Stable control method, system and equipment of intelligent soft switch and storage medium

Abstract

The invention discloses a stable control method, system and device for an intelligent soft switch and a storage medium, and the method comprises the steps: obtaining system parameters of the intelligent soft switch, and building an impedance model of a DC side of the intelligent soft switch according to the system parameters of the intelligent soft switch; calculating the stability margin of the DC side of the intelligent soft switch based on the impedance model of the DC side of the intelligent soft switch; judging the stability margin, and introducing a stabilizer into a control loop of the intelligent soft switch according to a judgment result; the parameters of the stabilizer are set, the stability margin of the direct current side of the intelligent soft switch is recalculated until the judgment condition is met, stable control over the intelligent soft switch is achieved, and the method has great significance in improving the overall performance of the flexible interconnection power distribution network.

Description

Technical Field

[0001] This invention relates to the field of flexible interconnected power distribution network technology, and in particular to a stable control method, system, device and storage medium for intelligent soft switching. Background Technology

[0002] To enhance the regulation capabilities of traditional distribution networks, flexible interconnection technology based on power electronics has gradually attracted the attention of scholars both domestically and internationally. Flexible interconnected distribution networks use smart soft switches (SOPs) to replace traditional tie switches, enabling interconnection between adjacent distribution substations. This promotes the flow of power generated by distributed generation sources between different substations and enhances the absorption capacity of renewable energy generation. Furthermore, smart soft switches also reduce distribution network losses, improve power supply reliability, and optimize power flow distribution, playing a crucial role in improving the "closed-loop design, open-loop operation" characteristics of traditional AC distribution networks. A typical smart soft switch consists of two back-to-back voltage source converters (VSCs). One converter uses active power control mode to regulate the power magnitude and direction of the smart soft switch; the other converter uses DC voltage control mode to maintain a constant DC-side voltage. Similar to conventional grid-connected converters, the voltage source converters in smart soft switches also suffer from interactive instability issues with the distribution network. Considering that the distribution network is located at the end of the grid and has high line impedance, smart soft switches are prone to abnormal interactions with the distribution network, leading to problems such as subsynchronous oscillations and harmonic oscillations.

[0003] On the other hand, due to the bidirectional power transmission characteristics of intelligent soft switches, there is a risk of interactive instability on the DC side. For back-to-back voltage source converters, the DC side can operate stably when the converter in active power control mode supplies power to the converter in DC voltage control mode; otherwise, the system will become unstable. Furthermore, existing studies analyzing the DC-side stability of cascaded converters mostly employ graphical analysis methods based on the Nyquist criterion, making it difficult to quantitatively determine the DC-side stability margin. Additionally, for intelligent soft switches with bidirectional power transmission, further research is needed to develop stabilization control strategies to enhance their DC-side stability. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] This invention provides a stable control method, system, device, and storage medium for intelligent soft switches to solve the problem of interactive instability on the DC side during bidirectional power transmission.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a stable control method for an intelligent soft switch, comprising: Obtain the parameters of the intelligent soft-switching system, and establish the impedance model of the DC side of the intelligent soft-switching system based on the parameters of the intelligent soft-switching system; The stability margin of the DC side of the intelligent soft switch is calculated based on the impedance model of the DC side of the intelligent soft switch. The stability margin is determined, and a stabilizer is introduced into the control loop of the intelligent soft switch based on the determination result. The parameters of the stabilizer are tuned, and the stability margin of the DC side of the intelligent soft switch is recalculated until the judgment condition is met, thereby achieving stable control of the intelligent soft switch.

[0007] As a preferred embodiment of the stable control method for an intelligent soft switch according to the present invention, the establishment of the impedance model of the DC side of the intelligent soft switch includes: DC impedance model of the first voltage source converter, DC impedance model of the second voltage source converter, and DC impedance model of the capacitor; The DC impedance model of the first voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the first voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the first voltage source converter. The DC impedance model of the second voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the second voltage source converter. The DC impedance model of a capacitor is established through the relationship between the capacitor's voltage and current.

[0008] The beneficial effect of this preferred technical solution is that it can quantify the stability margin of the DC side of the intelligent soft switch, providing a theoretical basis for stability analysis and control.

[0009] As a preferred embodiment of the stable control method for an intelligent soft switch according to the present invention, the calculation of the stability margin on the DC side of the intelligent soft switch includes: Characteristic equations are constructed based on the DC impedance models of the first voltage source converter, the second voltage source converter, and the capacitor. Solving the characteristic equation yields multiple eigenvalues; The stability margin of the DC side of the intelligent soft switch is determined based on multiple characteristic values.

[0010] The beneficial effect of this preferred technical solution is that it can accurately assess the DC-side stability of the intelligent soft switch and provide early warning of potential oscillation risks.

[0011] As a preferred embodiment of the stable control method for an intelligent soft switch according to the present invention, the determination of the stability margin includes: If the stability margin is greater than or equal to the first threshold, the smart switch can operate stably without the need for stable control of the smart soft switch. If the stability margin is less than the first threshold, a sedator is introduced into the control loop of the intelligent soft switch.

[0012] As a preferred embodiment of the stable control method for an intelligent soft switch according to the present invention, the tuning of the parameters of the stabilizer includes: Based on the stability margin, the characteristic value corresponding to the minimum damping ratio is obtained, and the potential oscillation frequency of the DC side of the intelligent soft switch is determined based on the corresponding characteristic value. The range of the sedator time constant is determined based on the potential oscillation frequency; The gain parameters of the stamina are tuned iteratively until the stability margin meets the judgment condition.

[0013] The beneficial effects of this preferred technical solution are that it can effectively enhance the DC-side damping of the intelligent soft switch, suppress oscillation, and improve stability.

[0014] As a preferred embodiment of the stable control method for an intelligent soft switch according to the present invention, the recalculation of the stability margin on the DC side of the intelligent soft switch includes: The stability margin of the DC side of the intelligent soft switch after adding the ballast is recalculated using the DC impedance model of the second voltage source converter with the ballast added. If the stability margin is greater than or equal to the first threshold, the sedator parameter tuning is complete; If the stability margin is less than the first threshold, the stamina parameters are readjusted until the stability margin is greater than or equal to the first threshold. The DC impedance model of the second voltage source converter after adding the ballast is established by the transfer function reflecting the relationship between the change in active power and the change in DC voltage of the second voltage source converter after adding the ballast, based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the change in DC voltage.

[0015] Secondly, the present invention provides a stable control system for an intelligent soft switch, comprising: The intelligent soft switch consists of a first voltage source converter and a second voltage source converter; The DC sides of the first voltage source converter and the second voltage source converter are connected; The first voltage source converter adopts a dual-loop control structure consisting of an outer loop for DC voltage and reactive power control and an inner loop for current control. The second voltage source converter adopts a dual-loop control structure with a power control outer loop and a current control inner loop, and a stamina is provided in the current control inner loop. The sedator takes the feedback value of the DC voltage of the second voltage source converter as input, and the output signal is superimposed on the reference value of the current control inner loop of the second voltage source converter.

[0016] As a preferred embodiment of the stable control system for an intelligent soft switch according to the present invention, the stabilizer includes: The relationship between the sedator input and output is described by a transfer function that includes a gain parameter and a time constant.

[0017] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned stable control method for an intelligent soft switch.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the aforementioned stable control method for an intelligent soft switch.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention can quantify the stability margin of the DC side of intelligent soft switches, overcoming the limitations of existing graphical analysis methods based on the Nyquist criterion, which are difficult to quantify, and providing accurate quantitative indicators for system stability assessment. For potential oscillation frequencies, the proposed stabilizer can specifically enhance the damping of the intelligent soft switch at those oscillation frequencies, effectively improving DC side stability and suppressing oscillations. Through accurate impedance model establishment and stability margin calculation, this invention provides a scientific basis for the design, operation, and control of intelligent soft switches, enhancing system reliability and adaptability, and is of great significance for improving the overall performance of flexible interconnected distribution networks. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic diagram of the overall flow logic of a stable control method for an intelligent soft switch provided in one embodiment of the present invention; Figure 2 The curves showing the stability margin of an intelligent soft switch as a function of active power under different grid intensities are provided in an embodiment of the present invention. Figure 3The instability waveform of an intelligent soft switch under varying grid strength is provided in an embodiment of the present invention for a stable control method of an intelligent soft switch. Figure 4 The curves showing the stability margin of the intelligent soft switch as a function of active power under different ballast gains in a stable control method for an intelligent soft switch according to an embodiment of the present invention. Figure 5 Simulation waveform of the intelligent soft switch when the ballast gain is 0.4, provided in an embodiment of the present invention, for a stable control method of an intelligent soft switch; Figure 6 The simulation waveform of the intelligent soft switch when the ballast gain is 1.0 is provided in a stabilization control method for an intelligent soft switch according to an embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 As an embodiment of the present invention, a stable control method for an intelligent soft switch is provided, comprising: S100: Obtain the parameters of the intelligent soft-switching system and establish the impedance model of the DC side of the intelligent soft-switching system based on the parameters of the intelligent soft-switching system; S200: Calculate the stability margin of the DC side of the intelligent soft switch based on the impedance model of the DC side of the intelligent soft switch. S300: Determines the stability margin and introduces a stabilizer into the control loop of the intelligent soft switch based on the determination result; S400: Adjusts the parameters of the stabilizer, recalculates the stability margin of the DC side of the intelligent soft switch until the judgment conditions are met, and realizes stable control of the intelligent soft switch.

[0024] It should be noted that this invention accurately quantifies the DC-side stability margin and provides early warning of instability risks by acquiring system parameters and establishing an impedance model. The introduction of a sedator and parameter tuning effectively enhances damping, suppresses oscillations, and significantly improves the DC-side stability of the intelligent soft switch. This invention provides a reliable guarantee for the stable operation of flexible interconnected distribution networks, enhances system reliability and adaptability, and has significant practical application value.

[0025] In this embodiment of the invention, step S100 includes the following sub-steps A1-A4; In A1: DC impedance model of the first voltage source converter, DC impedance model of the second voltage source converter, and DC impedance model of the capacitor; In A2: The DC impedance model of the first voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the first voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the first voltage source converter; In A3: The DC impedance model of the second voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the second voltage source converter; In A4: The DC impedance model of a capacitor is established through the relationship between the voltage and current of the capacitor.

[0026] In one optional embodiment, the DC impedance model of the first voltage source converter (VSC1) can be a DC impedance model based on a small-signal linearized model. The DC impedance model of VSC1 is obtained by linearizing the nonlinear equations of VSC1 using a small-signal method. Near the steady-state operating point, the state equations of VSC1 are linearized to obtain the small-signal model. The DC impedance model is defined as the ratio of the change in DC voltage to the change in DC current, that is, the DC impedance is equal to the ratio of the small-signal change in DC voltage to the small-signal change in DC current flowing into VSC1, which is obtained by solving the linearized differential equation or transfer function. In another alternative embodiment, the DC impedance model of the first voltage source converter (VSC1) can also be a DC impedance model based on an equivalent circuit model. The DC impedance model of VSC1 is represented by constructing an equivalent circuit, which consists of passive components and controlled sources. The DC side is usually modeled as a combination of a parallel capacitor and a controlled current source. The controlled current source depends on the AC side control variables of VSC1. The DC impedance model is the input impedance of the equivalent circuit at the DC port, which is calculated by circuit analysis. In this embodiment of the invention, the DC impedance model of the first voltage source converter (VSC1) Represented as:

[0027] in, This represents the steady-state value of the DC voltage of VSC1. This represents the steady-state value of the active power flowing into VSC1. The transfer function of the change in active power of input VSC1 with respect to the change in DC voltage; in, Represented as:

[0028] in, This is the steady-state value matrix of the AC voltage of VSC1. Here is the transfer function matrix of the AC side inductor of VSC1. This is the steady-state value matrix of the AC current in VSC1. Here is the transfer function matrix of the VSC1 current controller. This is the steady-state value matrix of the VSC1 modulation ratio. This is the transfer function matrix of the VSC1 DC voltage controller.

[0029] In one optional embodiment, the DC impedance model of the second voltage source converter (VSC2) can be a DC impedance model based on a state-space average model, derived by establishing and linearizing its state-space average model; a nonlinear state-space average model is established, including state variables such as the AC-side inductor current and DC-side voltage of VSC2. At a given steady-state operating point, this model is subjected to small-signal linearization to obtain a linearized small-signal state-space model. By solving this linearized model, the transmission relationship between small perturbations in the DC-side current and small perturbations in the DC-side voltage is established; this transmission relationship is the DC impedance model of VSC2. In another alternative embodiment, the DC impedance model of the second voltage source converter (VSC2) can be characterized by constructing its equivalent circuit in the frequency domain. This equivalent circuit represents the effects of VSC2 and its control system as a combination of a series of controlled sources and passive components. The DC impedance model is the equivalent impedance seen from the DC port of this equivalent circuit, which can be obtained by circuit analysis in the frequency domain using Kirchhoff's laws. In this embodiment of the invention, the DC impedance model of the second voltage source converter (VSC2) Represented as:

[0030] in, This represents the steady-state value of the DC voltage of VSC1. This represents the steady-state value of the active power flowing into VSC1. The transfer function of the change in active power output by VSC2 with respect to the change in DC voltage; in, Represented as:

[0031] in, This is the steady-state value matrix of the AC current in VSC2. Here is the transfer function matrix of the AC side inductor of VSC2. This is the steady-state value matrix of the AC voltage of VSC2. Here is the transfer function matrix of the VSC2 current controller. The transfer function matrix of the VSC2 active power controller is shown below. This is the steady-state value matrix of the VSC2 modulation ratio.

[0032] It should be noted that the electrical characteristics of the DC side of the intelligent soft-switching converter were quantified through precise modeling. The DC impedance models of the first and second voltage source converters were constructed based on their steady-state DC voltage, active power values, and transfer functions, respectively, meticulously characterizing their dynamic behavior. Furthermore, the DC impedance model of the capacitor was established through voltage-current relationships, further refining the overall characteristic description of the DC side. This provides an accurate basis for subsequent stability margin calculations and ballast parameter tuning, serving as a crucial foundation for achieving stable control of the intelligent soft-switching converter.

[0033] In this embodiment of the invention, step S200 includes the following sub-steps B1-B3; In B1: Characteristic equations are constructed based on the DC impedance models of the first voltage source converter, the second voltage source converter, and the capacitor. In B2: Solve the characteristic equation to obtain multiple eigenvalues; In B3: The stability margin of the DC side of the intelligent soft switch is determined based on multiple characteristic values.

[0034] In one alternative embodiment, the stability margin can be the absolute value of the real part with the smallest absolute value among all eigenvalues. That is, first find the eigenvalue whose real part is closest to the imaginary axis, and then take the absolute value of its real part as the stability margin index; In another alternative embodiment, the stability margin can also be the shortest distance from all eigenvalues ​​to a preset stability boundary, which is typically a vertical line on the complex plane. The stability margin is the minimum value among the differences of the real parts of all eigenvalues. In this embodiment of the invention, the stability margin is the minimum damping ratio; Specifically, based on the DC impedance models of the first voltage source converter, the second voltage source converter, and the capacitor, the characteristic equation is expressed as follows:

[0035] in, This is the DC impedance model of the first voltage source converter. This is the DC impedance model for the second voltage source converter. This is the DC impedance model for a capacitor.

[0036] make The value is 0, calculate the eigenvalue. ; Stability margin of the DC side of intelligent soft switching Eigenvalues The corresponding minimum damping ratio.

[0037] It should be noted that by constructing characteristic equations and solving for eigenvalues, the stability margin of the DC side of the intelligent soft switch was quantified, providing accurate indicators for stability assessment, effectively warning of potential oscillation risks, and enhancing the reliability of the system.

[0038] In this embodiment of the invention, step S300 includes the following sub-steps C1-C2; In C1: If the stability margin is greater than or equal to the first threshold, the smart switch can operate stably without the need for stable control of the smart soft switch; In C2: If the stability margin is less than the first threshold, a sedator is introduced into the control loop of the smart soft switch.

[0039] In one alternative embodiment, the sedator can be a band-stop filter-based sedator, wherein when tuning parameters, the center frequency is set according to the potential oscillation frequency, and the gain and quality factor are adjusted iteratively until the stability margin is met. In another alternative embodiment, the sedator can also be a state observation and feedback based sedator, with the current inner loop of VSC2 embedded. When tuning the parameters, the tuning objects are the gain of the state observer and the gain of the state feedback controller. In this embodiment of the invention, the sedator is a sedator that enhances the stability of the DC side of the intelligent soft switch; the first threshold is 0.1; Specifically, if the stability margin is greater than or equal to 0.1, the smart switch can operate stably without the need for stable control of the smart soft switch. If the stability margin is less than 0.1, a sedator is introduced into the control loop of the intelligent soft switch.

[0040] It should be noted that by setting a threshold to determine the stability margin, the stability state of the system can be accurately identified. When the stability margin is insufficient, a sedator is introduced in a timely manner to enhance damping, effectively suppress oscillations, ensure stable operation of the DC side of the intelligent soft switch, and improve system reliability.

[0041] In this embodiment of the invention, step S400 includes the following sub-steps D1-D3; In D1: The characteristic value corresponding to the minimum damping ratio is obtained based on the stability margin, and the potential oscillation frequency of the DC side of the intelligent soft switch is determined based on the corresponding characteristic value. In D2: The range of the sedator time constant is determined based on the potential oscillation frequency; In D3: The gain parameters of the stamina are tuned using an iterative method until the stability margin meets the judgment condition.

[0042] Specifically, the stability margin of the DC side of the intelligent soft switch is used to find the eigenvalue corresponding to the minimum damping ratio, and the potential oscillation frequency is determined based on the imaginary part of the corresponding eigenvalue. The range of the stabilizer time constant determined based on the potential oscillation frequency is expressed as follows:

[0043]

[0044] in, The oscillation frequency is... , These are the time constants of the stabilizer; Initially, a small sedator gain value is given, and during retuning, the sedator gain value is gradually increased.

[0045] In this embodiment of the invention, after completing steps D1-D3, step S400 further includes steps D4-D7; In D4: The stability margin of the DC side of the intelligent soft switch after adding the ballast is recalculated using the DC impedance model of the second voltage source converter after adding the ballast. In D5: If the stability margin is greater than or equal to the first threshold, the sedator parameter tuning is complete; In D6: If the stability margin is less than the first threshold, the stamina parameters are readjusted until the stability margin is greater than or equal to the first threshold. In D7: The DC impedance model of the second voltage source converter after adding the ballast is established by the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the second voltage source converter after adding the ballast, based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the change in DC voltage.

[0046] Specifically, a DC impedance model of the voltage source converter VSC2 with the ballast added is established, and the stability margin of the DC side of the intelligent soft switch is calculated after the ballast is added. ; like If the value is greater than or equal to 0.1, then the parameters of the stabilizer have been tuned. like If the value is less than 0.1, the gain and time constant of the ballast are readjusted until... The value is greater than or equal to 0.1.

[0047] The DC impedance model of the second voltage source converter after adding the ballast is expressed as follows:

[0048] in, This represents the steady-state value of the DC voltage of VSC1. This represents the steady-state value of the active power flowing into VSC1. This is the transfer function of the change in the output active power of VSC2 with respect to the change in DC voltage after the addition of the ballast.

[0049] in, Represented as:

[0050] in, This is the steady-state value matrix of the AC current in VSC2. Here is the transfer function matrix of the AC side inductor of VSC2. This is the steady-state value matrix of the AC voltage of VSC2. Here is the transfer function matrix of the VSC2 current controller. The transfer function matrix of the VSC2 active power controller is shown below. This is the steady-state value matrix of the VSC2 modulation ratio. This is the transfer function matrix of the stabilizer in VSC2.

[0051] It should be noted that by iteratively optimizing the stabilizer parameters, the stability margin of the DC side of the intelligent soft switch is ensured to reach above the threshold, which effectively enhances the stability of the system, improves the reliability of the intelligent soft switch under different operating conditions, and ensures the stable operation of the flexible interconnected distribution network.

[0052] The above is an illustrative scheme of a stable control method for an intelligent soft switch according to this embodiment. It should be noted that the technical solution of the stable control system for this intelligent soft switch belongs to the same concept as the technical solution of the stable control method for the intelligent soft switch described above. Details not described in detail in the technical solution of the stable control system for the intelligent soft switch in this embodiment can be found in the description of the technical solution of the stable control method for the intelligent soft switch described above.

[0053] The stable control system of the intelligent soft switch in this embodiment includes: The model building module is used to obtain the parameters of the intelligent soft-switching system and establish the impedance model of the DC side of the intelligent soft-switching system based on the parameters of the intelligent soft-switching system. The calculation module is used to calculate the stability margin of the DC side of the intelligent soft switch based on the impedance model of the DC side of the intelligent soft switch. The judgment module is used to judge the stability margin and, based on the judgment result, introduce a stabilizer into the control loop of the intelligent soft switch. The tuning module is used to tune the parameters of the stabilizer, recalculate the stability margin of the DC side of the intelligent soft switch until the judgment conditions are met, and realize stable control of the intelligent soft switch.

[0054] Specifically, the intelligent soft switch consists of a first voltage source converter and a second voltage source converter; The DC sides of the first voltage source converter and the second voltage source converter are connected; The first voltage source converter adopts a dual-loop control structure consisting of an outer loop for DC voltage and reactive power control and an inner loop for current control. The second voltage source converter adopts a dual-loop control structure with a power control outer loop and a current control inner loop, and a stamina is provided in the current control inner loop. The sedator takes the feedback value of the DC voltage of the second voltage source converter as input, and the output signal is superimposed on the reference value of the current control inner loop of the second voltage source converter.

[0055] In this embodiment of the invention, the control structure of VSC2 includes a dual-loop control structure consisting of a power control outer loop and a current control inner loop; a phase-locked loop is used to observe the phase of the grid voltage connected to VSC2. ,use The three-phase current output from VSC2 Current transformed into a rotating coordinate system , ; In the power control outer loop, VSC2 outputs a reference value for active power. With feedback value The difference is fed into a controller, whose output serves as a reference value for the d-axis component of the VSC2 output current. Feedback value of reactive power output from VSC2 Compared with reference value The difference is fed into a controller, whose output serves as a reference value for the q-axis component of the VSC2 output current. ; In the current control inner loop, the feedback value of the VSC2 DC voltage Entering the sedator, the sedator's output is ; Overlay Later and The difference is fed into a controller, whose output is a d-axis modulated voltage. ; Overlay Later and The difference is fed into a controller, whose output is a q-axis modulated voltage. ; , After rotational coordinate transformation and pulse width modulation, the trigger pulse for VSC2 is generated.

[0056] The control structure of VSC1 includes a dual-loop control structure with an outer loop for DC voltage and reactive power control and an inner loop for current control; a phase-locked loop is used to monitor the phase of the grid voltage connected to VSC1. ,use The three-phase current input to VSC1 Current transformed into a rotating coordinate system , ; In the outer loop of DC voltage and reactive power control, the difference between the reference value of DC voltage VSC1 and the feedback value of DC voltage VSC2 is fed into a controller, the output of which is the reference value of the d-axis component of the input current VSC1. Input reactive power feedback value for VSC1 Compared with reference value The difference is fed into a controller, whose output is a reference value for the q-axis component of the input VSC1 current. ; In the current control inner loop. and The difference is fed into a controller, whose output is the d-axis modulated voltage of VSC1. ; and The difference is fed into a controller, whose output is the q-axis modulated voltage of VSC1. ; , After rotational coordinate transformation and pulse width modulation, the trigger pulse of VSC1 is generated.

[0057] Specifically, the relationship between the output and input of the stabilizer is expressed as follows:

[0058] in, For the gain of the sedator, , These are the time constants, For the Laplace operator.

[0059] It should be noted that by introducing a sedator into the current control inner loop of VSC2, the stability of the DC side of the intelligent soft switch is effectively enhanced. The sedator adjusts the current reference value based on DC voltage feedback, precisely suppressing oscillations, ensuring stable system operation, and improving the reliability of the flexible interconnected distribution network.

[0060] This embodiment also provides a computer device suitable for stable control of intelligent soft switches, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a stable control method for an intelligent soft switch as described in the above embodiments.

[0061] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a stable control method for an intelligent soft switch as proposed in the above embodiments.

[0062] The storage medium proposed in this embodiment and the stable control method for realizing intelligent soft switching proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0063] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computing device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0064] Example 2, refer to Table 1, Figures 2-6 This embodiment differs from the first embodiment, and provides a verification test of a stable control method for an intelligent soft switch, verifying and explaining the technical effects used in this method.

[0065] like Figure 2 As shown in Table 1, the stability margin of the intelligent soft switch varies with active power under different grid strengths. The relevant electrical and control parameters of the intelligent soft switch are also shown in Table 1. Under a weak grid with a short-circuit ratio (SCR) of 2, as the output active power P2 of the intelligent soft switch increases from 0 to its rated value, the quantitative indicator ζ, which measures the DC-side stability margin of the intelligent soft switch, gradually decreases to a negative value, indicating that the intelligent soft switch is prone to DC-side instability as the output active power increases. Under a strong grid with a short-circuit ratio (SCR) of 20, the values ​​of the damping characteristic curves are all greater than 0, indicating that the DC-side of the intelligent soft switch is less prone to interactive instability under strong grid conditions.

[0066] Table 1 Main parameters of intelligent soft switch

[0067] like Figure 3 As shown, the intelligent soft switch can operate stably when the grid short-circuit ratio (SCR) is 10 and the output active power P2 is 0.4 pu. At 25 seconds, the grid short-circuit ratio (SCR) drops to 2, and the intelligent soft switch's output active power P2, AC current i2abc, and DC voltage udc oscillate and diverge, indicating that the intelligent soft switch is unstable. Figure 3 The simulation results shown are consistent with Figure 2 The results are consistent with the theoretical analysis, further verifying the correctness of the theoretical analysis.

[0068] like Figure 4 As shown, the short-circuit ratio (SCR) is 2, and the ballast time constants T1 and T2 are 1 and 0.001, respectively. When the ballast gain... When the value is 0, it corresponds to no stabilizer being added. As the output power P2 increases, the value of damping ζ gradually decreases to a negative value. When When the damping value is 0.4, the damping characteristic curve gradually shifts upward, and the critical output power corresponding to DC-side instability is 0.85 pu. When the value is 1, the damping characteristic curve is always positive, corresponding to the DC side of the intelligent soft switch always being stable. Figure 4 The analysis results show that the proposed stabilizer can effectively enhance the DC-side stability of the intelligent soft switch.

[0069] like Figure 5 , Figure 6 As shown, the short-circuit ratio (SCR) is 2, and the ballast time constants T1 and T2 are 1 and 0.001, respectively. Figure 5 In the middle, sedator gain The value was 0.4. As the active power output of the intelligent soft switch gradually increased to 0.85 pu, the output active power, AC current, and DC voltage began to oscillate and diverge. The active power at which the intelligent soft switch became critically unstable was 0.85 pu. Figure 4 The critical instability value obtained from the theoretical analysis is close to the expected value, which verifies the correctness of the theoretical analysis. Figure 6 In the test, the sedator gain is 1. As the active power output of the intelligent soft switch increases to its rated value, the output active power P2, AC current, and DC voltage all operate stably. (Comparison) Figure 4 , Figure 5 , Figure 6 It is easy to see that the proposed sedator can enhance the interactive stability of the DC side of the intelligent soft switch and increase the sedator gain. This can further enhance the DC-side interaction stability of intelligent soft switches.

[0070] In summary, this invention can quantify the stability margin of the DC side of intelligent soft switches, overcoming the limitations of existing graphical analysis methods based on the Nyquist criterion; for potential oscillation frequencies, the proposed stabilizer can specifically enhance the damping of intelligent soft switches at those oscillation frequencies, effectively improving the stability of the DC side and suppressing oscillations.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A stable control method for an intelligent soft switch, characterized in that, include: Obtain the parameters of the intelligent soft-switching system, and establish the impedance model of the DC side of the intelligent soft-switching system based on the parameters of the intelligent soft-switching system; The stability margin of the DC side of the intelligent soft switch is calculated based on the impedance model of the DC side of the intelligent soft switch. The stability margin is determined, and a stabilizer is introduced into the control loop of the intelligent soft switch based on the determination result. The parameters of the stabilizer are tuned, and the stability margin of the DC side of the intelligent soft switch is recalculated until the judgment condition is met, thereby achieving stable control of the intelligent soft switch.

2. The stable control method for an intelligent soft switch as described in claim 1, characterized in that, Establishing the impedance model for the DC side of the intelligent soft switch includes: DC impedance model of the first voltage source converter, DC impedance model of the second voltage source converter, and DC impedance model of the capacitor; The DC impedance model of the first voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the first voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the first voltage source converter. The DC impedance model of the second voltage source converter is established based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the transfer function that reflects the relationship between the change in active power and the change in DC voltage of the second voltage source converter. The DC impedance model of a capacitor is established through the relationship between the capacitor's voltage and current.

3. The stable control method for an intelligent soft switch as described in claim 2, characterized in that, The calculation of the stability margin on the DC side of the intelligent soft switch includes: Characteristic equations are constructed based on the DC impedance models of the first voltage source converter, the second voltage source converter, and the capacitor. Solving the characteristic equation yields multiple eigenvalues; The stability margin of the DC side of the intelligent soft switch is determined based on multiple characteristic values.

4. The stable control method for an intelligent soft switch as described in claim 3, characterized in that, Determining the stability margin includes: If the stability margin is greater than or equal to the first threshold, the smart switch can operate stably without the need for stable control of the smart soft switch. If the stability margin is less than the first threshold, a sedator is introduced into the control loop of the intelligent soft switch.

5. The stable control method for an intelligent soft switch as described in claim 4, characterized in that, Tuning the parameters of the stabilizer includes: Based on the stability margin, the characteristic value corresponding to the minimum damping ratio is obtained, and the potential oscillation frequency of the DC side of the intelligent soft switch is determined based on the corresponding characteristic value. The range of the sedator time constant is determined based on the potential oscillation frequency; The gain parameters of the stamina are tuned iteratively until the stability margin meets the judgment condition.

6. A stable control method for an intelligent soft switch as described in claim 1 or 5, characterized in that, The recalculation of the stability margin on the DC side of the smart soft switch includes: The stability margin of the DC side of the intelligent soft switch after adding the ballast is recalculated using the DC impedance model of the second voltage source converter with the ballast added. If the stability margin is greater than or equal to the first threshold, the sedator parameter tuning is complete; If the stability margin is less than the first threshold, the stamina parameters are readjusted until the stability margin is greater than or equal to the first threshold. The DC impedance model of the second voltage source converter after adding the ballast is established by the transfer function reflecting the relationship between the change in active power and the change in DC voltage of the second voltage source converter after adding the ballast, based on the steady-state value of the DC voltage, the steady-state value of the active power of the second voltage source converter, and the change in DC voltage.

7. A stable control system for an intelligent soft switch, employing the stable control method for an intelligent soft switch as described in any one of claims 1-6, characterized in that, include: The intelligent soft switch consists of a first voltage source converter and a second voltage source converter; The DC sides of the first voltage source converter and the second voltage source converter are connected; The first voltage source converter adopts a dual-loop control structure consisting of an outer loop for DC voltage and reactive power control and an inner loop for current control. The second voltage source converter adopts a dual-loop control structure with a power control outer loop and a current control inner loop, and a stamina is provided in the current control inner loop. The sedator takes the feedback value of the DC voltage of the second voltage source converter as input, and the output signal is superimposed on the reference value of the current control inner loop of the second voltage source converter.

8. The stable control system for an intelligent soft switch as described in claim 7, characterized in that, The stabilizer includes: The relationship between the sedator input and output is described by a transfer function that includes a gain parameter and a time constant.

9. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a stable control method for an intelligent soft switch according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of a stable control method for an intelligent soft switch according to any one of claims 1 to 6.

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