current recovery ratio optimization coefficient:

[0175]

[0176] in, The active current component of the converter of the wind turbine. This is the optimization coefficient for the active current recovery ratio. The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The rated current of the fan;

[0177] A static var generator reactive current component control unit is used to control the reactive current component of the static var generator to be: based on the voltage regulation optimization gain of the static var generator.

[0178]

[0179] in, The reactive circuit component output by the static var generator. Optimize the gain for the voltage regulation. The rated current of the static var generator is given.

[0180] Optionally, the device may also include:

[0181] A real-time current monitoring unit is used to monitor the first output current of the wind turbine and the second output current of the static var generator in real time.

[0182] The active current recovery ratio optimization coefficient reduction unit is used to reduce the active current recovery ratio optimization coefficient according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm if the first output current is greater than the first preset rated current.

[0183] The voltage regulation optimization gain reduction unit is used to reduce the voltage regulation optimization gain according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm if the second output current is greater than the second preset rated current.

[0184] Optionally, the objective function optimization unit includes:

[0185] The first unit of the particle swarm optimization algorithm is used to initialize the particle population of the particle swarm algorithm and determine the search boundary of each particle, wherein each particle represents the low-pressure crossing cooperative control parameters to be optimized.

[0186] The second unit of the particle swarm optimization algorithm is used to calculate the fitness of each particle.

[0187] The third unit of the particle swarm optimization algorithm is used to update the extreme values ​​and global optimal positions of each particle based on their fitness.

[0188] The fourth unit of the particle swarm optimization algorithm is used to output the low-pressure crossing cooperative control parameters represented by the optimal particle as the low-pressure crossing optimization parameters if the particle swarm algorithm meets the convergence condition or reaches the maximum number of iterations. Otherwise, it updates each particle according to the velocity update formula and the position update formula to perform iteration and return to execute the second unit of the particle swarm optimization algorithm.

[0189] The device for coordinated low-voltage ride-through of wind turbines and static var generators provided in this application embodiment can be applied to equipment for coordinated low-voltage ride-through of wind turbines and static var generators, such as terminals: cloud computing, computers, etc. Optionally, Figure 3 The hardware structure block diagram of the equipment for coordinated low-voltage ride-through of wind turbine and static var generator is shown, with reference to... Figure 3 The hardware structure of the equipment for low-voltage ride-through of the wind turbine and static var generator may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0190] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0191] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0192] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0193] The memory stores a program, which the processor can call. The program is used for:

[0194] Obtain the low-voltage ride-through parameters of wind turbines and static var generators in the power distribution network to be optimized for coordinated control.

[0195] Based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network, a low-voltage ride-through optimization objective function is constructed to optimize the low-voltage ride-through collaborative control parameters to be optimized.

[0196] The low-pressure crossing optimization objective function is optimized using the particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters;

[0197] Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled.

[0198] Optionally, the refined and extended functions of the program can be found in the description above.

[0199] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0200] Obtain the low-voltage ride-through parameters of wind turbines and static var generators in the power distribution network to be optimized for coordinated control.

[0201] Based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network, a low-voltage ride-through optimization objective function is constructed to optimize the low-voltage ride-through collaborative control parameters to be optimized.

[0202] The low-pressure crossing optimization objective function is optimized using the particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters;

[0203] Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled.

[0204] Optionally, the refined and extended functions of the program can be found in the description above.

[0205] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0206] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0207] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for coordinated low-voltage ride-through of a wind turbine and a static var generator, characterized in that, include: Obtain the low-voltage ride-through parameters of wind turbines and static var generators in the power distribution network to be optimized for coordinated control. Based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network, a low-voltage ride-through optimization objective function is constructed to optimize the low-voltage ride-through collaborative control parameters to be optimized. The low-pressure crossing optimization objective function is optimized using the particle swarm optimization algorithm to obtain the low-pressure crossing optimization parameters; Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled.

2. The method according to claim 1, characterized in that, The acquisition of low-voltage ride-through parameters for the coordinated control of wind turbines and static var generators within the distribution network includes: Monitor the voltage amplitude at the wind farm connection point of the power distribution network; When the voltage amplitude is greater than the low-voltage ride-through trigger threshold of the wind farm and the rate of change of the voltage amplitude is negative, the low-voltage ride-through coordinated control parameters to be optimized for wind turbines and static var generators in the distribution network are obtained.

3. The method according to claim 1, characterized in that, The low-voltage ride-through optimized collaborative control parameters include the active current recovery ratio coefficient of the wind turbine and the voltage regulation gain of the static var generator. The objective function for low-pressure ride-through optimization is: in, This refers to the active current recovery ratio coefficient. The voltage regulation gain, The objective function for the low-pressure ride-through is optimized. Let be the objective function for the voltage support capability of the distribution network. This represents the target value for the active power recovery performance of the distribution network. This is a penalty item for exceeding the current limit. As the first weighting factor, As the second weighting factor, It is the third weighting factor.

4. The method according to claim 3, characterized in that, The objective function for voltage support capability is: in, The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The time when a low-voltage ride-through fault occurs in the aforementioned distribution network. This refers to the time when the low-voltage ride-through fault is cleared; The target value for active power recovery performance is: in, The moment when the voltage at the grid connection point of the wind farm recovers to 90% of its rated value; The current over-limit penalty item is as follows: in, The output current of the fan is [value]. The output current of the static var generator is... This is the maximum allowable current value for the fan. This is the maximum permissible current value of the static var generator. This represents the initial moment of the current constraint. This is the time when the current constraint ends.

5. The method according to claim 1, characterized in that, The low-voltage ride-through optimization parameters include the active current recovery ratio optimization coefficient of the wind turbine and the voltage regulation optimization gain of the static var generator. Based on the low-voltage ride-through optimization parameters, the converter current component of the wind turbine and the reactive current component of the static var generator are controlled, including: Based on the active current recovery ratio optimization coefficient, the active current component of the wind turbine converter is controlled as follows: in, The active current component of the converter of the wind turbine. The active current recovery ratio optimization coefficient is given. The voltage amplitude at the wind farm grid connection point of the aforementioned distribution network. The rated current of the fan; Based on the voltage regulation optimization gain of the static var generator, the reactive current component of the static var generator is controlled as follows: in, The reactive circuit component output by the static var generator. Optimize the gain for the voltage regulation. The rated current of the static var generator is given.

6. The method according to claim 5, characterized in that, Also includes: Real-time monitoring of the first output current of the wind turbine and the second output current of the static var generator; If the first output current is greater than the first preset rated current, the active current recovery ratio optimization coefficient is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm. If the second output current is greater than the second preset rated current, the voltage regulation optimization gain is reduced according to the ratio of the short-circuit capacity of the wind farm grid connection point to the rated capacity of the wind farm.

7. The method according to any one of claims 1-6, characterized in that, The low-pressure ride-through optimization objective function is optimized using a particle swarm optimization algorithm to obtain low-pressure ride-through optimization parameters, including: Initialize the particle population for the particle swarm optimization algorithm and determine the search boundary for each particle, wherein each particle represents the low-pressure crossing cooperative control parameters to be optimized. Calculate the fitness of each particle; Update the extreme values ​​and global optimal positions of each particle based on their fitness. If the particle swarm optimization algorithm satisfies the convergence condition or reaches the maximum number of iterations, the low-pressure crossing cooperative control parameters represented by the optimal particle are output as the low-pressure crossing optimization parameters. Otherwise, each particle is updated according to the velocity update formula and the position update formula to perform iteration, and the algorithm returns to the step of calculating the fitness of each particle.

8. A low-voltage ride-through device for a combined wind turbine and a static var generator, characterized in that, include: The unit for acquiring the coordinated control parameters to be optimized is used to acquire the low-voltage ride-through coordinated control parameters of wind turbines and static var generators in the distribution network. The objective function construction unit is used to construct a low-voltage ride-through optimization objective function for optimizing the low-voltage ride-through cooperative control parameters based on the voltage support capability, active power recovery performance, and current over-limit penalty mechanism of the distribution network. The objective function optimization unit is used to optimize the low-pressure ride-through objective function using a particle swarm optimization algorithm to obtain the low-pressure ride-through optimization parameters. The current component control unit is used to control the converter current component of the wind turbine and the reactive current component of the static var generator based on the low-voltage ride-through optimization parameters.

9. A low-voltage ride-through device that combines a wind turbine and a static var generator, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement the various steps of the coordinated low-voltage ride-through method of wind turbine and static var generator as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the various steps of the coordinated low-voltage ride-through method of wind turbine and static var generator as described in any one of claims 1-7.

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