A current limiting method and device for a modular multilevel converter

Abstract

The application belongs to the field of electric power, and discloses a current limiting method and device for a modular multilevel converter, which comprises the following steps: a dynamic nonlinear overcurrent limiting boundary model of the modular multilevel converter is established, the boundary model is jointly defined by a sub-module capacitor voltage peak value constraint, a sub-module capacitor voltage ripple constraint and a bridge arm modulation signal constraint; a critical current limit value under different grid voltage amplitudes and instruction current phase angles is obtained based on the boundary model; a grid voltage amplitude, a d-axis current instruction reference value and a q-axis current instruction reference value are obtained, and a current instruction amplitude and a current instruction phase angle are calculated; the grid voltage amplitude and the current instruction phase angle are input into a pre-trained neural network model, and a corresponding current instruction amplitude is output; the current instruction amplitude is compared with the current critical current limit value, and the original d-axis current instruction reference value and the original q-axis current instruction reference value are dynamically limited in amplitude according to a comparison result, so that a final output current instruction is obtained.

Description

Technical Field

[0001] This invention belongs to the field of power, and particularly relates to a current limiting method and device for a modular multilevel converter. Background Technology

[0002] Grid-type modular multilevel converters (MMCs), with their modular structure, high output waveform quality, and strong scalability, have become the core topology for high-voltage, high-capacity power transmission and large-scale renewable energy grid integration. However, when severe faults such as short circuits occur in the power grid, grid-type MMCs can generate fault currents far exceeding their equipment safety thresholds. This can not only cause internal power electronic switching devices to break down due to overheating, but also trigger cascading faults such as overvoltage damage to submodule capacitors, posing a serious threat to the transient stability and controllability of the power system. Therefore, the ability to quickly and reliably limit fault currents is a critical capability that grid-type MMCs must possess.

[0003] Traditional current limiting methods typically employ fixed current limits or consider only a single constraint, which inherently limits their ability to achieve precise current limiting and ensure effective support for the power grid. The fundamental reason is that the actual overcurrent capacity of a Modulation Controller (MMC) is not determined by a simple threshold, but rather by a dynamic, asymmetric safety operating boundary formed by the combined effects of three core physical constraints: the linear region constraint of the modulation signal, the peak voltage constraint of the submodule capacitor, and the capacitor voltage ripple constraint. Traditional methods neglect this complex dynamic boundary, and while forcibly suppressing the output current, they can easily lead to capacitor voltage ripple exceeding safety limits or the modulation signal entering the saturation region (overmodulation), thereby causing new safety hazards or control instability. Summary of the Invention

[0004] In view of this, the present invention discloses a current limiting method and device for a modular multilevel converter, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: According to a first aspect of the present invention, a current limiting method for a modular multilevel converter is proposed, comprising: A dynamic nonlinear overcurrent limiting boundary model for a grid-type modular multilevel converter is established. The boundary model is jointly defined by the peak voltage constraint of the submodule capacitor, the ripple voltage constraint of the submodule capacitor, and the bridge arm modulation signal constraint. Based on the dynamic nonlinear overcurrent limiting boundary model, the critical current limit values ​​under different grid voltage amplitudes and command current phase angles are obtained. Obtain the grid voltage amplitude, d-axis and q-axis current command reference values, and calculate the current command current phase angle and current command amplitude; The grid voltage amplitude and command current phase angle are input into a pre-trained neural network model, which is used to fit the dynamic nonlinear overcurrent limit boundary and output the corresponding current critical current limit. The current command amplitude is compared with the current critical current limit, and the original d-axis and q-axis current command reference values ​​are dynamically limited based on the comparison result to obtain the final output current command, which controls the grid-type modular multilevel converter.

[0006] Optionally, the dynamic nonlinear overcurrent limiting boundary model is determined by the minimum of the maximum allowable currents that satisfy the three constraints of the boundary model, wherein the peak value of the capacitor voltage does not exceed 1.1 times its rated voltage, the capacitor voltage ripple does not exceed 0.1 times its rated voltage, and the value of the bridge arm modulation signal is between 0 and 1.

[0007] Optionally, obtaining the critical current limit under different grid voltage amplitudes and command current phase angles includes: By iterating through and calculating the critical current limit determined by the dynamic nonlinear overcurrent limiting boundary model under different combinations of grid voltage amplitude and command current phase angle, a series of overcurrent limiting boundary operating points are obtained and stored to form a boundary dataset for training the neural network model.

[0008] Furthermore, the neural network model is obtained by offline training using the grid voltage amplitude and command current phase angle in the boundary dataset as inputs and the corresponding critical current limit as the expected output. Its functional relationship is used to calculate the dynamic current limit.

[0009] Optionally, the command current phase angle is calculated using the arctangent function based on the d-axis and q-axis current command reference values, and the current command amplitude is calculated using the square root of the square of the d-axis and q-axis current command reference values.

[0010] Optionally, the dynamic limiting processing of the original d-axis and q-axis current command reference values ​​based on the comparison results includes: When the current command amplitude is less than the current critical current limit, the current operating point is determined to be safe, and the output current command is not modified. When the current command amplitude is greater than or equal to the current critical current limit, dynamic limiting is triggered, and the current scaling factor is calculated. This factor is the ratio of the current command amplitude to the current critical current limit. The original d-axis and q-axis current command reference values ​​are scaled according to this ratio to obtain the output current command, so as to keep the current phase angle unchanged.

[0011] Furthermore, it also includes: When the current command amplitude is not less than 0.955 times the current critical current limit and is less than the current critical current limit, the dynamic limiting module maintains the output state of the previous control cycle to prevent control command jitter.

[0012] According to a second aspect of the present invention, a current limiting device for a modular multilevel converter is provided, comprising: Construction Unit: Establish a dynamic nonlinear overcurrent limiting boundary model for a grid-type modular multilevel converter. The boundary model is jointly defined by the peak voltage constraint of the sub-module capacitor, the ripple voltage constraint of the sub-module capacitor, and the modulation signal constraint of the bridge arm. First acquisition unit: Based on the dynamic nonlinear overcurrent limiting boundary model, acquire the critical current limit under different grid voltage amplitudes and command current phase angles; The second acquisition unit acquires the grid voltage amplitude, the current command reference values ​​of the d-axis and q-axis, and calculates the current command current phase angle and current command amplitude. Input unit: Inputs the grid voltage amplitude and command current phase angle into a pre-trained neural network model. The neural network model is used to fit the dynamic nonlinear overcurrent limiting boundary and output the corresponding current critical current limit. Comparison Unit: Compares the current command amplitude with the current critical current limit, and performs dynamic amplitude limiting processing on the original d-axis and q-axis current command reference values ​​according to the comparison result to obtain the final output current command, so as to control the grid-type modular multilevel converter.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0014] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0015] As can be seen from the above technical solutions, the current limiting method and device for modular multilevel converters disclosed in this invention have the following beneficial effects: On the one hand, by establishing a dynamic boundary model that accurately represents the real safe operating area of ​​MMC, the risks of capacitor overvoltage, capacitor voltage ripple exceeding the limit, and modulation signal overmodulation caused by neglecting constraint coupling in traditional methods are avoided. This reduces the electrical stress of key components, extends equipment life, and improves the overall reliability of the system.

[0016] On the other hand, the current limit is adaptively adjusted according to the grid voltage drop, so that the converter can output the largest possible current within the safety boundary during the fault to support the grid voltage, thereby enhancing the fault ride-through (FRT) capability of the grid-type MMC and its support strength for weak grids.

[0017] Furthermore, by introducing neural networks as high-precision function approximators for complex dynamic boundaries, the problem of complex analytical forms of boundary models and difficulties in online real-time calculation is solved, enabling fast, online querying and precise flow limiting control of safe boundaries. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a current limiting method for a modular multilevel converter, provided in an exemplary embodiment. Figure 2 This is a flowchart illustrating a specific circuit method provided in an exemplary embodiment; Figure 3 This is a schematic diagram of a multi-constraint dynamic nonlinear overcurrent limiting boundary operating point provided in an exemplary embodiment; Figure 4 This is a schematic diagram of a multi-constraint dynamic nonlinear overcurrent limiting boundary fitting surface provided in an exemplary embodiment; Figure 5 This is a schematic diagram of the experimental results waveform of the power grid support performance of a dynamic nonlinear current limiting method provided in an exemplary embodiment; Figure 6 This is a schematic diagram illustrating a comparison of steady-state waveforms of capacitor voltage provided in an exemplary embodiment; Figure 7 This is a schematic diagram illustrating a comparison of steady-state waveforms of capacitor voltage ripple provided in an exemplary embodiment; Figure 8 This is a schematic diagram illustrating a comparison of steady-state waveforms of a modulated signal, provided in an exemplary embodiment. Figure 9 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 10 This is a block diagram of a current limiting device for a modular multilevel converter, provided in an exemplary embodiment. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0020] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention. The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0021] Grid-type modular multilevel converters (MMCs), with their modular structure, high output waveform quality, and strong scalability, have become the core topology for high-voltage, high-capacity power transmission and large-scale renewable energy grid integration. However, when severe faults such as short circuits occur in the power grid, grid-type MMCs can generate fault currents far exceeding their equipment safety thresholds. This can not only cause internal power electronic switching devices to break down due to overheating, but also trigger cascading faults such as overvoltage damage to submodule capacitors, posing a serious threat to the transient stability and controllability of the power system. Therefore, the ability to quickly and reliably limit fault currents is a critical capability that grid-type MMCs must possess.

[0022] Traditional current limiting methods typically employ fixed current limits or consider only a single constraint, which inherently limits their ability to achieve precise current limiting and ensure effective support for the power grid. The fundamental reason is that the actual overcurrent capacity of a Modulation Controller (MMC) is not determined by a simple threshold, but rather by a dynamic, asymmetric safety operating boundary formed by the combined effects of three core physical constraints: the linear region constraint of the modulation signal, the peak voltage constraint of the submodule capacitor, and the capacitor voltage ripple constraint. Traditional methods neglect this complex dynamic boundary, and while forcibly suppressing the output current, they can easily lead to capacitor voltage ripple exceeding safety limits or the modulation signal entering the saturation region (overmodulation), thereby causing new safety hazards or control instability.

[0023] To address the shortcomings of traditional methods, this invention proposes a current limiting method and device for modular multilevel converters.

[0024] Figure 1 This is a flowchart illustrating a current limiting method for a modular multilevel converter, as provided in an exemplary embodiment. Figure 2 As shown, the method may include the following steps: Step 101: Establish a dynamic nonlinear overcurrent limiting boundary model for the grid-type modular multilevel converter. The boundary model is jointly defined by the peak voltage constraint of the sub-module capacitor, the ripple voltage constraint of the sub-module capacitor, and the modulation signal constraint of the bridge arm.

[0025] Specifically, the dynamic nonlinear overcurrent limiting boundary model is determined by the minimum of the three constraints of the boundary model, wherein the peak value of the capacitor voltage does not exceed 1.1 times its rated voltage, the capacitor voltage ripple does not exceed 0.1 times its rated voltage, and the value of the bridge arm modulation signal is between 0 and 1.

[0026] Based on the three core physical constraints that MMC must simultaneously satisfy during operation (capacitor voltage ripple constraint, capacitor voltage peak constraint, and modulation signal constraint), a dynamic nonlinear overcurrent limiting boundary model is constructed. Under a given real-time grid voltage Us and current phase angle β, the safe overcurrent limit I is determined. l Determined by the minimum of the maximum allowable currents among all individual constraints, its mathematical model can be expressed as: ; Among them, I cap,rip I cap,max and Is represent the maximum allowable current function satisfying the capacitor voltage ripple constraint, capacitor voltage peak constraint, and modulation signal linear region constraint, respectively; U N This is the rated voltage of the submodule capacitor.

[0027] Step 102: Based on the dynamic nonlinear overcurrent limiting boundary model, obtain the critical current limit under different grid voltage amplitudes and command current phase angles.

[0028] In one embodiment, such as Figure 3 As shown, obtaining the critical current limit under different grid voltage amplitudes and command current phase angles includes: calculating the critical current limit determined by the dynamic nonlinear overcurrent limiting boundary model under different combinations of grid voltage amplitudes and command current phase angles, obtaining a series of overcurrent limiting boundary operating points, and storing them to form a boundary dataset for training the neural network model.

[0029] These boundary running points are set to (Us, β, I) l The data is stored in the form of an overcurrent limit boundary lookup table.

[0030] Step 103: Obtain the grid voltage amplitude, d-axis and q-axis current command reference values, and calculate the current command current phase angle and current command amplitude.

[0031] Step 104: Input the grid voltage amplitude and command current phase angle into a pre-trained neural network model. The neural network model is used to fit the dynamic nonlinear overcurrent limit boundary and output the corresponding current critical current limit.

[0032] Due to the complexity of the analytical expression of the boundary model, a neural network is used to perform high-precision fitting of the boundary running points extracted in step 102 to improve the efficiency of online applications. The trained neural network can represent dynamic boundary surfaces (such as...). Figure 4 As shown), its functional relationship can be expressed as: ; Here, fn() represents the trained neural network mapping function.

[0033] The neural network model is obtained by offline training using the grid voltage amplitude and command current phase angle in the boundary dataset as inputs and the corresponding critical current limit as the expected output. Its functional relationship is used to calculate the dynamic current limit.

[0034] The control system acquires the grid voltage amplitude Us in real time and calculates the phase angle β of the current command using the following formula: ; By inputting Us and β into the neural network model fn trained in step 103, the precise critical current limit I under the current operating conditions can be calculated in real time. Lim .

[0035] Step 105: Compare the current command amplitude with the current critical current limit, and perform dynamic amplitude limiting processing on the original d-axis and q-axis current command reference values ​​according to the comparison result to obtain the final output current command, so as to control the grid-type modular multilevel converter.

[0036] The command current phase angle is calculated using the arctangent function based on the d-axis and q-axis current command reference values, and the current command amplitude is calculated using the square root of the square of the d-axis and q-axis current command reference values.

[0037] The current command amplitude I is calculated using the following formula. sref : ; Subsequently, I sref The dynamic current limit I output by the neural network Lim Compare them.

[0038] In one embodiment, the dynamic limiting processing of the original d-axis and q-axis current command reference values ​​based on the comparison result includes: when the current command amplitude is less than the current critical current limit, determining that the current operating point is safe and not modifying the output current command; when the current command amplitude is greater than or equal to the current critical current limit, triggering dynamic limiting, calculating the current scaling factor, which is the ratio of the current command amplitude to the current critical current limit, and scaling the original d-axis and q-axis current command reference values ​​according to this ratio to obtain the output current command, so as to keep the current phase angle unchanged.

[0039] Furthermore, when the current command amplitude is not less than 0.955 times the current critical current limit and is less than the current critical current limit, the dynamic limiting module maintains the output state of the previous control cycle to prevent control command jitter.

[0040] When the instruction amplitude I sref The critical current limit I was not exceeded. Lim At this time, the original instruction reference value is output directly without modification: .

[0041] When the instruction amplitude I sref Not less than 0.955 times I Lim And not exceeding I Lim When the dynamic limiting module is in a hysteresis comparison state, it will maintain the output state of the previous control cycle; when the command amplitude I... sref Exceeding the critical current limit I Lim When this occurs, overcurrent limiting will be triggered, and the current command vector will be scaled according to the current scaling factor k. ; .

[0042] The final current command i obtained after the above processing dout and i qout The value is sent to the inner loop current controller of the MMC as a reference value for its tracking.

[0043] The current limiting method for modular multilevel converters (MMCs) proposed in this invention addresses several issues. First, by establishing a dynamic boundary model that accurately represents the true safe operating range of the MMC, it avoids risks such as capacitor overvoltage, capacitor voltage ripple exceeding limits, and modulation signal overmodulation caused by neglecting constraint coupling in traditional methods. This reduces electrical stress on key components, extends equipment life, and improves overall system reliability. Second, by adaptively adjusting the current limit based on grid voltage dips, the converter can output the maximum possible current within the safe boundary during faults to support the grid voltage, enhancing the fault ride-through (FRT) capability and support strength for weak grids in grid-connected MMCs. Furthermore, by introducing a neural network as a high-precision function approximator for complex dynamic boundaries, it solves the problem of complex analytical forms of boundary models and difficulties in online real-time calculation, enabling rapid, online querying of safe boundaries and precise current limiting control.

[0044] To verify the proposed multi-constraint network-type MMC dynamic nonlinear current limiting method, the following verification is performed in conjunction with embodiments, where the main circuit parameters of the MMC are shown in Table 1.

[0045] Table 1

[0046] Assume a symmetrical three-phase voltage dip fault occurs in the power grid. Figure 5 The experimental results waveforms show the grid support performance using the dynamic nonlinear overcurrent limiting method. Figure 5 As shown in (a), the system initially operates stably at rated power. At t=1.0s, U S The power suddenly dropped from 1.0 PU to 0.85 PU. Figure 5 As can be seen in (b), after the voltage drop, in order to support the grid voltage, the MMC significantly increases the reactive power output, and the reactive power Q rapidly rises from 300 Mvar to approximately 600 Mvar. The analysis of the data is as follows.

[0047] According to the real-time grid voltage U S =0.85 pu and the command current phase angle β, a new dynamic current limit I is calculated by the neural network-based dynamic boundary calculation module. Lim ,Depend on Figure 5 I can be seen in (e) of the middle. Lim Approximately 3.75 kA, this limit accurately reflects the true safe operating boundary of the MMC under the current operating conditions. Then, the dynamic limiting module, through a scaling mechanism, constrains the d-axis and q-axis current commands within this dynamic boundary, such as... Figure 5 (b) and Figure 5 As shown in (c), the transient i dref The spike was suppressed from 3.04 kA to 2.6 kA, iqref The spike was suppressed from -3.4 kA to -3.2 kA. Finally, as... Figure 5 As shown in (e), the actual output current IS is precisely limited to the dynamic boundary I. pre Under these conditions, the reactive power support capability of the converter is maximized, ensuring the safety, efficiency and stability of the entire transient process.

[0048] To further verify the effectiveness of the proposed MMC dynamic nonlinear current limiting method, comparative waveforms of MMC using the dynamic nonlinear current limiting method and the traditional current limiting method are provided. Figure 6 , Figure 7 and Figure 8 The waveforms of the upper bridge arm capacitor voltage uc,ap, capacitor voltage ripple uc,ap,r, and modulation signal SAP of phase A are given under steady-state conditions using both the dynamic nonlinear current limiting method and the traditional current limiting method.

[0049] Firstly, when using traditional current limiting methods, although the output current is limited, the internal dynamic process causes the peak capacitor voltage to rise. Figure 6 As can be seen, its peak voltage reached 2495 V, which is very close to the hardware protection boundary of 1.1 times the rated voltage, resulting in a very small safety margin. In contrast, by adopting the dynamic nonlinear current limiting method of this invention, the peak voltage is stabilized at 2439 V because its boundary model considers the peak voltage constraint of the capacitor, which significantly reduces the voltage stress on the capacitor and increases the operational safety margin.

[0050] Secondly, by Figure 7 A comparison of the capacitor voltage ripple waveforms reveals that, under traditional methods, a large fault current causes the capacitor voltage ripple amplitude to reach as high as 249 V. This exceeds the commonly used safety limit of 10% of the rated voltage in engineering practice, and long-term operation will accelerate component aging. In contrast, the dynamic nonlinear current limiting method, with its precise boundary sensing capability, actively constrains the voltage ripple to 172.5 V, a 30.7% reduction compared to traditional methods, and remains entirely within a safe range, effectively extending the lifespan of the submodule capacitors.

[0051] also, Figure 8The diagram shows a comparison of the steady-state waveforms of the modulation signal Sap in the upper arm of phase A using the dynamic nonlinear current limiting method and the traditional current limiting method. It can be seen that the traditional method, in order to output the required voltage after a voltage drop, forces the minimum value of the modulation signal to reach 0.003, almost touching the lower limit of saturation. This puts the system on the verge of overmodulation, making it highly susceptible to harmonic distortion and control instability. In contrast, the dynamic nonlinear current limiting method of this invention directly incorporates modulation signal constraints into its boundary model, maintaining the modulation signal within the healthy linear region of 0.059 to 0.901. This ensures that the system maintains high-quality control performance even during faults, demonstrating the effectiveness of the method proposed in this application.

[0052] Figure 9 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 9 At the hardware level, the device includes a processor 902, an internal bus 904, a network interface 906, memory 908, and non-volatile memory 910, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 902 reads the corresponding computer program from the non-volatile memory 910 into memory 908 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0053] Please refer to Figure 10 A current limiting device for a modular multilevel converter can be applied to, for example... Figure 10 The device shown, in order to implement the technical solution of the present invention, includes: Construction unit 1001 is used to establish a dynamic nonlinear overcurrent limiting boundary model for a grid-type modular multilevel converter. The boundary model is jointly defined by the peak voltage constraint of the sub-module capacitor, the ripple voltage constraint of the sub-module capacitor, and the bridge arm modulation signal constraint. The first acquisition unit 1002 is used to acquire critical current limits under different grid voltage amplitudes and command current phase angles based on the dynamic nonlinear overcurrent limiting boundary model. The second acquisition unit 1003 is used to acquire the grid voltage amplitude, the current command reference values ​​of the d-axis and q-axis, and to calculate the current command current phase angle and current command amplitude. The input unit 1004 is used to input the grid voltage amplitude and the command current phase angle into a pre-trained neural network model. The neural network model is used to fit the dynamic nonlinear overcurrent limit boundary and output the corresponding current critical current limit. The comparison unit 1005 is used to compare the current command amplitude with the current critical current limit, and to perform dynamic amplitude limiting processing on the original d-axis and q-axis current command reference values ​​according to the comparison result, so as to obtain the final output current command to control the grid-type modular multilevel converter.

[0054] Optionally, the dynamic nonlinear overcurrent limiting boundary model is determined by the minimum of the maximum allowable currents that satisfy the three constraints of the boundary model, wherein the peak value of the capacitor voltage does not exceed 1.1 times its rated voltage, the capacitor voltage ripple does not exceed 0.1 times its rated voltage, and the value of the bridge arm modulation signal is between 0 and 1.

[0055] Optionally, the first acquisition unit 1002 is specifically used for: By iterating through and calculating the critical current limit determined by the dynamic nonlinear overcurrent limiting boundary model under different combinations of grid voltage amplitude and command current phase angle, a series of overcurrent limiting boundary operating points are obtained and stored to form a boundary dataset for training the neural network model.

[0056] Furthermore, the neural network model is obtained by offline training using the grid voltage amplitude and command current phase angle in the boundary dataset as inputs and the corresponding critical current limit as the expected output. Its functional relationship is used to calculate the dynamic current limit.

[0057] Optionally, the command current phase angle is calculated using the arctangent function based on the d-axis and q-axis current command reference values, and the current command amplitude is calculated using the square root of the square of the d-axis and q-axis current command reference values.

[0058] Optionally, the comparison unit 1005 is specifically used for: When the current command amplitude is less than the current critical current limit, the current operating point is determined to be safe, and the output current command is not modified. When the current command amplitude is greater than or equal to the current critical current limit, dynamic limiting is triggered, and the current scaling factor is calculated. This factor is the ratio of the current command amplitude to the current critical current limit. The original d-axis and q-axis current command reference values ​​are scaled according to this ratio to obtain the output current command, so as to keep the current phase angle unchanged.

[0059] Furthermore, it also includes: The holding unit 1006 is used to maintain the output state of the dynamic limiting module in the previous control cycle when the current command amplitude is not less than 0.955 times the current critical current limit and is less than the current critical current limit, so as to prevent control command jitter.

[0060] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0061] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0062] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0063] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0064] For any other form of computer-readable medium (or computer-readable storage medium) as described above, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0065] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.

[0066] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0068] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0069] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0070] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A current limiting method for a modular multilevel converter, characterized in that, include: A dynamic nonlinear overcurrent limiting boundary model for a grid-type modular multilevel converter is established. The boundary model is jointly defined by the peak voltage constraint of the submodule capacitor, the ripple voltage constraint of the submodule capacitor, and the bridge arm modulation signal constraint. Based on the dynamic nonlinear overcurrent limiting boundary model, the critical current limit values ​​under different grid voltage amplitudes and command current phase angles are obtained. Obtain the grid voltage amplitude, d-axis and q-axis current command reference values, and calculate the current command current phase angle and current command amplitude; The grid voltage amplitude and command current phase angle are input into a pre-trained neural network model, which is used to fit the dynamic nonlinear overcurrent limit boundary and output the corresponding current critical current limit. The current command amplitude is compared with the current critical current limit, and the original d-axis and q-axis current command reference values ​​are dynamically limited based on the comparison result to obtain the final output current command, which controls the grid-type modular multilevel converter.

2. The current limiting method for a modular multilevel converter according to claim 1, characterized in that, The dynamic nonlinear overcurrent limiting boundary model is determined by the minimum of the maximum allowable currents that satisfy the three constraints of the boundary model, wherein the peak value of the capacitor voltage does not exceed 1.1 times its rated voltage, the capacitor voltage ripple does not exceed 0.1 times its rated voltage, and the value of the bridge arm modulation signal is between 0 and 1.

3. The current limiting method for a modular multilevel converter according to claim 1, characterized in that, The acquisition of critical current limits under different grid voltage amplitudes and command current phase angles includes: By iterating through and calculating the critical current limit determined by the dynamic nonlinear overcurrent limiting boundary model under different combinations of grid voltage amplitude and command current phase angle, a series of overcurrent limiting boundary operating points are obtained and stored to form a boundary dataset for training the neural network model.

4. The current limiting method for a modular multilevel converter according to claim 3, characterized in that, The neural network model is obtained by offline training using the grid voltage amplitude and command current phase angle in the boundary dataset as inputs and the corresponding critical current limit as the expected output. Its functional relationship is used to calculate the dynamic current limit.

5. The current limiting method for a modular multilevel converter according to claim 1, characterized in that, The command current phase angle is calculated using the arctangent function based on the d-axis and q-axis current command reference values, and the current command amplitude is calculated using the square root of the square of the d-axis and q-axis current command reference values.

6. The current limiting method for a modular multilevel converter according to claim 1, characterized in that, The dynamic limiting processing of the original d-axis and q-axis current command reference values ​​based on the comparison results includes: When the current command amplitude is less than the current critical current limit, the current operating point is determined to be safe, and the output current command is not modified. When the current command amplitude is greater than or equal to the current critical current limit, dynamic limiting is triggered, and the current scaling factor is calculated. This factor is the ratio of the current command amplitude to the current critical current limit. The original d-axis and q-axis current command reference values ​​are scaled according to this ratio to obtain the output current command, so as to keep the current phase angle unchanged.

7. The current limiting method for a modular multilevel converter according to claim 6, characterized in that, Also includes: When the current command amplitude is not less than 0.955 times the current critical current limit and is less than the current critical current limit, the dynamic limiting module maintains the output state of the previous control cycle to prevent control command jitter.

8. A current limiting device for a modular multilevel converter, characterized in that, include: Construction Unit: Establish a dynamic nonlinear overcurrent limiting boundary model for a grid-type modular multilevel converter. The boundary model is jointly defined by the peak voltage constraint of the sub-module capacitor, the ripple voltage constraint of the sub-module capacitor, and the modulation signal constraint of the bridge arm. First acquisition unit: Based on the dynamic nonlinear overcurrent limiting boundary model, acquire the critical current limit under different grid voltage amplitudes and command current phase angles; The second acquisition unit acquires the grid voltage amplitude, the current command reference values ​​of the d-axis and q-axis, and calculates the current command current phase angle and current command amplitude. Input unit: Inputs the grid voltage amplitude and command current phase angle into a pre-trained neural network model. The neural network model is used to fit the dynamic nonlinear overcurrent limiting boundary and output the corresponding current critical current limit. Comparison Unit: Compares the current command amplitude with the current critical current limit, and performs dynamic amplitude limiting processing on the original d-axis and q-axis current command reference values ​​according to the comparison result to obtain the final output current command, so as to control the grid-type modular multilevel converter.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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