Wind driven generator fault ride-through sensorless virtual test method and device based on ground test platform, medium and product

Abstract

The invention discloses a wind driven generator fault ride-through sensorless virtual test method and device based on a ground test platform, a medium and a product, and relates to the technical field of wind power generation. Obtaining target variable information according to the operation state information by using a sensorless state estimation formula; and performing the wind driven generator fault ride-through sensorless virtual test according to the target variable information. According to the method, the operation state information is directly input into the sensorless state estimation formula, so that the target variable information can be obtained, the wind driven generator fault ride-through sensorless virtual test is carried out, and the state of a unit is monitored in real time without depending on a sensor. According to the method, the dependence on the sensor can be reduced through virtual testing under the operation states of the wind turbine generator under different working conditions, especially under the extreme working conditions such as power grid faults, the testing precision and efficiency are improved, and the fault ride-through capacity of the wind turbine generator is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation, in particular to a wind turbine fault ride-through sensorless virtual testing method, device, medium and product based on a ground test platform. BACKGROUND

[0002] The rapid development of offshore wind power technology puts forward higher requirements for wind turbine generators, especially in complex sea and wind conditions. Traditional wind turbine fault ride-through testing usually relies on sensors to monitor the unit state in real time, but this method not only has high cost, but also has certain testing limitations. SUMMARY

[0003] The purpose of the present application is to provide a wind turbine fault ride-through sensorless virtual testing method, device, medium and product based on a ground test platform, which can monitor the unit state in real time without relying on sensors.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a wind turbine fault ride-through sensorless virtual testing method based on a ground test platform, comprising:

[0006] Obtaining operating state information of a simulated wind turbine generator;

[0007] Using a sensorless state estimation formula to obtain target variable information according to the operating state information; the sensorless state estimation formula is:

[0008] Wherein, is the time derivative of the wind wheel torsion angle, i.e. its dynamic change; is the estimated value of the wind wheel angular velocity; fal(e, a, d) is a nonlinear error function about e; is the time derivative of the wind wheel angular velocity; D wt is the wind wheel self-damping coefficient; H wt is the wind wheel equivalent inertia time constant; is the estimated value of the wind wheel aerodynamic torque; is the estimated value of the transmission chain torque; is the time derivative of the generator set torsion angle; is the estimated value of the generator set angular velocity; is the estimated value of the generator set angular velocity; D g is the generator self-damping coefficient; H g is the generator equivalent inertia time constant; is the estimated value of the generator electromagnetic torque; is the time derivative of the torque of the transmission chain; β1 and β2 are respectively the observer estimated gain coefficients of the wind wheel torsion angle and angular velocity, β3 and β4 are respectively the observer estimated gain coefficients of the generator set torsion angle and angular velocity, and β5 is the observer estimated gain coefficient of the transmission chain torque; a step signal test is performed on the dynamic model of the simulated wind turbine generator set, and the rise time t r is obtained based on the dynamic characteristics of the simulated wind turbine generator set , and the bandwidth information of the wind turbine generator set is calculated based on the rise time c , the bandwidth w0 = 3.6w

[0009] According to the target variable information, the wind turbine fault ride-through sensorless virtual test is performed.

[0010] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the wind turbine fault ride-through sensorless virtual test method based on the ground test platform according to any one of the above.

[0011] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the wind turbine fault ride-through sensorless virtual test method based on the ground test platform according to any one of the above.

[0012] In a fourth aspect, the present application provides a computer program product comprising a computer program, and the computer program is executed by a processor to implement the wind turbine fault ride-through sensorless virtual test method based on the ground test platform according to any one of the above.

[0013] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0014] The present application provides a wind turbine fault ride-through sensorless virtual test method, device, medium and product based on a ground test platform, which comprises: obtaining the running state information of a simulated wind turbine generator set; using a sensorless state estimation formula to obtain target variable information according to the running state information; the sensorless state estimation formula is: wherein, is the time derivative of the wind wheel torsion angle, i.e., its dynamic change; is the estimated value of the wind wheel angular velocity; fal(e, α, δ) is a nonlinear error function about e; is the time derivative of the wind wheel angular velocity; Dwt is the wind rotor self-damping coefficient; H wt is the wind rotor equivalent inertia time constant; is the estimated value of the wind rotor aerodynamic torque; is the estimated value of the transmission chain torque; is the time derivative of the generator set torsion angle; is the estimated value of the generator set angular velocity; is the estimated value of the generator set angular velocity; D g is the generator self-damping coefficient; H g is the generator equivalent inertia time constant; is the estimated value of the generator electromagnetic torque; is the time derivative of the transmission chain torque; β1 and β2 are respectively the observer estimated gain coefficients of the wind rotor torsion angle and angular velocity, β3 and β4 are respectively the observer estimated gain coefficients of the generator set torsion angle and angular velocity, and β5 is the observer estimated gain coefficient of the transmission chain torque; a step signal test is performed on the dynamic model of the simulated wind turbine generator set, the rising time t r is obtained based on the dynamic characteristics of the simulated wind turbine generator set, the bandwidth information of the wind turbine generator set is calculated based on the rising time so as to determine the bandwidth w0=3.6w in the sensorless state estimation algorithm c The bandwidth method is used to obtain the estimated gain coefficients of each variable as follows: β1=5w0, The target variable information is obtained by directly inputting the operating state information into the sensorless state estimation formula, so as to perform the sensorless virtual test of the wind turbine fault ride-through. The method does not rely on sensors to monitor the operating state of the wind turbine generator in real time. The method can reduce the dependence on sensors, improve the test accuracy and efficiency, and enhance the fault ride-through capability of the wind turbine generator under different operating conditions, especially under extreme conditions such as power grid faults. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 is an application environment diagram of a wind turbine fault ride-through sensorless virtual test method based on a ground test platform in an embodiment of the present application.

[0017] Figure 2A two-mass system schematic diagram provided for an embodiment of the present application.

[0018] Figure 3 A simulation schematic diagram of a wind turbine fault ride-through sensorless virtual test method based on a ground test platform provided for an embodiment of the present application.

[0019] Figure 4 A structure schematic diagram of a wind turbine fault ride-through sensorless virtual test system based on a ground test platform provided for an embodiment of the present application.

[0020] Figure 5 A flowchart of a wind turbine fault ride-through sensorless virtual test method based on a ground test platform provided for an embodiment of the present application.

[0021] Figure 6 A structure schematic diagram of a computer device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0023] The application provides a wind turbine fault ride-through sensorless virtual test method based on a ground test platform, which estimates load data of a wind turbine in real time to ensure stable operation of the wind turbine under power grid fault conditions. In order to avoid confusion of terms, the generator connected to the power grid is defined as the'motor side' in the application, and the series double drag motor used to simulate the dynamic of the wind wheel is defined as the 'rotor side'. The method comprises the following steps: obtaining internal state information of a simulated wind turbine, including inertia time constant of the wind wheel and the generator, damping coefficient of the transmission shaft, stiffness coefficient, self-damping coefficient of the wind wheel and the generator and other parameters; obtaining initial working condition information of the rotor side and the motor side, including aerodynamic torque, initial angle and angular velocity of the wind wheel, initial electromagnetic torque of the generator and the like; constructing a wind wheel rotor model and a generator model based on the state information of the simulated wind turbine, and constructing a voltage ride-through fault model through data processing and modeling; performing unit conversion processing on the data by using a two-mass block transmission chain model to estimate real-time load data of the simulated wind turbine; simulating the torsion angle and angular velocity of the wind wheel and the generator by using a dynamic equation, and calculating the aerodynamic torque and electromagnetic torque of the simulated wind turbine; introducing a state observer to optimize the estimation of the torsion angle and angular velocity of the wind wheel on the rotor side by calculating the correction error, and further improving the load estimation accuracy. The application can effectively reduce the dependence on sensors, optimize the fan control strategy by monitoring the fan operating state in real time, enhance the fault ride-through capability, and thus improve the operation stability and reliability of the wind turbine.

[0024] The above objects, features and advantages of the application will become more apparent from the following detailed description of the application, taken in conjunction with the accompanying drawings and specific embodiments.

[0025] The wind turbine fault ride-through sensorless virtual test method based on the ground test platform provided by the embodiments of the application can be applied to the application environment as shown in the figure. Figure 1 The terminal 102 communicates with the server 104 through the network. The data storage system can store the data required to be processed by the server 104. The data storage system can be separately arranged, or integrated on the server 104, or placed on the cloud or other servers.

[0026] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart televisions, smart air conditioners, smart vehicle devices and the like. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices and the like. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0027] The application does not need to rely on expensive sensor equipment. By modeling and estimating various operating parameters of the wind turbine generator, the load change of the wind turbine generator under grid fault condition can be accurately predicted.

[0028] Embodiment one:

[0029] In an exemplary embodiment, a ground test platform-based wind turbine fault ride-through sensorless virtual testing method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or can be executed by a terminal and a server together. In the embodiment of the application, the method is applied to the server 104 in Figure 1 The embodiment accurately calculates the transmission chain load information of the wind turbine by modeling the operating state of the simulated wind turbine generator and estimating the key variables by the state observer, while reducing the use of sensors and improving the stability of the wind turbine during fault ride-through. Specifically as follows:

[0030] Obtain the operating state information of the simulated wind turbine generator. First, obtain the key state parameters of the simulated wind turbine generator, including rotor-side parameters: wind wheel equivalent inertia time constant, wind wheel self-damping coefficient, wind wheel initial torsion angle, wind wheel initial angular velocity, wind wheel aerodynamic torque; transmission chain parameters: damping coefficient of transmission shaft, stiffness coefficient; motor-side parameters: generator equivalent inertia time constant, generator self-damping coefficient, generator initial electromagnetic torque, generator initial torsion angle, generator initial angular velocity, torque constant of motor, and voltage at motor end.

[0031] The above parameters can be obtained from historical data, operating state estimation or online monitoring system.

[0032] Construct a two-mass dynamic model of the simulated wind turbine generator.

[0033] Please refer to Figure 2 In this embodiment, the transmission chain modeling of the simulated wind turbine generator adopts a two-mass system, in which the wind wheel and the generator are connected by an elastic transmission shaft as independent masses. The wind wheel is driven by aerodynamic force, and the generator is subjected to electromagnetic torque, while the stiffness and damping of the transmission shaft determine the torque transmission between the two. The dynamic equation is described as follows:

[0034]

[0035] Where H wt is the wind wheel equivalent inertia time constant, θ wt is the torsion angle of the wind wheel, ω wt is the angular velocity of the wind wheel, D wt is the wind wheel self-damping coefficient, T wt is the wind wheel aerodynamic torque, Ts H is the torque of the transmission chain, g θ is the equivalent inertia time constant of the generator, g ω is the torsional angle of the generator, g D is the angular velocity of the generator, g T is the self-damping coefficient of the generator, s T is the torque of the transmission chain, g T is the electromagnetic torque of the generator.

[0036] Based on the two-mass system, the model can more accurately describe the dynamic response of the wind turbine under different working conditions, especially under the action of wind speed change and electromagnetic torque, the dynamic coupling relationship between the wind wheel and the generator.

[0037] Fault ride-through model:

[0038] When the voltage of the power grid drops, the electromagnetic torque T g of the simulation wind turbine changes abruptly, causing changes in the dynamic response of the system. In order to describe this phenomenon, a fault ride-through simulation model is established in this embodiment, and the following mathematical expression is used:

[0039]

[0040] where T g is the electromagnetic torque, k e is the torque constant of the motor, V is the current grid voltage, V nom is the rated voltage of the motor, T nom is the electromagnetic torque at the rated voltage. When the grid voltage is temporarily reduced, the electromagnetic torque T g will be reduced in proportion, thereby affecting the output power and stability of the unit. In this process, the control system must adjust the output torque according to the change of voltage to avoid the unit being disconnected too early or having an unstable dynamic response.

[0041] Sensorless state estimation:

[0042] In this embodiment, in order to reduce the dependence on physical sensors, a state observer-based method is used to estimate the key variables in the simulation wind turbine system, including the key variable information of the rotor side, the transmission chain side and the motor side. In this way, the demand for sensors can be effectively reduced, while the operating stability and system reliability of the wind turbine can be improved. The designed observer is:

[0043]

[0044] where, is the time derivative of the wind wheel torsional angle, i.e. its dynamic change; is an estimated value of the wind wheel angular velocity; fal(e, a, d) is a nonlinear error function about e; is a time derivative of the wind wheel angular velocity; D wt is a wind wheel self-damping coefficient; H wt is an equivalent inertia time constant of the wind wheel; is an estimated value of the wind wheel aerodynamic torque; is an estimated value of the transmission chain torque; is a time derivative of the generator set torsion angle; is an estimated value of the generator set angular velocity; is an estimated value of the generator set angular velocity; D g is a generator self-damping coefficient; H g is an equivalent inertia time constant of the generator; is an estimated value of the generator electromagnetic torque; is a time derivative of the transmission chain torque; is an estimated value of the generator set torsion angle; is an estimated value of the wind wheel torsion angle, b1 and b2 are respectively an observer estimated gain coefficient of the wind wheel torsion angle and angular velocity, b3 and b4 are respectively an observer estimated gain coefficient of the generator set torsion angle and angular velocity, and b5 is an observer estimated gain coefficient of the transmission chain torque; a step signal test is performed on a dynamic model of the simulated wind turbine generator set, and a rise time t r is calculated based on the rise time, and bandwidth information of the wind turbine generator set is calculated based on the rise time The bandwidth w0=3.6w in the sensorless state estimation algorithm can be determined c Based on the bandwidth method, a design method of the related estimated gain coefficient is: b1=5w0,

[0045] The wind turbine fault ride-through sensorless virtual test is performed according to the target variable information.

[0046] wherein,

[0047]

[0048] In the formula, e is an estimated error, and wt is a torsion angle of the wind wheel, is an estimated value of the torsion angle of the wind wheel, fal(e, a, d) is a nonlinear error function about e, a is an adjustment parameter of the estimated error, and d is a threshold parameter of the estimated error,

[0049] Noise information n of a wind wheel torsion angle output port of the wind turbine generator set is collected under a zero state input of the simulated wind turbine generator set, and a noise standard deviation where n i is the noise information of the rotor torsion angle output port of the i-th collected wind turbine, γ is the mean value of the noise (usually taken as 0), and N is the total number of sampled noises. At this time, the threshold parameter δ of the estimation error is 2.1σ.

[0050] Since the estimation error designed in the application is a first-order state error, based on Lyapunov stability theory, the adjustment parameter α of the estimation error is set to 0.5.

[0051] Based on the above designed extended state observer, the key variables in the simulated wind turbine system are estimated, including the key variable information of the rotor side, the transmission chain side and the motor side. In order to ensure the stability and accuracy of the whole system, accurate gain coefficients (such as β1, β2, β3, β4, β5) are used to adjust the estimation process of each key variable to optimize the performance of the state observer. These gain coefficients reflect the response rate of different physical quantities, ensuring accurate coupling and coordination between variables.

[0052] Load information solution of the simulated wind turbine:

[0053] Based on the designed state observer, the transmission chain torque at any time can be obtained, and the calculation method is as follows:

[0054]

[0055] where, is the initial value of the transmission chain torque, β5 is the estimation gain coefficient of the transmission chain torque observer, fal(e, α, δ) is a nonlinear error function about e, is the transmission chain torque at time t.

[0056] Based on the designed state observer, the estimation values of the rotor angle and the generator set torsion angle at any time can be obtained, and the transmission chain torsion angle reflects the coupling relationship between the rotor and the generator, helping to estimate the state of the transmission chain and the torque transmission therein. The transmission chain torsion angle of the wind turbine is estimated in the following form:

[0057]

[0058] where, is the estimated value of the transmission chain torsion angle, is the estimated value of the rotor torsion angle, is the estimated value of the generator set torsion angle. The "transmission chain torsion angle of the wind turbine" here is the relative angle difference between the "rotor angle and the generator set torsion angle", which is used to describe the relative torsion deformation amount in the transmission chain, and is an important physical quantity reflecting the coupling stiffness and load state.

[0059] Based on the designed state observer of the step, the estimated values of the wind wheel angular velocity and the generator set angular velocity at any time can be obtained, and the torque of the transmission chain is the difference between the wind wheel angular velocity and the generator angular velocity, so as to reflect the load condition of the transmission chain. The torsional vibration of the wind turbine transmission chain can be estimated in the following form:

[0060]

[0061] wherein, is the estimated value of the transmission chain torsional vibration, is the estimated value of the wind wheel angular velocity, is the estimated value of the generator set angular velocity. The "torsional vibration" here refers to the relative torsional vibration caused by the difference between the wind wheel angular velocity and the generator angular velocity, that is, the periodic torque change caused by the difference between the two angular velocities. The "torsional vibration" here not only refers to the difference between the two angular velocities, but also includes the dynamic vibration response caused by the difference, which belongs to the dynamic behavior of the system transmission chain.

[0062] For example: at a certain time, the simulated wind turbine is affected by a power grid fault, and the voltage sag causes the wind turbine to enter the fault ride-through mode. It is assumed that the fault occurs between 10 seconds and 30 seconds, and the wind turbine experiences a sharp drop in grid voltage during this period. The voltage sag triggers the fault ride-through mechanism of the unit, and the control system of the unit begins to adjust the output torque of the wind turbine to maintain the stable operation of the wind turbine and avoid shutdown.

[0063] Under this working condition, the transmission chain torque simulation analysis is carried out in this embodiment, and the transmission chain torque change of the simulated wind turbine during the fault ride-through period is focused on. As shown in Figure 3 , by comparing the changes of the estimated value and the actual value, the simulation results are as follows:

[0064] In the process of implementing this embodiment, Figure 3 The transmission chain torque estimation graph in (a) shows the estimated transmission chain torque change of the simulated wind turbine based on the sensorless detection method of the present application. Through the real-time estimation method of this embodiment, the load change of the simulated wind turbine during the power grid fault can be accurately calculated, and the simulated wind turbine can be ensured to operate stably and effectively reduce the mechanical impact during the power grid recovery process.

[0065] Figure 3 The voltage ride-through graph shown in (b) represents the operating state of the simulated wind turbine when the power grid experiences voltage sag or short circuit fault. The grid voltage fluctuates during the fault and gradually recovers. Such voltage fluctuation will directly affect the electromagnetic torque and aerodynamic torque of the wind turbine, thereby affecting the transmission chain torque.

[0066] The embodiment proposes a wind turbine fault ride-through sensorless virtual testing method based on a ground test platform. The method realizes real-time state information detection of the simulated wind turbine through dynamic modeling of the simulated wind wheel and generator and construction of an extended state observer.

[0067] Embodiment two:

[0068] To perform the method in embodiment one, the embodiment provides a wind turbine fault ride-through sensorless virtual testing system based on a ground test platform, which is used for real-time monitoring of the operating state of the simulated wind turbine under grid fault conditions and realizing sensorless estimation of the load of the simulated wind turbine. The system is composed of a fault simulation unit, an information acquisition unit, a state estimation unit, a load calculation unit and the like, and realizes modeling, simulation and real-time calculation of the dynamic characteristics of the simulated wind turbine.

[0069] As shown in Figure 4 , the detection system of the embodiment mainly includes the following modules:

[0070] Fault simulation unit: used for simulating grid voltage drop or short circuit fault and calculating the sudden change of electromagnetic torque of the simulated wind turbine;

[0071] Information acquisition unit: acquires the basic operating state information of the simulated wind turbine, including the key parameters of the wind wheel, transmission chain and motor side;

[0072] State estimation unit: based on the two-mass block model, the state variables of the simulated wind turbine are estimated in real time, and the error is corrected in combination with the state observer;

[0073] Load calculation unit: obtains the information such as torque and torsional vibration of the simulated wind turbine through dynamic calculation to replace the physical sensor for monitoring.

[0074] The obtained results are used for wind turbine operating state monitoring and can be used as input for wind turbine control strategy optimization.

[0075] Compared with the existing technology, the testing system provided by the embodiment does not need to rely on sensors, estimates key variables by using a state observer, reduces the dependence on mechanical sensors and reduces maintenance costs. It has high fault ride-through capability: real-time calculation of the state of the wind turbine improves the stability of the wind turbine under grid fault conditions. Moreover, it can be used for intelligent wind turbine control, so that the obtained load information can be used as input to improve the intelligent level of wind turbine operation control.

[0076] Embodiment three:

[0077] The embodiment provides a wind turbine fault ride-through sensorless virtual test method based on an electronic device and a ground test platform, the method realizes real-time monitoring of a wind turbine operation state and fault ride-through optimization control through computer program running of the electronic device. The electronic device of the embodiment comprises a memory, a processor and a data interface, is used for storing and executing a simulation algorithm of wind turbine fault ride-through, and provides state estimation and control optimization functions.

[0078] Optionally, the electronic device comprises a memory, a processor, a data interface and the like.

[0079] In addition, the working process of the electronic device is data input, state estimation, fault response and control signal output.

[0080] The electronic device receives real-time operation state information of the wind turbine through the data interface, including key parameters of the rotor side, the transmission chain side and the motor side. The information can be provided by the wind turbine control system or estimated by the electronic device based on historical data and operation conditions.

[0081] The processor estimates the operation state of the wind turbine based on a two-mass block model, and combines a state observer to correct errors, so as to improve the estimation accuracy. The state estimation process comprises calculating the aerodynamic torque of the rotor side, calculating the torque change of the transmission chain, and calculating the electromagnetic torque of the motor side. The state observer is used to compensate the estimation error, and the estimation accuracy of the wind turbine state variable is optimized.

[0082] When the electronic device detects that the grid voltage is abnormal (such as voltage drop or short circuit), the fault response mode is automatically entered. The processor estimates the transmission chain load information according to the operation state of the wind turbine and the type of grid fault.

[0083] The electronic device sends adjustment instructions to the wind turbine control system according to the calculated transmission chain load estimation value, and adjusts the operation state of the wind turbine. The specific adjustment comprises adjusting the wind wheel pitch angle, optimizing the aerodynamic characteristics of the wind turbine; sending the generator excitation control signal, improving the adaptability of the wind turbine to the grid fault; and feeding back the estimated wind turbine operation state information, optimizing the fault response capability of the wind turbine control system.

[0084] The embodiment provides a wind turbine fault ride-through sensorless virtual test method based on an electronic device and a ground test platform, the method realizes real-time monitoring of a wind turbine operation state and fault ride-through optimization control through computer program running of the electronic device. The electronic device of the embodiment comprises a memory, a processor and a data interface, is used for storing and executing a simulation algorithm of wind turbine fault ride-through, and provides state estimation and control optimization functions.

[0085] Embodiment four

[0086] Please refer toFigure 5 The embodiment provides a wind turbine fault ride-through sensorless virtual testing method based on a ground test platform, characterized in that the ground test platform adopts a series double-towed motor as a driving device to simulate the driving process of a wind wheel on a generator in an actual working condition. Mechanical input generated by a wind wheel rotor in a wind turbine generator set is simulated and realized on the ground test platform. In order to avoid confusion, the generator connected to the power grid is defined as the'motor side' in the application, and the series double-towed motor responsible for simulating the wind wheel torque input is defined as the 'rotor side'.

[0087] Obtaining internal state information of the simulated wind turbine generator set, the state information including equivalent inertia time constant H wt and H g , damping coefficient B s of a transmission shaft, stiffness coefficient K s of the transmission shaft, wind wheel self-damping coefficient D wt , generator self-damping coefficient D g , torque constant k e of the motor, and voltage V of the motor end.

[0088] Obtaining initial working condition information of the rotor side of the simulated wind turbine generator set, the initial working condition information including aerodynamic torque T wt , initial torsion angle θ wt (0) of the wind wheel, and initial angular velocity ω wt (0) of the wind wheel.

[0089] Obtaining initial working condition information of the motor side of the simulated wind turbine generator set, the initial working condition information including initial electromagnetic torque T g (0) of the generator, initial torsion angle θ g (0) of the generator set, and initial angular velocity ω g (0).

[0090] Data processing is performed on the state information, and a wind wheel rotor model and a generator model are constructed based on the state information.

[0091] Obtaining voltage operation information of the simulated wind turbine generator set, and constructing a wind turbine generator set voltage ride-through fault model based on the voltage information.

[0092] Converting the working condition information to the low-speed shaft side of the transmission chain, performing normalized data processing, and then estimating the load data of the wind turbine generator set by using a two-mass block transmission chain model.

[0093] Establishing a voltage fault ride-through model, grid voltage sag will cause changes in electromagnetic torque and aerodynamic torque of the wind turbine generator set, and then affect the dynamic response of the unit.

[0094] The relationship between electromagnetic torque and motor terminal voltage can be expressed as:

[0095]

[0096] where T g is the electromagnetic torque, k e is the torque constant of the motor, V nom is the rated voltage of the motor, T nom is the electromagnetic torque at rated voltage. When the grid voltage is reduced, the electromagnetic torque T g will be reduced in proportion, thereby affecting the output power and stability of the unit. In this process, the control system must adjust the output torque according to the change of voltage to avoid premature disconnection or unstable dynamic response of the unit.

[0097] The method further comprises:

[0098] The processed operating condition data is applied to the pre-established wind rotor model and generator model, and the aerodynamic torque T wt on the rotor side of the wind turbine generator and the electromagnetic torque T g on the motor side are calculated in real time, respectively.

[0099]

[0100]

[0101] where H wt is the equivalent inertia time constant of the wind wheel, θ wt is the torsion angle of the wind wheel, ω wt is the angular velocity of the wind wheel, D wt is the self-damping coefficient of the wind wheel, T wt is the aerodynamic torque of the wind wheel, T s is the transmission chain torque, H g is the equivalent inertia time constant of the generator, θ g is the torsion angle of the generator, ω g is the angular velocity of the generator, D g is the self-damping coefficient of the generator, T g is the electromagnetic torque of the generator.

[0102] Based on the estimated data of the aerodynamic torque and the electromagnetic torque, the torque T s of the transmission shaft is calculated to obtain real-time information of the wind turbine load.

[0103] The method further comprises:

[0104] The wind wheel torsion angle and angular velocity on the rotor side are estimated using the dynamic equation;

[0105]

[0106] wherein, is an estimated value of the rotor torsion angle, is an estimated value of the rotor angular velocity, is an estimated value of the drive train torque, fal(e, a, d) is a non-linear error function with respect to e, H wt is an equivalent inertia time constant of the rotor, D wt is a rotor self-damping coefficient, b1 and b2 are respectively observer estimated gain coefficients of the rotor torsion angle and angular velocity.

[0107] The generator set torsion angle and angular velocity on the motor side are estimated by using the dynamic equation.

[0108]

[0109] wherein, is an estimated value of the generator set torsion angle, is an estimated value of the generator set angular velocity, is an estimated value of the drive train torque, is an estimated value of the generator electromagnetic torque, fal(e, a, d) is a non-linear error function with respect to e, H g is an equivalent inertia time constant of the generator, D g is a generator self-damping coefficient, b3 and b4 are respectively observer estimated gain coefficients of the generator set torsion angle and angular velocity.

[0110] The method further comprises:

[0111] The drive train load of the simulated wind turbine is estimated based on the principle of extended state observer.

[0112]

[0113] wherein, is an estimated value of the drive train torque, b5 is a drive train torque observer estimated gain coefficient, fal(e, a, d) is a non-linear error function with respect to e.

[0114] The method further comprises designing an observer to estimate the rotor torsion angle and angular velocity on the rotor side by calculating a correction error, and the correction formula is:

[0115]

[0116] wherein, e is an estimation error, 0 wt is a torsion angle of the rotor, is an estimated value of the rotor torsion angle, fal(e, a, d) is a non-linear error function with respect to e, a is an adjustment parameter of the estimation error, and d is a threshold parameter of the estimation error.

[0117] The method further comprises:

[0118] The torsion angle and torsion vibration data of the transmission chain are used to further optimize the operation control strategy of the fan to improve the stability of the fan during fault ride-through.

[0119] Furthermore, a corresponding system is also provided, comprising:

[0120] A fault simulation unit is configured to simulate the change of electromagnetic torque of the generator after the voltage fault;

[0121] An information acquisition unit is configured to acquire internal state information and voltage operation information of the simulated wind turbine generator system;

[0122] A data processing unit is connected to the information acquisition unit and is configured to process the state information and voltage information, and construct a voltage ride-through fault model of the simulated wind turbine generator system based on the information;

[0123] A load solving unit is connected to the data processing unit and is configured to estimate load data of the simulated wind turbine generator system according to the model.

[0124] Embodiment five:

[0125] Based on the project "Key technologies and equipment for full-condition simulation and grid-connected test of large-capacity offshore wind turbine generators", this embodiment proposes a sensorless virtual test method based on a ground test platform, which simulates the wind turbine torque input through a series of double drag motors. This method can make the wind turbine run in different conditions, especially in extreme conditions such as power grid failure, reduce the dependence on sensors through virtual testing, improve testing accuracy and efficiency, and enhance the fault ride-through capability of the unit.

[0126] Specifically, the method comprises the following steps:

[0127] The ground test platform uses a series of double drag motors as driving devices to simulate the driving process of the generator by the wind turbine in actual working conditions. The mechanical input generated by the wind turbine rotor in the wind turbine generator system is simulated on the ground test platform. To avoid confusion, the generator connected to the power grid side is defined as "motor side" in this embodiment, and the series of double drag motors responsible for simulating the wind turbine torque input are defined as "rotor side".

[0128] Obtain internal state information of the simulated wind turbine generator system; the state information includes: equivalent inertia time constant of the wind turbine and the generator, damping coefficient of the transmission shaft, stiffness coefficient of the transmission shaft, wind turbine self-damping, generator self-damping; torque constant of the motor; voltage at the motor end.

[0129] Obtaining initial operating condition information of a simulated wind turbine rotor side; the rotor side operating condition information includes: aerodynamic torque, initial torsion angle of the wind wheel, initial angular velocity of the wind wheel; obtaining initial operating condition information of a simulated wind turbine motor side; the motor side operating condition information includes: initial electromagnetic torque of the generator, initial torsion angle of the generator set, initial angular velocity of the generator set.

[0130] After data processing of the state information, a wind wheel rotor model and a generator model are constructed based on the state information.

[0131] Obtaining voltage operating information of a simulated wind turbine, and constructing a wind turbine voltage ride-through fault model based on the voltage information.

[0132] The voltage ride-through fault model of the simulated wind turbine aims to ensure that the wind turbine can continue to operate stably and gradually recover to the normal working state when the power grid has a voltage fault (such as voltage sag or voltage interruption). Voltage sag in the power grid is usually caused by short-circuit fault or other power grid disturbances, which will cause changes in electromagnetic torque and aerodynamic torque of the wind turbine, and then affect the dynamic response of the unit. In order to ensure that the wind turbine does not shut down immediately when a fault occurs, the unit must be able to withstand voltage fluctuations in the power grid, especially when the voltage is lower than normal, to avoid shutdown of the unit due to voltage fault.

[0133] When the power grid has a voltage sag, the electromagnetic torque and aerodynamic torque of the simulated wind turbine will change accordingly. The electromagnetic torque T g is directly proportional to the voltage V at the motor end, which can usually be represented by the following formula:

[0134]

[0135] Where T g is the electromagnetic torque, k e is the torque constant of the motor, V nom is the rated voltage of the motor, T nom is the electromagnetic torque at rated voltage. When the power grid voltage sags, the electromagnetic torque T g will be reduced in proportion, thereby affecting the output power and stability of the unit. In this process, the control system must adjust the output torque according to the change of voltage to avoid premature disconnection or unstable dynamic response of the unit.

[0136] At the same time, the aerodynamic torque T wt of the wind turbine is mainly determined by the interaction of the speed of the wind wheel and the airflow. Generally, the aerodynamic torque is relatively unaffected by the power grid voltage in the short term, unless the wind speed changes significantly. Therefore, the aerodynamic torque T wtThe grid voltage can be considered to remain relatively constant during the voltage dip. The change in grid voltage mainly affects the electromagnetic torque on the motor side, while the aerodynamic torque and the overall torque of the wind turbine are transmitted through the drive train.

[0137] The total torque T(t) of the wind turbine is a combination of the electromagnetic torque and the aerodynamic torque. The total torque T(t) is affected by the grid voltage V(t), so the effect of voltage on torque can be represented by a correction function f(V(t)). This correction function is usually inversely proportional to the drop in voltage. For example, when the grid voltage V(t) is below a certain threshold V min , the torque will decrease accordingly until the voltage returns to normal. The expression of the total torque is:

[0138] T(t) = k · T wt · f(V(t)) + T g · f(V(t))

[0139] where f(V(t)) represents the function of the effect of voltage change on torque, which gradually decreases as the grid voltage decreases. For example, the following correction function can be used to describe the effect of voltage dip:

[0140]

[0141] where f(V(t)) gradually decreases as the voltage decreases, indicating that when the grid voltage dips, the torque output of the unit gradually decreases to reduce the impact on the unit and the grid, avoiding shutdown or equipment damage.

[0142] The core of the fault ride-through model is to ensure that the wind turbine can adjust the output torque according to the voltage change when the grid voltage changes, so as to ensure the normal operation of the wind turbine. In this way, not only can the fault ride-through capability of the wind turbine under grid fault be improved, but also the dependence on external sensors can be reduced, realizing real-time monitoring and load estimation without sensors, and improving the stability and reliability of the system.

[0143] After converting the working condition information to the low-speed shaft side of the drive train and processing the data in the unit, the relationship between the wind turbine rotor model and the generator model is estimated using the designed two-mass block drive train model to simulate the load data of the wind turbine.

[0144] Optionally, the processed working condition data are respectively applied to the pre-established rotor model and fault ride-through model, and the aerodynamic torque on the rotor side of the wind turbine and the electromagnetic torque on the motor side are respectively calculated in real time; based on the estimated data of the aerodynamic torque and the electromagnetic torque, the drive shaft torque is calculated, which is the real-time information of the wind turbine load, specifically including:

[0145] The processed working condition data is applied to the pre-established rotor model and generator model to determine the wind wheel torsion angle and angular velocity of the rotor side, and the generator set torsion angle and angular velocity of the motor side;

[0146] According to the wind wheel torsion angle and angular velocity of the rotor side and the generator set torsion angle and angular velocity of the motor side, the aerodynamic torque of the rotor side and the electromagnetic torque of the motor side of the wind turbine generator set are calculated by using a dynamic equation;

[0147] Optionally, the wind wheel torsion angle and angular velocity of the rotor side are simulated by using the following formula:

[0148]

[0149] Wherein, H wt is the equivalent inertia time constant of the wind wheel, θ wt is the torsion angle of the wind wheel, ω wt is the angular velocity of the wind wheel, D wt is the self-damping coefficient of the wind wheel, T wt is the aerodynamic torque of the wind wheel, and T s is the transmission chain torque.

[0150] Optionally, the generator set torsion angle and angular velocity of the motor side are simulated by using the following formula:

[0151]

[0152] Wherein, H g is the equivalent inertia time constant of the generator, θ g is the torsion angle of the generator, ω g is the angular velocity of the generator, D g is the self-damping coefficient of the generator, T s is the transmission chain torque, and T g is the electromagnetic torque of the generator.

[0153] Considering the uncertain disturbances in the rotor side, the transmission chain and the motor side of the simulated wind turbine generator, the frequency of using the wind turbine generator data detection device is reduced, and an observer is designed according to the wind turbine generator model.

[0154] Optionally, the estimation error of the observer is designed by using the following formula:

[0155]

[0156] Wherein, e is the estimation error, θ wt is the torsion angle of the wind wheel, is the estimated value of the torsion angle of the wind wheel, fal(e, α, δ) is a nonlinear error function about e, α is an adjustment parameter of the estimation error, and δ is a threshold parameter of the estimation error.

[0157] Optionally, the rotor-side wind turbine torsion angle and angular velocity size are estimated in the following form:

[0158]

[0159] wherein, is the estimated value of the wind turbine torsion angle, is the estimated value of the wind turbine angular velocity, is the estimated value of the transmission chain torque, fal(e, a, d) is a nonlinear error function about e, H wt is the equivalent inertia time constant of the wind turbine, D wt is the self-damping coefficient of the wind turbine, and β1 and β2 are respectively the observer estimation gain coefficients of the wind turbine torsion angle and angular velocity.

[0160] Optionally, the generator set torsion angle and angular velocity size on the motor side are estimated in the following form:

[0161]

[0162] wherein, is the estimated value of the generator set torsion angle, is the estimated value of the generator set angular velocity, is the estimated value of the transmission chain torque, is the estimated value of the generator electromagnetic torque, fal(e, a, d) is a nonlinear error function about e, H g is the equivalent inertia time constant of the generator, D g is the self-damping coefficient of the generator, and β3 and β4 are respectively the observer estimation gain coefficients of the generator set torsion angle and angular velocity.

[0163] According to the aerodynamic torque on the rotor side of the wind turbine and the electromagnetic torque on the motor side, the transmission chain load of the wind turbine is calculated by using the two-mass transmission chain dynamics equation, specifically including:

[0164] According to the aerodynamic torque on the rotor side of the wind turbine and the electromagnetic torque on the motor side, the transmission chain torque of the wind turbine is calculated by using the dynamics equation.

[0165] The transmission chain torque of the wind turbine is estimated in the following form:

[0166]

[0167] wherein, is the estimated value of the transmission chain torque, β5 is the transmission chain torque observer estimation gain coefficient, and fal(e, a, d) is a nonlinear error function about e.

[0168] The transmission chain torsion angle of the simulated wind turbine is estimated in the following form:

[0169]

[0170] wherein, is an estimated value of the transmission chain torsion angle, is an estimated value of the wind wheel torsion angle, is an estimated value of the generator set torsion angle.

[0171] The torsional vibration of the transmission chain of the simulated wind turbine is estimated in the following form:

[0172]

[0173] wherein, is an estimated value of the transmission chain torsional vibration, is an estimated value of the wind wheel angular velocity, is an estimated value of the generator set angular velocity.

[0174] To achieve the above object, the embodiment further provides the following scheme: a wind turbine fault ride-through sensorless virtual test system based on a ground test platform, comprising:

[0175] a fault simulation unit configured to simulate a change in electromagnetic torque of a generator after a voltage fault, simulate fluctuation of the electromagnetic torque with voltage change by acquiring grid voltage information, and provide real-time data of the electromagnetic torque.

[0176] an information acquisition unit configured to acquire internal state information of a simulated wind turbine generator set, wherein the state information comprises equivalent inertia time constants of a wind wheel and a generator, a damping coefficient of a transmission shaft, a stiffness coefficient of the transmission shaft, self-damping of the wind wheel, and self-damping of the generator; acquire initial operating condition information of a rotor side of the simulated wind turbine generator set, wherein the rotor side operating condition information comprises aerodynamic torque, an initial torsion angle of the wind wheel, and an initial angular velocity of the wind wheel; and acquire initial operating condition information of a motor side of the simulated wind turbine generator set, wherein the motor side operating condition information comprises an initial electromagnetic torque of the generator, an initial torsion angle of the generator set, and an initial angular velocity of the generator set.

[0177] a state estimation unit connected with the information acquisition unit and configured to establish a transmission model of the wind turbine generator set based on the internal state information, and design an observer to estimate the rotor side operating condition information and the motor side operating condition information of the simulated wind turbine generator set.

[0178] a load solution unit connected with the state estimation unit and configured to determine a transmission chain torsion angle, a torsion angular velocity, and a torsional vibration of the wind turbine generator based on the transmission chain model according to the rotor side operating condition information and the motor side operating condition information of the simulated wind turbine generator set, so as to simulate load information of the wind turbine generator set.

[0179] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in FIG. 1. Figure 6 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a wind turbine fault ride-through sensorless virtual testing method based on a ground test platform.

[0180] Those skilled in the art can understand that Figure 6 The structure shown in FIG. 1 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0181] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0182] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0183] In an exemplary embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0184] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0185] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0186] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0187] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0188] The principles and implementation modes of the present application are described by applying specific examples in the present application. The above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A sensorless virtual test method for fault ride of wind turbines based on a ground test platform, characterized in that, include: Obtain the operating status information of the simulated wind turbine generator set; Using the sensorless state estimation formula, the target variable information is obtained based on the operating state information; The sensorless state estimation formula is as follows: in, This is the time derivative of the wind turbine's torsion angle, i.e., its dynamic change; is the estimated value of the wind turbine angular velocity; fal(e,α,δ) is the nonlinear error function with respect to E; D is the time derivative of the wind turbine's angular velocity; wt H is the self-damping coefficient of the wind turbine; wt The equivalent inertia time constant of the wind turbine; This is an estimated value for the aerodynamic torque of the wind turbine; This is an estimated value for the torque of the transmission chain; The time derivative of the generator set's torsion angle; This is an estimated value for the angular velocity of the generator set; D is an estimated value of the generator set's angular velocity. g H is the generator self-damping coefficient; g The equivalent inertia time constant of the generator; This is an estimated value for the electromagnetic torque of the generator; β1 represents the time derivative of the transmission chain torque; β2 and β3 represent the observer estimation gain coefficients for the wind turbine's torsional angle and angular velocity, respectively; β4 and β5 represent the observer estimation gain coefficients for the generator set's torsional angle and angular velocity, respectively; and β5 represents the observer estimation gain coefficient for the transmission chain torque. Step signal testing is performed on the dynamic model of the simulated wind turbine generator set, and the rise time t is obtained based on the dynamic characteristics of the simulated wind turbine generator set. r Calculate the bandwidth information of wind turbine generators based on rise time. Therefore, the bandwidth w0 = 3.6w in the sensorless state estimation algorithm is determined. c Based on the bandwidth method, the estimated gain coefficients are: β1 = 5w0. Sensorless virtual test of wind turbine fault ride is performed based on the target variable information.

2. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, Where e is the estimation error, θ wt The angle of twist of the wind turbine. Let fal(e,α,δ) be the estimated value of the wind turbine's torsion angle, α be the adjustment parameter for the estimation error, and δ be the threshold parameter for the estimation error. With the simulated wind turbine generator set at zero input, the noise information n at the wind turbine generator set's torsion angle output port is collected, and the noise standard deviation is calculated. Where n i Let γ be the noise information of the output port of the wind turbine torsion angle of the i-th wind turbine generator set, where γ is the mean of the noise, N is the total number of noise samples, and the threshold parameter for estimation error is δ = 2.1σ. Based on the Lyapunov stability theory, the adjustment parameter α for estimation error is set to 0.

5.

3. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, The process of obtaining target variable information based on the operating state information using the sensorless state estimation formula specifically includes: The formula for obtaining the transmission chain torque at any given time is as follows: in, Let be the initial value of the transmission chain torque, β5 be the gain coefficient for the transmission chain torque observer estimation, and fal(e,α,δ) be the nonlinear error function with respect to e. Let t be the torque of the transmission chain at time t.

4. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, The sensorless virtual test of wind turbine fault ride based on the target variable information specifically includes: Using the sensorless state estimation formula, the target variable information is obtained based on the operating state information; Based on the target variable information, the estimated values ​​of the wind turbine angle and the generator set torsion angle at any given time are obtained, as shown in the following formula: in, This is an estimated value for the torsion angle of the transmission chain. This is an estimated value for the wind turbine's twist angle. This is an estimated value for the torsion angle of the generator set.

5. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, The sensorless virtual test of wind turbine fault ride based on the target variable information specifically includes: Using the sensorless state estimation formula, the target variable information is obtained based on the operating state information; Based on the target variable information, the estimated values ​​of the wind turbine angular velocity and the generator set angular velocity at any given time are obtained. The calculation formula is as follows: in, This is an estimate of the torsional vibration of the transmission chain. This is an estimate of the wind turbine's angular velocity. This is an estimated value for the angular velocity of the generator set.

6. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, After performing the sensorless virtual test of wind turbine fault ride based on the target variable information, the method further includes: When a voltage sag causes the wind turbine to enter fault ride-through mode, the unit control system adjusts the output torque of the wind turbine to maintain stable operation and avoid shutdown. The fault ride-through simulation model is as follows: Among them, T g It is electromagnetic torque, k e V is the torque constant of the motor, and V is the current grid voltage. nom It is the rated voltage of the motor, T nom It is the electromagnetic torque under rated voltage.

7. The sensorless virtual test method for wind turbine fault ride based on a ground test platform according to claim 1, characterized in that, The operating status information specifically includes: rotor-side parameters, transmission chain parameters, and motor-side parameters; The rotor-side parameters include: the wind turbine equivalent inertia time constant, the wind turbine self-damping coefficient, the wind turbine initial torsional angle, the wind turbine initial angular velocity, and the wind turbine aerodynamic torque; The transmission chain parameters include: the damping coefficient and stiffness coefficient of the transmission shaft; The motor-side parameters include: generator equivalent inertia time constant, generator self-damping coefficient, generator initial electromagnetic torque, generator initial torsional angle, generator initial angular velocity, motor torque constant, and motor terminal voltage.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the sensorless virtual test method for wind turbine fault ride based on a ground test platform according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the sensorless virtual test method for wind turbine fault ride based on a ground test platform as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the sensorless virtual test method for wind turbine fault ride based on a ground test platform as described in any one of claims 1-7.

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