Three-phase mmc current modeling method and device, computer device, storage medium and program product

Abstract

The application relates to a three-phase MMC current modeling method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: establishing a full state space modeling equation of a three-phase MMC current; converting the full state space modeling equation into a matrix form modeling equation, wherein a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation are constructed in the matrix form modeling equation; performing row echelon reduction on the coefficient matrix to obtain a simplified matrix; and based on the simplified matrix, extracting independent state variables of the matrix form modeling equation to obtain a reduced state space model. The method can reduce the modeling difficulty and the calculation amount.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power electronic converters, and particularly relates to a three-phase MMC current modeling method and device, computer equipment, a storage medium and a program product. BACKGROUND

[0002] As a key device in the technical field of power electronic converters, the three-phase modular multilevel converter (MMC) is widely used in high-voltage direct current transmission, flexible alternating current transmission and other fields due to its high efficiency, high power quality and flexible scalability. In order to fully tap the performance potential of the three-phase MMC and realize its stable and efficient operation, accurate current modeling is an indispensable foundation.

[0003] However, the traditional modeling method has the problems of high modeling difficulty and large amount of calculation when facing the complex electrical elements and current dynamic behavior inside the three-phase MMC, because the state space representation of the three-phase MMC involves many variables, resulting in high model dimension. SUMMARY

[0004] Therefore, it is necessary to provide a three-phase MMC current modeling method, device, computer equipment, storage medium and program product which can reduce the modeling difficulty and has small amount of calculation.

[0005] In a first aspect, the present application provides a three-phase MMC current modeling method, comprising the following steps:

[0006] establishing a full state space modeling equation of three-phase MMC current;

[0007] transforming the full state space modeling equation into a matrix form modeling equation, wherein a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation are constructed in the matrix form modeling equation;

[0008] performing row echelon reduction on the coefficient matrix to obtain a simplified matrix;

[0009] extracting independent state variables of the matrix form modeling equation based on the simplified matrix to obtain a reduced state space model.

[0010] In a second aspect, the present application further provides a three-phase MMC current modeling device, comprising:

[0011] a establishing module configured to establish a full state space modeling equation of three-phase MMC current;

[0012] a transformation module, configured to transform the full state space modeling equation into a matrix form modeling equation, the matrix form modeling equation comprising a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation;

[0013] a simplification module, configured to perform row echelon simplification on the coefficient matrix to obtain a simplified matrix;

[0014] a dimension reduction module, configured to extract independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced state space model.

[0015] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory storing a computer program, and the processor realizing the following steps when executing the computer program:

[0016] establishing a full state space modeling equation of three-phase MMC current;

[0017] transforming the full state space modeling equation into a matrix form modeling equation, the matrix form modeling equation comprising a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation;

[0018] performing row echelon simplification on the coefficient matrix to obtain a simplified matrix;

[0019] extracting independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced state space model.

[0020] In a fourth aspect, the present application further provides a computer readable storage medium, storing a computer program, the computer program being executed by a processor to realize the following steps:

[0021] establishing a full state space modeling equation of three-phase MMC current;

[0022] transforming the full state space modeling equation into a matrix form modeling equation, the matrix form modeling equation comprising a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation;

[0023] performing row echelon simplification on the coefficient matrix to obtain a simplified matrix;

[0024] extracting independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced state space model.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, the computer program being executed by a processor to realize the following steps:

[0026] establish a full state space modeling equation of three-phase MMC current;

[0027] convert the full state space modeling equation into a matrix form modeling equation, wherein a plurality of vectors and coefficient matrices corresponding to the full state space modeling equation are constructed in the matrix form modeling equation;

[0028] simplify the coefficient matrix by row echelonization to obtain a simplified matrix;

[0029] extract independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced dimension state space model.

[0030] The three-phase MMC current modeling method, device, computer equipment, storage medium and program product combine modeling of a state space model of three-phase MMC with dimension reduction technology, realize high-precision and low-complexity MMC dynamic modeling, and include key state variables in the modeling process, thereby ensuring that the reduced dimension state space model can still accurately reflect core dynamic characteristics of MMC while simplifying the model. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0032] Figure 1 An application environment diagram of a three-phase MMC current modeling method in an embodiment;

[0033] Figure 2 A flowchart of a three-phase MMC current modeling method in an embodiment;

[0034] Figure 3 A structure diagram of three-phase MMC in an embodiment;

[0035] Figure 4 A flowchart of a three-phase MMC current modeling method in another embodiment;

[0036] Figure 5Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0037] Figure 6 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0038] Figure 7 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0039] Figure 8 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0040] Figure 9 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0041] Figure 10 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0042] Figure 11 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0043] Figure 12 Control logic diagram for controlling the three-phase MMC in an embodiment;

[0044] Figure 13 MMC AC output voltage and current and upper and lower bridge arm current in the inverter mode and at rated power in an embodiment;

[0045] Figure 14 MMC AC side voltage and current and upper and lower bridge arm current in the rectifier mode and at rated power in an embodiment;

[0046] Figure 15 Flow chart of the method for modeling the three-phase MMC current in another embodiment;

[0047] Figure 16 Structure block diagram of the three-phase MMC current modeling device in an embodiment. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0049] As a key device in the field of power electronic converter technology, the three-phase modular multilevel converter (MMC) is widely used in high-voltage direct current transmission, flexible alternating current transmission and other fields due to its high efficiency, high power quality and flexible scalability. To fully exploit the performance potential of the three-phase MMC and achieve its stable and efficient operation, accurate current modeling is indispensable.

[0050] However, the traditional modeling method has the following problems when facing the complex electrical elements and current dynamic behavior inside the three-phase MMC:

[0051] (1) Current modeling: To fully exploit the potential of the three-phase MMC, accurate modeling is crucial. The traditional modeling method is difficult to systematically describe the dynamic behavior of the internal current of the three-phase MMC. For example, the three-phase MMC contains numerous complex electrical elements, and the internal current dynamics are complex. Existing methods cannot well adapt to this complexity. At the same time, the state space representation of the three-phase MMC often involves a large number of variables, resulting in a high-dimensional model, which not only increases the difficulty of modeling and challenges the modeling accuracy, but also affects the effectiveness of subsequent control strategies. High-dimensional models greatly increase the amount of calculation, making it difficult to process quickly and effectively in practical applications.

[0052] (2) Control: To ensure the stable and reliable operation of the three-phase MMC, effective control strategies are needed. Existing control strategies have shortcomings when dealing with the complex operating characteristics of the three-phase MMC. For example, it is difficult to ensure fast transient response while achieving minimum steady-state error and excellent dynamic performance. There is complex interaction between the branch currents of the three-phase MMC, which increases the computational demand and difficulty of control. Existing control strategies are difficult to handle this interaction well and cannot fully optimize the performance of the three-phase MMC. In addition, under different operating conditions, the dynamic response of the three-phase MMC needs to ensure power quality, but existing control strategies are not adaptable in this regard.

[0053] Based on this, the present application proposes a three-phase MMC current modeling method and device with low modeling difficulty and small amount of calculation, computer equipment, storage medium and program product.

[0054] The three-phase MMC current modeling method provided by the embodiments of the present application can be applied to, for example Figure 1The computer device shown in FIG. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, and the wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a control method for an MMC-DES integrated system. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0055] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0056] In one embodiment, Figure 2 As shown in the figure, a three-phase MMC current modeling method is provided, which is applied to Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0057] S201, establish a full state space modeling equation for the three-phase MMC current.

[0058] Among them, the full state-space modeling equation of the three-phase MMC circuit is an expression used to describe the dynamic change process of the electromagnetic energy inside the three-phase MMC. The full state-space modeling equation includes the rate of change of state variables over time, as well as the current state of the three-phase MMC current and the precise relationship between external inputs. State variables include current, capacitor voltage, etc., and external inputs include control signals, grid voltage, DC voltage, etc.

[0059] In an embodiment of the present application, each phase of a three-phase MMC has an upper bridge arm and a lower bridge arm. The upper and lower bridge arms of each phase are connected to the AC grid or load through a circuit node. Each bridge arm consists of N submodules connected in series and a bridge arm inductor. The positive and negative poles of the DC bus are respectively connected to the common circuit node of the three-phase upper bridge arm and the common circuit node of the lower bridge arm. The full state space modeling equation of the three-phase MMC current can be constructed based on the connection relationship between the bridge arms in the three-phase MMC, the current direction between the circuit nodes, the voltage between the bridge arms, and other circuit parameters.

[0060] In the embodiment of the present application, the three-phase MMC topology diagram is as follows: Figure 3 As shown, the three-phase output voltages on the AC side are defined as v a 、v b 、v c , the DC bus voltage is V dc The three-phase voltages of the upper and lower bridge arms are v ua 、v ub 、v uc 、v la 、v lb 、v lc The inflow currents corresponding to the voltage sources are i va 、i vb 、i vc 、i Vdc 、i vua 、i vub 、i vuc 、i vla 、i vlb 、i vlc The three-phase output inductance and bridge arm inductance are: L a , L b , L c , L ua , L ub , L uc , L la , L lb , L lc The corresponding inductor current is i a 、i b 、i c 、i ua 、i ub 、i uc 、i la 、i lb 、i lc The reference direction of voltage and current is as follows: Figure 1 As shown. The three-phase MMC has a total of 15 circuit nodes, such as Figure 3As shown, the 15 circuit nodes can be represented as node 0, node 1, ..., node 14. Define the voltage of node 0 as zero, and the voltages of the remaining nodes are defined as v1, v2, v3, v4, v5, v6, v7, v8, v9, v 10 、v 11 、v 12 、v 13 、v 14 , Figure 3 The SM in FIG is a submodule connected to the bridge arm inductor.

[0061] S202, converting the full state space modeling equation into a matrix form modeling equation, wherein the matrix form modeling equation is constructed with multiple vectors and coefficient matrices corresponding to the full state space modeling equation.

[0062] In an embodiment of the present application, the matrix form conversion of the full state-space modeling equations can be completed through structured encapsulation. Optionally, all differential equations describing the dynamics of the bridge arm current and capacitor voltage can be systematically reorganized into a state-space expression framework, wherein all state variables including the upper and lower bridge arm currents of each phase and the total capacitor voltage are integrated into a single multidimensional state vector, and the external input quantities including the insertion index, AC voltage and DC voltage constitute the input vector.

[0063] S203, performing row echelon simplification on the coefficient matrix to obtain a simplified matrix.

[0064] In the embodiments of the present application, a stepwise simplification operation is performed on the coefficient matrix, gradually eliminating linearly dependent rows in the matrix through elementary row transformations, and converting the original coefficient matrix into a simplified form with a trapezoidal structure. The stepwise arrangement of the trapezoidal matrix can clearly expose the hidden linear dependencies in the state equation, explicitly identify redundant differential equations, and thus reveal the true number of independent degrees of freedom inherent in the system.

[0065] S204 , extracting independent state variables from the matrix modeling equation based on the simplified matrix to obtain a state space model after dimensionality reduction.

[0066] In an embodiment of the present application, independent state variables are extracted based on a simplified ladder matrix. By identifying the pivot columns and their corresponding state variables in the ladder structure, a minimum set of independent state variables that can fully characterize the core dynamics of the system is directly screened out. State variables not selected by the pivot are identified as redundant variables, and their dynamic behavior can be fully reconstructed through linear combinations of the independent variables. This establishes a reduced-dimensional state space model: this model retains only the independent state variables and their corresponding simplified differential equations, while simultaneously reconstructing the reduced-dimensional system matrix and input matrix.

[0067] In the three-phase MMC current modeling method, a full state space modeling equation of three-phase MMC current is established; the full state space modeling equation is converted into a matrix form modeling equation, a plurality of vectors and coefficient matrices corresponding to the full state space modeling equation are constructed in the matrix form modeling equation; the coefficient matrix is subjected to row echelon reduction to obtain a reduced matrix; based on the reduced matrix, extraction of independent state variables is performed on the matrix form modeling equation to obtain a reduced dimension state space model. The modeling and dimension reduction technology of the state space model of the three-phase MMC are combined, high-precision and low-complexity MMC dynamic modeling is realized, and the key state variables are included in the modeling process, so that the reduced dimension state space model can still accurately reflect the core dynamic characteristics of the MMC while simplifying the model.

[0068] In one embodiment, an implementation of S201 is provided as shown in Figure 4 The establishment of the full state space modeling equation of the three-phase MMC current includes:

[0069] S301, determining a first relationship between the node voltage across the inductor and the inductor current in the three-phase MMC.

[0070] In the embodiments of the present application, the state of the three-phase MMC is determined as nine inductor current variables, i a , i b , i c , i ua , i ub , i uc , i la , i lb , i lc , and the first relationship between the node voltage across the inductor and the inductor current is determined according to the topological graph as shown in Figure 3 The first relationship between the node voltage across the inductor and the inductor current is shown in formula 1.

[0071] (Formula 1)

[0072] In formula 1, the nine relationships are i a , i b , i c , i ua , i ub , i uc , i la , i lb , i lc The first relationship between the node voltage across the inductor and the inductor current.

[0073] S302, determining a second relationship between the voltage of the voltage source and the node voltage in the three-phase MMC.

[0074] In the embodiments of the present application, the input in the three-phase MMC is determined as 10 voltage sources, i.e. a , b , c , dc , ua , ub , uc , la , lb , lc According to the topological graph as shown in Figure 3 , the second relationship between the voltage source power supply and the node voltage is determined as shown in formula 2:

[0075] (Formula 2)

[0076] In formula 2, the 10 relationships are v a , b , c , dc , ua , ub , uc , la , lb , lc The second relationship between the node voltage and the node voltage.

[0077] S303, determining the third relationship between the inflow current and the outflow current of the inductance end node in the three-phase MMC.

[0078] In the embodiments of the present application, the current inflow and outflow relationship of each node is described according to the Kirchhoff current law, as shown in formula 3:

[0079] (Formula 3)

[0080] S304, obtaining the full state space modeling equation of the three-phase MMC current based on the first relationship, the second relationship and the third relationship.

[0081] As an optional implementation, the first relationship, the second relationship and the third relationship can be determined as the full state space modeling equation of the three-phase MMC current.

[0082] As another optional implementation, the first relationship, the second relationship and the third relationship can be combined to obtain the full state space modeling equation of the three-phase MMC current.

[0083] In the above application examples, the full state space modeling equation of the three-phase MMC current is constructed from three angles, i.e., the relationship between the node voltage and the inductor current, the relationship between the voltage source and the node voltage, and the relationship between the current flowing into and out of each node, so that the full state space modeling equation of the three-phase MMC current is more comprehensive and accurate.

[0084] In one embodiment, an implementation of S202 is provided as shown in Figure 5 The above "transforming the full state space modeling equation into a matrix form modeling equation" includes:

[0085] S401, defining a state vector containing 9 inductor currents, an input vector containing 10 voltage sources, a current vector containing 10 voltage source currents, and a voltage vector containing 14 node voltages of the inductors.

[0086] In the embodiments of the present application, in combination with the three-phase MMC topology diagram as shown in Figure 3 The state vector containing 9 inductor currents can be represented as x = [i a , i b , i c , i ua , i ub , i uc , i la , i lb , i lc ] ', the current vector containing 10 voltage source currents can be represented as i u = [i va , i vb , i vc , i Vdc , i vua , i vub , i vuc , i vla , i vlb , i vlc ] ', and the voltage vector containing 14 node voltages of the inductors can be represented as v = [v 10 , v 11 , v 12 , v 13 , v 14 ] '.

[0087] S402, constructing 4 coefficient matrices.

[0088] In the embodiments of the present application, according to the current vector of the voltage source current and the voltage vector of the node voltage, the full state vector z representing the coupling relationship between the node voltage and the voltage source current is constructed as shown in formula 4, and the dimension of the full state vector is 14+10=24.

[0089] (Formula 4)

[0090] In the embodiment of the present application, for 9 inductance currents and 14 nodes, the sub-matrix elements corresponding to the inductance currents flowing into the nodes are defined as 1, and the sub-matrix elements corresponding to the inductance currents flowing out of the nodes are defined as -1, to form the coefficient matrix corresponding to the inductance current dynamic characteristics as shown in Formula 5:

[0091] (Formula 5)

[0092] In the embodiment of the present application, the coefficient matrix corresponding to the algebraic constraint equation can describe the coupling relationship between the input quantity and the nodes, for 10 voltage sources and 14 nodes, according to the reference direction, the sub-matrix elements corresponding to the nodes connected to the positive poles of the voltage sources are defined as -1, and the sub-matrix elements corresponding to the nodes connected to the negative poles of the voltage sources are defined as 1, to form the coefficient matrix corresponding to the algebraic constraint equation as shown in Formula 6:

[0093] (Formula 6)

[0094] In the embodiment of the present application, the system matrix corresponding to the input vector influence can describe the relationship between the circuit nodes and the voltage source currents, for 14 nodes, the sub-matrix elements corresponding to the voltage source currents flowing into the nodes are defined as -1, and the sub-matrix elements corresponding to the voltage source currents flowing out of the nodes are defined as 1, to form the system matrix corresponding to the input vector influence as shown in Formula 7:

[0095] (Formula 7)

[0096] S403, based on the state vector, the input vector, the current vector, the voltage vector and the four coefficient matrices, a matrix form modeling equation is obtained.

[0097] In the embodiment of the present application, based on the state vector, the input vector, the current vector, the voltage vector and the four coefficient matrices, a matrix form modeling equation is constructed: , wherein as shown in Formula 8, as shown in Formula 9, as shown in Formula 10:

[0098] (Formula 8)

[0099] , wherein G 11 , G 21 , G 32 are respectively the coefficient matrices corresponding to the inductance current dynamic characteristics, the algebraic constraint equation and the input vector influence, G 12 , G 22 , G31 All are zero matrix.

[0100] (Formula 9)

[0101] (Formula 10)

[0102] wherein, The sub-matrix A x1 , A x2 are all zero matrixes, and the sub-matrix A x3 is composed of a 9-dimensional unit matrix and a 5*9 zero matrix. . The sub-matrix B u1 , B u3 are all zero matrixes, and the sub-matrix B u2 is a 10-dimensional unit matrix.

[0103] In the above application embodiment, the three-phase MMC current model is described by four coefficient matrices and state vectors, input vectors, current vectors and voltage vectors, thereby improving the accuracy and reliability of the matrix form modeling equation.

[0104] In one embodiment, an implementation of S203 is provided, as shown in Figure 6 The above "performing row echelon reduction on the coefficient matrix to obtain a reduced matrix" includes:

[0105] S501, horizontally splicing the four coefficient matrices into a combined matrix.

[0106] In the embodiment of the application, the four coefficient matrices are horizontally spliced into a combined matrix [G, Adx, Ax, Bu], which lays a foundation for subsequent echelon reduction.

[0107] S502, performing row echelon reduction on the combined matrix to extract a reduced matrix composed of three sub-matrices.

[0108] In the embodiment of the application, the Gauss-Jordan elimination method is used to perform row echelon reduction on the combined matrix, extract key sub-matrices, and eliminate redundant variables. Specifically, the reduction process can be as shown in Formula 11:

[0109] (Formula 11)

[0110] Further, let E=M 22 ×E dx , A=-M 23 , B=-M 24 , and the reduced matrix can be represented as: wherein the three sub-matrices are E, A and B respectively.

[0111] In the embodiment of the present application, the coefficient matrix is row reduced to a simplest row echelon form, directly showing all key variables and free variables, and complete simplified information can be obtained without back substitution.

[0112] In one embodiment, an implementation of S204 is provided, as shown in Figure 7 The above "extracting independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced dimension state space model" includes:

[0113] S601, determining the target number of independent state variables according to the rank of the simplified matrix.

[0114] In the embodiment of the present application, the rank of the simplified matrix is the rank of the matrix M 22 The rank of the matrix M 22 is 5, which is less than the dimension of the state 9, so the target number of independent state variables is 5, and there are 9-5=4 zero vectors in M The decomposition of E, A, and B into a combination of submatrices is shown in equation 12:

[0115] (equation 12)

[0116] S602, based on the target number, reducing the original state variables in the matrix form modeling equation through zero space mapping to obtain the target number of independent state variables.

[0117] In the embodiment of the present application, in order to extract independent states from equation 12, the transition matrix is defined as the zero space of the matrix [A2, B2]: Further, the independent state vector x b and the independent input vector u b are determined according to the transition matrix , the state vector x, and the input vector u, as shown in equation 13:

[0118] (equation 13)

[0119] S603, generating a reduced dimension state space model based on the target number of independent state variables.

[0120] In the embodiment of the present application, the coefficient matrix of the target number of independent state variables is determined according to equation 13, that is, the coefficient matrix of 4 independent state variables, as shown in equation 14:

[0121] (equation 14)

[0122] Further, according to equation 14, a reduced dimension state space model is generated: wherein the independent state vector is x b = [i ub ,i uc ,i la ,i lb ,i lc ] and the coefficient matrix of the independent state vector is as shown in Formulas 15-18.

[0123] Formula 15

[0124] Formula 16

[0125] Formula 17

[0126] Formula 18

[0127] In the above application embodiments, the dimension of the state space model is significantly reduced by determining the independent variables based on the matrix rank and using zero space mapping for state reduction, thereby reducing the computational burden while maintaining the core dynamic characteristics of the model.

[0128] In one embodiment, as shown in Figure 8 the three-phase MMC current modeling method further includes:

[0129] S205, on the basis of the state space model, the input vector is reconstructed into a controlled power input vector and a disturbance input vector to obtain a reduced dimension state space equation.

[0130] In the embodiments of the present application, the input vector is reconstructed, the AC side voltage and the DC side voltage are regarded as disturbance inputs, the bridge arm voltage is regarded as a control input, and a reduced dimension state space equation is generated.

[0131] S206, a cost function of the reduced dimension state space equation is determined.

[0132] In the embodiments of the present application, the cost function is designed according to the state variable and the control input, and exemplarily, a mathematical optimization target can be constructed by the weighted sum of squares of the state variables and the weighted sum of squares of the control input energy.

[0133] Optionally, as shown in Figure 9 the “determining a cost function of the reduced dimension state space equation” includes:

[0134] S701, an independent state variable in the reduced dimension state space equation and a controlled power input vector are obtained.

[0135] In the embodiments of the present application, independent state variables are extracted from the original high-order model of the three-phase MMC, for example, circulating current components of the bridge arm current, fluctuation components of the sub-module capacitor voltage, AC side current tracking error, etc.; and physical quantities directly corresponding to the active control degrees of freedom are determined, for example, modulation signals of the upper / lower bridge arm insertion voltage, or dq-axis components of the equivalent bridge arm voltage.

[0136] In S702, a cost function including tracking errors and control quantity sizes is constructed based on the independent state variables and the controlled power input vector, where the tracking error is a tracking error between the independent state variable and a preset current reference value, and the control quantity size is a control quantity for the controlled power input vector.

[0137] In the embodiments of the present application, the cost function can be a linear quadratic regulator (LQR) cost function, for example, the LQR cost function is shown in formula 19:

[0138] (Formula 19)

[0139] Wherein, Q is a weighting matrix of the independent state variable, R is a weighting matrix of the control quantity, x ss is a system state under a steady state of the three-phase MMC current model, u ss is a system input under the steady state of the three-phase MMC current model; is the tracking error, is the control quantity size.

[0140] In S207, an optimal feedback gain matrix corresponding to the reduced dimension state space equation is calculated based on the cost function.

[0141] In the embodiments of the present application, the optimal feedback gain matrix corresponding to the reduced dimension state space equation can be determined by minimizing the cost function.

[0142] Optionally, the optimal feedback gain matrix corresponding to the reduced dimension state space equation is calculated based on the Riccati equation by minimizing the cost function.

[0143] Wherein, the Riccati equation is a steady-state algebraic equation, and the Riccati differential equation is a core mathematical tool for solving the optimal feedback gain matrix. The equation can be recursively or directly analytically integrated by integrating the structural parameters of the reduced dimension state space model and the cost function weight matrix, and a symmetric positive definite matrix is generated, and the steady-state solution of the equation directly generates the optimal feedback gain. In the embodiments of the present application, the optimal feedback gain matrix Klqr is obtained by solving the Riccati equation, so that the optimal control input .

[0144] Exemplarily, a cost function of a linear quadratic regulator (LQR) control algorithm designed according to a system rated power and a rated voltage effective value can be formula 20:

[0145] (formula 20)

[0146] wherein J represents the cost function; v n represents a three-phase alternating current voltage effective value; S n represents a rated power; represents a square sum of three-phase output currents, that is, ; represents a square sum of six bridge arm currents, that is, ; represents a square sum of six bridge arm voltages, that is, .

[0147] Further, the cost function is converted into a matrix form, and an optimal feedback gain matrix Klqr is calculated according to a Riccati equation as formula 21:

[0148] (formula 21)

[0149] S208, based on the optimal feedback gain matrix, controls the three-phase MMC.

[0150] In the embodiments of the present application, key operating state quantities of the three-phase MMC are acquired in real time, including upper and lower bridge arm currents of each phase, mean value and fluctuation component of a sub-module capacitor voltage set, and alternating current output current, etc. After signal conditioning and coordinate transformation, a state vector consistent with the dimension of a reduced dimension state space equation is formed, so that an increment instruction of a controlled power input vector is generated according to the real-time state vector and the optimal feedback gain matrix, and the three-phase MMC is controlled by using the increment instruction.

[0151] Optionally, the real-time state vector is multiplied by the optimal feedback gain matrix to generate the increment instruction of the controlled power input vector, the increment instruction is superimposed on a steady state working point reference to generate a final controlled power input vector, and the signal is converted into a switching sequence of a power semiconductor device by a pulse width modulation (PWM) unit to directly drive switching actions of a bridge arm sub-module.

[0152] In the above embodiments, a reduced dimension model with clear physical meaning is established by separating a controlled input and a disturbance input, and a balance relationship between a tracking error and a control quantity size is quantified based on a cost function, so that high-precision, high-efficiency, and strong-robust real-time control of the three-phase MMC is realized.

[0153] In one embodiment, an implementation of S205 is provided as follows: Figure 10As shown, the above "reconstructing the input vector into a controlled power input vector and a disturbance input vector on the basis of the state space model to obtain a reduced dimension state space equation" comprises:

[0154] S801, define the upper and lower bridge arm voltages in the three-phase MMC as controlled inputs, and define the three-phase AC side voltage and DC bus voltage as disturbance inputs.

[0155] In the embodiment of the application, on the basis of formula 12, the input vector u is reconstructed and split into a controlled power input vector u cs = [v ua , v ub , v uc , v la , v lb , v lc ] and a disturbance input vector u es = [v a , v b , v c , V dc ].

[0156] S802, on the basis of the state space model, separate the controlled input coefficient matrix and the disturbance input coefficient matrix to form a reduced dimension state space equation in which the control input and the disturbance input are separated.

[0157] In the embodiment of the application, y represents the output of the system, and a state space current model that can be directly used for designing LQR is constructed according to the control input and the disturbance input:

[0158] (formula 22)

[0159] wherein the controlled input coefficient matrix is formula 21, and the disturbance input coefficient matrix is formula 23:

[0160] (formula 23)

[0161] (formula 24)

[0162] In the above application embodiment, by separating the coefficient matrices of the controlled input and the disturbance input, a clear model basis is laid for subsequent design of a controller that can accurately track the controlled input and effectively suppress the voltage disturbance on the AC side and the DC side.

[0163] In one embodiment, an implementation of the above S208 is provided, as shown in Figure 11 The above "controlling the three-phase MMC on the basis of the optimal feedback gain matrix" comprises:

[0164] S901, collect the deviation between the independent state variable in the reduced dimension state space equation and the preset current reference value in real time.

[0165] In the embodiment of the application, the preset current reference value can be determined according to user input data, or can be determined according to the use scenario of the current three-phase MMC. In the embodiment of the application, the monitoring value corresponding to the independent state variable in the three-phase MMC is collected in real time, and the deviation between the independent state variable and the preset current reference value is determined.

[0166] In the embodiment of the application, the output y of the system should be equal to the current reference value r calculated by the power controller ss . To achieve this goal, as shown in Figure 12 , x ss = N x × r ss + N dx ×u e s and u ss = N u × r ss + N du ×u es , wherein u es is a disturbance input vector, and the transfer matrix composed of N x , N dx , N u , N du can be expressed by the coefficient matrix B bc , B be and C b . Wherein, the transfer matrix is as shown in formula 25:

[0167] (Formula 25)

[0168] S902, based on the deviation, the optimal feedback gain matrix and the disturbance input vector defined in the reduced dimension state space equation, generate the bridge arm voltage control quantity.

[0169] In the embodiment of the application, the difference between x ss and x b is input to the optimal feedback gain matrix to obtain the optimal feedback gain, and the optimal feedback gain and u ss are input to the state space model as shown in formula 22 to generate the bridge arm voltage control quantity.

[0170] S903, drive the power device of the three-phase MMC based on the bridge arm voltage control quantity to adjust the upper and lower bridge arm voltages.

[0171] In the embodiments of the present application, the bridge arm voltage control quantity is converted into a gate drive signal of a specific power semiconductor switching device, and the input and output states of the sub-modules in the upper and lower bridge arms of each phase are directly controlled by the drive signal, so that the instantaneous output voltage of each bridge arm is accurately regulated.

[0172] Optionally, the effect achieved by the technical solution can be verified by simulation of the three-phase MMC, and the simulation model parameters are shown in Table 1:

[0173] Table 1

[0174]

[0175] Exemplarily, under the mediation of the MMC current controller shown in Figure 12 , the MMC AC output voltage and current and the upper and lower bridge arm currents under the inverter mode and rated power are shown in Figure 13 . The characteristics of these waveforms show that the AC current presents a highly standard sinusoidal shape, and maintains an accurate phase relationship with the AC voltage. Even at a switching frequency of only 500 Hz, the total harmonic distortion (THD) of the AC current of the system in the inverter mode is only 0.32%. Figure 14 The AC side voltage and current of the MMC and the upper and lower bridge arm currents under the rectifier mode and rated power are shown. The AC current and the AC voltage are 180 degrees out of phase. Compared with the inverter mode, the bridge arm currents present a downward trend as a whole. In this mode, the total harmonic distortion is only 0.41%, which proves the effectiveness and strong performance of the control system under different operating conditions. Figure 13 and Figure 14 In the above, current represents current, kA represents kiloampere, Voltage represents voltage, and kV represents kilovolt.

[0176] In the above embodiments, the upper and lower bridge arms of the three-phase MMC are controlled by the reduced dimension state space equation, ensuring the stability, fast response, low overshoot and low control energy consumption of the MMC.

[0177] In one embodiment, a complete three-phase MMC current modeling method is provided, as shown in Figure 15 , which comprises the following steps:

[0178] S1, determining a first relationship between a node voltage of a node at both ends of an inductor and an inductor current in a three-phase MMC.

[0179] S2, determining a second relationship between a voltage of a voltage source and a node voltage in the three-phase MMC.

[0180] S3, determining a third relationship between an inflow current and an outflow current of a node at both ends of an inductor in the three-phase MMC.

[0181] S4, obtaining a full state space modeling equation of the three-phase MMC current based on the first relationship, the second relationship and the third relationship.

[0182] S5, defining a state vector containing 9 inductor currents, an input vector containing 10 voltage sources, a current vector containing 10 voltage source currents and a voltage vector containing 14 node voltages of the inductors.

[0183] S6, constructing 4 coefficient matrices respectively describing the coupling relationship between the node voltage and the voltage source current, the inductor current dynamic characteristic, the algebraic constraint equation and the influence of the input vector.

[0184] S7, obtaining a matrix form modeling equation based on the state vector, the input vector, the current vector, the voltage vector and the 4 coefficient matrices.

[0185] S8, transversely splicing the 4 coefficient matrices into a combined matrix.

[0186] S9, performing row echelon reduction on the combined matrix to extract a simplified matrix composed of three sub-matrices.

[0187] S10, determining the target number of independent state variables according to the rank of the simplified matrix.

[0188] S11, based on the target number, reducing the original state variables in the matrix form modeling equation through null space mapping to obtain the target number of independent state variables.

[0189] S12, generating a reduced dimension state space model based on the target number of independent state variables.

[0190] S13, defining the upper and lower bridge arm voltages in the three-phase MMC as controlled inputs and defining the three-phase AC side voltage and the DC bus voltage as disturbance inputs.

[0191] S14, based on the state space model, separating the controlled input coefficient matrix and the disturbance input coefficient matrix to form a reduced dimension state space equation in which the control input and the disturbance input are separated.

[0192] S15, obtaining the independent state variables in the reduced dimension state space equation and the controlled power input vector.

[0193] S16, based on the independent state variables and the controlled power input vector, constructing a cost function including a tracking error and a control amount size, wherein the tracking error is a tracking error between the independent state variables and a preset current reference value, and the control amount size is a control amount for the controlled power input vector.

[0194] S17, minimizing the cost function and calculating an optimal feedback gain matrix corresponding to the reduced dimension state space equation based on a Riccati equation.

[0195] S18, collecting a deviation between the independent state variable in the reduced dimension state space equation and the preset current reference value in real time.

[0196] S19, generating a bridge arm voltage control quantity based on the deviation, the optimal feedback gain matrix, and the disturbance input vector defined in the reduced dimension state space equation.

[0197] S20, driving power devices of the three-phase MMC based on the bridge arm voltage control quantity to adjust the upper and lower bridge arm voltages.

[0198] In the three-phase MMC current modeling method, a full state space modeling equation of three-phase MMC current is established; the full state space modeling equation is converted into a matrix form modeling equation, and a plurality of vectors and coefficient matrices corresponding to the full state space modeling equation are constructed in the matrix form modeling equation; the coefficient matrix is rowed and simplified to obtain a simplified matrix; and the independent state variable of the matrix form modeling equation is extracted based on the simplified matrix to obtain a reduced dimension state space model. The modeling of the state space model of the three-phase MMC is combined with the dimension reduction technology, the high-precision and low-complexity MMC dynamic modeling is realized, and the key state variable is included in the modeling process, so that the simplified model still accurately reflects the core dynamic characteristics of the MMC.

[0199] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0200] Based on the same inventive concept, the embodiments of the present application also provide a three-phase MMC current modeling device for implementing the three-phase MMC current modeling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more three-phase MMC current modeling device embodiments provided below can refer to the limitations of the three-phase MMC current modeling method described above, which will not be repeated here.

[0201] In one embodiment, as Figure 16As shown, a three-phase MMC current modeling device is provided, comprising: a building module 10, a conversion module 11, a simplifying module 12 and a dimension reduction module 13, wherein:

[0202] The building module 10 is configured to build a full state space modeling equation of the three-phase MMC current.

[0203] The conversion module 11 is configured to convert the full state space modeling equation into a matrix form modeling equation, in which a plurality of vectors and a coefficient matrix corresponding to the full state space modeling equation are constructed.

[0204] The simplifying module 12 is configured to perform row echelon simplification on the coefficient matrix to obtain a simplified matrix.

[0205] The dimension reduction module 13 is configured to extract independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a reduced state space model.

[0206] Each module in the three-phase MMC current modeling device described above can be realized by software, hardware or a combination thereof in whole or in part. Each module described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0207] In one embodiment, a computer device is also provided, comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each method embodiment described above.

[0208] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in each method embodiment described above.

[0209] In one embodiment, a computer program product is provided, comprising a computer program, and the computer program is executed by a processor to implement the steps in each method embodiment described above.

[0210] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0211] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0212] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A three-phase MMC current modeling method, characterized in that: The following steps are involved: Establish the full state space modeling equations of three-phase MMC current; Converting the full state space modeling equation into a matrix form modeling equation, wherein the matrix form modeling equation is constructed with multiple vectors and four coefficient matrices corresponding to the full state space modeling equation; horizontally concatenate the four coefficient matrices into a combined matrix; Performing row echelon simplification on the combined matrix to extract a simplified matrix consisting of three sub-matrices; Based on the simplified matrix, extracting independent state variables from the matrix form modeling equation to obtain a state space model after dimensionality reduction; The step of extracting independent state variables from the matrix modeling equation based on the simplified matrix to obtain a state space model after dimensionality reduction includes: determining a target number of independent state variables according to the rank of the simplified matrix; Based on the target number, reducing the original state variables in the matrix form modeling equation by null space mapping to obtain the target number of independent state variables; Based on the target number of independent state variables, a state space model after dimensionality reduction is generated.

2. The method according to claim 1, characterized in that The full state space modeling equation for establishing the three-phase MMC current includes: Determining a first relationship between a node voltage at two end nodes of an inductor and an inductor current in the three-phase MMC; determining a second relationship between a voltage of a voltage source in the three-phase MMC and the node voltage; determining a third relationship between an inflow current and an outflow current at two end nodes of an inductor in the three-phase MMC; Based on the first relationship, the second relationship and the third relationship, a full state-space modeling equation of the three-phase MMC current is obtained.

3. The method according to claim 1, characterized in that The converting of the full state space modeling equation into a matrix form modeling equation comprises: A state vector including 9 inductor currents, an input vector including 10 voltage source currents, a current vector including 10 voltage source currents, and a voltage vector including 14 node voltages at both ends of the inductor are defined; Constructing the four coefficient matrices; The matrix form modeling equation is obtained based on the state vector, the input vector, the current vector, the voltage vector and the four coefficient matrices.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Based on the state space model, the input vector is reconstructed into a controlled power input vector and a disturbance input vector to obtain a reduced-dimensional state space equation; determining a cost function for the reduced-dimensional state-space equation; Based on the cost function, calculating the optimal feedback gain matrix corresponding to the reduced-dimensional state-space equation; The three-phase MMC is controlled based on the optimal feedback gain matrix.

5. The method according to claim 4, characterized in that Based on the state space model, the input vector is reconstructed into a controlled power input vector and a disturbance input vector to obtain a reduced-dimensional state space equation, including: The upper and lower bridge arm voltages in the three-phase MMC are defined as controlled inputs, and the three-phase voltages on the AC side and the DC bus voltage are defined as disturbance inputs; On the basis of the state space model, a controlled input coefficient matrix and a disturbance input coefficient matrix are separated to form the reduced-dimensional state space equation in which the control input and the disturbance input are separated.

6. The method according to claim 4, characterized in that Determining the cost function of the reduced-dimensional state space equation includes: Obtaining the independent state variables in the reduced-dimensional state space equation and the controlled power supply input vector; Based on the independent state variable and the controlled power input vector, a cost function including a tracking error and a control amount size is constructed, wherein the tracking error is a tracking error between the independent state variable and a preset current reference value, and the control amount size is a control amount for the controlled power input vector.

7. The method according to claim 4, characterized in that The calculating, based on the cost function, an optimal feedback gain matrix corresponding to the reduced-dimensional state-space equation includes: The cost function is minimized, and an optimal feedback gain matrix corresponding to the reduced-dimensional state space equation is calculated based on the Riccati equation.

8. The method according to claim 4, characterized in that The controlling the three-phase MMC based on the optimal feedback gain matrix includes: real-time acquisition of deviations between independent state variables in the reduced-dimensional state space equation and preset current reference values; generating a bridge arm voltage control variable based on the deviation, the optimal feedback gain matrix, and the disturbance input vector defined in the reduced-dimensional state space equation; The power devices of the three-phase MMC are driven based on the bridge arm voltage control amount to adjust the upper and lower bridge arm voltages.

9. A three-phase MMC current modeling device, characterized in that: include: Establishing a module for establishing full state space modeling equations of three-phase MMC current; a conversion module, configured to convert the full state space modeling equation into a matrix form modeling equation, wherein the matrix form modeling equation is constructed with a plurality of vectors and four coefficient matrices corresponding to the full state space modeling equation; a simplification module, configured to horizontally concatenate the four coefficient matrices into a combined matrix; and perform row echelon simplification on the combined matrix to extract a simplified matrix consisting of three sub-matrices; A dimensionality reduction module is configured to extract independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a state space model after dimensionality reduction, wherein the extraction of independent state variables from the matrix form modeling equation based on the simplified matrix to obtain a state space model after dimensionality reduction comprises: determining a target number of independent state variables according to the rank of the simplified matrix; Based on the target number, the original state variables in the matrix form modeling equation are reduced by null space mapping to obtain the target number of independent state variables; based on the target number of independent state variables, a state space model after dimensionality reduction is generated.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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