A method and system for controlling a grid-forming static var generator

Abstract

The application discloses a network-constructing static reactive generator control method and system, and belongs to the technical field of static reactive generator control. The method constructs an affine nonlinear model with filter capacitor voltage, filter inductor current and DC capacitor energy as state variables, sets a system total energy storage and a filter capacitor d-axis voltage error output function, calculates relative steps and performs differential homeomorphic coordinate transformation. A pole placement method or a linear quadratic regulator is used to obtain linear control input, and d-axis modulation waves and q-axis modulation waves are obtained through input inverse transformation, and switching drive pulses are generated. The application can compensate for system nonlinear coupling, simplify parameter setting, and improve dynamic response and stable operation ability under disturbance conditions.

Description

Technical Field

[0001] This invention belongs to the field of power electronic converter and power system reactive power compensation control technology, specifically relating to a grid-type static var generator control method and system. Background Technology

[0002] Static Var Generators (SVGs) can regulate reactive power and support voltage at the grid connection point by controlling the amplitude and phase of the converter output voltage. With the increasing integration of new energy sources and power electronic equipment into the power grid, the equivalent short-circuit capacity of the grid is decreasing, and the characteristics of weak grid operation are becoming more pronounced. Traditional grid-connected SVGs rely on phase-locked loops (PLLs) to obtain the grid phase. When there are large disturbances in the grid voltage amplitude or frequency, they are prone to problems such as degraded synchronization performance, increased interaction of control loops, and even instability. Therefore, grid-connected SVGs capable of autonomously establishing voltage and frequency references are gradually gaining attention.

[0003] Existing grid-type static var generators typically employ droop control or virtual synchronous generator (VSG) control. Droop control achieves power distribution through the proportional relationship between active power and frequency, and reactive power and voltage amplitude. While its control structure is relatively simple, its dynamic performance is heavily dependent on the droop coefficient, making it difficult to simultaneously balance response speed, steady-state accuracy, and system damping. Virtual synchronous generator control improves the system's frequency support capability by simulating the inertia and damping characteristics of a synchronous generator; however, its controller is usually designed based on a simplified model established near a specific operating point.

[0004] In actual operation of a grid-type static var generator (SVR), nonlinear coupling exists between the DC bus voltage, DC capacitor energy storage, filter inductor current, filter capacitor voltage, and converter modulation wave. Furthermore, cross-coupling between the d-axis and q-axis exists in the rotating coordinate system. Controllers designed based on locally linearized models struggle to accurately describe these coupling relationships. When the operating point changes or large disturbances such as grid frequency or voltage drops occur, model errors increase, potentially leading to DC bus voltage fluctuations, filter capacitor voltage tracking lag, increased overshoot, and prolonged settling time. To ensure control performance under different operating conditions, it is often necessary to repeatedly adjust multiple control parameters, which are interdependent, making the tuning process complex.

[0005] While existing nonlinear control methods such as sliding mode control and active disturbance rejection control can improve control performance under large disturbance conditions to some extent, they usually have problems such as complex control structures, unclear correspondence between parameters and system physical states, large amount of calculation of control quantities, and difficulty in implementing digital controllers.

[0006] Furthermore, applying the state feedback precise linearization method to grid-type static var generators still faces several challenges: First, it is necessary to establish an affine nonlinear state-space model that can simultaneously describe the changes in DC-side energy storage and the power transfer process on the AC side. Second, it is necessary to select the output function appropriately and calculate the multi-order Lie derivatives to determine the relative order of each output channel; Third, it is necessary to construct a differential homeomorphic coordinate transformation and input transformation that satisfy the invertibility condition so that the system can be transformed into a controllable standard linear form. Fourth, it is also necessary to inversely transform the linear control quantity into the actual control input that meets the modulation requirements of the converter.

[0007] Therefore, how to balance the nonlinear compensation effect, the tuneability of control parameters, and the feasibility of engineering implementation remains an urgent problem to be solved in the field of grid-type static var generator control. Summary of the Invention

[0008] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a control method and system for a grid-type static var generator (SVR). This invention addresses the technical problems of existing grid-type SVR control methods, which rely on local linearization or simplified models, making it difficult to accurately compensate for the nonlinear coupling in the transmission process of DC-side energy and AC-side power. This results in dynamic mutual influence between active power, reactive power, voltage, and DC bus energy, complex control parameter tuning, inconsistent control performance under different operating conditions, and the complex structure, unclear physical meaning of parameters, and difficulty in engineering implementation of existing nonlinear control methods.

[0009] The present invention adopts the following technical solution: A control method for a grid-type static var generator includes the following steps: S1. Collect the active power and grid current at the common coupling point, the DC bus voltage on the DC side, and the filter capacitor voltage and filter inductor current of the filter circuit. Obtain the angular frequency based on the droop relationship between active power and angular frequency, obtain the DC capacitor energy based on the DC bus voltage, and obtain the reference value of the state variable. S2. Using the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, as well as the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, an affine nonlinear state-space model is constructed by combining the angular frequency and the grid current. S3. Construct the first output function of the total energy storage of the system and the second output function of the d-axis voltage error of the filter capacitor based on the state variables, calculate the Lie derivatives of the two output functions, and determine the relative orders as 3 and 2 respectively; S4. Perform a differential homeomorphic coordinate transformation on the model based on the output function and relative order to obtain new state variables. Calculate the reference values ​​of the new state variables based on the reference values ​​of the state variables, and perform difference calculation to obtain the deviation vector. S5. Determine the feedback gain using the pole placement method or a linear quadratic regulator, and obtain the linear control input based on the feedback gain and the deviation vector; S6. Perform inverse input transformation based on Lie derivative and linear control input to obtain d-axis and q-axis modulated waves, and transform the transformed system into Brunovsky canonical form; perform dq / abc coordinate transformation and sinusoidal pulse width modulation on d-axis and q-axis modulated waves to obtain switch drive pulses.

[0010] Furthermore, the d-axis component of the filter capacitor voltage... and q-axis components d-axis component of filter inductor current and q-axis components and DC capacitor energy Constructing a state vector The energy of the DC capacitor is:

[0011] in, This is the DC capacitance value. This is the DC bus voltage; The control input is:

[0012] in, and These are the d-axis modulated wave and the q-axis modulated wave, respectively, with superscripts indicating their respective characteristics. Represents the transpose of a matrix or vector; The affine nonlinear state-space model is as follows:

[0013]

[0014] in, For the drift vector field, For state vectors, To control the input vector; State vector The derivative with respect to time; and These are the first input vector field and the second input vector field, respectively. Let be the output vector of the affine nonlinear state-space model. For the state vector Determine the output vector The output function vector.

[0015] Furthermore, the first output function is:

[0016] The second output function is:

[0017] in, This is the value of the filter capacitor. This is the value of the filter inductance. This is the reference value for the d-axis voltage of the filter capacitor. The first output function represents the total energy stored in the system; The second output function represents the d-axis voltage error of the filter capacitor.

[0018] Furthermore, a differential homeomorphic coordinate transformation is performed on the first and second output functions to obtain new state variables:

[0019] in, Represents the field along the drift vector The first-order Lie derivative, , , , and The new state variables are obtained by the differential homeomorphic coordinate transformation, and together they constitute the new state vector. ; Represents the field along the drift vector Calculate the Lie derivative twice consecutively.

[0020] Furthermore, the state variable reference values ​​include the filter capacitor voltage reference value, the filter inductor current reference value, and the DC bus voltage reference value, which together form a reference state vector. The reference value for DC capacitor energy is:

[0021] in, This is the reference value for the DC bus voltage; The new state variable reference value is obtained based on the reference state vector:

[0022] The difference between the new state variable and its reference value is calculated to obtain the deviation vector:

[0023]

[0024] in, The reference state vector; This is a reference value for the DC capacitor energy. This serves as a reference value for the new state vector. Indicates the reference state vector The first output function value calculated at the location; The deviation vector; For the reason to The new state vector is formed; superscript This represents the transpose of a vector.

[0025] Furthermore, determining the feedback gain using the pole placement method includes: Desired closed-loop poles are set for the output subsystems with relative order of 3 and the output subsystems with relative order of 2, respectively, and the corresponding desired closed-loop characteristic polynomials are determined based on the desired closed-loop poles. By matching the closed-loop characteristic polynomials of the two output subsystems with their corresponding desired closed-loop characteristic polynomials, the feedback gain of the two output subsystems can be obtained. Based on the feedback gains of the two output subsystems, state feedback calculation is performed on the corresponding deviation components in the deviation vector to obtain the linear control input. and .

[0026] Furthermore, determining the feedback gain using the linear quadratic regulator includes: State weight matrices and control weights are set for the output subsystem with a relative order of 3 and the output subsystem with a relative order of 2, respectively. Quadratic performance indices are constructed based on the state weight matrices and control weights, and the corresponding feedback gain is obtained by solving the Riccati equation. The reference values ​​for the state weight matrix and control weights are:

[0027]

[0028]

[0029]

[0030] in, and These are the state weight matrix and control weights of the output subsystem with a relative order of 3, respectively. and These are the state weight matrix and control weights of the output subsystem with a relative order of 2, respectively.

[0031] Furthermore, the linear control input includes and The decoupling matrix is ​​composed of the Lie derivatives of the first and second output functions. Within the working domain where the determinant of the decoupling matrix is ​​not zero, the decoupling matrix is ​​inverted, and the input inverse transform is performed according to the following formula:

[0032] in, and These are the d-axis modulation wave and q-axis modulation wave of the converter, respectively. and These are the linear control inputs for the output subsystem with a relative order of 3 and the output subsystem with a relative order of 2, respectively. and They represent the fields along the input vector, respectively. and input vector field Li Daoshu, and Representing the fields along the drift vector respectively The determinant of the decoupling matrix and the q-axis component of the grid current are calculated consecutively two and three times. Proportional, the working domain satisfies ; Through the inverse transformation of the input, the transformed system satisfies the Brunovsky canonical form. , , , , .

[0033] Furthermore, the d-axis modulated wave and q-axis modulated wave A dq / abc coordinate transformation is performed to obtain a three-phase sinusoidal modulated wave. The three-phase sinusoidal modulated wave is then compared with a high-frequency carrier wave using sinusoidal pulse width modulation to obtain the switching drive pulses for the inverter power switching devices.

[0034] Secondly, embodiments of the present invention provide a grid-type static var generator control system, comprising: The data acquisition and power outer loop module is used to acquire active power and grid current at the common coupling point, DC bus voltage on the DC side, and filter capacitor voltage and filter inductor current in the filtering stage. It obtains the angular frequency based on the droop relationship between active power and angular frequency, obtains the DC capacitor energy based on the DC bus voltage, and acquires the reference value of the state variable. The model building module is used to construct an affine nonlinear state-space model with the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, as well as the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, combined with the angular frequency and grid current. The output function construction module is used to construct the first output function of the total energy storage of the system and the second output function of the d-axis voltage error of the filter capacitor based on the state variables. The Lie derivatives of the two output functions are calculated to determine the relative orders as 3 and 2, respectively. The coordinate transformation and deviation calculation module is used to perform a differential homeomorphic coordinate transformation on the model according to the output function and relative order to obtain new state variables, calculate the reference value of the new state variables according to the reference value of the state variables, and perform difference calculation to obtain the deviation vector. The linear control module is used to determine the feedback gain using the pole placement method or a linear quadratic regulator, and to obtain the linear control input based on the feedback gain and the deviation vector. The inverse input transformation module is used to perform inverse input transformation based on Lie derivative and linear control input to obtain the d-axis modulated wave and q-axis modulated wave, and to transform the transformed system into Brunofsky canonical form. The pulse generation module is used to perform dq / abc coordinate transformation and sinusoidal pulse width modulation on the d-axis modulated wave and q-axis modulated wave to obtain the switching drive pulse.

[0035] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described grid-type static var generator control method.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described grid-type static var generator control method.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects: A control method for a grid-type static var generator (SVA) is proposed. This method sequentially sets up steps including state variable acquisition, affine nonlinear modeling, output function construction, differential homeomorphic coordinate transformation, linear feedback control, inverse input transformation, and pulse generation, forming a complete closed-loop control chain for the grid-type SVA. The nonlinear coupling between DC-side energy storage, AC-side voltage and current, and modulation input in the main circuit is incorporated into a unified control framework. This transforms the complex nonlinear control problem into a linear control problem, thereby simplifying controller design and parameter tuning, and improving the system's dynamic response and stable operation under startup, grid frequency changes, and voltage dips.

[0038] Furthermore, an affine nonlinear state-space model is established using the filter capacitor voltage, filter inductor current, and DC capacitor energy as state variables, and the d-axis and q-axis modulation waves as control inputs. This model fully characterizes the dynamic relationship between the AC filtering stage, the DC energy storage stage, and the converter modulation input. This approach reduces model bias caused by locally linearized models, providing a unified and clear mathematical foundation for subsequent Lie derivative calculations, relative order determination, and precise linearization.

[0039] Furthermore, by setting the total system energy storage as the first output function and the d-axis voltage error of the filter capacitor as the second output function, the overall energy state of the system and the AC-side voltage tracking state are characterized, respectively. The relative orders of the two output subsystems are further determined to be 3 and 2, respectively, providing a basis for constructing a coordinate transformation covering all states of the fifth-order system. This is beneficial for balancing DC bus energy stability and filter capacitor voltage tracking, and reducing dynamic coupling between different control objectives.

[0040] Furthermore, by constructing new state variables based on the two output functions and their Lie derivatives, the original state variables are mapped to a new coordinate system that can directly reflect the dynamic changes of the output. This provides a coordinate basis for subsequent input feedback transformation and Brunofsky canonical form construction, enabling the dynamic relationship of the original nonlinear system to be described according to a series integral structure, thus facilitating controller design using mature linear control theory.

[0041] Furthermore, by constructing a reference state vector based on the reference values ​​of the filter capacitor voltage, filter inductor current, and DC bus voltage, and calculating the deviation vector between the new state variables and their reference values, the difference between the actual operating state of the system and the target steady-state operating point is quantified. This provides a unified error signal for state feedback control, enabling the controller to coordinate and adjust the energy state and voltage state around the target operating point, thereby improving the clarity of the control objective and the closed-loop tracking performance.

[0042] Furthermore, by configuring desired closed-loop poles for the third-order and second-order output subsystems respectively, and determining the feedback gain accordingly, the system's response speed and damping characteristics can be directly set using the closed-loop pole locations. This allows for adjustment of the dynamic performance of the two subsystems according to different operating requirements, which helps shorten settling time, suppress oscillations, and reduce the difficulty of repeatedly trial-and-error in adjusting control parameters.

[0043] Furthermore, by setting state weight matrices and control weights for the two output subsystems respectively, and using a linear quadratic regulator to determine the feedback gain, a comprehensive balance is struck between state deviation suppression capability and control input amplitude. By adjusting the elements in the weight matrix, control priorities are assigned to different state variables, thereby adjusting the system overshoot and settling time, and achieving an appropriate balance between dynamic response performance and control cost.

[0044] Furthermore, by performing an inverse transformation on the linear control input using a matrix composed of the Lie derivatives of the output function, the linear control quantity in the new coordinate system is restored to the d-axis and q-axis modulated waves that the converter can execute, while simultaneously compensating for drift terms and input coupling terms in the original system. Under the condition that the correlation matrix is ​​invertible, the transformed system can satisfy the Brunofsky canonical form, making the two output subsystems present relatively independent cascade integral structures, thus achieving nonlinear dynamic compensation and control channel decoupling.

[0045] Furthermore, by performing dq / abc coordinate transformation on the d-axis and q-axis modulated waves and generating switching drive pulses using sinusoidal pulse width modulation, the control algorithm output is converted into a three-phase drive signal that can be directly executed by the inverter's power switching devices. This completes the interface between the controller and the main power circuit, enabling the precise linearization control method to be implemented in digital controllers and actual converters, thus improving the engineering applicability of the solution.

[0046] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0047] In summary, this invention compensates for the nonlinear coupling of a grid-type static var generator by nonlinear modeling, relative order determination, differential homeomorphic coordinate transformation, and inverse input transformation. It also utilizes pole placement or a linear quadratic regulator to achieve state feedback control with well-defined parameters, offering advantages such as fast response, small overshoot, convenient tuning, stable operation, and strong engineering feasibility.

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the connection of the state feedback precise linearization controller in the method of the present invention; Figure 2 This is a flowchart of the method of the present invention; Figure 3 The method of this invention is used to replicate the system's response curve in MATLAB; Figure 4 The figure shows the response curves of DC bus voltage and filter capacitor voltage during the system startup process of the method of the present invention. Figure 5 This is a system response diagram of the method of the present invention when the power grid frequency drops by 1Hz and recovers; Figure 6 This is a system response diagram of the method of the present invention when the grid voltage drops by 20% and recovers; Figure 7 This is a system response diagram of the method of the present invention when the grid voltage drops by 50% and recovers; Figure 8 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 9 This is a block diagram of an electronic device according to an embodiment of the present invention.

[0050] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0053] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0054] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0055] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0056] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0057] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0058] This invention provides a control method for a grid-type static var generator (SVM). Using the filter capacitor voltage, filter inductor current, and DC capacitor energy as state variables, and the converter modulation wave as the control input, a fifth-order affine nonlinear state-space model is established, simultaneously reflecting the coupling relationship between DC-side energy storage changes and AC-side power transmission. The total system energy storage and the d-axis voltage error of the filter capacitor are selected as output functions. The relative orders of the two output channels are determined to be third and second, respectively, through Lie derivative calculations. Based on this, a differential homeomorphic coordinate transformation is constructed to accurately transform the original nonlinear system into a third-order and second-order cascaded integral subsystem of Brunofsky canonical form. Subsequently, a pole placement or linear quadratic regulator (LQR) is used to generate linear control quantities, which are then restored to d- and q-axis modulation waves through an inverse input transformation. The above improvements can accurately compensate for the nonlinear coupling between the DC and AC sides, reduce the mutual influence between active power, reactive power, voltage and DC bus energy dynamics, and make the controller parameters have a clearer correspondence with response speed, damping, overshoot and settling time, thereby simplifying parameter tuning and improving the dynamic response speed, DC bus stability and reactive power support capability of the system under disturbance conditions such as startup, frequency drop and voltage drop.

[0059] Please see Figure 1 and Figure 2 The present invention provides a control method for a grid-type static var generator, comprising the following steps: S1. Collect the active power and DC bus voltage at the common coupling point, and calculate the angular frequency of the grid-type static var generator through the droop characteristic of the power outer loop; specifically, the droop characteristic generates the system's reference angular frequency based on the proportional relationship between active power and angular frequency. S2. Collect the filter inductor current, filter capacitor voltage, grid current, DC voltage, and preset reference values ​​for filter capacitor voltage, filter inductor current, and DC voltage. Use these physical quantities, along with the angular frequency obtained in step 1, as inputs for nonlinear coordinate transformation. These input variables fully describe the current state and control objective of the system. The calculation of the reference value and the actual value is as follows: First, calculate or determine the reference values ​​of the state variables based on the system's expected steady-state operating point. Specifically, this includes the reference value of the filter capacitor voltage in the dq coordinate system. 、 (usually set) To maintain (axis orientation), the reference value of the filter inductor current in the dq coordinate system 、 and DC capacitor energy reference value ， in, This is the reference value for the DC bus voltage.

[0060] Simultaneously, the actual state variables of the system, namely the filter capacitor voltage, are measured in real time using voltage and current sensors. Filter inductor current DC bus voltage (and then calculate the actual DC capacitor energy) ), and grid current These actual values ​​are denoted as vectors. .

[0061] S3. Transform the original affine nonlinear system into a linear system through nonlinear coordinate transformation, that is, obtain the new linearized state variables using differential homeomorphic coordinate transformation, denoted as follows: z 1. z 2. z 3. z 4. z 5. This transformation rigorously compensates for the nonlinear coupling caused during power transmission between the DC and AC sides; S301. Construction of state-space equations and output function. First, the affine nonlinear state-space equations of the grid-type static var generator are established.

[0062] Selecting state variables Control input (i.e., the converter modulation wave), then the system is represented as:

[0063]

[0064] in, , , The specific expression is determined by the main circuit parameters (filter inductor). Filter capacitor DC capacitor Grid-connected reactor (etc.) decision.

[0065]

[0066] Meanwhile, this invention selects two output functions.

[0067] First output function The total energy stored in the system is the sum of the energy of the DC capacitor, the energy of the filter capacitor, and the energy of the filter inductor.

[0068] in, For filtering capacitors, This is a filter inductor.

[0069] Second output function For filter capacitors d Difference between shaft voltage and reference value:

[0070] S302. Calculation of Lie derivatives corresponding to state-space equations Based on nonlinear control theory, calculate the Lie derivatives of the two output functions respectively.

[0071] for The following items need to be calculated in sequence: Zeroth-order Lie derivative:

[0072] First-order Lie derivative:

[0073] Lie derivatives related to the input channel:

[0074]

[0075] Second-order Lie derivative:

[0076] Mixed Lie derivatives:

[0077]

[0078] Third-order Lie derivative:

[0079] Terms used for determining relative order:

[0080]

[0081] Through inspection It can be determined The relative order is 3.

[0082] for The following items need to be calculated in sequence: Zeroth-order Lie derivative:

[0083] First-order Lie derivative:

[0084] Lie derivatives related to the input channel:

[0085]

[0086] Second-order Lie derivative:

[0087] Terms used for determining relative order:

[0088]

[0089] Through inspection ,Sure The relative order is 2.

[0090] In the controller, real-time calculations are required. and The value, and the corresponding (terms), so as to facilitate the subsequent construction of linearized control laws; among which, For the relative order of each subsystem, have ,for have .

[0091] S303, Differential Homeomorphic Coordinate Transformation The original nonlinear system is transformed into the Brunofsky canonical form using differential homeomorphic coordinate transformation. The new state variables after the transformation are... Defined as:

[0092] Calculate the actual values ​​separately and reference value (At steady state, (The reference values ​​are all 0). Then the deviation vector is obtained. .

[0093] S4. For the linearized system, the control input in the linear system coordinates is calculated using the exact linearized optimal control law; this optimal control law is designed based on a linear quadratic regulator or the pole placement method. Deviation The input is fed into the linear optimal control law module. This invention provides two design methods: Extreme point placement method: For the third-order subsystem ( ) and second-order subsystems ( Let the desired closed-loop poles be set for each. For example, the desired pole of a third-order subsystem can be taken as... The expected pole of the second-order subsystem can be taken as Make the closed-loop characteristic polynomial and the expected characteristic polynomial equal. and Correspondingly, the feedback gain matrix is ​​calculated. and .

[0094] Linear Quadratic Regulator (LQR): Define performance metrics Solve the Riccati equation The optimal feedback gain is obtained, where, , To linearize the state matrix of the system, , This is the corresponding weight matrix.

[0095] The reference values ​​for the weight matrix given in this invention are:

[0096]

[0097]

[0098]

[0099] in, and Corresponding to the state weight matrix and control weight matrix of the third-order subsystem, and The state weight matrix and control weight matrix correspond to the second-order subsystem. The diagonal elements in the text correspond to respectively , , The weight, The diagonal elements in the text correspond to respectively , The weights are determined by adjusting the values ​​of these weight matrices. This alters the system's response characteristics (e.g., changing a 5×10 matrix). 6 Change to 5×10 2 (To accelerate response but increase overshoot), providing a basis for engineering parameter adjustment.

[0100] The output of the linear control law is:

[0101] in, The first row corresponds to the feedback gain of the third-order subsystem, which acts on the deviation. The second row corresponds to the feedback gain of the second-order subsystem, which acts on the deviation. The zero elements in the matrix indicate that there is no cross-coupling between different subsystems.

[0102] S5. The control input in the linear system coordinates obtained in step S4 is inversely transformed through the original nonlinear system optimal control law to obtain the control input required by the original nonlinear system, namely the modulation wave signal of the converter. Inverse transformation yields the original control input linear control quantity The control input of the original nonlinear system is obtained through inverse calculation. and (That is, the modulated wave signal of the converter). The inverse transform formula is:

[0103] Calculated and This is the control input of the original affine nonlinear system.

[0104] S6. The modulation wave signal is modulated by PWM to generate switching drive pulses, which control the power switching devices of the inverter, thereby realizing closed-loop control of the grid-type static var generator.

[0105] Control input , (The d- and q-axis modulated waves) are transformed into dq / abc coordinates to obtain a three-phase sinusoidal modulated wave. Then, SPWM modulation is used to compare with the high-frequency carrier wave to generate drive pulses for the inverter power switching devices, thereby realizing closed-loop control of the grid-type static var generator.

[0106] In another embodiment of the present invention, a grid-type static var generator (SVM) control system is provided. This system can be used to implement the above-mentioned grid-type SVM control method. Specifically, the grid-type SVM control system includes a data acquisition and power outer loop module, a model building module, an output function building module, a coordinate transformation and deviation calculation module, a linear control module, an input inverse transformation module, and a pulse generation module.

[0107] Among them, the data acquisition and power outer loop module is used to acquire active power, DC bus voltage, filter capacitor voltage, filter inductor current and grid current, obtain the angular frequency based on the droop relationship between active power and angular frequency, obtain the DC capacitor energy based on the DC bus voltage, and obtain the reference value of the state variable. The model building module is used to construct an affine nonlinear state-space model with the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, as well as the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, combined with the angular frequency and grid current. The output function construction module is used to construct the first output function of the total energy storage of the system and the second output function of the d-axis voltage error of the filter capacitor based on the state variables. The Lie derivatives of the two output functions are calculated to determine the relative orders as 3 and 2, respectively. The coordinate transformation and deviation calculation module is used to perform a differential homeomorphic coordinate transformation on the model according to the output function and relative order to obtain new state variables, calculate the reference value of the new state variables according to the reference value of the state variables, and perform difference calculation to obtain the deviation vector. The linear control module is used to determine the feedback gain using the pole placement method or a linear quadratic regulator, and to obtain the linear control input based on the feedback gain and the deviation vector. The inverse input transformation module is used to perform inverse input transformation based on Lie derivative and linear control input to obtain the d-axis modulated wave and q-axis modulated wave, and to transform the transformed system into Brunofsky canonical form. The pulse generation module is used to perform dq / abc coordinate transformation and sinusoidal pulse width modulation on the d-axis modulated wave and q-axis modulated wave to obtain the switching drive pulse.

[0108] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a grid-type static var generator control method, including: Active power and grid current at the common coupling point, DC bus voltage on the DC side, and filter capacitor voltage and filter inductor current in the filtering stage are collected. The angular frequency is obtained based on the droop relationship between active power and angular frequency, and the DC capacitor energy is obtained based on the DC bus voltage. Reference values ​​for state variables are also acquired. Using the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, and the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, an affine nonlinear state-space model is constructed in conjunction with the angular frequency and grid current. Based on the state variables, a first output function for the total system energy storage and a second output function for the filter capacitor d-axis voltage error are constructed, and the two are calculated. The Lie derivative of the output function is used to determine the relative orders as 3 and 2. Based on the output function and relative order, a differential homeomorphic coordinate transformation is performed on the model to obtain new state variables. Reference values ​​for the new state variables are calculated based on their reference values, and the difference is calculated to obtain the deviation vector. The feedback gain is determined using the pole placement method or a linear quadratic regulator. The linear control input is obtained based on the feedback gain and the deviation vector. An inverse input transformation is performed based on the Lie derivative and the linear control input to obtain d-axis and q-axis modulated waves. The transformed system is then converted to the Brunofsky canonical form. The dq / abc coordinate transformation and sinusoidal pulse width modulation are performed on the d- and q-axis modulated waves to obtain the switching drive pulse.

[0109] Please see Figure 8The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the grid-type static var generator control method of this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the grid-type static var generator control system of this embodiment. To avoid repetition, details are omitted here.

[0110] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 8 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0111] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0112] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0113] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0114] Please see Figure 9 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0115] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0116] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0117] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0118] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0119] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0120] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0121] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0122] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0123] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the grid-type static var generator control method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Active power and grid current at the common coupling point, DC bus voltage on the DC side, and filter capacitor voltage and filter inductor current in the filtering stage are collected. The angular frequency is obtained based on the droop relationship between active power and angular frequency, and the DC capacitor energy is obtained based on the DC bus voltage. Reference values ​​for state variables are also acquired. Using the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, and the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, an affine nonlinear state-space model is constructed in conjunction with the angular frequency and grid current. Based on the state variables, a first output function for the total system energy storage and a second output function for the filter capacitor d-axis voltage error are constructed, and the two are calculated. The Lie derivative of the output function is used to determine the relative orders as 3 and 2. Based on the output function and relative order, a differential homeomorphic coordinate transformation is performed on the model to obtain new state variables. Reference values ​​for the new state variables are calculated based on their reference values, and the difference is calculated to obtain the deviation vector. The feedback gain is determined using the pole placement method or a linear quadratic regulator. The linear control input is obtained based on the feedback gain and the deviation vector. An inverse input transformation is performed based on the Lie derivative and the linear control input to obtain d-axis and q-axis modulated waves. The transformed system is then converted to the Brunofsky canonical form. The dq / abc coordinate transformation and sinusoidal pulse width modulation are performed on the d- and q-axis modulated waves to obtain the switching drive pulse.

[0124] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0125] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0126] Please see Figure 3 In this embodiment of the invention, the original nonlinear system was replicated and verified in the MATLAB environment. To avoid the coupling effect of grid frequency fluctuations and grid current changes on the controller parameter tuning process, this embodiment uses the grid angular frequency... ω d-axis component of grid current and q-axis components By substituting fixed steady-state values ​​into the system, the dynamic performance regulation law of each element in the weight matrix can be clearly analyzed without being disturbed by external time-varying disturbances.

[0127] Figure 3 The simulation comparison results under two different weight parameter settings are given, where the solid line corresponds to the weight matrix:

[0128]

[0129]

[0130]

[0131] The dashed line corresponds to only The relevant weights are reduced (e.g., from 5×10). 6 Adjusted to 5×10 2The remaining parameters remain unchanged. This example demonstrates how changing the voltage-related weights affects the system response; in practical engineering, any weight matrix element can be adjusted as needed. Figure 3 It contains six subplots, which respectively show the time-domain response of the state variables, control inputs, and the transformed state variables. The solid lines correspond to the response curves under the initial weights, and the dashed lines correspond to the adjusted weights. z 1. Response curve after relevant weighting.

[0132] The first subgraph shows the state variables. x 1. x 2. x The time-domain response of 3, the second subgraph shows the state variables. x 4. x The time-domain response of 5. Comparing the solid and dashed lines shows that, compared to the initial weights, the adjusted... z After 1 relevant weight, x Although the overshoot of 1 has decreased, the time required to reach steady state still exceeds 5 seconds, while the initial weights are... x The adjustment time for 1 is approximately 1.1 seconds, the former being much slower than the latter; in x In response 4, the initial weights reach steady state approximately 0.3 seconds faster than the adjusted weights; x The difference is more pronounced with 5; it reaches steady state in about 0.8 seconds with the initial weights, and the overshoot is reduced by about 542% (relative to the adjusted weights). x 2 and x The response speed of 3 is basically the same under both weights. The third sub-graph shows the control input. u The time-domain variation of 1, the fourth sub-graph shows the control input. u The time-domain variation of 2. (Under the solid and dashed lines) u The responses to 1 remained largely consistent; while for u 2. Although the adjusted curve (dashed line) has a relatively smooth adjustment process, its adjustment time exceeds 5 seconds, while the curve (solid line) with the initial weights reaches steady state in about 1 second. The fifth sub-figure shows the transformed state variables. z 1. z 2. z The curve showing the change of 3, and the sixth sub-graph showing the state variables after the transformation. z 4. z The curve showing the change of 5. For z 3. The response under the initial weights reaches steady state faster than that under the adjusted weights; the trends of the other state variables are basically the same.

[0133] The above comparison leads to the conclusion that increasing only the weights of state variables reduces the deviation of the corresponding system states, but significantly extends the settling time. This pattern provides a clear and reproducible reference for the engineering tuning of controller parameters.

[0134] Please see Figure 4 This invention provides response curves of the DC bus voltage and filter capacitor voltage during system startup, and compares the proposed state feedback precise linearization control method with a parallel structure state feedback control method. The figures show that, using the method of this invention, the DC bus voltage rises rapidly from 0V, reaching the set value of 1200V in approximately 0.13s, with virtually no overshoot or oscillation. In contrast, the DC bus voltage rises more slowly and oscillates violently, reaching steady state in approximately 0.6s, with an overshoot of approximately 46%. The filter capacitor voltage transitions smoothly to the reference value under the method of this invention, without overshoot; under the contrast method, significant oscillations occur, stabilizing only after approximately 0.5s, with large fluctuations and an overshoot of 103%. These comparisons demonstrate that the method proposed in this invention has a faster response speed and smaller overshoot during system startup, exhibiting superior dynamic performance compared to the contrast method.

[0135] Please see Figure 5 This invention provides response curves of the DC bus voltage and filter capacitor voltage RMS values ​​during a 1Hz drop in grid frequency and their recovery process, and compares them with the same comparative method. The figures show that when the grid frequency drops from 50Hz to 49Hz in 1 second, the DC bus voltage fluctuation under this invention's method is small, and it quickly recovers to the set value; under the comparative method, the voltage change is large, and the recovery is slow. The RMS value of the filter capacitor voltage is almost unaffected by frequency disturbances under this invention's method, remaining stable; under the comparative method, a significant drop occurs, and it cannot recover to a stable state. When the grid frequency recovers to 50Hz in 1.2 seconds, this invention's method can quickly return to a steady state in about 0.1 seconds without overshoot, while the comparative method exhibits oscillations and takes about 0.3 seconds to recover to a steady state. The above comparison shows that the method of this invention has a better ability to support the DC bus voltage and filter capacitor voltage RMS values ​​under grid frequency disturbances than the comparative method.

[0136] Please see Figure 6This invention provides response curves of the DC bus voltage and filter capacitor voltage RMS values ​​during a 20% voltage dip and recovery process, and compares them with the same comparative method. The figures show that when the grid voltage drops by 20% in 1 second, the DC bus voltage under the method of this invention exhibits almost no fluctuation, while the voltage fluctuation under the comparative method is larger and difficult to stabilize. The RMS value of the filter capacitor voltage can quickly follow the grid voltage change under the method of this invention, while it is difficult to achieve rapid following under the comparative method. When the grid voltage recovers in 1.21 seconds, the overshoot of the method of this invention is less than 8.5%, and the recovery time is less than 0.04 seconds, achieving rapid tracking; the overshoot of the comparative method is approximately 11.6%, and the recovery time is approximately 0.06 seconds, indicating a relatively large overshoot. The above comparison shows that under a 20% voltage dip disturbance, the method of this invention has a better control effect on the DC bus voltage and filter capacitor voltage RMS values ​​than the comparative method.

[0137] Please see Figure 7 This invention provides response curves of the DC bus voltage and filter capacitor voltage RMS values ​​during a 50% voltage drop and recovery process, and compares them with the same comparative method. The figures show that when the grid voltage drops by 50% in 1.0s, the DC bus voltage under this invention's method remains relatively stable without fluctuations; under the comparative method, the DC voltage fluctuates significantly and is difficult to maintain a steady state. The RMS value of the filter capacitor voltage can quickly follow the grid voltage change under this invention's method; under the comparative method, prolonged oscillations occur, making it difficult to maintain stability. When the grid voltage recovers in 1.2s, this invention's method can return to a relatively smooth steady state (0.02s), with an overshoot of less than 11.7%; the comparative method, however, has a sluggish response (approximately 0.24s) and a large overshoot (approximately 26.8%). These comparisons demonstrate that under a large disturbance condition such as a 50% voltage drop, the robustness and dynamic support capability of this invention's method are significantly superior to the comparative method.

[0138] It should be noted that the embodiments of the present invention selected two typical voltage drop depths of 20% and 50% for verification, which have covered the range of moderate and deep disturbances commonly encountered in actual power grid operation. Deeper voltage drops (such as above 70%) have an extremely low probability of occurring in actual power grids, and at this point, the converter has usually already triggered the undervoltage ride-through protection mechanism, which is beyond the applicable scope of the precise linearization control method proposed in this invention, so no further verification was performed.

[0139] In summary, this invention provides a control method and system for a grid-type static var generator (SVR). Addressing the nonlinear coupling problem between DC-side energy storage, AC-side filtering, and converter modulation input in a grid-type SVR, an affine nonlinear state-space model is established, incorporating filter capacitor voltage, filter inductor current, and DC capacitor energy. This allows the controller design to move beyond the limitations of a local linear model near a single operating point. By selecting the total system energy storage and the d-axis voltage error of the filter capacitor as output functions, both overall energy stability and AC voltage tracking can be simultaneously considered. Through relative order determination, differential homeomorphic coordinate transformation, and inverse input transformation, the complex nonlinear system is transformed into a series integral structure that is easy to control, achieving compensation for nonlinear dynamics and control channel coupling. Furthermore, the pole placement method allows direct setting of response speed and damping, and the linear quadratic regulator can adjust overshoot, settling time, and control cost through weighted adjustments, resulting in clear control parameter effects and convenient tuning. After dq / abc transformation and sinusoidal pulse width modulation, the control quantity can be directly used to drive the inverter, providing a solid foundation for digital control implementation. Simulation results show that the method of the present invention can maintain stable operation under startup, frequency change and voltage drop conditions, and has fast dynamic response and reactive power support capability.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0141] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0147] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A control method for a grid-type static var generator, characterized in that, Includes the following steps: S1. Collect the active power and grid current at the common coupling point, the DC bus voltage on the DC side, and the filter capacitor voltage and filter inductor current of the filter circuit. Obtain the angular frequency based on the droop relationship between active power and angular frequency, obtain the DC capacitor energy based on the DC bus voltage, and obtain the reference value of the state variable. S2. Using the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, as well as the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, an affine nonlinear state-space model is constructed by combining the angular frequency and the grid current. S3. Construct the first output function of the total energy storage of the system and the second output function of the d-axis voltage error of the filter capacitor based on the state variables, calculate the Lie derivatives of the two output functions, and determine the relative orders as 3 and 2 respectively; S4. Perform a differential homeomorphic coordinate transformation on the model based on the output function and relative order to obtain new state variables. Calculate the reference values ​​of the new state variables based on the reference values ​​of the state variables, and perform difference calculation to obtain the deviation vector. S5. Determine the feedback gain using the pole placement method or a linear quadratic regulator, and obtain the linear control input based on the feedback gain and the deviation vector; S6. Perform inverse input transformation based on Lie derivative and linear control input to obtain d-axis modulated wave and q-axis modulated wave, and transform the transformed system into Brunofsky canonical form; perform dq / abc coordinate transformation and sinusoidal pulse width modulation on d-axis modulated wave and q-axis modulated wave to obtain switch drive pulse.

2. The control method for a grid-type static var generator according to claim 1, characterized in that, The d-axis component of the filter capacitor voltage and q-axis components d-axis component of filter inductor current and q-axis components and DC capacitor energy Constructing a state vector The energy of the DC capacitor is: in, This is the DC capacitance value. This is the DC bus voltage; The control input is: in, and These are the d-axis modulated wave and the q-axis modulated wave, respectively, with superscripts. Represents the transpose of a matrix or vector; The affine nonlinear state-space model is as follows: in, For the drift vector field, For state vectors, To control the input vector; State vector The derivative with respect to time; and These are the first input vector field and the second input vector field, respectively. Let be the output vector of the affine nonlinear state-space model. For the state vector Determine the output vector The output function vector.

3. The control method for a grid-type static var generator according to claim 2, characterized in that, The first output function is: The second output function is: in, This is the value of the filter capacitor. This is the value of the filter inductance. This is the reference value for the d-axis voltage of the filter capacitor. The first output function represents the total energy stored in the system; The second output function represents the d-axis voltage error of the filter capacitor.

4. The control method for a grid-type static var generator according to claim 3, characterized in that, Perform a homeomorphic coordinate transformation on the first and second output functions to obtain new state variables: in, Represents the field along the drift vector The first-order Lie derivative, , , , and The new state variables are obtained by the differential homeomorphic coordinate transformation, and together they constitute the new state vector. ; Represents the field along the drift vector Calculate the Lie derivative twice consecutively.

5. The control method for a grid-type static var generator according to claim 4, characterized in that, The state variable reference values ​​include the filter capacitor voltage reference value, the filter inductor current reference value, and the DC bus voltage reference value, which together form a reference state vector. The reference value for DC capacitor energy is: in, This is the reference value for the DC bus voltage; The new state variable reference value is obtained based on the reference state vector: The difference between the new state variable and its reference value is calculated to obtain the deviation vector: in, The reference state vector; This is a reference value for DC capacitor energy. This serves as a reference value for the new state vector. Indicates the reference state vector The first output function value calculated at the location; The deviation vector; For the reason to The new state vector is formed; superscript This represents the transpose of a vector.

6. The control method for a grid-type static var generator according to claim 5, characterized in that, Determining the feedback gain using the pole placement method includes: Desired closed-loop poles are set for the output subsystems with relative order of 3 and the output subsystems with relative order of 2, respectively, and the corresponding desired closed-loop characteristic polynomials are determined based on the desired closed-loop poles. By matching the closed-loop characteristic polynomials of the two output subsystems with their corresponding desired closed-loop characteristic polynomials, the feedback gain of the two output subsystems can be obtained. Based on the feedback gains of the two output subsystems, state feedback calculation is performed on the corresponding deviation components in the deviation vector to obtain the linear control input. and .

7. The control method for a grid-type static var generator according to claim 5, characterized in that, Determining the feedback gain using the linear quadratic regulator includes: State weight matrices and control weights are set for the output subsystem with a relative order of 3 and the output subsystem with a relative order of 2, respectively. Quadratic performance indices are constructed based on the state weight matrices and control weights, and the corresponding feedback gain is obtained by solving the Riccati equation. The reference values ​​for the state weight matrix and control weights are: in, and These are the state weight matrix and control weights of the output subsystem with a relative order of 3, respectively. and These are the state weight matrix and control weights of the output subsystem with a relative order of 2, respectively.

8. The control method for a grid-type static var generator according to claim 5, characterized in that, The linear control input includes and The decoupling matrix is ​​composed of the Lie derivatives of the first and second output functions. Within the working domain where the determinant of the decoupling matrix is ​​not zero, the decoupling matrix is ​​inverted, and the input inverse transformation is performed according to the following formula: in, and These are the d-axis modulation wave and q-axis modulation wave of the converter, respectively. and These are the linear control inputs for the output subsystem with a relative order of 3 and the output subsystem with a relative order of 2, respectively. and They represent the fields along the input vector, respectively. and input vector field Li Daoshu, and Representing the fields along the drift vector respectively The determinant of the decoupling matrix and the q-axis component of the grid current are calculated consecutively two and three times. Proportional, the working domain satisfies ; Through the inverse transformation of the input, the transformed system satisfies the Brunovsky canonical form. , , , , .

9. The control method for a grid-type static var generator according to claim 8, characterized in that, For the d-axis modulated wave and q-axis modulated wave A dq / abc coordinate transformation is performed to obtain a three-phase sinusoidal modulated wave. The three-phase sinusoidal modulated wave is then compared with a high-frequency carrier wave using sinusoidal pulse width modulation to obtain the switching drive pulses for the inverter power switching devices.

10. A grid-type static var generator control system, characterized in that, include: The data acquisition and power outer loop module is used to acquire active power and grid current at the common coupling point, DC bus voltage on the DC side, and filter capacitor voltage and filter inductor current in the filtering stage. It obtains the angular frequency based on the droop relationship between active power and angular frequency, obtains the DC capacitor energy based on the DC bus voltage, and acquires the reference value of the state variable. The model building module is used to construct an affine nonlinear state-space model with the d-axis and q-axis components of the filter capacitor voltage and filter inductor current, as well as the DC capacitor energy, as state variables, and the d-axis and q-axis modulation waves of the converter as control inputs, combined with the angular frequency and grid current. The output function construction module is used to construct the first output function of the total energy storage of the system and the second output function of the d-axis voltage error of the filter capacitor based on the state variables. The Lie derivatives of the two output functions are calculated to determine the relative orders as 3 and 2, respectively. The coordinate transformation and deviation calculation module is used to perform a differential homeomorphic coordinate transformation on the model according to the output function and relative order to obtain new state variables, calculate the reference value of the new state variables according to the reference value of the state variables, and perform difference calculation to obtain the deviation vector. The linear control module is used to determine the feedback gain using the pole placement method or a linear quadratic regulator, and to obtain the linear control input based on the feedback gain and the deviation vector. The inverse input transformation module is used to perform inverse input transformation based on Lie derivative and linear control input to obtain the d-axis modulated wave and q-axis modulated wave, and to transform the transformed system into Brunofsky canonical form. The pulse generation module is used to perform dq / abc coordinate transformation and sinusoidal pulse width modulation on the d-axis modulated wave and q-axis modulated wave to obtain the switching drive pulse.

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