A method for identifying a stable domain of a thermal mass energy storage converter broadband oscillation suppression parameter

Abstract

The present application relates to the field of power system stability analysis and control, in particular to a kind of thermal mass energy storage converter broadband oscillation suppression parameter stable domain identification method, comprising the following steps: establish general electric-thermal coupling mechanism model, construct the dynamic differential equation describing the evolution of input electric power and energy storage medium temperature state of converter;Mechanical dynamics equation is constructed;Establish the small signal state space model of whole system, linearization is carried out at steady state operating point and the state space matrix of whole system is derived;Define practical small disturbance stability domain, set the real part decay rate threshold and minimum damping ratio threshold of whole system eigenvalue;Algebraic singularity constraint equation in high-dimensional parameter space is constructed;Solve algebraic singularity constraint equation, establish parameter stable domain.This method avoids the repeated iteration of traditional eigenvalue analysis, can accurately depict parameter boundary, effectively improve the frequency support capability of high proportion of new energy power grid and system robustness.

Description

Technical Field

[0001] This invention relates to the field of power system stability analysis and control, specifically to a method for identifying the stability domain of broadband oscillation suppression parameters in a thermal mass storage converter. Background Technology

[0002] With the advancement of the "dual carbon" target, the penetration rate of renewable energy sources, represented by wind and solar power, in the power system is increasing daily. Power electronic converters are gradually replacing traditional synchronous generators as the main interface of the power grid, resulting in power systems exhibiting significant characteristics of "low inertia and weak damping," posing serious challenges to the system's frequency disturbance immunity and transient stability. To address this issue, exploring demand-side flexibility resources, particularly utilizing Virtual Energy Storage Systems (VESS) with huge thermal inertia, such as those found in buildings and industrial electric furnaces, to participate in grid regulation, has become a current research hotspot.

[0003] Thermal energy storage systems (VES) utilize electrothermal conversion devices (such as electric boilers and heat pumps) to convert difficult-to-store electrical energy into easily storable thermal energy. Through advanced control strategies, they exhibit power response characteristics similar to physical batteries or rotating electric motors. To enable VESS to actively support grid frequencies, the Virtual Synchronous Machine (VSG) control strategy is widely used. However, traditional VSG control simulates the rotor motion of a synchronous machine, and its "rigid" connection characteristics are prone to causing power oscillations in complex grid environments. In contrast, the Virtual Asynchronous Machine (VAM) control strategy, by introducing slip characteristics to simulate the asynchronous damping effect of an induction motor, provides more flexible power support and has a natural advantage in suppressing low-frequency oscillations.

[0004] While the VAM control strategy improves the dynamic performance of the system, it introduces complex nonlinear dynamic mechanisms. This is especially true for key parameters in the VAM controller (such as the virtual damping coefficient). Sag coefficient and adaptively adjusted virtual inertia The system is highly coupled with grid impedance and thermal load conditions. When these parameters are improperly set, or when grid operating conditions (such as changes in the short-circuit ratio (SCR)) change, the system is highly susceptible to wideband oscillations covering low frequencies (electromechanical mode, 0.1-2.5Hz) to sub / supersynchronous frequencies (electrical mode, 10-100Hz). This multi-band coupled oscillation phenomenon seriously threatens the safe and stable operation of power electronic power systems.

[0005] Existing methods for analyzing and tuning parameters for converter grid-connected stability have the following main shortcomings: (1) Fragmented physical model, lacking an electrothermal coupling perspective: Existing studies typically analyze thermodynamic processes (seconds to minutes) and electromagnetic transient processes (milliseconds) separately. However, in thermo-mass energy storage systems, changes in indoor temperature directly affect the virtual inertia in the control strategy. The value of (adaptive inertia control) changes the pole distribution of the system. Ignoring this electro-thermal bidirectional coupling characteristic will cause the stability analysis results to fail during long-term operation.

[0006] (2) Traditional eigenvalue analysis is inefficient and lacks a global perspective: The current mainstream small-signal stability analysis method is based on the calculation of eigenvalues ​​of a single parameter point. Engineers usually use the "trial and error method" or the "root locus method" to determine the control parameters. However, when faced with high-dimensional systems with multiple coupled parameters, this method is difficult to traverse all possible parameter combinations and cannot intuitively give the safety boundary (stability region) of the parameters. Once the system operating point drifts, the originally set parameters may fall into the unstable region.

[0007] (3) Insufficient quantification of the ability to suppress broadband oscillations: Traditional stability criteria only require that the real part of the eigenvalues ​​be less than zero (Lyapunov stability). However, in practical engineering, in order to quickly suppress broadband oscillations, the system not only needs to be "stable," but also needs to have a sufficient damping ratio and decay rate. Existing parameter tuning methods often cannot directly characterize the specific dynamic performance indicators (such as minimum damping ratio) in the parameter space. The "practical stability region" of ).

[0008] Therefore, there is an urgent need for a method that can integrate the thermo-mass energy storage electrothermal coupling mechanism and efficiently and accurately identify the stability domain of control parameters to suppress broadband oscillations, so as to guide the optimization and tuning of converter control parameters in engineering practice. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter, specifically including the following steps: S1: Based on the axiom of energy conservation in non-equilibrium thermodynamics, the thermal mass storage unit is equivalent to a lumped parameter thermal network. A general electrothermal coupling mechanism model is established, and a dynamic differential equation describing the evolution of the input power of the converter and the temperature state of the energy storage medium is constructed. S2: Based on the virtual asynchronous machine control strategy, an electromagnetic transient model is established, and a mechanical dynamics equation including a virtual stator voltage equation, a virtual rotor voltage equation, and a simulated induction motor slip characteristic is constructed. The mechanical dynamics equation also includes a virtual damping coefficient and a droop coefficient to be identified. S3: Based on the general electrothermal coupling mechanism model, the electromagnetic transient model, and the power grid impedance model, establish a small-signal state-space model of the whole system, perform linearization at the steady-state operating point, and derive the state-space matrix of the whole system. The elements of the state-space matrix are functions of the virtual damping coefficient and the droop coefficient. S4: Define a practical small disturbance stability region, set a threshold for the real part decay rate and a threshold for the minimum damping ratio of the eigenvalues ​​of the entire system, and the practical small disturbance stability region is a set of parameters enclosed by the eigenvalue distribution boundary that satisfies the threshold for the real part decay rate and the threshold for the minimum damping ratio. S5: Based on the double-substituent matrix operator, construct the mapping matrix corresponding to the real part decay rate boundary and the mapping matrix corresponding to the minimum damping ratio boundary. Based on the eigenvalue distribution constraints on the complex plane, construct the algebraic singularity constraint equation in the high-dimensional parameter space. S6: Solve the algebraic singularity constraint equations, and plot the closed parameter stability domain boundary curve on the two-dimensional control parameter plane composed of virtual damping coefficients and droop coefficients. Identify the region inside the parameter stability domain boundary curve as the parameter safe operating region for suppressing broadband oscillations, and establish the parameter stability domain.

[0010] In step S1, for the thermal mass storage unit (such as electric heating buildings, industrial electric furnaces, etc.), based on the axiom of energy conservation in non-equilibrium thermodynamics, it is equivalent to a lumped-parameter thermal resistance-thermal capacity (Thermal RC) network. A first-order linear non-homogeneous differential equation describing the evolution of the converter input power and the macroscopic temperature of the energy storage medium is established. In this model, the equivalent thermal capacity is used to characterize the inertia of the system's stored thermal energy, and the equivalent thermal resistance is used to characterize the system's energy dissipation characteristics to the environment. By introducing the electrothermal conversion efficiency coefficient, the dynamic coupling relationship between the power input and the temperature state variable is established. This model can reflect the influence of thermodynamic processes on the system's time scale, providing a physical basis for subsequent analysis of inertia adaptive characteristics.

[0011] Furthermore, the general electrothermal coupling mechanism model satisfies a first-order linear non-homogeneous differential equation:

[0012] in, For time, This refers to the macroscopic average temperature state quantity of the energy storage medium. The active power control quantity input to the converter. This represents the ambient temperature disturbance. The equivalent heat capacity of the system, The equivalent thermal resistance of the system, The electrothermal conversion efficiency coefficient; the general electrothermal coupling mechanism model uses the thermal time constant. Constrain the time scale of converter power regulation.

[0013] In step S2, an electromagnetic transient model of the converter is established in a synchronous rotating coordinate system (dq coordinate system). First, virtual stator voltage equations and virtual rotor voltage equations are established, where the virtual rotor side voltage is forced to zero to simulate the short-circuit rotor characteristics of an induction motor. Second, mechanical dynamic equations (swing equations) including virtual damping control and frequency droop control are established, introducing the slip frequency (i.e., the difference between the grid synchronous frequency and the virtual rotor frequency) to give the converter asynchronous damping characteristics similar to an induction motor. In this step, the virtual damping coefficient ( ) and droop coefficient ( () represents the key control parameters to be identified.

[0014] Furthermore, the virtual rotor voltage equation is constructed based on the dq synchronous rotating coordinate system, and the short-circuit rotor characteristics of the squirrel-cage induction motor are simulated by forcing the rotor-side voltage to zero.

[0015] in, For virtual rotor resistance, For virtual rotor current in Components of the axis, For virtual rotor current in Components of the axis, For virtual rotor flux in Components of the axis, For virtual rotor flux in Components of the axis, For the grid synchronization angular velocity, For virtual rotor angular velocity, Defined as slip frequency, it is used to introduce asynchronous damping effect in the control loop.

[0016] Furthermore, the mechanical dynamics equations are swing equations that include frequency droop control and damping control:

[0017] in, For virtual inertia coefficient, The virtual damping coefficient to be identified. For reference angular velocity, For virtual electromagnetic torque, For load torque, frequency droop term Represented as:

[0018] in, The droop coefficient to be identified. This is a reference value for active power. This refers to the angular velocity of the power grid synchronization.

[0019] In step S3, the above electrothermal coupling mechanism model and electromagnetic transient model are combined, along with the grid-side impedance model (including line resistance and inductance), to form a closed-loop system. At the steady-state operating point of the system, the nonlinear differential equations are subjected to Taylor expansion and small-signal linearization to eliminate intermediate algebraic variables, deriving the state-space equations of the entire system: The resulting state matrix of the entire system It includes both modes describing slow thermodynamic dynamics and modes describing fast electromagnetic transient dynamics, and the matrix elements are parameters to be identified. and The function.

[0020] Furthermore, a small-signal state-space model of the entire system is established, linearization is performed at the steady-state operating point, and the state-space matrix of the entire system is derived, including: Selecting a state variable vector ,in and This is the stator current deviation. For virtual rotor flux in Shaft deviation components, For virtual rotor flux in Shaft deviation components, This represents the deviation of the virtual rotor angular velocity. The general electrothermal coupling mechanism model, the virtual stator voltage equation, the virtual rotor voltage equation, and the mechanical dynamics equation are Taylor expanded at the equilibrium point, and the first-order terms are retained. By substituting the grid-side impedance model as a boundary condition, eliminating intermediate algebraic variables, and simplifying, the state-space equations of the entire system are obtained:

[0021] in, For containing parameters to be identified and The total system state matrix includes modes describing slow thermodynamic dynamics and modes describing fast electromagnetic transient dynamics.

[0022] In step S4, to ensure that the system possesses not only Lyapunov stability but also dynamic qualities that meet engineering requirements while suppressing broadband oscillations, this invention defines a practical small-disturbance stability region. This stability region is bounded by two key performance constraints: the real part decay rate constraint and the minimum damping ratio constraint.

[0023] Furthermore, the practical small-perturbation stability region must simultaneously satisfy the following two mathematical constraints: Constraint 1: State matrix of the entire system All eigenvalues real part It must be less than or equal to the set negative constant. ,Right now ,in To minimize the decay rate, constraint one is used to control the convergence speed of the system after it is disturbed; Constraint 2: State Matrix of the Entire System All eigenvalues Damping ratio It must be greater than the set threshold. ,Right now The second constraint is used to control the system's ability to suppress wideband oscillations.

[0024] In step S5, the Guardian Map stability mapping theory is used to transform the eigenvalue distribution constraints on the complex plane into algebraic singularity constraints in a high-dimensional parameter space, thus avoiding the iterative calculation of eigenvalues ​​by traditional methods.

[0025] Furthermore, the process of establishing the algebraic singularity constraint equations includes two mapping transformations: Transformation 1: Construct a translation matrix based on the decay rate boundary of the real part of the eigenvalues. ,in It is the identity matrix. The state matrix of the entire system. and For the parameters to be identified, To minimize the decay rate, the algebraic singularity constraint equation corresponding to the translation matrix is: ; Transformation 2: Calculate the corresponding damping ratio threshold based on the minimum damping ratio boundary. rotation angle Construct a complex rotation matrix The corresponding algebraic singularity constraint equation is: .

[0026] Furthermore, the construction methods for the bivariate matrix operator include: For any 3D matrix The matrix bivariate matrix for A dimensional matrix, where the dimension is sum dimension satisfy ; Double substitution matrix Elements in the array are indexed by row and column indexes Arrange them, among which and ; The specific formula for calculating matrix elements is as follows:

[0027] in The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number The elements of the column.

[0028] In step S6, the virtual damping coefficient is used to... and droop coefficient On the constructed two-dimensional parameter plane, the solution trajectories of the two algebraic equations are numerically plotted. These trajectories divide the parameter plane into several regions. By selecting test points for verification, a closed region that simultaneously satisfies the attenuation rate and damping ratio constraints is identified, which is the final parameter stability region. Engineers can select the optimal control parameters based on this stability region and actual operating conditions, thereby effectively suppressing the broadband oscillations of the thermo-mass energy storage converter.

[0029] Furthermore, the steps for establishing the parameter stability region include: exist Numerical solution of equations in the parametric plane Draw the corresponding real part decay boundary trajectory; exist Numerical solution of equations in the parametric plane Draw the corresponding damping ratio boundary trajectory; The real part attenuation boundary trajectory and the damping ratio boundary trajectory divide the parameter plane into several closed or semi-closed candidate regions. Select test parameter points within the candidate region Substitute into the state matrix of the entire system Perform eigenvalue calculation; Extract valid test parameter points whose feature values ​​simultaneously satisfy both constraint one and constraint two, and determine the candidate region where the valid test parameter points are located as a valid parameter stability region.

[0030] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention abandons the traditional approach of separating thermodynamic processes from electromagnetic transient processes and establishes a full-dimensional coupled model that includes thermal resistance-thermal capacity network and virtual asynchronous machine dynamics. This model can accurately capture the dynamic characteristics of the system when the virtual inertia is adaptively adjusted due to the temperature change of the thermal mass storage medium, and avoids the stability assessment deviation caused by model simplification. It is particularly suitable for long-term operational analysis.

[0031] (2) Unlike traditional trial-and-error methods or single-point eigenvalue verification, which can only determine stability under specific parameters, this invention directly provides a closed safety boundary on a two-dimensional plane composed of virtual damping coefficients and droop coefficients by identifying the parameter stability region (P-SSSR). Engineers can intuitively observe the trend of parameter changes on stability, thereby quickly selecting the optimal control parameters at this moment, greatly reducing the difficulty of debugging.

[0032] (3) This invention utilizes a double substitution matrix and a mapping operator to transform the complex plane eigenvalue distribution problem of a high-dimensional complex system into a problem of solving an algebraic determinant, thus avoiding repeated iterative calculations of eigenvalues. This enables the method to quickly handle complex power grid impedance changes and multi-parameter coupling scenarios, and has the potential for online calculation and real-time application.

[0033] (4) The Practical Small Disturbance Stability Region (P-SSSR) defined in this invention not only requires the system to be stable (real part less than zero), but also introduces the dual strong constraints of minimum decay rate and minimum damping ratio. The control parameters tuned by this method can not only prevent system instability, but also ensure that the system has fast convergence capability and sufficient damping support when facing broadband disturbances in the range of 0.1Hz to 100Hz, effectively eliminating the risk of subsynchronous / supersynchronous oscillations. Attached Figure Description

[0034] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1This is a flowchart of a method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to the present invention.

[0036] Figure 2 This is a schematic diagram of the electrothermal coupling mechanism and control architecture of the thermal mass storage unit of the present invention.

[0037] Figure 3 This is a detailed logic block diagram of the multi-virtual asynchronous machine control strategy of the present invention.

[0038] Figure 4 This is a schematic diagram illustrating the definition of the practical small-perturbation stability region of the present invention on the complex plane.

[0039] Figure 5 This is a schematic diagram of the stability domain of the two-dimensional control parameters obtained by the present invention based on the double substitution matrix mapping identification. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] This invention discloses a method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter, such as... Figure 1 As shown, the main steps include: S1: Based on the axiom of energy conservation in non-equilibrium thermodynamics, the thermal mass storage unit is equivalent to a lumped parameter thermal network. A general electrothermal coupling mechanism model is established, and a dynamic differential equation describing the evolution of the input power of the converter and the temperature state of the energy storage medium is constructed.

[0043] As a preferred embodiment of this application, such as Figure 2As shown, for thermo-mass energy storage units (such as electric heating buildings and industrial electric furnaces), based on the axiom of energy conservation in non-equilibrium thermodynamics, they are equivalently represented as lumped-parameter thermal resistance-thermal capacity (Thermal RC) networks. A first-order linear non-homogeneous differential equation describing the evolution of the converter input power and the macroscopic temperature of the energy storage medium is established. In this model, the equivalent thermal capacity characterizes the inertia of the system's stored thermal energy, and the equivalent thermal resistance characterizes the system's energy dissipation to the environment. By introducing the electrothermal conversion efficiency coefficient, the dynamic coupling relationship between the power input and the temperature state variables is established. This model can reflect the influence of thermodynamic processes on the system's time scale, providing a physical basis for subsequent analysis of inertia adaptive characteristics.

[0044] As a preferred embodiment of this application, the general electrothermal coupling mechanism model satisfies a first-order linear non-homogeneous differential equation:

[0045] in, For time, This refers to the macroscopic average temperature state quantity of the energy storage medium. The active power control quantity input to the converter. This represents the ambient temperature disturbance. The equivalent heat capacity of the system, The equivalent thermal resistance of the system, The electrothermal conversion efficiency coefficient; the general electrothermal coupling mechanism model uses the thermal time constant. Constrain the time scale of converter power regulation.

[0046] S2: Based on the virtual asynchronous machine control strategy, an electromagnetic transient model is established, and a mechanical dynamics equation including a virtual stator voltage equation, a virtual rotor voltage equation, and a simulated induction motor slip characteristic is constructed. The mechanical dynamics equation also includes virtual damping coefficients and droop coefficients to be identified.

[0047] As a preferred embodiment of this application, such as Figure 3 As shown, the converter in this embodiment adopts a virtual asynchronous motor (VAM) control strategy. This control strategy includes voltage / current dual closed-loop control and a core mechanical dynamics simulation component. An electromagnetic transient model of the converter is established in a synchronous rotating coordinate system (dq coordinate system). First, virtual stator voltage equations and virtual rotor voltage equations are established, where the virtual rotor side voltage is forced to zero to simulate the short-circuit rotor characteristics of an induction motor. Second, mechanical dynamics equations (swing equations) including virtual damping control and frequency droop control are established, introducing a slip frequency (i.e., the difference between the grid synchronous frequency and the virtual rotor frequency) to give the converter asynchronous damping characteristics similar to an induction motor. In this step, the virtual damping coefficient (…) is defined. ) and droop coefficient ( () represents the key control parameters to be identified.

[0048] In a preferred embodiment of this application, the virtual rotor voltage equation is constructed based on the dq synchronous rotating coordinate system, and the short-circuit rotor characteristics of the squirrel-cage induction motor are simulated by forcing the rotor-side voltage to zero.

[0049] in, For virtual rotor resistance, For virtual rotor current in Components of the axis, For virtual rotor current in Components of the axis, For virtual rotor flux in Components of the axis, For virtual rotor flux in Components of the axis, For the grid synchronization angular velocity, For virtual rotor angular velocity, Defined as slip frequency, it is used to introduce asynchronous damping effect in the control loop.

[0050] In a preferred embodiment of this application, the mechanical dynamics equation is a swing equation that includes frequency droop control and damping control:

[0051] in, For virtual inertia coefficient, The virtual damping coefficient to be identified. For reference angular velocity, For virtual electromagnetic torque, For load torque, frequency droop term Represented as:

[0052] in, The droop coefficient to be identified. This is a reference value for active power. This refers to the angular velocity of the power grid synchronization.

[0053] S3: Based on the general electrothermal coupling mechanism model, the electromagnetic transient model, and the power grid impedance model, establish a small-signal state-space model of the entire system, perform linearization at the steady-state operating point, and derive the state-space matrix of the entire system. The elements of the state-space matrix are functions of the virtual damping coefficient and the droop coefficient.

[0054] In a preferred embodiment of this application, the electrothermal coupling mechanism model and the electromagnetic transient model are combined, along with the grid-side impedance model (including line resistance and inductance), to form a closed-loop system. At the steady-state operating point of the system, the nonlinear differential equations are subjected to Taylor expansion and small-signal linearization to eliminate intermediate algebraic variables, thereby deriving the state-space equations of the entire system: The resulting state matrix of the entire system It includes both modes describing slow thermodynamic dynamics and modes describing fast electromagnetic transient dynamics, and the matrix elements are parameters to be identified. and The function.

[0055] As a preferred embodiment of this application, a small-signal state-space model of the entire system is established, linearization is performed at the steady-state operating point, and the state-space matrix of the entire system is derived, including: Selecting a state variable vector ,in and This is the stator current deviation. For virtual rotor flux in Shaft deviation components, For virtual rotor flux in Shaft deviation components, This represents the deviation of the virtual rotor angular velocity. The general electrothermal coupling mechanism model, the virtual stator voltage equation, the virtual rotor voltage equation, and the mechanical dynamics equation are Taylor expanded at the equilibrium point, and the first-order terms are retained. By substituting the grid-side impedance model as a boundary condition, eliminating intermediate algebraic variables, and simplifying, the state-space equations of the entire system are obtained:

[0056] in, For containing parameters to be identified and The total system state matrix includes modes describing slow thermodynamic dynamics and modes describing fast electromagnetic transient dynamics.

[0057] S4: Define a practical small perturbation stability region, and set a threshold for the real part decay rate and a threshold for the minimum damping ratio of the eigenvalues ​​of the entire system. The practical small perturbation stability region is a set of parameters enclosed by the eigenvalue distribution boundary that satisfies the threshold for the real part decay rate and the threshold for the minimum damping ratio.

[0058] As a preferred embodiment of this application, in order to effectively suppress broadband oscillations, this embodiment requires not only system stability but also good dynamic performance. To ensure that the system possesses not only Lyapunov stability but also dynamic performance meeting engineering requirements while suppressing broadband oscillations, this invention defines a practical small-disturbance stability domain. For example... Figure 4 As shown, the stability region is bounded by two key performance indicators: the real part decay rate constraint and the minimum damping ratio constraint.

[0059] As a preferred embodiment of this application, the practical small-perturbation stability region must simultaneously satisfy the following two mathematical constraints: Constraint 1: State matrix of the entire system All eigenvalues real part It must be less than or equal to the set negative constant. ,Right now ,in To minimize the decay rate, constraint one is used to control the convergence speed of the system after it is disturbed; Constraint 2: State Matrix of the Entire System All eigenvalues Damping ratio It must be greater than the set threshold. ,Right now The second constraint is used to control the system's ability to suppress wideband oscillations.

[0060] S5: Based on the double-substituent matrix operator, construct the mapping matrix corresponding to the real part decay rate boundary and the mapping matrix corresponding to the minimum damping ratio boundary. Based on the eigenvalue distribution constraints on the complex plane, construct the algebraic singularity constraint equation in the high-dimensional parameter space.

[0061] As a preferred embodiment of this application, the Guardian Map stability mapping theory is used to transform the eigenvalue distribution constraints on the complex plane into algebraic singularity constraints in a high-dimensional parameter space, thus avoiding the iterative calculation of eigenvalues ​​by traditional methods.

[0062] As a preferred embodiment of this application, the process of establishing the algebraic singularity constraint equations includes two mapping transformations: Transformation 1: Construct a translation matrix based on the decay rate boundary of the real part of the eigenvalues. ,in It is the identity matrix. The state matrix of the entire system. and For the parameters to be identified, To minimize the decay rate, the algebraic singularity constraint equation corresponding to the translation matrix is: ; Transformation 2: Calculate the corresponding damping ratio threshold based on the minimum damping ratio boundary. rotation angle Construct a complex rotation matrix The corresponding algebraic singularity constraint equation is: .

[0063] As a preferred embodiment of this application, the method for constructing the double substitution matrix operator includes: For any 3D matrix The matrix bivariate matrix for A dimensional matrix, where the dimension is sum dimension satisfy ; Double substitution matrix Elements in the array are indexed by row and column indexes Arrange them, among which and ; The specific formula for calculating matrix elements is as follows:

[0064] in The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number The elements of the column.

[0065] S6: Solve the algebraic singularity constraint equations, and plot the closed parameter stability domain boundary curve on the two-dimensional control parameter plane composed of virtual damping coefficients and droop coefficients. Identify the region inside the parameter stability domain boundary curve as the parameter safe operating region for suppressing broadband oscillations, and establish the parameter stability domain.

[0066] As a preferred embodiment of this application, the virtual damping coefficient is used... and droop coefficient On the constructed two-dimensional parameter plane, the solution trajectories of the two algebraic equations are numerically plotted. These trajectories divide the parameter plane into several regions. By selecting test points for verification, a closed region that simultaneously satisfies the attenuation rate and damping ratio constraints is identified, which is the final parameter stability region. Engineers can select the optimal control parameters based on this stability region and actual operating conditions, thereby effectively suppressing the broadband oscillations of the thermo-mass energy storage converter.

[0067] In a preferred embodiment of this application, the step of establishing the parameter stability domain includes: exist Numerical solution of equations in the parametric plane Draw the corresponding real part decay boundary trajectory; exist Numerical solution of equations in the parametric plane Draw the corresponding damping ratio boundary trajectory; The real part attenuation boundary trajectory and the damping ratio boundary trajectory divide the parameter plane into several closed or semi-closed candidate regions. Select test parameter points within the candidate region Substitute into the state matrix of the entire system Perform eigenvalue calculation; Extract valid test parameter points whose feature values ​​simultaneously satisfy both constraint one and constraint two, and determine the candidate region where the valid test parameter points are located as a valid parameter stability region.

[0068] As a preferred embodiment of this application, such as Figure 5 As shown, the boundary trajectory forms a closed loop.

[0069] Safe Region: The area inside the curve (shaded area in the figure) is the set of parameters that satisfy the P-SSSR constraint. Design working points are selected within this region. The system can effectively suppress wideband oscillations.

[0070] Unstable region: The area outside the curve (the blank area in the figure) is a region of parametric instability or weak damping. Running in this region is very likely to cause system oscillation.

[0071] Engineers can rely on Figure 5 Based on the identification results shown, and combined with actual working conditions, the VAM control parameters are tuned to be near the geometric center of the shaded area to obtain the best robustness.

[0072] This invention provides a method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter. This method establishes a full-dimensional mathematical model that includes thermodynamic slow dynamics and electromagnetic transient fast dynamics, and utilizes the Guardian Map stability mapping theory to achieve accurate identification of the safety boundary of control parameters.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter, characterized in that, Includes the following steps: Based on the axiom of energy conservation in non-equilibrium thermodynamics, the thermal mass storage unit is equivalent to a lumped parameter thermal network. A general electrothermal coupling mechanism model is established, and a dynamic differential equation describing the evolution of the converter input power and the temperature state of the energy storage medium is constructed. An electromagnetic transient model is established based on a virtual asynchronous machine control strategy. A mechanical dynamics equation is constructed, including a virtual stator voltage equation, a virtual rotor voltage equation, and a mechanical dynamics equation simulating the slip characteristics of an induction motor. The mechanical dynamics equation also includes a virtual damping coefficient and a droop coefficient to be identified. Based on the general electrothermal coupling mechanism model, the electromagnetic transient model, and the power grid impedance model, a small-signal state-space model of the entire system is established. Linearization is performed at the steady-state operating point, and the state-space matrix of the entire system is derived. The elements of the state-space matrix are functions of the virtual damping coefficient and the droop coefficient. Define a practical small perturbation stability region, and set a threshold for the real part decay rate and a threshold for the minimum damping ratio of the eigenvalues ​​of the entire system. The practical small perturbation stability region is a set of parameters enclosed by the distribution boundary of the eigenvalues ​​that satisfy the threshold for the real part decay rate and the threshold for the minimum damping ratio. Based on the double-substituent matrix operator, a mapping matrix corresponding to the real part decay rate boundary and a mapping matrix corresponding to the minimum damping ratio boundary are constructed. Based on the eigenvalue distribution constraints on the complex plane, an algebraic singularity constraint equation in the high-dimensional parameter space is constructed. Solve the algebraic singularity constraint equations, and plot the closed parameter stability domain boundary curve on the two-dimensional control parameter plane composed of virtual damping coefficients and droop coefficients. Identify the region inside the parameter stability domain boundary curve as the parameter safe operating region that suppresses broadband oscillations, and establish the parameter stability domain.

2. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 1, characterized in that, The general electrothermal coupling mechanism model satisfies a first-order linear non-homogeneous differential equation: in, For time, This refers to the macroscopic average temperature state quantity of the energy storage medium. The active power control quantity input to the converter. This represents the ambient temperature disturbance. The equivalent heat capacity of the system, The equivalent thermal resistance of the system, The electrothermal conversion efficiency coefficient; The general electrothermal coupling mechanism model uses the thermal time constant. Constrain the time scale of converter power regulation.

3. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 1, characterized in that, The virtual rotor voltage equation is constructed based on the dq synchronous rotating coordinate system, and the short-circuit rotor characteristics of the squirrel-cage induction motor are simulated by forcing the rotor-side voltage to zero. in, For virtual rotor resistance, For virtual rotor current in Components of the axis, For virtual rotor current in Components of the axis, For virtual rotor flux in Components of the axis, For virtual rotor flux in Components of the axis, For the grid synchronization angular velocity, For virtual rotor angular velocity, Defined as slip frequency, it is used to introduce asynchronous damping effect in the control loop.

4. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 1, characterized in that, The mechanical dynamics equations are swing equations that include frequency droop control and damping control: in, For virtual inertia coefficient, The virtual damping coefficient to be identified. For reference angular velocity, For virtual electromagnetic torque, For load torque, frequency droop term Represented as: in, The droop coefficient to be identified. This is a reference value for active power. This refers to the angular velocity of the power grid synchronization.

5. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 1, characterized in that, A small-signal state-space model of the entire system is established, linearized at the steady-state operating point, and the state-space matrix of the entire system is derived, including: Selecting a state variable vector ,in and This is the stator current deviation. For virtual rotor flux in Shaft deviation components, For virtual rotor flux in Shaft deviation components, This represents the deviation of the virtual rotor angular velocity. The general electrothermal coupling mechanism model, the virtual stator voltage equation, the virtual rotor voltage equation, and the mechanical dynamics equation are Taylor expanded at the equilibrium point, and the first-order terms are retained. By substituting the grid-side impedance model as a boundary condition, eliminating intermediate algebraic variables, and simplifying, the state-space equations of the entire system are obtained: in, For containing parameters to be identified and The total system state matrix includes modes describing slow thermodynamic dynamics and modes describing fast electromagnetic transient dynamics.

6. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 1, characterized in that, The practical small perturbation stability region must simultaneously satisfy the following two mathematical constraints: Constraint 1: State matrix of the entire system All eigenvalues real part It must be less than or equal to the set negative constant. ,Right now ,in To minimize the decay rate, constraint one is used to control the convergence speed of the system after it is disturbed; Constraint 2: State Matrix of the Entire System All eigenvalues Damping ratio It must be greater than the set threshold. ,Right now The second constraint is used to control the system's ability to suppress wideband oscillations.

7. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 6, characterized in that, The process of establishing the algebraic singularity constraint equations involves two mapping transformations: Transformation 1: Construct a translation matrix based on the decay rate boundary of the real part of the eigenvalues. ,in It is the identity matrix. The state matrix of the entire system. and For the parameters to be identified, To minimize the decay rate, the algebraic singularity constraint equation corresponding to the translation matrix is: ; Transformation 2: Calculate the corresponding damping ratio threshold based on the minimum damping ratio boundary. rotation angle Construct a complex rotation matrix The corresponding algebraic singularity constraint equation is: .

8. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 7, characterized in that, The method for constructing the bivariate matrix operator includes: For any 3D matrix The matrix bivariate matrix for A dimensional matrix, where the dimension is sum dimension satisfy ; Double substitution matrix Elements in the array are indexed by row and column indexes Arrange them, among which and ; The specific formula for calculating matrix elements is as follows: in The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number Column elements, The original matrix No. Line number The elements of the column.

9. The method for identifying the stability domain of broadband oscillation suppression parameters in a thermo-mass energy storage converter according to claim 7, characterized in that, The steps to establish the parameter stability region include: exist Numerical solution of equations in the parametric plane Draw the corresponding real part decay boundary trajectory; exist Numerical solution of equations in the parametric plane Draw the corresponding damping ratio boundary trajectory; The real part attenuation boundary trajectory and the damping ratio boundary trajectory divide the parameter plane into several closed or semi-closed candidate regions. Select test parameter points within the candidate region Substitute into the state matrix of the entire system Perform eigenvalue calculation; Extract valid test parameter points whose feature values ​​simultaneously satisfy both constraint one and constraint two, and determine the candidate region where the valid test parameter points are located as a valid parameter stability region.

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