Virtual inertia control-containing wind turbine generator set order reduction method oriented to large interference power angle stability

Abstract

The invention provides a large interference power angle stability-oriented virtual inertia control-containing wind turbine generator set order reduction method, and relates to the field of power systems, and the method comprises the steps of introducing virtual inertia control into a wind turbine generator set, and constructing a control model of the virtual inertia control-containing wind turbine generator set; simulating the control model to obtain a dynamic trajectory, sampling an unbalanced point on the dynamic trajectory, constructing a linear model at the unbalanced point, and decoupling a state variable in the linear model to obtain a state space model; calculating a trajectory feature root dominance and a dominant participation factor based on the state space model and the state variable; and dividing a state variable through the trajectory feature root dominance, the dominant participation factor and a preset division rule, and establishing a wind turbine generator set order reduction model containing virtual inertia control based on a division result. According to the method, redundant state variables and complex coupling relations are removed through targeted order reduction processing, so that the complexity of the wind turbine generator set order reduction model containing virtual inertia control is remarkably reduced on the premise that main dynamic characteristics of the model are reserved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method for reducing the order of wind turbine generators with virtual inertia control for stabilizing large disturbance power angles. Background Technology

[0002] With the goals of carbon peaking and carbon neutrality being proposed, building a new power system based on new energy sources is a crucial measure for the power industry to achieve low-carbon transformation. As the penetration rate of new energy sources continues to rise, the overall inertia level of the power system is relatively weakened, severely deteriorating the system's stability characteristics. To ensure the safe and stable operation of the power system, my country requires that wind power connected to the grid possess a certain degree of inertia effect and damping capacity.

[0003] Currently, new power systems can improve system inertia levels through virtual inertial control technology. Compared to traditional wind turbines, which do not respond to system disturbances without additional control and mainly affect the system's dynamic response by altering power flow, wind turbines incorporating virtual inertial control can provide inertial support and directly respond to system power imbalances, thereby changing output power. However, in practical applications, wind turbines are widely distributed within power systems, with varying distances from fault points. Furthermore, the depth and type of faults are diverse, leading to significant differences in the operating conditions of wind turbines during fault occurrences. Moreover, the virtual inertia of wind turbines interacts complexly with various controls in the full-order model of the wind turbine in both space and time. Spatially, the impact of faults on wind turbines at different locations varies, resulting in different responses to their virtual inertial control. Temporally, the dynamic characteristics of the system continuously change at different stages after a fault, altering the interaction between virtual inertial control and other control strategies, exhibiting complex spatiotemporal distribution characteristics.

[0004] This complexity is further exacerbated, especially when large disturbances occur in the power grid. Large disturbances can cause drastic changes in system parameters such as frequency and voltage, and the operating state of wind turbines can also change dramatically. At this time, the interaction between the virtual inertia of the wind turbine and various controls in the full-order model becomes even more difficult to predict, and the inertial response characteristics become more complex and variable, introducing significant uncertainty to the stable operation of the power system. The full-order model of wind turbines that considers virtual inertia also has many problems. Due to the intertwining of different control systems, the model contains numerous variables and complex mathematical relationships, resulting in a high model order and complex structure. This makes the analysis of the transient stability mechanism of wind turbines extremely difficult, and traditional analytical methods are unable to accurately reveal its inherent laws, failing to meet the needs of new power systems for precise analysis and calculation. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a method for reducing the order of wind turbines with virtual inertia control that is stable under large disturbances. By reducing the model complexity according to the different time scales of each control link, a reduced-order model of wind turbines with virtual inertia control suitable for large disturbance scenarios is established, which can be used for simulation calculations of power systems containing wind power.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] Virtual inertia control is introduced into wind turbine units, and a control model for wind turbine units with virtual inertia control is constructed.

[0008] The control model is simulated to obtain a dynamic trajectory. Non-equilibrium points are sampled on the dynamic trajectory, a linear model is constructed at the non-equilibrium points, and the state variables in the linear model are decoupled to obtain a state-space model.

[0009] Based on the state-space model and the state variables, calculate the dominance degree and dominance participation factor of the trajectory eigenvalues;

[0010] The state variables are divided using the dominance degree and dominance participation factor of the trajectory feature root and the preset division rules, and a reduced-order model of the wind turbine with virtual inertia control is established based on the division results.

[0011] In some embodiments, the step of simulating the control model to obtain a dynamic trajectory and sampling non-equilibrium points on the dynamic trajectory includes:

[0012] The control model is simulated, a three-phase short-circuit fault is set, and the dynamic trajectory of the system after the disturbance is generated;

[0013] Non-equilibrium points are sampled on the dynamic trajectory;

[0014] At the non-equilibrium point, the control model is approximated by a nonlinear function to obtain the state matrix and input matrix at the non-equilibrium point.

[0015] In some embodiments, the step of constructing a linear model at a non-equilibrium point and decoupling the state variables in the linear model to obtain a state-space model includes:

[0016] Based on the non-equilibrium point, state matrix, and input matrix, the control model is transformed into a linear model, where the non-equilibrium point is a state vector, and the state vector and state matrix together constitute the state variables.

[0017] The state vector is transformed by the feature matrix corresponding to the state matrix, and decoupled into a new state vector;

[0018] A state-space model is constructed based on the new state vector and the linear model.

[0019] In some embodiments, the step of calculating the dominance degree of the trajectory eigenvalues ​​and the dominance participation factor based on the state space model and the state variables includes:

[0020] The state-space model is transformed to obtain the time-domain solution of the state variables;

[0021] The dominance of the trajectory eigenvalues ​​is calculated in the time-domain solution of the state variables, and the degree of participation of the state variables and the trajectory eigenvalues ​​is used as the dominance participation factor.

[0022] In some embodiments, the step of calculating the dominance of the trajectory eigenvalues ​​in the time-domain solution of the state variables includes:

[0023] Calculate the sub-dominance degrees of several trajectory eigenvalues ​​with respect to the state variables by calculating all state variables;

[0024] Sum all the sub-dominance degrees to get the trajectory feature root dominance degree.

[0025] In some embodiments, the preset partitioning rule is as follows: if the dominant participation factor of a state variable in any trajectory feature root is greater than 0.05, it is considered a slow variable; otherwise, it is considered a fast variable.

[0026] The resulting partition is: a set of fast variables and a set of slow variables.

[0027] In some embodiments, the step of dividing the state variables using the trajectory feature root dominance and dominance participation factor and a preset partitioning rule, and establishing a reduced-order model of the wind turbine with virtual inertia control based on the partitioning results includes:

[0028] Establish fast-state models and slow-state models for the fast variable set and the slow variable set, respectively;

[0029] The fast-state model is transformed, and the steady-state solution is calculated.

[0030] The steady-state solution and the slow-state model are combined to obtain a reduced-order model of the wind turbine with virtual inertia control.

[0031] This invention proposes a reduced-order system for wind turbine generators with virtual inertia control for large disturbance power angle stability, comprising:

[0032] An introduction unit is configured to introduce virtual inertia control into wind turbine units and construct a control model for wind turbine units with virtual inertia control.

[0033] The decoupling unit is configured to simulate the control model to obtain a dynamic trajectory, sample non-equilibrium points on the dynamic trajectory, construct a linear model at the non-equilibrium points, and decouple the state variables in the linear model to obtain a state-space model.

[0034] The dominant unit is configured to calculate the dominance degree of the trajectory eigenvalues ​​and the dominant participation factor based on the state space model and the state variables.

[0035] The construction unit is configured to divide state variables using the dominance degree and dominance participation factor of the trajectory feature root and a preset division rule, and to establish a reduced-order model of the wind turbine with virtual inertia control based on the division results.

[0036] This invention proposes a computer device, comprising:

[0037] At least one processor; and a memory storing a computer program executable on the processor, wherein the processor, when executing the program, performs the steps of the method for reducing the order of a wind turbine with virtual inertia control for stabilizing large disturbance power angles.

[0038] This invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method for reducing the order of a wind turbine generator with virtual inertia control for stabilizing large disturbance power angles.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention proposes a method for order reduction of wind turbines with virtual inertia control for large disturbance power angle stability. The method includes: introducing virtual inertia control into the wind turbine to construct a control model of the wind turbine with virtual inertia control; simulating the control model to obtain a dynamic trajectory, sampling non-equilibrium points on the dynamic trajectory, constructing a linear model at the non-equilibrium points and decoupling the state variables in the linear model to obtain a state-space model; calculating the dominance degree and dominance participation factor of the trajectory eigenvalues ​​based on the state-space model and the state variables; dividing the state variables using the dominance degree and dominance participation factor of the trajectory eigenvalues ​​and a preset partitioning rule, and establishing a wind turbine order reduction model with virtual inertia control based on the partitioning results.

[0041] This invention employs targeted order reduction techniques to remove redundant state variables and complex coupling relationships, thereby significantly reducing the complexity of wind turbine model reduction with virtual inertia control while preserving the main dynamic characteristics of the model. For wind turbines with virtual inertia control, a wind turbine model with virtual inertia control suitable for large disturbance scenarios is established. Considering the impact of virtual inertia control on the dynamic characteristics of the wind turbine, it can accurately simulate key dynamic behaviors of the wind turbine under large disturbances, such as frequency response and power output. The reduced-order model greatly reduces the number of state variables and computational complexity while maintaining a certain level of accuracy. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0043] Figure 1 The flowchart of the method for reducing the order of wind turbine generators with virtual inertia control for stabilizing large disturbance power angles provided by the present invention is shown below.

[0044] Figure 2 This invention provides a module diagram of a wind turbine generator with virtual inertia control for stabilizing large disturbance power angles.

[0045] Figure 3 A schematic diagram of the structure of an embodiment of the computer device provided by the present invention;

[0046] Figure 4 A schematic diagram of an embodiment of the computer-readable storage medium provided by the present invention;

[0047] Figure 5 The grid-connected control logic diagram of a wind turbine with virtual inertia control, which is provided by the present invention for the order reduction method of wind turbine with virtual inertia control for stabilizing large disturbance power angle.

[0048] Figure 6 The diagram shows a wind turbine model with virtual inertia control, which is provided by the present invention for the method of reducing the order of wind turbines with virtual inertia control for stabilizing large disturbance power angles. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application.

[0050] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0051] This invention proposes a method for order reduction of wind turbine generators with virtual inertia control for large disturbance power angle stability. Please refer to [link / reference]. Figure 1 , Figure 5 and Figure 6 ,include:

[0052] S1. Introduce virtual inertia control into wind turbine units and construct a control model for wind turbine units with virtual inertia control.

[0053] S2. Simulate the control model to obtain a dynamic trajectory, sample non-equilibrium points on the dynamic trajectory, construct a linear model at the non-equilibrium points and decouple the state variables in the linear model to obtain a state-space model.

[0054] S3. Calculate the dominance degree and dominance participation factor of the trajectory eigenvalues ​​based on the state space model and the state variables;

[0055] S4. Using the dominance degree and dominance participation factor of the trajectory feature root and the preset partitioning rules, the state variables are partitioned, and a reduced-order model of the wind turbine with virtual inertia control is established based on the partitioning results.

[0056] This invention addresses the issue of wind turbines with inertial support capabilities. Due to the complexity of their control structure, traditional full-order models are unsuitable for power system large disturbance simulation analysis. Therefore, a reduction-order method is proposed. Step 1: Establish the differential-algebraic equations of the wind turbine control logic. Step 2: Generate a dynamic trajectory and sample non-equilibrium points. Calculate the state matrix A and input matrix B using nonlinear function approximation (small disturbance deviation method), construct a linearized model, and decouple it. Step 3: Calculate the dominance degree and dominance participation factor of the trajectory eigenvalues. Step 4: Divide the fast / slow variable sets, minimize the model order while satisfying accuracy requirements, and establish a reduced-order model for the wind turbine.

[0057] Step 1: Establish the differential algebraic equations for the wind turbine control logic.

[0058] Grid-connected control logic of wind turbines with virtual inertia control, as follows Figure 5 As shown, virtual inertia control, by introducing a delay element, can slow down the frequency response speed and improve the inertia effect of the system. Its transfer function is expressed as:

[0059]

[0060] In the formula: K1 and ω1 are the control coefficient and control parameter of virtual inertia control, respectively.

[0061] The phase synchronization method employs droop control based on the active power-frequency droop characteristic, where ω0 is the frequency reference value, and the output active power reference value P0 is compared with the actual value P. g The difference is the active power deviation, and the power deviation is related to the droop coefficient D. p The product of is the frequency adjustment term, and the mathematical model of droop control is shown in equation (2).

[0062] ω g =ω0+(P g -P0) / D p (2)

[0063] Virtual inertia control couples the change in DC bus voltage with the change in grid angular frequency Δω, enabling the DC bus voltage to respond to the frequency changes of the system and providing transient energy support for the system.

[0064] The output x8 of the virtual inertia control and the reference value of the DC bus voltage Together, they serve as the setpoint for the outer loop of the DC voltage control, and are related to the actual value V of the DC bus voltage. dc The difference is calculated, and after passing through the PI controller output, it is used as the reference value for the inner loop of the q-axis current control. Compared with the actual value of q-axis current I gq Together, they achieve closed-loop current control. The difference between the reactive power output of the wind turbine and the reference value is calculated, and after passing through the PI controller, the reference value of the d-axis component of the grid-side current is obtained. With respect to the actual value of d-axis current I gd Together, they achieve closed-loop current control.

[0065] according to Figure 5 V, taking into account virtual inertia control dc and the reference value of the q-axis current control inner loop They can be written as:

[0066]

[0067] When a large disturbance fault occurs in the system, the active power of the wind turbine generator is difficult to deliver, and the voltage V on the DC bus of the high-voltage side of the wind turbine generator decreases. dc Because the unbalanced power accumulation will suddenly increase, at which point the system frequency ω g It will decrease. As can be seen from equations (3)-(4), virtual inertia control provides transient inertia support to the system by releasing the energy of the DC bus capacitor on the high-voltage side, thereby causing V to decrease. dc decline.

[0068] The state model of a wind turbine grid-connected system considering virtual inertia control can be mathematically represented by the following system of differential-algebraic equations:

[0069]

[0070] In the formula, f(X, U) and g(X, U) are the state equation and output equation of the wind turbine considering virtual inertia control, respectively; X is the corresponding state vector, with a total of 14 state variables, namely I gd I gq V dc I sd I sq ω s and x 1-8 , where I gd I gq These are the d-axis and q-axis components of the grid-side current, U. dc For DC side voltage, I sd I sq These are the d-axis and q-axis components of the stator current, respectively, ω s x is the rotor angular velocity. 1-8 U is an intermediate variable introduced into the PI link of the converter control system, where U is the voltage vector at the grid connection point of the wind turbine.

[0071] This invention employs targeted order reduction techniques to remove redundant state variables and complex coupling relationships, thereby significantly reducing the complexity of wind turbine model reduction with virtual inertia control while preserving the main dynamic characteristics of the model. For wind turbines with virtual inertia control, a wind turbine model with virtual inertia control suitable for large disturbance scenarios is established. Considering the impact of virtual inertia control on the dynamic characteristics of the wind turbine, it can accurately simulate key dynamic behaviors of the wind turbine under large disturbances, such as frequency response and power output. The reduced-order model greatly reduces the number of state variables and computational complexity while maintaining a certain level of accuracy.

[0072] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The step of simulating the control model to obtain a dynamic trajectory and sampling non-equilibrium points on the dynamic trajectory includes:

[0073] The control model is simulated, a three-phase short-circuit fault is set, and the dynamic trajectory of the system after the disturbance is generated;

[0074] Non-equilibrium points are sampled on the dynamic trajectory;

[0075] At the non-equilibrium point, the control model is approximated by a nonlinear function to obtain the state matrix and input matrix at the non-equilibrium point.

[0076] Step 2: Generate dynamic trajectories and sample non-equilibrium points. Calculate the state matrix A and input matrix B using nonlinear function approximation (small perturbation deviation method), construct a linearized model, and decouple the components.

[0077] Simulations were performed on the full-order model obtained in step 1, assuming a three-phase short-circuit fault, generating the system's state trajectory after disturbance, and selecting the unbalanced point. The system is in equilibrium when no disturbance is applied. For a period after the large disturbance disappears, the system is in an unbalanced operating state. The wind turbine state vector (unbalanced point) corresponding to the instant the fault disappears is selected as the first linearization point, and subsequent linearization points are defined using t = t... c For each time interval, z non-equilibrium points are sampled on the dynamic trajectory of the wind turbine as linearization points. According to Equation (6), the full-order model of the wind turbine considering virtual inertia control is approximated by a nonlinear function at the non-equilibrium points to obtain the n×n-order state matrix and the n×p-order input matrix [A,B] at the non-equilibrium points.

[0078]

[0079] In the formula, XΔ i-m and UΔ i-m These are the wind turbine units at the unbalance point X. i The small perturbation deviation between the m-th component of the state vector and the input vector.

[0080] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The steps of constructing a linear model at non-equilibrium points and decoupling the state variables in the linear model to obtain a state-space model include:

[0081] Based on the non-equilibrium point, state matrix, and input matrix, the control model is transformed into a linear model, where the non-equilibrium point is a state vector, and the state vector and state matrix together constitute the state variables.

[0082] The state vector is transformed by the feature matrix corresponding to the state matrix, and decoupled into a new state vector;

[0083] A state-space model is constructed based on the new state vector and the linear model.

[0084] The linear model of the full-order wind turbine model considering virtual inertia control at non-equilibrium points is shown below:

[0085]

[0086] As shown in (7), the rate of change of each state variable is a linear combination of all state variables and inputs. Due to the cross-coupling between state variables and inputs, it is difficult to directly isolate the state variables that dominate the dynamic behavior of the system. To eliminate the mutual coupling between state variables, the state vector ΔX is transformed into a new state vector ΔZ, resulting in:

[0087] ΔX=VΔZ (8)

[0088] In the formula: V is the characteristic matrix of the state matrix A at the non-equilibrium point.

[0089] After replacing the state variables, we obtain the new system state-space equations (diagonal canonical form), that is, the state-space model is:

[0090]

[0091] In the formula: Λ and B Λ As shown in equations (10)-(11) respectively.

[0092] Λ=V -1 AV = diag(λ1Lλ) k Lλ n (10)

[0093] B Λ =V -1 B (11)

[0094] Equation (10) represents n decoupled first-order equations, which shows that the state variables in the full-order model of the wind turbine are decoupled, that is, the transformed state variable ΔZ i The dynamic response is determined solely by the corresponding trajectory feature roots λ. i The decision was made.

[0095] By simulating a three-phase short-circuit fault in the control model, the dynamic trajectory of the system after disturbance is generated. Three-phase short-circuit faults are one of the most severe and common fault types in power systems. This simulation can highly reproduce the extreme disturbances that the system may encounter in actual operation. The dynamic trajectory obtained based on the three-phase short-circuit fault simulation includes rapid frequency changes, large voltage fluctuations, and instantaneous power imbalances.

[0096] The power system itself is a highly nonlinear and complex system. Under normal operation, the system is in a relatively stable equilibrium state. However, when subjected to fault disturbances, the system rapidly deviates from the equilibrium point and enters a complex nonlinear dynamic process. The dynamic characteristics of the system at the non-equilibrium point are fundamentally different from those at the equilibrium point, containing more crucial information about the system's nonlinearity. By extensively sampling non-equilibrium points along the dynamic trajectory, it is possible to comprehensively capture the system's behavioral characteristics under different nonlinear states.

[0097] Nonlinear function approximations are performed on the control model at nonequilibrium points to obtain the state and input matrices. These matrices describe the dynamic characteristics of the system, reflecting the coupling relationships between internal states and the influence of external inputs on the system state, respectively. Accurately obtaining these two matrices at nonequilibrium points clarifies how the system state changes over time and how external inputs affect these changes. By sampling nonequilibrium points on the dynamic trajectory and performing local nonlinear function approximations, computational complexity is reduced. Focusing only on key nonequilibrium points on the dynamic trajectory, rather than performing a comprehensive analysis of the entire system state space, significantly reduces the amount of data and computation required.

[0098] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The steps of calculating the dominance degree of the trajectory eigenvalues ​​and the dominance participation factor based on the state-space model and the state variables include:

[0099] The state-space model is transformed to obtain the time-domain solution of the state variables;

[0100] The dominance of the trajectory eigenvalues ​​is calculated in the time-domain solution of the state variables, and the degree of participation of the state variables and the trajectory eigenvalues ​​is used as the dominance participation factor.

[0101] Step 3: Calculate the dominance degree of the trajectory eigenvalues, and then obtain the dominant participation factor.

[0102] Expanding each differential equation in equation (9) and performing a Laplace transform on each equation, we can obtain the s-domain expression shown in equation (16).

[0103]

[0104] In the formula: i = 1:n; j = 1:n, b Λij It is matrix B Λ The element in the i-th row and j-th column, v ji It is the element in the j-th row and i-th column of the characteristic matrix V. Further expansion of equations (12)-(13) yields...

[0105]

[0106] The time-domain solution of the state variables is obtained by inverse Laplace transformation of equation (15).

[0107]

[0108] The right eigenvector ψ of the characteristic matrix V i and the left eigenvector φ i TCombined, we define the participation factor p. ki =φ ki ψ ki λ represents the i-th trajectory feature root. i With the k-th state variable ΔX k The degree of mutual participation. Traditional eigenvalue dominance only considers the influence of eigenvalues ​​on system output, severing the connection between eigenvalue dominance and state variables. If the trajectory eigenvalue λ i Corresponding dominance L i It is a relatively large value, and the relationship between each state variable and the trajectory characteristic root λ i The participation factor might be a small value, which could lead to an incorrect result in the subsequent partitioning of the fast and slow variable sets of the wind turbine. Therefore, the trajectory characteristic root λ can be... i The dominance and participation factors are coupled to strengthen the correlation between trajectory eigenvalues ​​and state variables. λ i The corresponding participation factor and dominance degree can be combined to obtain:

[0109] γ ki =φ ki ψ ki ×L i (19)

[0110] γ ki The participation degree of the k-th state variable and the i-th trajectory eigenvalue of the wind turbine, taking into account the dominance of the trajectory eigenvalues, is defined as the dominance participation factor. This is achieved by calculating all γ values. i The trajectory feature roots λ can be obtained. i The degree of participation among all state variables. The higher the participation, the stronger the correlation between them. By defining the dominant participation factor, the inverse mapping from the eigenvalues ​​of the state trajectory to the state variables is completed, realizing the quantitative analysis of the transient behavior of each state variable on the system. This provides an intuitive basis for the multi-timescale partitioning of wind turbine state variables that takes into account virtual inertia control.

[0111] Transforming the state-space model to obtain the time-domain solution of the state variables allows us to present the dynamic behavior of the system state in an intuitive time function form. This demonstrates the values ​​and trends of each state variable at different times.

[0112] Trajectory eigenvalues ​​are parameters describing the dynamic characteristics of a system, while the dominance of these eigenvalues ​​quantifies the contribution of each eigenvalue to the dynamic changes in the system's state variables. By calculating dominance, it is possible to accurately identify which trajectory eigenvalues ​​play a dominant role in the system's dynamic processes. In practical power systems, multiple oscillation modes may exist, some with a significant impact on system stability, while others have a relatively smaller impact. The calculation of trajectory eigenvalue dominance distinguishes between these oscillation modes of varying importance.

[0113] In power systems incorporating wind power, the state variables of different wind turbines and other parts of the power grid are intertwined, collectively influencing the overall dynamic response of the system. By using dominant participation factors, it can be determined which state variables play a key role in driving system oscillations or instability under a specific fault scenario, and which have a relatively smaller impact. Dominant participation factors are used to assess the degree of coupling between different parts of the system, providing efficient strategies for power system structural optimization and coordinated control.

[0114] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The step of calculating the dominance of the trajectory eigenvalues ​​in the time-domain solution of the state variables includes:

[0115] Calculate the sub-dominance degrees of several trajectory eigenvalues ​​with respect to the state variables by calculating all state variables;

[0116] Sum all the sub-dominance degrees to get the trajectory feature root dominance degree.

[0117] In equation (16), l i-jh The term means that in v ji b Λih Under the influence of the action, the trajectory feature root λ i For state variable X j The dominant role of λ is therefore defined as the sub-dominance degree. i It usually appears in the form of a complex number, so the formula for calculating the dominance degree of the child in the complex form shown in equation (17) is given.

[0118]

[0119] Trajectory eigenvalues ​​affect every state variable in the full-order model of the wind turbine. The trajectory eigenvalue λ... i λ can be calculated by summing the child dominance degrees of all state variables ΔX. i The dominance of ΔX is shown in Equation (18).

[0120]

[0121] Each trajectory eigenvalue represents a dynamic pattern of the system, and the sub-dominance degree indicates the degree to which the trajectory eigenvalue contributes to the dynamic changes of a specific state variable. The sub-dominance degree for each state variable is calculated separately to identify which state variables are more susceptible to the influence of a specific trajectory eigenvalue. All sub-dominance degrees are then summed to obtain the dominance degree.

[0122] While sub-dominance reflects the influence of trajectory eigenvalues ​​on individual state variables, each sub-dominance cannot fully assess the importance of that trajectory eigenvalue in the overall system dynamics. Dominance is obtained by summing all sub-dominances. The trajectory eigenvalues ​​determine the main oscillation modes and stability characteristics of the system.

[0123] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The preset division rule is as follows: if the dominant participation factor of a state variable in any trajectory feature root is greater than 0.05, it is considered a slow variable; otherwise, it is considered a fast variable.

[0124] The resulting partition is: a set of fast variables and a set of slow variables.

[0125] Iterate through the dominant participating factors of each trajectory feature root, if the wind turbine state variable ΔX k γ corresponding to any trajectory feature root ki If the value is greater than 0.05, it is considered a slow variable; otherwise, it is considered a fast variable, thus completing the division between fast and slow variables.

[0126] When the dominant participation factor is greater than 0.05, it indicates that the state variable makes a significant contribution to the dynamic process dominated by the corresponding trajectory eigenvalues. Its rate of change is relatively slow, and it has an important influence on the long-term dynamic behavior of the system. Therefore, it is classified as a slow variable. Conversely, state variables with a dominant participation factor less than or equal to 0.05 change rapidly in the system's dynamic process and play a dominant role in the system's short-term dynamic response. They are classified as fast variables.

[0127] In some embodiments, please refer to Figure 1 , Figure 5 and Figure 6 The steps of dividing the state variables using the trajectory feature root dominance and dominance participation factor and a preset partitioning rule, and establishing a reduced-order model of the wind turbine with virtual inertia control based on the partitioning results, include:

[0128] Establish fast-state models and slow-state models for the fast variable set and the slow variable set, respectively;

[0129] The fast-state model is transformed, and the steady-state solution is calculated.

[0130] The steady-state solution and the slow-state model are combined to obtain a reduced-order model of the wind turbine with virtual inertia control.

[0131] Step 4: Divide the fast / slow variable sets, minimize the model order while satisfying the accuracy requirements, and establish the wind turbine model.

[0132] The following criteria should be followed when partitioning the state variables of wind turbines with virtual inertia control across multiple time scales:

[0133] 1) In order to maintain the stability of the wind turbine model, all trajectory feature roots must be less than zero.

[0134] 3) On the basis of meeting the above requirements, the order of the wind turbine model should be reduced as much as possible.

[0135] After completing the partitioning of the fast and slow variable sets of the wind turbine, the state equation sets corresponding to the slow variable set Xslow and the fast variable set Xfast are also divided into two parts. The state equations related to the slow variables are retained, and the state equations corresponding to the fast variables are rewritten in singular perturbation form:

[0136]

[0137] In the formula, ε is the singular perturbation parameter matrix that reflects the characteristics of the system.

[0138] Let ε = 0, the system of state equations (21) degenerates from differential equations into steady-state equations, and then we obtain X. fast Steady-state solutions for each state variable:

[0139] X fast =h(X) slow ,U) (22)

[0140] Substituting equation (22) into equation (20) yields a reduced-order model of a wind turbine with virtual inertia control suitable for large disturbance analysis:

[0141]

[0142] The steady-state solution of the state model represents the system's state after reaching stability during a rapid dynamic process. Combining it with a slow-state model considers the system's rapid dynamic response characteristics while retaining key information from the slow dynamic process. This allows for an accurate description of the system's main dynamic characteristics while effectively ignoring secondary rapid dynamic details, avoiding model distortion due to oversimplification. In wind turbines with virtual inertia control, this model can accurately capture the dynamic changes of the system under virtual inertia control, providing reliable model support for the stable operation and efficient control of wind turbines.

[0143] This invention proposes a reduced-order system for wind turbine generators with virtual inertia control for stabilizing large disturbance power angles. Please refer to [link / reference]. Figure 2 ,include:

[0144] Unit 100 is configured to introduce virtual inertia control in wind turbine units and construct a control model for wind turbine units with virtual inertia control.

[0145] The decoupling unit 200 is configured to simulate the control model to obtain a dynamic trajectory, sample non-equilibrium points on the dynamic trajectory, construct a linear model at the non-equilibrium points, and decouple the state variables in the linear model to obtain a state-space model.

[0146] The dominant unit 300 is configured to calculate the dominance degree of the trajectory eigenvalues ​​and the dominant participation factor based on the state space model and the state variables.

[0147] The construction unit 400 is configured to divide state variables using the dominance degree and dominance participation factor of the trajectory feature root and a preset division rule, and to establish a reduced-order model of the wind turbine with virtual inertia control based on the division results.

[0148] This invention addresses the problems existing in the prior art by providing a method for reducing the order of wind turbines with virtual inertia control that is stable under large disturbances. By reducing the model complexity according to the different time scales of each control link, a reduced-order model of wind turbines with virtual inertia control suitable for large disturbance scenarios is established, which can be used for simulation calculations of power systems containing wind power.

[0149] In some embodiments, please refer to Figure 6 A simulation model of the wind power grid-connected system was built and connected to a single-unit infinite bus system. The simulation parameters are shown in Table 1.

[0150] Table 1 System Simulation Parameters

[0151]

[0152]

[0153] A large disturbance is applied at the grid connection point, with the following settings: the total simulation time is set to 0.8 s. When t = 0.2 s, a three-phase ground fault occurs at the wind power grid connection point of the test system, with a fault duration of 0.2 s. The state trajectory of the nonlinear response of the full-order model of the wind turbine, considering virtual inertia control, is obtained and characterized using the output current. To balance computational efficiency and fully extract the system's multiple nonlinear information, the first sampling point is at the fault clearing time, with a preset sampling time interval t. c =0.05s, number of sampling points z=6, sampling points are denoted as X s (s = 1, K, 6).

[0154] After sampling the unbalanced points of the dynamic trajectory, the wind turbine with virtual inertia control was modeled according to the nonlinear dynamic modeling process. The maximum values ​​of the dominant participation factors of each state variable at the fault clearing time are shown in Table 2.

[0155] Table 2. Maximum values ​​of the dominant participation factors for each state variable.

[0156] State variables Maximum value of dominant participation factor State variables Maximum value of dominant participation factor Igd 0.0741 Isq 0 Igq 0.2433 ωs 0 x4 0.8440 Vdc 1.3008 x6 0.0154 x1 0 x7 0.0532 x2 0 x5 3.7014 x3 0 Isd 0 x8 1.2589

[0157] Based on the maximum value of the dominant participation factor of each state variable, the fast and slow variable set partitioning scheme of the wind turbine model taking into account virtual inertia control is shown in Table 3.

[0158] Table 3 Results of Fast and Slow Variable Classification

[0159]

[0160] After reducing the order of the full-order wind turbine model considering virtual inertia control according to singular perturbation theory, the reduced-order model neglects rotor dynamics, the turbine-side converter and its control system, and retains the intermediate capacitor. The grid-side converter control structure only neglects the q-axis inner loop control; other control strategies, especially virtual inertia control, are retained. The grid-side converter control block diagram of the reduced-order wind turbine model considering virtual inertia control is shown below. Figure 6 As shown.

[0161] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 3 As shown, an embodiment of the present invention also provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, it performs the steps of the method described above.

[0162] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 4 As shown, embodiments of the present invention also provide a computer-readable storage medium 40, which stores a computer program 410 that, when executed by a processor, performs the methods described above.

[0163] Embodiments of the present invention may also include a corresponding computer device. The computer device includes a memory, at least one processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes any of the methods described above when executing the program.

[0164] The memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules in the embodiments of this application. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in the memory, thereby implementing the above-described method.

[0165] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the device. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0166] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium for the program can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The above computer program embodiments can achieve the same or similar effects as any of the corresponding foregoing method embodiments.

[0167] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0168] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. The sequence numbers of the disclosed embodiments of this invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0169] It should be understood that, as used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, “and / or” refers to any and all possible combinations of one or more of the associated listed items.

[0170] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A reduced order method for wind turbine generator with virtual inertia control for large disturbance power angle stability, characterized in that, The method comprises the following steps: introducing virtual inertia control into a wind turbine to build a control model of the wind turbine with virtual inertia control; simulating the control model to obtain a dynamic trajectory, sampling non-equilibrium points on the dynamic trajectory, building a linear model at the non-equilibrium points and decoupling state variables in the linear model to obtain a state space model; calculating trajectory eigenvalue dominance and dominant participation factors based on the state space model and the state variables; dividing the state variables by using the trajectory eigenvalue dominance and the dominant participation factors and a preset division rule, and establishing a reduced-order model of the wind turbine with virtual inertia control based on the division result.

2. The reduced order method for wind turbine with virtual inertia control for large disturbance power angle stability according to claim 1, wherein, The step of simulating the control model to obtain a dynamic trajectory and sampling non-equilibrium points on the dynamic trajectory comprises the following steps: simulating the control model, setting a three-phase short-circuit fault, and generating a dynamic trajectory of the system after being disturbed; sampling non-equilibrium points on the dynamic trajectory; performing non-linear function approximation calculation on the control model at the non-equilibrium points to obtain a state matrix and an input matrix of the non-equilibrium points.

3. The reduced order method of wind turbine with virtual inertia control for large disturbance power angle stability according to claim 1, wherein, The step of building a linear model at the non-equilibrium points and decoupling state variables in the linear model to obtain a state space model comprises the following steps: transforming the control model into a linear model based on the non-equilibrium points, the state matrix and the input matrix, wherein the non-equilibrium points are state vectors, and the state vectors and the state matrix together constitute state variables; transforming the state vectors by using a characteristic matrix corresponding to the state matrix to decouple the state vectors into new state vectors; building a state space model based on the new state vectors and the linear model.

4. The reduced order method of wind turbine with virtual inertia control for large disturbance power angle stability according to claim 3, characterized in that, The step of calculating trajectory eigenvalue dominance and dominant participation factors based on the state space model and the state variables comprises the following steps: transforming the state space model to obtain a time-domain solution of the state variables; calculating the trajectory eigenvalue dominance of the state variables in the time-domain solution, and taking the participation degree of the state variables and the trajectory eigenvalues as the dominant participation factors.

5. The reduced order method of wind turbine with virtual inertia control for large disturbance power angle stability according to claim 4, wherein, The step of calculating the trajectory eigenvalue dominance of the state variables in the time-domain solution comprises the following steps: calculating all the state variables to obtain sub-dominance of a plurality of trajectory eigenvalues with respect to the state variables; summing all the sub-dominance to obtain the trajectory eigenvalue dominance.

6. The reduced order method for wind turbine with virtual inertia control for large disturbance power angle stability according to claim 1, wherein, The preset division rule is that, in response to the dominant participation factor of the state variables in any trajectory eigenvalue being greater than 0.05, the state variables are regarded as slow variables, otherwise, the state variables are regarded as fast variables; the division result is a fast variable set and a slow variable set.

7. The reduced order method of wind turbine with virtual inertia control for large disturbance power angle stability according to claim 1, characterized in that, The step of dividing the state variables by using the trajectory eigenvalue dominance and the dominant participation factors and the preset division rule, and establishing a reduced-order model of the wind turbine with virtual inertia control based on the division result comprises the following steps: establishing a fast state model and a slow state model for the fast variable set and the slow variable set, respectively; transforming the fast state model to calculate a steady-state solution; combining the steady-state solution and the slow state model to obtain the reduced-order model of the wind turbine with virtual inertia control.

8. A reduced order system of a wind turbine with virtual inertia control for large disturbance power angle stability, characterized in that, The method comprises the following steps: introducing a unit configured to introduce virtual inertia control into a wind turbine to build a control model of the wind turbine with virtual inertia control; The decoupling unit is configured to simulate the control model to obtain a dynamic trajectory, sample a non-equilibrium point on the dynamic trajectory, construct a linear model at the non-equilibrium point, and decouple state variables in the linear model to obtain a state space model; The dominant unit is configured to calculate a trajectory eigenvalue dominant degree and a dominant participation factor based on the state space model and the state variables; The construction unit is configured to divide the state variables by using the trajectory eigenvalue dominant degree and the dominant participation factor and a preset division rule, and establish a wind turbine generator set reduced-order model containing virtual inertia control based on a division result. 9.A computer device, comprising: at least one processor; and a memory, the memory storing a computer program executable on the processor, characterized in that the processor executes the program to execute the steps of the wind turbine generator set reduced-order method containing virtual inertia control for large disturbance power angle stability according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to execute the steps of the wind turbine generator set reduced-order method containing virtual inertia control for large disturbance power angle stability according to any one of claims 1 to 7.

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