Multi-mode switching method and system for grid-connected converter based on non-intrusive perception

Abstract

The invention provides a grid-connected converter multi-mode switching method and system based on non-intrusive perception, and belongs to the technical field of grid-connected converter control, and the method comprises the steps: obtaining a grid-connected point voltage, a power grid voltage and a grid-connected current; performing online identification on the obtained grid-connected point voltage, the grid voltage and the grid-connected current to obtain a grid impedance observation value; and calculating a power grid short-circuit ratio based on the power grid impedance observation value, determining the converter to work in a grid following mode when a strong power grid is determined based on the power grid short-circuit ratio, and determining the converter to work in a grid construction mode when a weak power grid is determined.

Description

Technical Field

[0001] This invention belongs to the field of grid-connected converter control technology, and particularly relates to a method and system for multi-mode switching of grid-connected converters based on non-intrusive sensing. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Renewable energy sources are environmentally friendly and energy-secure, leading to the rapid development of distributed generation systems. Power electronic converters are a crucial component of distributed generation systems, with grid-connected converters converting direct current (DC) into high-quality alternating current (AC) for feeding into the grid. Grid-connected converters are available in single-phase and three-phase versions; the former is primarily used in smaller residential generation systems, while the latter is applied in large-scale distributed generation systems.

[0004] For large-scale distributed generation systems, existing grid-connected converter control schemes are mainly divided into two types: grid-following control and grid-connecting control. Grid-following control typically employs constant power control, which can quickly respond to fluctuations in renewable energy, track the maximum power point, ensure the energy output of the generation system, and offers fast power response and high grid-connected current quality. However, grid-following control exhibits poor stability under weak grid conditions and cannot provide voltage and frequency support to the system. Grid-connecting control typically uses droop control or virtual synchronous machine control, adjusting the voltage frequency and amplitude to control the active and reactive power output of the converter. Under weak grid conditions, grid-connecting control offers a larger stability margin than grid-following control. However, grid-connecting control is prone to oscillations under strong grid conditions.

[0005] Non-intrusive sensing grid-connected converters refer to intelligent devices that utilize the inherent control system and sensors of the grid-connected converter itself, and through advanced algorithms, to achieve real-time monitoring and evaluation of the grid's operating status (such as impedance, harmonics, load type, faults, etc.) without increasing additional hardware costs.

[0006] However, traditional control strategies often employ a PI control architecture at their core. The two control structures mentioned above require a total of six PI controllers, making parameter design complex, and the cascaded structure can affect the system's dynamic response speed. During control mode switching—either from grid-following mode to grid-building mode or vice versa—integral saturation can negatively impact the switching speed.

[0007] In addition, traditional impedance identification methods mostly adopt active methods. Currently, there are mode switching methods that periodically inject non-characteristic subharmonic disturbances into the power grid, use the discrete Fourier algorithm to extract harmonic responses, and then calculate the power grid impedance and short-circuit ratio. When the short-circuit ratio reaches the boundary, the control state is switched. However, this method injects harmonic disturbances, which will affect the normal operation of converter equipment and reduce the power quality of grid connection. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, this invention provides a multi-mode switching method and system for grid-connected converters based on non-intrusive sensing. The system can ensure stable operation under both strong and weak power grid conditions, thereby improving the economy and reliability of the new power system with power electronic equipment as its core.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: Firstly, a multi-mode switching method for grid-connected converters based on non-intrusive sensing is disclosed, including: Obtain the grid connection point voltage, grid voltage, and grid connection current; The obtained grid connection point voltage, grid voltage, and grid connection current are identified online to obtain the grid impedance observation value; The grid short-circuit ratio is calculated based on the grid impedance observation. When the grid short-circuit ratio is determined to be strong, the converter operates in grid-following mode; when the grid is determined to be weak, the converter operates in grid-connecting mode.

[0010] As a further technical solution, when obtaining the grid impedance observation value by online identification of the acquired grid connection point voltage, grid voltage, and grid connection current, the following are included: The grid-connected converter is modeled, and the grid impedance parameters, initialization iteration matrix, observation matrix, and feedback matrix are defined based on the grid-side equivalent resistance, grid-side equivalent inductance, three-phase output current, three-phase voltage source, and grid connection point voltage of the grid-connected converter. Update the gain according to the defined matrix above. 、 Update the iteration matrix; The observation error is the difference between the product of the feedback matrix and the transpose of the grid impedance parameters and the observation matrix. The observed grid impedance values ​​are updated based on the observation error, and the observed grid impedance values ​​can be obtained after iterative updates.

[0011] As a further technical solution, when the converter is judged to be switching from grid-following mode to grid-building mode when the grid is weak, the output voltage phase angle and amplitude of the grid-building control strategy need to be synchronized with the grid voltage in advance.

[0012] As a further technical solution, when obtaining the grid-connected current, the predicted value of the grid-connected current is obtained based on the current prediction model. Based on minimizing the cost function of the predicted current, the optimal switching state is selected to realize the control of the converter in grid-connected mode or grid-connected mode.

[0013] As a further technical solution, the current prediction model is constructed as follows: Get α、β Axis current components, α、β Axis voltage components, α、β The components of the grid voltage and the filter inductance; The circuit equations are represented based on the parameters of the acquired grid-connected converter; Discretize and rearrange the above circuit equations to obtain the current prediction model.

[0014] As a further technical solution, based on minimizing the cost function of the predicted current, the optimal switching state is selected, including: Define the switching states of the grid-connected converter; The composite vector of grid-connected converter switching functions is obtained based on the defined grid-connected converter switching states; Using the sum of squares of current errors as the cost function, we iterate through the above composite vectors to find the switching vector that minimizes the cost function.

[0015] Secondly, a multi-mode switching system for grid-connected converters based on non-intrusive sensing is disclosed, including: The data acquisition module is configured to acquire the grid connection point voltage, grid voltage, and grid connection current. The identification module is configured to: perform online identification of the acquired grid connection point voltage, grid voltage and grid connection current to obtain the grid impedance observation value; The mode switching module is configured to: calculate the grid short-circuit ratio based on the grid impedance observation value; determine that the converter operates in grid-following mode when the grid is strong and in grid-connecting mode when the grid is weak.

[0016] The above one or more technical solutions have the following beneficial effects: This invention, within the framework of finite set model predictive control, uses the least squares method to identify grid impedance online and calculate the system's short-circuit ratio. Based on the short-circuit ratio calculation, the grid-connected converter is adjusted to operate in either grid-following or grid-connected mode, thereby ensuring stable system operation under different grid strengths. This invention requires no additional hardware circuitry. Employing finite set model predictive control, it is designed for grid-connected converter equipment in renewable energy generation grid-connected systems, energy storage, and microgrids, offering faster transient response compared to traditional modulation methods. By using the least squares method to identify grid impedance, grid strength can be calculated while ensuring power quality, enabling multi-mode switching of the grid-connected converter. Stable system operation is guaranteed under both strong and weak grid conditions, improving the economy and reliability of new power systems centered on power electronic equipment.

[0017] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 This is a topology diagram of an LC-type filtered two-level grid-connected converter according to an embodiment of the present invention; Figure 2 This is a structural diagram of the multimodal predictive control strategy for a grid-connected converter according to an embodiment of the present invention; Figure 3 This is a diagram of the VSG control structure according to an embodiment of the present invention; Figure 4 This is a logic block diagram of a multimodal switching method based on the least squares method according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] Example 1 This embodiment discloses a multimodal switching method for grid-connected converters based on non-invasive sensing. The LC-type two-level grid-connected converter topology on which it is based is as Figure 1 shown. This converter system includes a DC bus capacitor ( ), a three-phase inverter, a grid-side filter inductor ( ), a filter capacitor ( ), a grid-side equivalent resistance ( ), a grid-side equivalent inductance ( ), a three-phase voltage source ( ), and a point of common coupling voltage ( ). And are the three-phase output voltage and current of the inverter, is the DC bus voltage. By controlling the conduction state of the switching tubes, the converter can output two levels, and the corresponding voltage values are and 0.

[0024] The above-mentioned multimodal switching method for grid-connected converters based on non-invasive sensing includes: Obtain the point of common coupling voltage, grid voltage, and grid-connected current; Perform online identification on the obtained point of common coupling voltage, grid voltage, and grid-connected current to obtain an observed value of the grid impedance; Calculate the grid short-circuit ratio based on the observed value of the grid impedance. When it is judged to be a strong grid based on the grid short-circuit ratio, the converter operates in the grid-following mode, and when it is judged to be a weak grid, the converter operates in the grid-forming mode.

[0025] In this embodiment, the online identification of the line impedance is realized by the non-invasive least squares method with a forgetting factor. The system grid short-circuit ratio ( , is the rated capacity of the distributed generation system connected to the grid, is the grid short-circuit capacity, is the rated effective value of the grid voltage, is the grid impedance) is calculated through the identification result. According to the IEEE standard, when SCR > 3, it is called a strong grid, and when 2 < SCR < 3, it is called a weak grid. Whether the grid-connected converter needs to switch the operating mode is judged through the grid short-circuit ratio. The specific steps are as Figure 4 shown.

[0026] The least squares method impedance identification method can realize impedance identification through the point of common coupling voltage , the grid voltage and the grid-connected current .

[0027] (1) First, model the grid-connected converter. The grid-side equivalent model is a series-connected resistive-inductive circuit, and the voltage equation is:

[0028] Discretize both sides and rearrange them into a linear regression form:

[0029] Wherein: the grid impedance parameter is the parameter to be estimated, i.e. , Observation matrix for: Feedback matrix for: , To measure noise and modeling error.

[0030] (2) Implementation process of the least squares algorithm: First, initialize the iteration matrix.

[0031] At each sampling time, the gain is updated according to the following matrix. K : ,in, r Forgetting factor, I It is an identity matrix.

[0032] Update iteration matrix:

[0033] Calculate the observation error: y is the feedback matrix.

[0034] Update grid impedance observations:

[0035] After iterative updates, the grid impedance observation value can be obtained. The SCR (Self-Regulating Voltage) is detected using the grid impedance observation value to determine the grid strength and whether the converter needs to switch operating modes. Under strong grid conditions, the converter operates in grid-following mode; under weak grid conditions, the converter operates in grid-building mode, providing voltage and frequency support. To ensure a smooth and shock-free switch between grid-following and grid-building modes, the following measures are adopted: pre-synchronization. When the grid strength decreases, if a switch from grid-following mode to grid-building mode is required, the output voltage phase angle and amplitude of the grid-building control strategy must be synchronized with the grid voltage in advance, and the voltage phase angle and amplitude output by the grid-building mode must be synchronized with the actual grid voltage. To achieve a smooth and shock-free switch from grid-following mode to grid-building mode, the specific implementation method of pre-synchronization is: before triggering the switch, although the grid-building control module (such as the VSG controller) is activated, its output does not immediately affect the system. During this pre-synchronization phase, the internal state variables of the grid controller are forced to align with the grid state. Specifically, the frequency of its internal oscillator and the initial value of its phase integrator are forcibly set to the grid frequency and phase angle detected in real-time by the phase-locked loop (PLL), respectively. Simultaneously, the reference value of its output voltage amplitude is set to the currently measured effective value of the grid connection point voltage, temporarily bypassing its inherent power-voltage regulation logic. When the error between the voltage reference vector generated internally by the grid controller and the actual grid voltage vector in terms of amplitude, frequency, and phase converges to a preset minimum threshold, the system determines that synchronization is complete. At this point, control is seamlessly transferred to the grid controller, effectively suppressing voltage and current surges during switching and ensuring stable system operation.

[0036] In one implementation example, the grid-connected converter and grid-connected modal predictive control structure are as follows: Figure 2 As shown, the predicted value of the grid-connected current is obtained based on the prediction model. Based on minimizing the cost function of the predicted current, the optimal switching state is selected to realize the control of the converter and grid mode. The specific steps are as follows.

[0037] The switching states of the grid-connected converter are defined as follows:

[0038] The composite vector of the switching functions of a grid-connected converter can be expressed as: .

[0039] There are eight possible vector combinations for the output voltage of a two-level grid-connected converter, which can be represented as follows:

[0040] Based on the DC bus voltage and switch status, the two-level grid-connected converter can be used to obtain the following information: αβ The expression for the output voltage in the coordinate system is as follows:

[0041] To simplify the analysis, a current prediction model is established using an L-filter as an example. αβ In a coordinate system, the circuit equations can be expressed as:

[0042] Discretizing and rearranging the above equations, we can obtain the current prediction model:

[0043] in, They are respectively in k+ Predicted grid-connected converter output current at time 1 α、β Quantity, Output current at the current moment α、β Axial components, The current converter output voltage α、β Axial components, The current grid voltage α、β Axial components.

[0044] Based on the deadbeat control concept, the goal is for the actual current at time k+1 to completely track its reference value, i.e., let...

[0045] Substituting this condition into the current prediction model, the reference voltage that the converter should output at time k can be calculated. and :

[0046] in, and The reference values ​​for the α and β axis components of the grid-connected current at time k+1 are given. After calculating the required reference voltage, this voltage vector can be synthesized using space vector pulse width modulation (SVPWM) technology, and a corresponding switching signal can be generated.

[0047] The grid-connected current reference value of the grid-connected converter can be directly given, or the grid-connected mode predictive control can be achieved through the above steps based on changes in new energy demand.

[0048] For grid-connected mode control, its underlying deadbeat control logic is consistent with that of grid-connected converter control. The difference lies in that its current reference value is obtained from the outer loop controller. Taking VSG as an example, the control structure is as follows: Figure 3 As shown. First, the voltage reference value and its phase are obtained through VSG control. The VSG characteristic equation is as follows:

[0049] in, These are the phase and voltage reference values ​​generated by the VSG controller, respectively. J Let be the inertia constant. For active and reactive power VSG coefficients, These are reference values ​​for active and reactive power. The actual output active and reactive power of the converter. The actual and reference angular frequency values ​​are controlled by the network structure.

[0050] This embodiment employs a predictive model for deadbeat control. Based on the predictive model, the state variables for the next moment are obtained, and then the voltage reference value is calculated. This voltage vector is synthesized using Space Vector Pulse Width Modulation (SVPWM) technology, and corresponding switching signals are generated to complete control of different modes. This method solves the problems of slow response speed and integrator saturation in traditional PI control. This invention uses a least squares method based on a forgetting factor for impedance identification. Compared to the harmonic disturbance problem of traditional methods, this method uses the voltage and current at the common coupling point to estimate line impedance without injecting disturbance signals, thus ensuring power quality. The method proposed in this invention can ensure the stable operation of grid-connected converters under different grid strengths.

[0051] This paper proposes a non-intrusive sensing-based multi-mode switching method for grid-connected converter systems. This method ensures that the converter can switch control modes under different grid strengths, guaranteeing stable operation of the converter. The following sections will introduce the grid-connected converter model and control method, the least squares impedance identification method based on the forgetting factor, and the proposed non-intrusive sensing-based multi-mode switching control method.

[0052] Compared with traditional control methods, this invention employs model predictive control (MMC). Based on a predictive model, the state variables for the next time step are obtained. The corresponding switching signal is output by minimizing the cost function, thus completing control of different modes. This method solves the problems of slow response speed and integrator saturation in traditional PI control. This invention uses a least-squares method based on a forgetting factor for impedance identification. The method uses the voltage and current at the common coupling point to estimate line impedance without injecting disturbance signals, ensuring power quality. Pre-synchronization is used during switching to ensure shock-free switching. The method proposed in this invention can ensure the stable operation of the grid-connected converter under different grid strengths, avoiding system instability caused by grid strength changes.

[0053] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0054] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0055] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0056] Example 4 The purpose of this embodiment is to provide a multi-mode switching system for grid-connected converters based on non-intrusive sensing, including: The data acquisition module is configured to acquire the grid connection point voltage, grid voltage, and grid connection current. The identification module is configured to: perform online identification of the acquired grid connection point voltage, grid voltage and grid connection current to obtain the grid impedance observation value; The mode switching module is configured to: calculate the grid short-circuit ratio based on the grid impedance observation value; determine that the converter operates in grid-following mode when the grid is strong and in grid-connecting mode when the grid is weak.

[0057] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments. The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0058] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0059] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for multi-modal switching of grid-tied converters based on non-intrusive sensing, characterized in that, The method comprises the following steps: acquiring grid point voltage, grid voltage and grid current; performing online identification on the acquired grid point voltage, grid voltage and grid current to obtain grid impedance observation value; calculating grid short-circuit ratio based on the grid impedance observation value, and determining that the converter operates in grid-following mode when the grid is strong, and determining that the converter operates in grid-constructing mode when the grid is weak.

2. The non-intrusive sensing based grid-tie converter multi-modal switching method of claim 1, wherein, When performing online identification on the acquired grid point voltage, grid voltage and grid current to obtain grid impedance observation value, the method comprises the following steps: modeling the grid-connected converter, defining grid impedance parameters, initializing iteration matrix, observation matrix and feedback matrix based on grid-side equivalent resistance, grid-side equivalent inductance, three-phase output current, three-phase voltage source and grid point voltage of the grid-connected converter; Updating the gain matrix according to the definition above 、 updating the iteration matrix; the difference between the product of the feedback matrix and the transpose of the grid impedance parameters and the observation matrix is the observation error; updating the grid impedance observation value based on the observation error, and obtaining the grid impedance observation value after iterative update.

3. The non-intrusive sensing based grid-tie converter multi-modal switching method of claim 1, wherein, When the grid is weak, the converter needs to switch from grid-following mode to grid-constructing mode, and the output voltage phase angle and amplitude of the grid-constructing control strategy need to be synchronized with the grid voltage in advance.

4. The non-intrusive sensing based grid-tie converter multi-modal switching method of claim 1, wherein, When acquiring the grid current, the predicted value of the grid current is obtained based on the current prediction model, the optimal switching state is selected based on the cost function of the predicted current, and the control of the converter in grid-following mode or grid-constructing mode is realized.

5. The non-intrusive sensing based grid-tie converter multi-modal switching method of claim 1, wherein, When the current prediction model is constructed: acquisition α、β axial current component, α、β axial voltage component, α、β component of the axial network voltage and filter inductance; the circuit equation is represented based on the acquired parameters of the grid-connected converter; the current prediction model is obtained by discretizing and arranging the above circuit equation.

6. The non-intrusive sensing based grid-tie converter multi-modal switching method of claim 1, wherein, The optimal switching state is selected based on the cost function of the predicted current, which comprises the following steps: defining the switching state of the grid-connected converter; obtaining the resultant vector of the grid-connected converter switching function based on the defined switching state of the grid-connected converter; using the sum of the squares of the current errors as the cost function, and traversing the above resultant vector to find the switching vector that minimizes the cost function.

7. A grid-connected converter multi-mode switching system based on non-intrusive sensing, comprising: a data acquisition module configured to acquire grid point voltage, grid voltage and grid current; an identification module configured to perform online identification on the acquired grid point voltage, grid voltage and grid current to obtain grid impedance observation value; a mode switching module configured to calculate grid short-circuit ratio based on the grid impedance observation value, and determine that the converter operates in grid-following mode when the grid is strong, and determine that the converter operates in grid-constructing mode when the grid is weak.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the method of any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the method of any one of claims 1-6.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to perform the steps of the method of any one of claims 1-6.

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