A method for suppressing sub / super synchronous oscillation of a direct-drive wind power grid-connected system based on state feedback control

Abstract

The application discloses a kind of based on state feedback control's direct-drive wind power grid-connected system sub / super synchronous oscillation suppression method, current direct-drive wind power converter's mainstream control mode is solved, sub / super synchronous oscillation instability is easily induced by machine network dynamic interaction, leading to the technical problem that the closed-loop stability of direct-drive wind power grid-connected system is poor.Method includes obtaining direct-drive wind power grid-connected system dynamic characteristic parameter, constructs original state space equation;Error state variable is introduced to original state space equation and controlled object reference input vector, and augmented state space equation is obtained;Based on augmented state space equation design full state feedback control law and deduce, obtain closed-loop system dynamic equation;According to the formula of dissipation theory, construct closed-loop system energy dissipation deviation constraint condition, equivalent conversion into linear matrix inequality;Concave optimization problem is constructed and solved, combined with full state feedback control law to obtain sub super synchronous oscillation suppression instruction.

Description

Technical Field

[0001] This invention relates to the field of power system subsynchronous oscillation suppression technology, and in particular to a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control. Background Technology

[0002] With the fossil fuel crisis and environmental problems becoming increasingly prominent, wind power, as a core form of clean and renewable energy, has become an important direction for global energy transformation. Direct-drive permanent magnet synchronous wind turbines are widely used in wind power projects due to their advantages such as simple structure, high operating efficiency, and excellent low voltage ride-through performance.

[0003] With the large-scale grid connection of direct-drive wind power, the grid strength of high-proportion renewable energy power systems continues to decrease. In weak grid scenarios, the dynamic interaction between direct-drive wind power converters and the grid is significantly enhanced, which can easily induce subsynchronous / supersynchronous oscillations. This problem has become the core bottleneck restricting the safe and stable operation of high-proportion renewable energy power systems.

[0004] Traditional PI (Proportional-Integral) control strategy is the mainstream control method for direct-drive wind power converters. Based on the single-input single-output frequency domain design concept, it can achieve stable control in strong grid scenarios. However, in weak grid access scenarios, it has inherent defects such as insufficient suppression capability of sub / supersynchronous frequency band oscillations and poor robustness. It is prone to sub / supersynchronous oscillation instability induced by dynamic interaction between the generator and the grid, resulting in poor closed-loop stability of the direct-drive wind power grid-connected system. Summary of the Invention

[0005] This invention provides a method for suppressing subsynchronous / supersynchronous oscillations in direct-drive wind power grid-connected systems based on state feedback control. This method solves the technical problem that the mainstream control method of current direct-drive wind power converters is prone to subsynchronous / supersynchronous oscillation instability induced by dynamic interaction between the generator and the grid, resulting in poor closed-loop stability of the direct-drive wind power grid-connected system.

[0006] The first aspect of this invention provides a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control, comprising:

[0007] Obtain the dynamic characteristic parameters of the direct-drive wind power grid-connected system, and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system;

[0008] By introducing error state variables into the original state-space equations and the reference input vector of the controlled object, an augmented state-space equation is obtained.

[0009] Based on the augmented state-space equations, a full-state feedback control law is designed and derived to obtain the dynamic equations of the closed-loop system.

[0010] Based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index, the energy dissipation deviation constraint condition of the closed-loop system is constructed.

[0011] By performing an equivalent transformation on the energy dissipation deviation constraint of the closed-loop system and the augmented state space equation, a linear matrix inequality is obtained.

[0012] A convex optimization problem is constructed based on the linear matrix inequality, and a preset convex optimization numerical solver is used to solve the convex optimization problem. The sub-supersynchronous oscillation suppression command is obtained by combining the full-state feedback control law.

[0013] Optionally, the dynamic characteristic parameters of the direct-drive wind power grid-connected system include DC bus voltage, dq-axis current, and inverter-side output voltage; the step of constructing the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system includes:

[0014] The original state-space equations are constructed by taking the square of the DC bus voltage and the dq-axis current as the original state variables, the inverter-side output voltage as the control input vector, and the DC bus voltage and dq-axis current as the system output vectors.

[0015] Optionally, the step of introducing error state variables into the original state-space equation and the reference input vector of the controlled object to obtain the augmented state-space equation includes:

[0016] The tracking error is calculated based on the output vector of the original state-space equation and the reference input vector of the controlled object.

[0017] Based on the tracking error, determine the error state variables;

[0018] The original state variables in the original state-space equation are combined with the error state variables to obtain the augmented state variable vector;

[0019] Based on the augmented state variable vector, the system matrix of the original state space equation, and the reference input vector of the controlled object, the augmented system matrix is ​​derived.

[0020] An augmented state space equation is constructed based on the augmented state variable vector, the control input vector, the system output vector, and the augmented system matrix.

[0021] Optionally, the step of designing and deriving a full-state feedback control law based on the augmented state-space equations to obtain the dynamic equations of the closed-loop system includes:

[0022] Based on the augmented state variable vector of the augmented state space equation, the structure of the full-state feedback control law is determined;

[0023] By introducing the state feedback gain matrix to be solved into the full-state feedback control law structure, the full-state feedback control law is obtained.

[0024] Substituting the full-state feedback control law into the augmented state-space equations, eliminating the control input vector, and rearranging the equations, we obtain the closed-loop system state matrix.

[0025] Based on the closed-loop system state matrix and the augmented state variable vector, the dynamic equations of the closed-loop system are constructed.

[0026] Optionally, the step of constructing the energy dissipation deviation constraint condition of the closed-loop system based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index includes:

[0027] Substituting the preset positive definite symmetric matrix into the dissipation theory formula yields the energy storage function;

[0028] Substitute the dynamic equation of the closed-loop system into the energy storage function to derive the expression for the rate of energy change.

[0029] Based on the energy change rate expression and the preset energy dissipation deviation index, a dissipation deviation inequality is constructed.

[0030] By combining the energy storage function and the dissipation deviation inequality, a closed-loop system energy dissipation deviation constraint condition is constructed.

[0031] Optionally, the equivalent transformation of the energy dissipation deviation constraints of the closed-loop system and the augmented state-space equations to obtain linear matrix inequalities includes:

[0032] Extract the state matrix, input matrix, and output matrix from the augmented state-space equation;

[0033] Substitute the state matrix, the input matrix, and the output matrix into the energy dissipation deviation constraint of the closed-loop system, and perform algebraic simplification to obtain the simplified constraint expression;

[0034] Based on the rules of matrix inequality transformation, the simplified constraint expression is reconstructed into standard matrix form to obtain a linear matrix inequality.

[0035] Optionally, the step of constructing a convex optimization problem based on the linear matrix inequality, solving the convex optimization problem using a preset convex optimization numerical solver, and obtaining a sub-supersynchronous oscillation suppression command by combining the full-state feedback control law includes:

[0036] With minimizing the energy dissipation deviation index as the optimization objective, a convex optimization problem is constructed by combining the aforementioned linear matrix inequalities.

[0037] The preset convex optimization numerical solver is invoked to numerically solve the convex optimization problem, and the optimal state feedback gain matrix is ​​obtained.

[0038] Obtain the real-time operating state variables of the direct-drive wind power grid-connected system, substitute the optimal state feedback gain matrix into the full-state feedback control law, and calculate the subsynchronous oscillation suppression command in combination with the real-time operating state variables of the direct-drive wind power grid-connected system.

[0039] The second aspect of this invention provides a subsynchronous / supersynchronous oscillation suppression system for a direct-drive wind power grid-connected system based on state feedback control, comprising:

[0040] The acquisition module is used to acquire the dynamic characteristic parameters of the direct-drive wind power grid-connected system and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system.

[0041] An introduction module is used to introduce error state variables into the original state space equation and the reference input vector of the controlled object to obtain the augmented state space equation.

[0042] The derivation module is used to design and derive the full-state feedback control law based on the augmented state-space equations, thereby obtaining the dynamic equations of the closed-loop system.

[0043] The module is used to construct the energy dissipation deviation constraint conditions of the closed-loop system based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index.

[0044] The transformation module is used to perform an equivalent transformation on the energy dissipation deviation constraint of the closed-loop system and the augmented state space equation to obtain a linear matrix inequality.

[0045] The solution module is used to construct a convex optimization problem based on the linear matrix inequality, solve the convex optimization problem using a preset convex optimization numerical solver, and obtain a sub-supersynchronous oscillation suppression command by combining the full-state feedback control law.

[0046] The third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the above-described method for suppressing sub / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control.

[0047] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control as described above.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] The above-mentioned technical solution of the present invention provides a method for suppressing sub- / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control. The method involves obtaining the dynamic characteristic parameters of the direct-drive wind power grid-connected system and constructing the original state-space equation based on these parameters. An error state variable is introduced into the original state-space equation and the reference input vector of the controlled object to obtain an augmented state-space equation. Based on the augmented state-space equation, a full-state feedback control law is designed and derived to obtain the closed-loop system dynamic equation. Based on the dissipation theory formula, the closed-loop system dynamic equation, a preset positive definite symmetric matrix, and a preset energy dissipation deviation index, a closed-loop system energy dissipation deviation constraint is constructed. The closed-loop system energy dissipation deviation constraint and the augmented state-space equation are equivalently transformed to obtain a linear matrix inequality. A convex optimization problem is constructed based on the linear matrix inequality, and a preset convex optimization numerical solver is used to solve the convex optimization problem. Combined with the full-state feedback control law, a sub- / supersynchronous oscillation suppression command is obtained. Based on the above solution, the present invention constructs the original state-space equation by obtaining the system's dynamic characteristic parameters and then introduces an error state variable into the reference input vector of the controlled object to obtain an augmented state-space equation. An augmented state-space equation is constructed using the difference state variables. Based on this equation, a full-state feedback control law is designed, and the dynamic equation of the closed-loop system is derived, allowing for flexible configuration of the closed-loop poles. Energy dissipation deviation constraints are constructed by combining the closed-loop system dynamic equation, a preset positive definite symmetric matrix, and a preset energy dissipation deviation index. This establishes a system stability criterion from the physical essence of energy dissipation. State feedback control of the grid-side converter is designed based on dissipation theory, breaking through the strict constraints of traditional dissipation control. The energy dissipation deviation constraints and the augmented state-space equation are equivalently transformed into linear matrix inequalities. A convex optimization problem is then constructed using these inequalities and solved by a preset convex optimization numerical solver. This eliminates the need for trial-and-error parameter adjustments based on engineering experience, directly yielding globally optimal control parameters and ensuring precise adaptation of control parameters to system dynamics. Finally, a sub-supersynchronous oscillation suppression command is generated using the full-state feedback control law. This command can be directly applied to the direct-drive wind power grid-connected system, precisely suppressing sub / supersynchronous oscillations induced by dynamic interaction between the generator and the grid, thus ensuring the closed-loop stability of the system from the physical essence of energy dissipation. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1This is a flowchart illustrating the steps of a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control, as provided in Embodiment 1 of the present invention.

[0052] Figure 2 This is a schematic diagram of the simulation waveform of the DC voltage positive step system provided in Embodiment 1 of the present invention;

[0053] Figure 3 This is a schematic diagram of the simulation waveform of the DC voltage negative step system provided in Embodiment 1 of the present invention;

[0054] Figure 4 This is a structural block diagram of a subsynchronous / supersynchronous oscillation suppression system for a direct-drive wind power grid-connected system based on state feedback control, provided in Embodiment 2 of the present invention. Detailed Implementation

[0055] This invention provides a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control. This method solves the technical problem that the mainstream control method of current direct-drive wind power converters is prone to subsynchronous / supersynchronous oscillation instability induced by dynamic interaction between the generator and the grid, resulting in poor closed-loop stability of the direct-drive wind power grid-connected system.

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of relevant departments, and in compliance with relevant laws, regulations, and standards. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0057] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control, as provided in Embodiment 1 of the present invention.

[0058] This invention provides a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control, comprising:

[0059] Step 101: Obtain the dynamic characteristic parameters of the direct-drive wind power grid-connected system, and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system.

[0060] Dynamic characteristic parameters of direct-drive wind power grid-connected systems refer to key parameters that reflect the changing patterns, interrelationships, and dynamic response characteristics of various physical quantities during the operation of the direct-drive wind power grid-connected system. These parameters include a set of physical parameters that can quantify the dynamic behavior of the system, such as DC bus voltage, dq-axis current (including d-axis current and q-axis current), and inverter-side output voltage.

[0061] It should be noted that after obtaining the dynamic characteristic parameters of the direct-drive wind power grid-connected system, the original state-space equations are constructed based on them to complete the basic modeling of the system's dynamic behavior, providing core model support for subsequent control design.

[0062] Furthermore, step 101 can be achieved by performing the following steps:

[0063] The original state-space equations are constructed by taking the square of the DC bus voltage and the dq-axis current as the original state variables, the inverter-side output voltage as the control input vector, and the DC bus voltage and dq-axis current as the system output vectors.

[0064] It should be noted that this invention targets a multi-input multi-output direct-drive wind power grid-connected system, defining the DC bus voltage and dq current as control targets; selecting the square of the DC bus voltage and the dq-axis current, which characterize the system's dynamic characteristics, as the original state variables; and defining the inverter-side output voltage as the system control input u, and the DC bus voltage and dq current as the output y; constructing the system's original state-space equations in the form:

[0065] (1)

[0066] In the formula, x o Let u be the original state variable vector, y be the control input vector, and y be the control input vector. o Let A be the system output vector. o B o C o It is a system matrix with appropriate dimensions.

[0067] In this implementation, the sum of the squares of the DC bus voltage and the dq-axis current (direct-quad-axis current) are selected as the original state variables. The inverter-side output voltage is defined as the control input vector, and the DC bus voltage and dq-axis current are defined as the system output vector. Based on the dynamic coupling relationship between the variables, the original state-space equations are constructed simultaneously. This can accurately characterize the internal dynamics of the system and the correlation between input and output, making up for the shortcomings of traditional control modeling in not fully characterizing the dynamic features of the system. This provides reliable variable modeling support for subsequent suppression of subsynchronous / supersynchronous oscillations induced by machine-grid interaction.

[0068] Step 102: Introduce error state variables into the original state-space equations and the reference input vector of the controlled object to obtain the augmented state-space equations.

[0069] The controlled object reference input vector refers to the set of target state variables that are pre-set to guide the stable operation of the direct-drive wind power grid-connected system. It is a reference signal that needs to be accurately tracked during the system control process, specifically including preset ideal operating parameters such as DC bus voltage reference value, d-axis current reference value, and q-axis current reference value.

[0070] Error state variables refer to variables obtained by subtracting the corresponding components of the system's actual output from the reference input vector of the controlled object. These variables are used to characterize the dynamic changes in the system's tracking deviation and can reflect the degree of deviation between the system's actual operating state and the target state. Specifically, they include deviation variables such as DC bus voltage tracking error, d-axis current tracking error, and q-axis current tracking error.

[0071] It should be noted that by combining the system dynamic relationship represented by the original state-space equations with the target benchmark set by the reference input vector of the controlled object, error state variables are introduced to incorporate the system tracking deviation dynamics. The augmented state-space equations are derived by fusing the original state and the error state, thus completing the joint modeling of the system's original dynamics and error dynamics.

[0072] Furthermore, step 102 may include the following sub-steps:

[0073] S21. Calculate the tracking error based on the output vector of the original state-space equation and the reference input vector of the controlled object;

[0074] S22. Determine the error state variables based on the tracking error;

[0075] S23. Combine the original state variables and error state variables in the original state space equation to obtain the augmented state variable vector;

[0076] S24. Derive the augmented system matrix based on the augmented state variable vector, the system matrix of the original state space equation, and the reference input vector of the controlled object;

[0077] S25. Construct the augmented state space equation based on the augmented state variable vector, control input vector, system output vector, and augmented system matrix.

[0078] It should be noted that the error state variable is defined. Specifically, the square of the DC bus voltage and the q-axis current are defined as error state variables. :

[0079] (2)

[0080] Where r is the reference input vector of the controlled object; The first component of the error state variable is the tracking error variable of the square of the DC bus voltage, which characterizes the dynamic change of the deviation between the actual value and the reference value of the square of the DC bus voltage over time. The second component of the error state variable is the tracking error variable of the q-axis current, which characterizes the dynamic change of the deviation between the actual value and the reference value of the q-axis current over time. Let be the reference value of the square of the DC bus voltage in the reference input vector r of the controlled object, be the target state variable of the square of the DC bus voltage set in advance, and be the error state variable. The tracking benchmark; The reference value of the q-axis current in the reference input vector r of the controlled object is the pre-set target state variable of the q-axis current, and the error state variable is the reference value of the reference input vector r of the controlled object. The tracking benchmark; The square of the DC bus voltage of the direct-drive wind power grid-connected system is one of the original state variables selected when constructing the original state-space equations. It is also the actual output value of the system corresponding to the error state variable and is used to characterize the dynamic operating state of the DC side voltage of the system. The grid-side q-axis current (i.e., q-axis current) of the direct-drive wind power grid-connected system is one of the original state variables selected when constructing the original state-space equations. It is also the actual output value of the system corresponding to the error state variable and is used to characterize the dynamic operating state of the AC side current of the system.

[0081] Furthermore, the original state variables and the error state variables are combined to form an augmented state variable vector. :

[0082] (3)

[0083] in, The original state variable vector is the set of state variables selected when constructing the original state space equations. It includes variables such as the square of the DC bus voltage and the dq-axis current, and is used to characterize the basic dynamic operating state of the direct-drive wind power grid-connected system.

[0084] Furthermore, based on the original state equation and the error variable equation, the augmented state-space model is derived:

[0085] (4)

[0086] in, Let x be the derivative of the augmented state variable vector with respect to time, representing the dynamic rate of change of the augmented state variable over time, and reflecting the dynamic evolution trend of the original state and error state of the direct-drive wind power grid-connected system; A, B, C, and E are the augmented system matrices jointly determined by the original system matrix and the error state variables.

[0087] In this embodiment, the system tracking error is first calculated by subtracting the actual operating state variables of the system output from the original state-space equation from the target value of the controlled object's reference input vector. Then, the corresponding error state variables are determined based on this tracking error. Subsequently, the original state variables and error state variables in the original state-space equation are combined to construct an augmented state variable vector that simultaneously contains the system's basic dynamics and tracking error dynamics. Next, by combining the augmented state variable vector, the system matrix of the original state-space equation, and the controlled object's reference input vector, the augmented system matrix that can characterize the coupling relationship between the augmented state, control input, and reference signal is derived. Finally, based on the augmented state variable vector, control input vector, system output vector, and augmented system matrix, the complete augmented state-space equation is constructed simultaneously, achieving a complete characterization of the system's dynamic characteristics across all dimensions. This provides a model foundation for subsequent control design that takes into account both the original dynamics and error dynamics.

[0088] Step 103: Based on the augmented state-space equations, design and derive the full-state feedback control law to obtain the dynamic equations of the closed-loop system.

[0089] It should be noted that, based on the constructed augmented state space equations, the full-state feedback control design method is used to configure feedback gains for the augmented state variables, and the corresponding control laws are formed. After these laws are substituted into the augmented state space equations for derivation, the control input terms are eliminated, and the closed-loop system dynamic equations that can characterize the closed-loop operation dynamics of the system are obtained.

[0090] Furthermore, step 103 may include the following sub-steps:

[0091] S31. Determine the structure of the full-state feedback control law based on the augmented state variable vector of the augmented state space equation;

[0092] S32. Introduce the state feedback gain matrix to be solved into the full-state feedback control law structure to obtain the full-state feedback control law.

[0093] S33. Substitute the full-state feedback control law into the augmented state-space equation, eliminate the control input vector, and rearrange the equation to obtain the closed-loop system state matrix.

[0094] S34. Based on the closed-loop system state matrix and the augmented state variable vector, construct the dynamic equations of the closed-loop system.

[0095] It should be noted that the state feedback control law has the following form:

[0096] (5)

[0097] Where K is the state feedback gain matrix to be designed.

[0098] The dynamic equations of the closed-loop system are:

[0099] (6)

[0100] In the formula, The system state matrix after introducing state feedback.

[0101] Specifically, firstly, based on the augmented state variable vector of the augmented state space equation, a full-state feedback control law structure with the augmented state variables as all feedback information is defined. This structure establishes a linear mapping relationship between the augmented state variables and the control input. Then, the state feedback gain matrix to be solved is introduced into this control law structure and combined with the augmented state variable vector to obtain a full-state feedback control law that can dynamically generate control inputs based on the current system state. Next, the obtained full-state feedback control law is substituted into the augmented state space equation, the control input vector is eliminated, and the matrix terms are merged and rearranged to obtain a closed-loop system state matrix that can characterize the coupling relationship between closed-loop states. Finally, based on the closed-loop system state matrix and the augmented state variable vector, a closed-loop system dynamic equation that can completely describe the closed-loop operation dynamics of the system is constructed, achieving accurate modeling of the system behavior under the action of the control law.

[0102] Step 104: Based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index, construct the energy dissipation deviation constraint conditions of the closed-loop system.

[0103] The dissipative theory formula refers to the mathematical expression describing the stability of a system based on its energy storage and dissipation characteristics. It characterizes the dynamic behavior of the system from an energy perspective and provides a theoretical basis for the construction of system stability criteria. Its core consists of three parts: energy storage function, supply rate function, and dissipation inequality. The energy storage function represents the energy level accumulated inside the system, the supply rate function describes the rate at which external input injects energy into the system, and the dissipation inequality defines the non-negative constraint relationship that the system's energy dissipation must satisfy. It is the core theoretical framework for subsequently constructing the energy dissipation deviation constraint conditions of a closed-loop system.

[0104] The pre-defined positive definite symmetric matrix refers to a pre-defined symmetric positive definite matrix used to construct the system's energy storage function. It is a key parameter in dissipative theory that characterizes the system's energy level. It must satisfy the mathematical requirements of positive definiteness (all eigenvalues ​​are greater than zero) and symmetry (the transpose of the matrix is ​​equal to itself). In this scheme, the matrix constructs a quadratic energy storage function using the augmented state variable vector as variables. Its symmetric positive definite property ensures the non-negativity of the energy function, providing a reliable mathematical basis for the quantitative characterization of the system's energy level.

[0105] The preset energy dissipation deviation index refers to a pre-set non-negative threshold parameter used to limit the degree to which the system's energy dissipation rate deviates from the strict dissipation characteristics. It can characterize the maximum deviation range that the system is allowed to deviate from the strict dissipation conditions. In this scheme, this index is used to construct the dissipation deviation inequality. By controlling the deviation of the system's energy dissipation rate from the ideal dissipation characteristics through its value, it provides a clear boundary benchmark for the closed-loop system's energy dissipation deviation constraint conditions, taking into account both system stability requirements and the flexibility of control design.

[0106] It should be noted that the dynamic equations of the closed-loop system are substituted into the dissipation theory formula, the system energy storage function is constructed with a preset positive definite symmetric matrix, and a preset energy dissipation deviation index is introduced to constrain the system energy dissipation rate. The energy dissipation deviation constraint conditions of the closed-loop system are obtained through simultaneous derivation.

[0107] Furthermore, step 104 may include the following sub-steps:

[0108] S41. Substitute the pre-defined positive definite symmetric matrix into the dissipative theory formula to obtain the energy storage function;

[0109] S42. Substitute the dynamic equation of the closed-loop system into the energy storage function to derive the expression for the rate of energy change.

[0110] S43. Construct the dissipation deviation inequality based on the energy change rate expression and the preset energy dissipation deviation index;

[0111] S44. By combining the energy storage function and the dissipation deviation inequality, construct the energy dissipation deviation constraint condition for the closed-loop system.

[0112] It should be noted that the system energy function (i.e., energy storage function) is defined as follows:

[0113] (7)

[0114] In the formula, P is a positive definite symmetric matrix.

[0115] Furthermore, this invention derives and establishes a closed-loop system that satisfies the energy dissipation deviation constraint condition. The energy dissipation deviation constraint condition is that, for a certain constant ϵ>0, the following inequality holds:

[0116] (8)

[0117] The system is said to satisfy the input energy dissipation deviation condition, where ϵ quantifies the energy dissipation deviation index and the degree to which the system deviates from strict dissipation.

[0118] Wherein, V is the system energy function (i.e., energy storage function), which is a quadratic function constructed based on the augmented state variable vector and the positive definite symmetric matrix. It is used to characterize the energy level accumulated inside the direct-drive wind power grid-connected system and is the core indicator for quantifying the system energy state in dissipation theory. The derivative of the system energy function V with respect to time represents the dynamic rate of change of energy in a direct-drive wind power grid-connected system over time. It reflects the rate of energy dissipation or accumulation in the system and is a key variable for analyzing the energy dissipation characteristics of the system.

[0119] In this embodiment, a pre-defined positive definite symmetric matrix is ​​first substituted into the dissipation theory formula, and an energy storage function in quadratic form is constructed using the augmented state variable vector as variables to achieve a quantitative representation of the accumulated energy within the system. Then, the dynamic equation of the closed-loop system is substituted into the energy storage function, differentiated, and rearranged to derive an expression for the energy change rate reflecting the dynamic change of system energy over time. Next, based on the energy change rate expression and the pre-defined energy dissipation deviation index, a dissipation deviation inequality is established to limit the degree of deviation of the system's energy dissipation rate. Finally, the energy storage function and the dissipation deviation inequality are combined to construct the energy dissipation deviation constraint condition of the closed-loop system. This constraint condition establishes a system stability criterion from the physical essence of energy dissipation, breaking through the strict constraints of traditional dissipation control and providing a reliable constraint basis for the subsequent optimization of control parameters.

[0120] Step 105: Perform an equivalent transformation on the energy dissipation deviation constraints and augmented state space equations of the closed-loop system to obtain linear matrix inequalities.

[0121] It should be noted that by linearizing the quadratic energy terms in the energy dissipation deviation constraints of the closed-loop system, and by combining the matrix form of the augmented state space equation with variable substitution and matrix rearrangement, the nonlinear energy constraint relationship is equivalently transformed into a linear matrix inequality with matrix variables as the core, thus obtaining a standardized constraint form suitable for convex optimization solutions.

[0122] Furthermore, step 105 may include the following sub-steps:

[0123] S51. Extract the state matrix, input matrix, and output matrix from the augmented state-space equation;

[0124] S52. Substitute the state matrix, input matrix and output matrix into the energy dissipation deviation constraint of the closed-loop system, perform algebraic simplification, and obtain the simplified constraint expression.

[0125] S53. Based on the rules of matrix inequality transformation, the simplified constraint expression is reconstructed into standard matrix form to obtain the linear matrix inequality.

[0126] It should be noted that by substituting the state equation and output equation, the energy dissipation deviation constraint is transformed into an equivalent linear matrix inequality form that is easy to solve numerically:

[0127] (9)

[0128] in, The closed-loop system state matrix is ​​obtained by substituting the full-state feedback control law into the augmented state-space equation. It is determined by the original augmented state matrix and the state feedback gain matrix, and is used to describe the dynamic coupling relationship of the augmented state variables in the closed-loop system. I is the identity matrix. The matrix inequality M≤0 constitutes the core constraint for the design of the state feedback gain K and parameter ϵ.

[0129] Specifically, firstly, the state matrix describing the dynamic coupling relationship between augmented state variables, the input matrix representing the driving effect of control input on the state, and the output matrix reflecting the state-to-output mapping relationship are extracted from the augmented state-space equations. Then, these three types of matrices are substituted into the energy dissipation deviation constraint of the closed-loop system. Combining the derivation relationship between the system energy storage function and the rate of energy change, the quadratic terms and matrix product terms in the constraints are algebraically simplified to eliminate the dynamic derivative terms of the state variables, resulting in a simplified constraint expression without state derivatives. Next, based on rules such as the Schur complement transformation of matrix inequalities, the simplified constraint expression is reconstructed into a standard matrix inequality in block matrix form, completing the equivalent transformation of linear matrix inequalities. This process transforms the nonlinear energy constraint into a linear matrix inequality form that can be solved with convex optimization, providing standardized constraint conditions for the numerical solution of the state feedback gain matrix. It eliminates the need for complex trial-and-error methods and can efficiently solve for control parameters that meet the energy dissipation deviation requirements, providing a reliable optimization basis for subsequently generating control commands to suppress subsynchronous / supersynchronous oscillations caused by machine-network interaction.

[0130] Step 106: Construct a convex optimization problem based on linear matrix inequalities, and solve the convex optimization problem using a preset convex optimization numerical solver. Combine this with the full-state feedback control law to obtain the sub-supersynchronous oscillation suppression command.

[0131] It should be noted that, with linear matrix inequalities as constraints, a convex optimization problem is constructed with the state feedback gain matrix and a preset positive definite symmetric matrix as optimization variables. Then, the preset convex optimization numerical solver is used to solve the convex optimization problem to obtain the optimal state feedback gain matrix that satisfies the energy dissipation deviation constraint. This matrix is ​​then substituted into the full-state feedback control law to generate a sub-supersynchronous oscillation suppression command that can be adjusted in real time for the direct-drive wind power grid-connected system.

[0132] Furthermore, step 106 may include the following sub-steps:

[0133] S61. With minimizing the energy dissipation deviation index as the optimization objective, a convex optimization problem is constructed by combining linear matrix inequalities.

[0134] S62. Call the preset convex optimization numerical solver to numerically solve the convex optimization problem and obtain the optimal state feedback gain matrix;

[0135] S63. Obtain the real-time operating state variables of the direct-drive wind power grid-connected system, substitute the optimal state feedback gain matrix into the full-state feedback control law, and calculate the sub-supersynchronous oscillation suppression command in combination with the real-time operating state variables of the direct-drive wind power grid-connected system.

[0136] It should be noted that the optimization objective is to minimize the energy dissipation deviation index φ, so that the system can obtain the best possible dynamic performance and robustness; then, positive definite matrix constraints are established:

[0137] (10)

[0138] Furthermore, energy dissipation deviation inequality constraints are established:

[0139] (11)

[0140] Establish stability constraints for the closed-loop system:

[0141] (12)

[0142] Furthermore, the standard convex optimization problem is constructed as follows:

[0143] (13)

[0144] The above convex optimization problem is solved efficiently by calling a convex optimization numerical solver; the specific elements of the state feedback gain matrix K are extracted from the solution. The obtained specific elements of the feedback gain K are substituted into the state feedback control law to output a subsynchronous oscillation suppression command, thereby achieving subsynchronous / supersynchronous oscillation suppression and stable operation of the system.

[0145] Specifically, the optimization objective is to minimize the energy dissipation deviation index φ to obtain the best possible dynamic performance and robustness of the system. A convex optimization problem is constructed using the aforementioned linear matrix inequalities. The constraints of this problem include: positive definiteness constraints that the pre-defined positive definite symmetric matrix P must satisfy. Linear matrix inequality constraints derived from energy dissipation deviation conditions and the state matrix of the closed-loop system Stability constraints to be satisfied This process ultimately forms a standard convex optimization problem. Subsequently, a preset convex optimization numerical solver is called to efficiently solve the convex optimization problem. The specific elements of the state feedback gain matrix are extracted from the solution results. Then, the real-time operating state variables of the direct-drive wind power grid-connected system are obtained. The optimal state feedback gain matrix is ​​substituted into the full-state feedback control law. Combined with the real-time operating state variables of the system, the sub-supersynchronous oscillation suppression command is calculated to achieve the suppression and stable operation control of the system's sub / supersynchronous oscillations.

[0146] For example, this invention builds a simulation model of a direct-drive wind power grid-connected system on the Matlab / Simulink platform. The system short-circuit ratio is 2.5, and it operates in a weak grid scenario. Two control schemes are set up for comparative analysis: Scheme 1 is the traditional PI dual closed-loop control, and Scheme 2 is the state feedback control based on the energy dissipation deviation index proposed in this invention, referred to as energy dissipation control.

[0147] In implementation, the system proposed in this invention first establishes an augmented state-space equation based on system parameters through an offline optimization design module, sets optimization objectives and constraints, and obtains the state feedback gain matrix K using a convex optimization solver. Then, the state variable acquisition module in the online control module collects state variables such as grid-side current, voltage, and DC voltage in real time. The control law calculation module calculates the control signal based on K, which is then applied to the grid-side converter via the control command output module to achieve subsynchronous / supersynchronous oscillation suppression. The main system parameters are shown in Table 1.

[0148] Table 1 Parameters of Direct-Drive Wind Turbine Grid-Connected Model

[0149]

[0150] like Figures 2-3As shown, this invention sets up two sets of experiments: a positive DC voltage step (rising from 1200V to 1600V) and a negative DC voltage step (falling from 1200V to 800V). Simulation results show that under a positive step, the voltage overshoot of traditional PI control is approximately 9.2%, accompanied by small oscillations during the response; the voltage step response curve under the energy dissipation control proposed in this invention is smooth with no overshoot and a shorter settling time. Under a negative step, the voltage of traditional PI control drops to a minimum of approximately 670V, with an overshoot of 15%; while the voltage response curve under the control of this invention decreases smoothly without significant oscillations, demonstrating superior transient regulation capability and damping characteristics.

[0151] For comparison of technical effects, existing technologies can be referenced. Traditional PI control is currently the mainstream control method for direct-drive wind power converters. Based on a single-input, single-output frequency domain design, it can achieve stable control in strong grid scenarios. However, in weak grid scenarios, it has inherent defects such as insufficient suppression of sub- / supersynchronous frequency band oscillations and poor robustness, making it prone to sub- / supersynchronous oscillation instability induced by dynamic interaction between the generator and the grid. State feedback control can flexibly configure the closed-loop poles of the system while taking into account the system's dynamic response and disturbance rejection performance. Compared with PI control, it has better stability control potential. However, its traditional design method requires solving multiple sets of feedback gains step by step, which is cumbersome. Parameter design relies on engineering experience, making it difficult to achieve global optimization and limiting its engineering applications.

[0152] Dissipation theory, starting from the physical nature of energy dissipation, provides theoretical support for system stability analysis and control design. However, traditional strict dissipation control has strict constraints, and most practical physical systems and power electronic devices in engineering cannot meet the strict dissipation requirements, making it unsuitable for the multivariable, strongly coupled, and nonlinear characteristics of direct-drive wind power grid-connected systems. Analysis methods based on energy dissipation deviation indices overcome the constraints of strict dissipation. By quantifying the degree of energy dissipation deviation and establishing stability criteria, stability constraints can be transformed into linear matrix inequalities to achieve parameter convex optimization, providing a new theoretical path for the stable control of direct-drive wind power grid-connected systems. However, there is currently no mature technical solution for applying this theory to the suppression of subsynchronous / supersynchronous oscillations in direct-drive wind power grid-connected systems.

[0153] To address the above problems, this invention provides a method for suppressing subsynchronous / supersynchronous oscillations in direct-drive wind power grid-connected systems based on state feedback control, specifically including the following steps:

[0154] Step 1: Based on the general dynamic characteristics of the controlled direct-drive wind power grid-connected system, determine the control objective and select state variables to establish the original state-space equations of the system.

[0155] Step 2: Introduce error state variables to form augmented state-space equations.

[0156] Step 3: Design the full-state feedback control law, substitute the control law into the augmented state-space equation, and obtain the dynamic equation of the closed-loop system containing the state feedback gain matrix to be designed.

[0157] Step 4: Define the system energy storage function, derive and establish the closed-loop system to satisfy the energy dissipation deviation constraint.

[0158] Step 5: Transform the stability constraints into the standard form of linear matrix inequalities.

[0159] Step 6: Determine the optimization objective, and construct a complete set of matrix inequality constraints by combining closed-loop stability constraints and positive definite matrix constraints.

[0160] Step 7: Based on the optimization objective and the set of inequality constraints, construct a standard convex optimization problem, solve it using a convex optimization numerical solver, and obtain the specific elements of the globally optimal state feedback gain matrix K.

[0161] Step 8: Substitute the specific elements of the feedback gain K obtained in Step 7 into the state feedback control law, collect the system operating status in real time and output control commands to achieve system sub / supersynchronous oscillation suppression and stable operation.

[0162] Overall, this invention transforms the energy dissipation deviation condition into a linear matrix inequality constraint, and completes the optimal design of the controller through convex optimization, thereby ensuring system stability from the energy theory level and achieving synergistic optimization of damping characteristics and dynamic performance.

[0163] As can be seen from the above, the advantages of the present invention include:

[0164] 1. This invention designs a state feedback control for grid-side converters based on dissipation theory, which breaks through the strict constraints of traditional dissipation control and solves the inherent defects of traditional PI control in weak grid scenarios, such as insufficient suppression of subsynchronous / supersynchronous oscillations and poor robustness. It ensures the closed-loop stability of the system from the physical essence of energy dissipation.

[0165] 2. This invention transforms the input energy dissipation deviation condition into a linear matrix inequality constraint, and directly obtains the globally optimal state feedback gain matrix through convex optimization. This avoids the cumbersome process of solving multiple sets of feedback gains step by step in traditional state feedback control, and eliminates the need to rely on engineering experience for parameter trial and error, thus greatly improving the controller design efficiency and engineering practicality.

[0166] 3. This invention aims to minimize the deviation of energy dissipation index, enabling precise control of the system. While ensuring system stability, it also takes into account dynamic response performance and anti-disturbance capability. Under typical operating conditions such as DC voltage step and wind speed fluctuation, it can significantly reduce system overshoot and transient impact, rapidly attenuate sub / supersynchronous oscillation components, and the damping effect is far superior to traditional PI control.

[0167] In this embodiment of the invention, a method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control is provided. The method involves obtaining dynamic characteristic parameters of the direct-drive wind power grid-connected system and constructing an original state-space equation based on these parameters. An error state variable is introduced into the original state-space equation and the reference input vector of the controlled object to obtain an augmented state-space equation. Based on the augmented state-space equation, a full-state feedback control law is designed and derived to obtain the closed-loop system dynamic equation. Energy dissipation deviation constraints are constructed for the closed-loop system based on dissipation theory formulas, the closed-loop system dynamic equation, a preset positive definite symmetric matrix, and a preset energy dissipation deviation index. An equivalent transformation is performed on the closed-loop system energy dissipation deviation constraints and the augmented state-space equation to obtain a linear matrix inequality. A convex optimization problem is constructed based on the linear matrix inequality and solved using a preset convex optimization numerical solver. The subsynchronous / supersynchronous oscillation suppression command is obtained by combining the full-state feedback control law. Based on the above scheme, this invention constructs an original state-space equation by obtaining the system's dynamic characteristic parameters and then introduces... An augmented state-space equation is constructed using error state variables. Based on this equation, a full-state feedback control law is designed, and the dynamic equation of the closed-loop system is derived, allowing for flexible configuration of the system's closed-loop poles. Energy dissipation deviation constraints are constructed by combining the closed-loop system dynamic equation, a preset positive definite symmetric matrix, and a preset energy dissipation deviation index. This establishes a system stability criterion from the physical essence of energy dissipation. State feedback control of the grid-side converter is designed based on dissipation theory, breaking through the strict constraints of traditional dissipation control. The energy dissipation deviation constraints and the augmented state-space equation are equivalently transformed into linear matrix inequalities. A convex optimization problem is then constructed using these inequalities and solved by a preset convex optimization numerical solver. This eliminates the need for trial-and-error parameter adjustments based on engineering experience, directly yielding globally optimal control parameters and ensuring precise adaptation of control parameters to system dynamics. Finally, a sub-supersynchronous oscillation suppression command is generated using the full-state feedback control law. This command can be directly applied to direct-drive wind power grid-connected systems, precisely suppressing sub / supersynchronous oscillations induced by dynamic interaction between the generator and the grid, thus ensuring the closed-loop stability of the system from the physical essence of energy dissipation.

[0168] Please see Figure 4 , Figure 4 This is a structural block diagram of a subsynchronous / supersynchronous oscillation suppression system for a direct-drive wind power grid-connected system based on state feedback control, provided in Embodiment 2 of the present invention.

[0169] This invention provides a subsynchronous / supersynchronous oscillation suppression system for direct-drive wind power grid-connected systems based on state feedback control, comprising:

[0170] The acquisition module 401 is used to acquire the dynamic characteristic parameters of the direct-drive wind power grid-connected system and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system.

[0171] Module 402 is introduced to introduce error state variables into the original state space equation and the reference input vector of the controlled object to obtain the augmented state space equation.

[0172] Derivation module 403 is used to design and derive the full-state feedback control law based on the augmented state-space equations, and obtain the dynamic equations of the closed-loop system.

[0173] Module 404 is used to construct energy dissipation deviation constraints of the closed-loop system based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index.

[0174] The transformation module 405 is used to perform equivalent transformations on the energy dissipation deviation constraints and augmented state space equations of the closed-loop system to obtain linear matrix inequalities.

[0175] The solver module 406 is used to construct convex optimization problems based on linear matrix inequalities, solve convex optimization problems using a preset convex optimization numerical solver, and obtain sub-hypersynchronous oscillation suppression commands by combining the full-state feedback control law.

[0176] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0177] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the subsynchronous / supersynchronous oscillation suppression method for direct-drive wind power grid-connected systems based on state feedback control as described in the above embodiments.

[0178] This invention also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the steps of the subsynchronous / supersynchronous oscillation suppression method for direct-drive wind power grid-connected systems based on state feedback control as described in the above embodiments.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0180] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0182] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0183] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control, characterized in that, include: Obtain the dynamic characteristic parameters of the direct-drive wind power grid-connected system, and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system; By introducing error state variables into the original state-space equations and the reference input vector of the controlled object, an augmented state-space equation is obtained. Based on the augmented state-space equations, a full-state feedback control law is designed and derived to obtain the dynamic equations of the closed-loop system. Based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index, the energy dissipation deviation constraint condition of the closed-loop system is constructed. By performing an equivalent transformation on the energy dissipation deviation constraint of the closed-loop system and the augmented state space equation, a linear matrix inequality is obtained. A convex optimization problem is constructed based on the linear matrix inequality, and a preset convex optimization numerical solver is used to solve the convex optimization problem. The sub-supersynchronous oscillation suppression command is obtained by combining the full-state feedback control law.

2. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, The dynamic characteristic parameters of the direct-drive wind power grid-connected system include DC bus voltage, dq-axis current, and inverter-side output voltage; the construction of the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system includes: The original state-space equations are constructed by taking the square of the DC bus voltage and the dq-axis current as the original state variables, the inverter-side output voltage as the control input vector, and the DC bus voltage and dq-axis current as the system output vectors.

3. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, The process of introducing error state variables into the original state-space equations and the reference input vector of the controlled object to obtain the augmented state-space equations includes: The tracking error is calculated based on the output vector of the original state-space equation and the reference input vector of the controlled object. Based on the tracking error, determine the error state variables; The original state variables in the original state-space equation are combined with the error state variables to obtain the augmented state variable vector; Based on the augmented state variable vector, the system matrix of the original state space equation, and the reference input vector of the controlled object, the augmented system matrix is ​​derived. An augmented state space equation is constructed based on the augmented state variable vector, the control input vector, the system output vector, and the augmented system matrix.

4. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, Based on the augmented state-space equations, the full-state feedback control law is designed and derived to obtain the dynamic equations of the closed-loop system, including: Based on the augmented state variable vector of the augmented state space equation, the structure of the full-state feedback control law is determined; By introducing the state feedback gain matrix to be solved into the full-state feedback control law structure, the full-state feedback control law is obtained. Substituting the full-state feedback control law into the augmented state-space equations, eliminating the control input vector, and rearranging the equations, we obtain the closed-loop system state matrix. Based on the closed-loop system state matrix and the augmented state variable vector, the dynamic equations of the closed-loop system are constructed.

5. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, The energy dissipation deviation constraint conditions of the closed-loop system are constructed based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index, including: Substituting the preset positive definite symmetric matrix into the dissipation theory formula yields the energy storage function; Substitute the dynamic equation of the closed-loop system into the energy storage function to derive the expression for the rate of energy change. Based on the energy change rate expression and the preset energy dissipation deviation index, a dissipation deviation inequality is constructed. By combining the energy storage function and the dissipation deviation inequality, a closed-loop system energy dissipation deviation constraint condition is constructed.

6. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, The equivalent transformation of the energy dissipation deviation constraints of the closed-loop system and the augmented state-space equations to obtain linear matrix inequalities includes: Extract the state matrix, input matrix, and output matrix from the augmented state-space equation; Substitute the state matrix, the input matrix, and the output matrix into the energy dissipation deviation constraint of the closed-loop system, and perform algebraic simplification to obtain the simplified constraint expression; Based on the rules of matrix inequality transformation, the simplified constraint expression is reconstructed into standard matrix form to obtain a linear matrix inequality.

7. The method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control according to claim 1, characterized in that, The process of constructing a convex optimization problem based on the linear matrix inequality, solving the convex optimization problem using a preset convex optimization numerical solver, and obtaining a sub-hypersynchronous oscillation suppression command by combining the full-state feedback control law includes: With minimizing the energy dissipation deviation index as the optimization objective, a convex optimization problem is constructed by combining the aforementioned linear matrix inequalities. The preset convex optimization numerical solver is invoked to numerically solve the convex optimization problem, and the optimal state feedback gain matrix is ​​obtained. Obtain the real-time operating state variables of the direct-drive wind power grid-connected system, substitute the optimal state feedback gain matrix into the full-state feedback control law, and calculate the subsynchronous oscillation suppression command in combination with the real-time operating state variables of the direct-drive wind power grid-connected system.

8. A subsynchronous / supersynchronous oscillation suppression system for a direct-drive wind power grid-connected system based on state feedback control, characterized in that, include: The acquisition module is used to acquire the dynamic characteristic parameters of the direct-drive wind power grid-connected system and construct the original state-space equations based on the dynamic characteristic parameters of the direct-drive wind power grid-connected system. An introduction module is used to introduce error state variables into the original state space equation and the reference input vector of the controlled object to obtain the augmented state space equation. The derivation module is used to design and derive the full-state feedback control law based on the augmented state-space equations, thereby obtaining the dynamic equations of the closed-loop system. The module is used to construct the energy dissipation deviation constraint conditions of the closed-loop system based on the dissipation theory formula, the dynamic equation of the closed-loop system, the preset positive definite symmetric matrix, and the preset energy dissipation deviation index. The transformation module is used to perform an equivalent transformation on the energy dissipation deviation constraint of the closed-loop system and the augmented state space equation to obtain a linear matrix inequality. The solution module is used to construct a convex optimization problem based on the linear matrix inequality, solve the convex optimization problem using a preset convex optimization numerical solver, and obtain a sub-supersynchronous oscillation suppression command by combining the full-state feedback control law.

9. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for suppressing sub / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for suppressing subsynchronous / supersynchronous oscillations in a direct-drive wind power grid-connected system based on state feedback control as described in any one of claims 1-7.

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