Following-networking virtual parallel SVG (static var generator) control method adaptive to power grid intensity change

Abstract

The invention discloses a following-networking virtual parallel SVG control method adaptive to power grid intensity change, and the method comprises the steps: constructing following-networking virtual parallel SVG equipment, so as to achieve the equivalent simulation of the dynamic characteristics of a virtual following grid converter and a parallel combination of a virtual networking converter; the method comprises the following steps: establishing a feed-in system closed-loop characteristic equation according to a typical application scene of a follow-to-construct network virtual parallel SVG device, and describing a power system small-interference stability evaluation framework based on the power grid strength depicted by a short-circuit ratio index; and finally, when the power grid intensity changes, based on the small interference stability evaluation framework, realizing adaptive adjustment of the virtual component proportionality coefficient of the following-networking virtual parallel SVG. According to the control method, continuous switching of dual-mode control can be realized when the power grid strength changes, so that the influence of instant impact on the power grid is avoided, flexible support is provided for small-interference stability of the system, and the control method has practical engineering significance.

Description

Adaptive SVG control method based on grid strength variation and virtual parallel operation Technical Field

[0001] This invention relates to the field of new energy grid-connected system operation, and in particular to a grid-connected virtual parallel SVG control method that adapts to changes in grid intensity. Background Technology

[0002] The new power system exhibits the dual characteristics of "high proportion of renewable energy" and "high proportion of power electronic equipment," with grid-connected control and grid-connected control being the main control modes for these electronic devices. These two control modes are adapted to different grid strengths: grid-connected control can operate stably in strong grids but is prone to instability under small disturbances in weak grids; grid-connected control is the opposite. However, fluctuations in factors such as the number of grid-connected devices, load, and system operating modes cause changes in grid strength, exceeding the stable operating range of a single control mode, leading to system instability and serious consequences.

[0003] Although some scholars have studied the complementary characteristics of grid-following control and grid-based control under different grid strengths, most studies focus on ensuring the stability of the system under small disturbances when the grid impedance changes significantly by fast dual-mode switching, but neglect the impact of nonlinear switching on the instantaneous impact on the grid during large-scale switching. Liu Pengyin et al. proposed an adaptive weighted fusion method for dual-mode synchronization links, which adaptively adjusts the weighting coefficients of the two synchronization methods based on the current grid SCR identification results, but lacks theoretical basis (Liu P, Xie X, Shair J. AdaptiveHybrid Grid-Forming and Grid-Following Control of IBRs with Enhanced Small-Signal Stability under Varying SCRs[J]. IEEE Transactions on PowerElectronics, 2024, 39(6): 6603-6607.)

[0004] Therefore, there is an urgent need for an adaptive grid strength variation-based virtual parallel SVG control method that can be applied to grid-connected renewable energy power plants / SVG interconnection systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an adaptive grid strength variation-based virtual parallel SVG control method. This method enables continuous switching between dual-mode control when grid strength changes, thereby avoiding the impact of instantaneous shocks on the grid and providing flexible support for the system's small-disturbance stability. It has practical engineering significance.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] Construct a virtual parallel SVG device to equivalently simulate the dynamic characteristics of the parallel combination of virtual grid-connected converters and virtual grid-connected converters;

[0008] Based on circuit principles such as Thevenin's equivalent theorem, the modulation voltage reference value of the output of the virtual parallel SVG with a grid-connected structure is calculated. The measured current signal is distributed, processed, and then sent to the control calculation units of the virtual grid-connected transformer and the virtual grid-connected converter, respectively. The relationship between the filter inductance in the virtual parallel SVG with the filter inductance in the virtual grid-connected transformer and the virtual grid-connected converter is analyzed.

[0009] Based on the application scenario of virtual parallel SVG devices in the grid-connected network, the closed-loop characteristic equation of the feed system is established, and a small-disturbance stability evaluation framework for the power system based on the grid strength characterized by the short-circuit ratio index is constructed.

[0010] When the grid strength changes, the virtual component ratio coefficient of the virtual parallel SVG is adjusted in real time based on the small disturbance stability assessment framework to enhance the small disturbance stability of the feed-in system when the grid strength changes.

[0011] Compared with the prior art, the advantages of this invention are as follows:

[0012] (1) When the grid strength changes, the continuous switching of the grid-following / grid-building dual-mode control is realized to avoid the impact of the instantaneous impact on the grid caused by nonlinear switching, and to provide flexible support for the small disturbance stability of the system.

[0013] (2) The method of the present invention can be extended to the multi-machine feed-in scenario of power electronic equipment, and has good scalability, theoretical basis and interpretability. Attached Figure Description

[0014] Figure 1 is a flowchart of obtaining the virtual component ratio coefficient of the root-structured virtual parallel SVG in an embodiment.

[0015] Figure 2 is a schematic diagram of a typical application scenario for a virtual parallel SVG device.

[0016] Figure 3 shows the control structure of a grid-connected new energy power station.

[0017] Figure 4 shows the control structure of the virtual parallel SVG.

[0018] Figure 5 illustrates the basic idea of ​​the virtual parallel SVG control method based on the network structure.

[0019] Figure 6 is a flowchart of the adjustment of the virtual component ratio coefficient of the virtual parallel SVG in the adaptive grid strength change.

[0020] Figure 7 shows the specific simulation results of the embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer and more explicit, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0022] Example 1

[0023] This example demonstrates a grid-connected virtual parallel SVG (Static Var Generator) control method adapted to grid intensity changes, as shown in Figure 1. This example tests the feedin system corresponding to a typical application scenario of the grid-connected virtual parallel SVG device to illustrate the grid-connected virtual parallel SVG control method provided by this invention. The feedin system schematic diagram, the grid-connected renewable energy power station, and the control structure of the grid-connected virtual parallel SVG are shown in Figures 2, 3, and 4, and include the following steps:

[0024] In this embodiment, the control parameters of the grid-connected new energy power station and the grid-connected virtual parallel SVG in the feed-in system corresponding to typical application scenarios are obtained, as shown in Table 1 and Table 2.

[0025] Table 1 Control parameters for grid-connected new energy power stations

[0026] Table 2 Control parameters for virtual parallel SVG network

[0027] S1. Construct a virtual parallel SVG device to equivalently simulate the dynamic characteristics of the parallel combination of virtual grid-connected converters and virtual grid-connected converters;

[0028] In this way, the original structure of grid-type and network-type control can be completely preserved, so that it has both the "current source" characteristics of grid-type control and the "voltage source" characteristics of network-type control.

[0029] The grid-connected virtual parallel SVG is equivalent to a virtual system in which a converter using a grid-connected control computing unit and a converter using a grid-connected control computing unit are connected in parallel (for ease of description later, it is referred to as a virtual grid-connected transformer and a virtual grid-connected converter). Its basic concept is shown in Figure 5. Among them, Figure 5(a) is a virtual system in which a virtual grid-connected converter and a grid-connected converter are connected in parallel; Figure 5(b) is a schematic diagram of the grid connection of the grid-connected virtual parallel SVG equipment.

[0030] The virtual parallel SVG with grid connection completely retains the original structure of grid connection and grid-connection control. Its external characteristics can have both the "current source" characteristics of grid connection control and the "voltage source" characteristics of grid-connection control, as shown in Figure 4.

[0031] Virtual grid-connected converters use phase-locked loops for grid synchronization, with DC voltage control and reactive power control as the outer loop and current control as the inner loop, exhibiting external characteristics of a "current source".

[0032] Specifically, the control calculation unit of the virtual grid converter includes: phase-locked loop synchronization equations: DC voltage control equation: Reactive power control equation: Current control equation:

[0033] Where s is the Laplace operator, ω0 is the power frequency angular velocity; L f,GFL For the virtual grid-type converter filter inductor, θ PLL and ω PLL These are the synchronization angle and angular frequency of the virtual mesh converter, respectively; v d,GFL (v q,GFL ), i d,GFL (i q,GFL ) and i dref,GFL (i qref,GFL These represent the grid connection point voltage, the current used for feedback control of the virtual grid-connected converter, and its reference value, respectively. d,GFL (e q,GFL The reference value for the modulation voltage output of the virtual grid-connected converter is denoted as U, which, when the control delay is ignored, is the terminal electromotive force of the virtual grid-connected converter (all the above variables are located in the controller dq coordinate system of the virtual grid-connected converter); dc and U dcref Q GFL and Q ref,GFL These represent the DC voltage, reactive power, and reference values ​​of the virtual grid-connected converter, H. PLL (s), H dc (s), H Q (s), H C,GFL (s) and fV,GFL (s) are the transfer functions of the controllers and voltage feedforward loops of the virtual grid converter, including the phase-locked loop, DC voltage loop, reactive power loop, and current loop.

[0034] Virtual grid-type converters use virtual synchronous machine control, with AC voltage control as the outer loop and current control as the inner loop, exhibiting "voltage source" characteristics.

[0035] Specifically, the control calculation unit of the virtual grid converter includes: virtual synchronous machine control equations: AC voltage control equation: Current control equation:

[0036] Where s is the Laplace operator, ω0 is the power frequency angular velocity; L f,GFM For the filter inductor of the virtual network converter, θ PBS and ω PBS Here, ω represents the synchronization angle and angular frequency of the virtual network converter, and J and D represent the inertia coefficient and damping coefficient of the virtual synchronous machine control, respectively; v d,GFM (v q,GFM ), i d,GFM (i q,GFM ) and i dref,GFM (i qref,GFM These represent the grid connection point voltage, the current used for feedback control in the virtual grid-type converter, and its reference value, respectively. d,GFM (e q,GFM These are the modulation voltage reference values ​​output by the virtual network converter, which, when control delay is ignored, are the terminal electromotive force of the virtual network converter (all variables are located in the controller dq coordinate system of the virtual network converter); P GFM With P ref,GFM These are the measured and reference values ​​of the active power of the virtual grid converter, H. V (s), H C,GFM (s) and f I (s), f V,GFM (s) are the transfer functions of the controllers such as the AC voltage loop and current loop, and the current and voltage feedforward loops of the virtual network converter, respectively.

[0037] S2. Based on circuit principles such as Thevenin's equivalent theorem, calculate the modulation voltage reference value of the output of the virtual parallel SVG with a root-grid connection. Distribute and process the measured current signal, then send it to the control calculation units of the virtual root-grid transformer and the virtual grid converter respectively. Analyze the relationship between the filter inductance in the virtual parallel SVG with the filter inductance in the virtual root-grid transformer and the virtual grid converter; the specific calculations are as follows:

[0038] According to Thevenin's equivalent theorem, the reference value of the modulation voltage output of the virtual parallel SVG should be numerically equal to the open-circuit voltage at the parallel point of the virtual system, specifically:

[0039] Among them, e xy,HCC = [e x,HCC , e y,HCC ] T and e xy,GFL =[e x,GFL , e y,GFL ] T , e xy,GFM =[e x,GFM ,e y,GFM ] T These are the modulation voltage reference values ​​output by the virtual parallel SVG, virtual grid-connected transformer, and virtual grid-connected converter, respectively. Ignoring control delay, these are the terminal electromotive forces; k1=L f,GFM / (L f,GFM+ L f,GFM ) and k2=L f,GFL / (L f,GFM+ L f,GFM ) represents the reference weighting coefficient for the modulation voltage.

[0040] The measured current signals are distributed, processed, and then sent to the control calculation units of the virtual grid-type and virtual grid-type converters, respectively, as follows:

[0041] First, according to Kirchhoff's circuit theory, the virtual three-phase current i flowing to the grid from the virtual grid-connected transformer and the virtual grid-connected converter is measured. xy,GFL,V = [i x,GFL,V i y,GFL,V ] T and i xy,GFM,V = [i x,GFM,V i y,GFM,V ] T Current measurement value i flowing from the virtual parallel SVG of the grid to the power grid xy,HCC = [i x,HCC i y,HCC ] T The calculation yielded the following: ; Among them, B GFL =1 / ω0L GFL B GFM =1 / ω0L GFM ,

[0042] The calculated virtual current measurements of the virtual grid-connected transformer and virtual grid-connected converter flowing to the power grid are processed and then sent to the control calculation units of the virtual grid-connected transformer and virtual grid-connected converter, respectively. The specific processing steps are as follows:

[0043] Where α and β are the proportional coefficients of the virtual grid-connected transformer and the virtual grid-connected converter, respectively, and always satisfy α>0, β>0 and α+β=1; xy,GFL and i xy,GFM These are the current signals fed into the control calculation units of the virtual grid-connected transformer and the virtual grid-connected converter, respectively.

[0044] In the control calculation units of virtual grid-connected transformers and virtual grid-connected converters, i can be transformed by coordinate transformation. xy,GFL and i xy,GFM Transform to the respective controller's dq coordinate system to obtain the current i used for feedback control in the control calculation unit of the virtual grid-type and virtual grid-type converters. d,GFL (i q,GFL ) and i d,GFM (i q,GFM ).

[0045] The relationship between the filter inductance in a virtual parallel SVG with a virtual grid-connected network and the filter inductance in a virtual grid-connected transformer and a virtual grid-connected converter is analyzed as follows:

[0046] According to Thevenin's equivalent theorem, the inductor L of the virtual parallel SVG filter inductor is connected to the network. f,HCC The filter inductor L of the virtual grid-type transformer and the virtual grid-type converter f,GFL and L f,GFM The specific relationship between them is as follows: .

[0047] S3. Based on the application scenario of virtual parallel SVG devices connected to the grid, establish the closed-loop characteristic equation of the feed system, and construct a power system small-disturbance stability assessment architecture characterized by grid strength and short-circuit ratio; specifically including:

[0048] A matrix model of the small-signal complex frequency domain admittance / impedance transfer function for a virtual parallel SVG device with a root-structure network, suitable for small-disturbance stability analysis, is established as follows:

[0049] Where Δ represents the incremental change of the variable; i xy,HCC =[i x,HCC i y,HCC ] T and v xy,HCC =[v x,HCC, v y,HCC ] T These represent the measured values ​​of the grid connection point voltage and current flowing into the grid for a virtual parallel SVG in the global xy coordinate system; Y HCC (s) and Y GFL (s), Y GFM (s) represent the small-signal complex frequency domain admittance transfer function matrices of the virtual parallel SVG and the virtual grid-type and grid-type converters under the equipment capacity reference (the impedance transfer function matrix and the admittance transfer function matrix are inverses of each other).

[0050] The typical application scenario for grid-connected virtual parallel SVG devices is as follows: A renewable energy power station using grid-connected control is connected in parallel with a grid-connected virtual parallel SVG via admittance B0, and then both are connected to the AC power grid to form a feed-in system. The power grid is modeled using the Thevenin equivalent circuit, i.e., modeled as equivalent admittance B0. g A series combination with an ideal voltage source.

[0051] Establish small-signal complex frequency domain admittance transfer function matrix models for the above-mentioned feed system's equipment side and network side. The specific models are as follows: Equipment side: Network side:

[0052] In the formula, Δ represents the infinitesimal increment of the variable, and represents the Kronecker product; i xy,IBR= [i x,IBR i y,IBR ] T v xy,IBR= [v x,IBR , v y,IBR ] T and i xy,HCC= [i x,HCC i y,HCC ] T and v xy,HCC= [v x,HCC , v y,HCC ] T These are the measured values ​​of grid connection point voltage and current flowing to the grid for grid-connected renewable energy power stations and grid-connected virtual parallel SVG, respectively, in the global xy coordinate system; S IBR and S HCC These are the rated capacities of grid-connected new energy power stations and grid-connected virtual parallel SVG, respectively; Y IBR (s) represents the small-signal complex frequency domain admittance transfer function matrix of a grid-type renewable energy power station; .

[0053] The closed-loop characteristic equation of the feed system is established as follows:

[0054] Where det{·} represents the determinant of the matrix; I2 represents the second-order identity matrix.

[0055] S3. When the grid strength changes, the virtual component ratio coefficient of the virtual parallel SVG in the feed-in system is adjusted in real time based on the small disturbance stability assessment framework to enhance the small disturbance stability of the feed-in system when the grid strength changes; specifically including:

[0056] When studying broadband oscillation problems dominated by grid-connected renewable energy power plants and grid-connected virtual parallel SVG, the short-circuit ratio (SCR) and its critical short-circuit ratio (CSCR) are established as follows:

[0057] When studying the broadband oscillation problem dominated by grid-connected renewable energy power plants, the "current source" characteristics (approximately equivalent to an ideal current source) of the virtual grid-connected converter and the "voltage source" characteristics (approximately equivalent to the equivalent susceptance B determined by the control loop and control parameters) of the virtual grid-connected SVG are considered. eq The specific expressions for the closed-loop characteristic equation of the feed system (and the series combination with an ideal voltage source), the short-circuit ratio correction value SCR1, and its critical value CSCR1 are as follows: ;

[0058] Where arg{·} is the solution to the equation, j is the imaginary unit; CSCR1 is the short-circuit ratio corresponding to the characteristic root of the oscillation problem dominated by grid-type new energy power stations (mainly phase-locked loops) which is located exactly on the imaginary axis of the complex plane, and ω1 is the oscillation angular frequency at this time.

[0059] When studying the broadband oscillation problem dominated by the virtual grid-connected SVG, considering that the probability of broadband oscillation in a grid-connected SVG is generally low under reasonable parameter settings, the study mainly focuses on the broadband oscillation problem dominated by the virtual grid-connected converter in the grid-connected SVG. In this case, considering the "current source" characteristics (approximately equivalent to an ideal current source) of the virtual grid-connected converter in the renewable energy power plant and the grid-connected SVG, the expressions for the closed-loop characteristic equation of the feed system, the short-circuit ratio correction value SCR2, and its critical value CSCR2 are as follows: ;

[0060] Where arg{·} is the solution to the equation, j is the imaginary unit; CSCR2 is the short-circuit ratio corresponding to the characteristic root of the oscillation problem dominated by the virtual parallel SVG (mainly the synchronization link of the virtual grid converter) located exactly on the imaginary axis of the complex plane, and ω2 is the oscillation angular frequency at this time.

[0061] The proportional coefficients α and β of the virtual grid-connected transformer and virtual grid-connected converter described in claim 6 are adaptively adjusted based on the short-circuit ratio SCR1 / SCR2 and its critical value CSCR1 / CSCR2. The adjustment process is shown in Figure 6, and specifically:

[0062] Let the equivalent admittance of the virtual network converter be B. eq The critical short-circuit ratios are CSCR1 and CSCR2, respectively. Let S be the capacity of the grid-connected renewable energy power station at time t. IBR,t The capacity of the virtual parallel SVG structure is S. HCC,t The virtual component proportion coefficient is α t and β t If at this time the admittance B0 or / and B g If a change occurs, regulation will begin:

[0063] Step A: Based on α at this time t and β t Calculate the short-circuit ratio SCR2 using parameters (when β) t When β = 0, SCR2 = 0). If δ = SCR2 - (1 - n%)CSCR2 < 0, where n% is the preset small disturbance stability margin when studying the broadband oscillation problem dominated by virtual parallel SVG in a grid-connected system, mainly virtual grid-connected converters, then proceed to step B; otherwise, if β t If the value is greater than or equal to 1, then the virtual network converter will stop operating, and β will be set to 1. t If δ = 0 and Flag = 1, proceed to step B; otherwise, adjust α in real time according to δ. t and β t Repeat step A.

[0064] Step B: Based on α at this time t and β t Calculate the short-circuit ratio SCR1 using parameters such as ε = (1 + m%)CSCR1 - SCR1 < 0, where m% is the preset small disturbance stability margin when studying the broadband oscillation problem dominated by grid-connected new energy power stations. Then update the system's operating state, such as α. t and β t Parameters; otherwise, if β t If Flag=1 or Flag=1, the system's active support capability is severely insufficient. Output actions such as machine switching and load shedding are used to maintain the system's stability under small disturbances, and Flag=0 is set, returning to step A; otherwise, proceed to step C.

[0065] Step C: If β t If ε = 0, then the virtual network converter will start running, and α will be adjusted in real time according to ε. t and β t If necessary, return to step A; otherwise, adjust α in real time according to ε. t and β t Return to step B.

[0066] After the system operating status update is completed, once admittance B0 or / and B... g If a change occurs, return to step A.

[0067] In other words, this method first proposes a virtual parallel SVG control method based on the idea of ​​simulating the parallel combination of grid-connected and grid-connected converters, which completely retains the original control structure of grid-connected and grid-connected converters. Then, for typical application scenarios of the virtual parallel SVG device, a closed-loop characteristic equation of the feed system is established, and a novel small-disturbance stability assessment architecture for the power system is constructed based on the grid strength characterized by the short-circuit ratio index. Finally, when the grid strength changes, the virtual component proportional coefficient of the virtual parallel SVG is adaptively adjusted based on this small-disturbance stability assessment architecture. This control method can achieve continuous switching between dual-mode control when the grid strength changes, thereby avoiding the impact of instantaneous shocks on the grid and providing flexible support for the system's small-disturbance stability margin, which has practical engineering significance.

[0068] In this embodiment, the equivalent admittance B is determined by the virtual grid converter control structure and parameters shown in Figure 4 and Table 2. eq =10p.u.;Let S IBR =1p.u., S HCC =0.7pu; preset stability margin m%=20%, n%=20%. At time 0, admittance B0=10p.u. and B g =5p.u., α0=0.7 and β0=0.3; change B during operation g =10p.u. The results of adaptive grid strength variation-based virtual parallel SVG virtual component proportional coefficient control are obtained: α t =0.49 and β t =0.51, the adjusted SCR1=5.75≥(1+m%)CSCR1=2.748, SCR2=28.24≤(1-n%)CSCR2=28.24, that is, the system meets the requirements of small disturbance stability. The specific results are shown in Figure 7(a).

[0069] Example 2

[0070] In Example 2, during operation, B0=4p.u., B g =2p.u., and the remaining parameter settings and preset stability margin are consistent with those in Example 1.

[0071] The result of the adaptive grid strength variation-based virtual parallel SVG virtual component proportional coefficient control in Example 2 is: α t =0.04 and β t =0.96, after adjustment, SCR1=2.75≥(1+m%)CSCR1=2.748, SCR2=2.97≤(1-n%)CSCR2=28.24, that is, the system meets the requirements of small disturbance stability. This result shows that, facing different degrees of changes in power grid conditions, this adaptive control method can flexibly provide support for the system's small disturbance stability margin. Specific results are shown in Figure 7(b).

[0072] Example 3

[0073] In Example 3, B is changed first during operation. g =25p.u.; then change B g =10 p.u.; Finally, change B0 = 5 p.u., B g =2p.u. Then change the other parameter settings and the preset stability margin to be consistent with that in Example 1.

[0074] The result of the adaptive grid strength change-based virtual parallel SVG virtual component proportional coefficient control in Example 3 is: B g The minimum value of SCR2 (i.e., β=1) when SCR changes from 5 p.u. to 25 p.u. 2,min >(1-n%)CSCR2, therefore the virtual grid converter is stopped and α t =1 and β t =0, after regulation, SCR1 = 7.14 ≥ (1 + m%)CSCR1 = 2.748; B g When the value changes from 25 p.u. to 10 p.u., the SCR1=5≥(1+m%)CSCR1=2.748 still holds true, so there is no need to adjust the virtual component scaling factor. At this time, α t =1 and β t =0 remains unchanged; B0 changes from 10 p.u. to 5 p.u. and B g When the value changes from 10 p.u. to 2 p.u., SCR1 = 1.43 < (1 + m%)CSCR1 = 2.748, therefore adjustment is needed to obtain α. t =0.42 and β t=0.58, after adjustment, SCR1=2.75≥(1+m%)CSCR1=2.748, SCR2=4.93≤(1-n%)CSCR2=28.24. The system remained stable under small disturbances throughout the above changes. Specific results are shown in Figure 7(c).

[0075] Obviously, the above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. An adaptive grid strength variation-based virtual parallel SVG control method, characterized in that, include: A virtual parallel SVG device with a grid-connected structure is constructed to equivalently simulate the dynamic characteristics of the parallel combination of a virtual grid-connected converter and a virtual grid-connected converter. Based on circuit principles such as Thevenin's equivalent theorem, the modulation voltage reference value of the output of the virtual parallel SVG with a grid-connected structure is calculated. The measured current signal is distributed, processed, and then sent to the control calculation units of the virtual grid-connected transformer and the virtual grid-connected converter, respectively. The relationship between the filter inductance in the virtual parallel SVG with a grid-connected structure and the filter inductance in the virtual grid-connected transformer and the virtual grid-connected converter is analyzed. Based on the application scenario of the virtual parallel SVG device in the grid-connected system, a closed-loop characteristic equation of the feed system is established, and a small-disturbance stability assessment framework for the power system based on the grid strength characterized by the short-circuit ratio index is constructed. When the grid strength changes, the virtual component ratio coefficient of the virtual parallel SVG in the grid-connected system is adjusted in real time based on the small-disturbance stability assessment framework to enhance the small-disturbance stability of the feed system when the grid strength changes.

2. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 1, characterized in that, The virtual grid-connected converter uses a phase-locked loop for grid synchronization, with DC voltage control and reactive power control as the outer loop and current control as the inner loop, exhibiting current source characteristics in its external characteristics. The virtual grid-connected converter uses a virtual synchronous machine for control, with AC voltage control as the outer loop and current control as the inner loop, exhibiting voltage source characteristics in its external characteristics.

3. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 1 or 2, characterized in that, The control calculation unit of the virtual grid-connected converter specifically includes: phase-locked loop synchronization equations: DC voltage control equation: Reactive power control equation: Current control equation: Where s is the Laplace operator, ω0 is the power frequency angular velocity; L f,GFL For the virtual grid-type converter filter inductor, θ PLL and ω PLL These are the synchronization angle and angular frequency of the virtual mesh converter, respectively; v d,GFL (v q,GFL ), i d,GFL (i q,GFL ) and i dref,GFL (i qref,GFL These represent the grid connection point voltage, the current used for feedback control of the virtual grid-connected converter, and its reference value, respectively. d,GFL (e q,GFL The modulated voltage reference value of the virtual grid-connected converter output is U; when control delay is ignored, it is the terminal electromotive force of the virtual grid-connected converter. dc and U dcref Q GFL and Q ref,GFL These represent the DC voltage, reactive power, and reference values ​​of the virtual grid-connected converter, H. PLL (s), H dc (s), H Q (s), H C,GFL (s) and f V,GFL (s) are the transfer functions of the virtual grid-connected converter's phase-locked loop, DC voltage loop, reactive power loop, current loop controller, and voltage feedforward loop, respectively.

4. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 1 or 2, characterized in that, The control calculation unit of the virtual grid converter specifically includes: virtual synchronous machine control equations: AC voltage control equation: Current control equation: Where s is the Laplace operator, ω0 is the power frequency angular velocity; L f,GFM For the filter inductor of the virtual network converter, θ PBS and ω PBS Here, ω represents the synchronization angle and angular frequency of the virtual network converter, and J and D represent the inertia coefficient and damping coefficient of the virtual synchronous machine control, respectively; v d,GFM (v q,GFM ), i d,GFM (i q,GFM ) and i dref,GFM (i qref,GFM These represent the grid connection point voltage, the current used for feedback control in the virtual grid-type converter, and its reference value, respectively. d,GFM (e q,GFM These are the modulation voltage reference values ​​output by the virtual network converter, which, when control delay is ignored, are the terminal electromotive force of the virtual network converter (all variables are located in the controller dq coordinate system of the virtual network converter); P GFM With P ref,GFM These are the measured and reference values ​​of the active power of the virtual grid converter, H. V (s), H C,GFM (s) and f I (s), f V,GFM (s) are the transfer functions of the controllers such as the AC voltage loop and current loop, and the current and voltage feedforward loops of the virtual network converter, respectively.

5. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 1, characterized in that, The modulation voltage reference value is calculated in the following manner: ; where e xy,HCC = [e x,HCC , e y,HCC ] T and e xy,GFL =[e x,GFL , e y,GFL ] T , e xy,GFM =[e x,GFM , e y,GFM ] T These are the modulation voltage reference values ​​output by the virtual parallel SVG, virtual grid-connected transformer, and virtual grid-connected converter, respectively. Ignoring control delay, these are the terminal electromotive forces; k1=L f,GFM / (L f,GFM+ L f,GFM ) and k2=L f,GFL / (L f,GFM+ L f,GFM ) represents the reference weighting coefficient for the modulation voltage.

6. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 5, characterized in that, The process of distributing and processing the measured current signals and then sending them to the control calculation units of the virtual grid-connected transformer and the virtual grid-connected converter includes: based on Kirchhoff circuit theory, the virtual three-phase current measurement value i flowing from the virtual grid-connected transformer and the virtual grid-connected converter to the power grid. xy,GFL,V = [i x,GFL,V i y,GFL,V ] T and i xy,GFM,V = [i x,GFM,V i y,GFM,V ] T Current measurement value i flowing from the virtual parallel SVG of the root-structured network to the power grid xy,HCC = [i x,HCC i y,HCC ] T The calculation yielded the following: ； ; among them,B GFL =1 / ω0L GFL , B GFM =1 / ω0L GFM , The calculated virtual current measurements of the virtual grid-connected transformer and virtual grid-connected converter flowing to the power grid are processed and then sent to the control calculation units of the virtual grid-connected transformer and virtual grid-connected converter, respectively. The specific processing steps are as follows: Where α and β are the proportional coefficients of the virtual grid-connected transformer and the virtual grid-connected converter, respectively, and always satisfy α>0, β>0 and α+β=1; xy,GFL and i xy,GFM These are the current signals fed into the control calculation units of the virtual grid-connected transformer and the virtual grid-structured converter, respectively; within the control calculation units of the virtual grid-connected transformer and the virtual grid-structured converter, i can be transformed using coordinates. xy,GFL and i xy,GFM Transform to the respective controller's dq coordinate system to obtain the current i used for feedback control in the control calculation unit of the virtual grid-type and virtual grid-type converters. d,GFL (i q,GFL ) and i d,GFM (i q,GFM ).

7. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 1, characterized in that, The analysis of the relationship between the filter inductance in the virtual parallel SVG and the filter inductance of the virtual grid-type transformer and the virtual grid-type converter includes: according to Thevenin's equivalent theorem, the filter inductance L in the virtual parallel SVG... f,HCC The filter inductance L of the virtual grid-type transformer and the virtual grid-type converter f,GFL and L f,GFM The specific relationship between them is as follows: 。 8. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 2, characterized in that, The application scenario based on the root-and-network virtual parallel SVG device establishes the closed-loop characteristic equation of the feed system, including: establishing a small-signal complex frequency domain admittance / impedance transfer function matrix model of the root-and-network virtual parallel SVG device suitable for small-disturbance stability analysis, specifically: Where Δ represents the incremental change of the variable; i xy,HCC =[i x,HCC i y,HCC ] T and v xy,HCC =[v x,HCC , v y,HCC ] T These represent the measured current flowing into the power grid and the grid connection point voltage of the virtual parallel SVG in the global xy coordinate system; Y HCC (s) and Y GFL (s), Y GFM (s) represent the small-signal complex frequency domain admittance transfer function matrices of the grid-connected virtual parallel SVG and the virtual grid-connected and grid-connected converters, respectively, under the equipment capacity reference. The specific application scenarios of the grid-connected virtual parallel SVG equipment are as follows: a new energy power station using grid-connected control is connected in parallel with the grid-connected virtual parallel SVG via admittance B0, and then connected to the AC grid together to form a feed-in system; wherein, the AC grid is modeled using the Thevenin equivalent circuit, i.e., modeled as admittance B0. g A series combination with an ideal voltage source; establish small-signal complex frequency domain admittance transfer function matrix models for the above-mentioned feed system's equipment side and network side, as follows: Equipment side: Network side: In the formula, Δ represents the infinitesimal increment of the variable, and represents the Kronecker product; i xy,IBR =[i x,IBR i y,IBR ] T v xy,IBR =[v x,IBR ,v y,IBR ] T and i xy,HCC =[i x,HCC i y,HCC ] T and v xy,HCC =[v x,HCC , v y,HCC ] T These are the measured values ​​of grid connection point voltage and current flowing to the grid for grid-connected renewable energy power stations and grid-connected virtual parallel SVG, respectively, in the global xy coordinate system; S IBR and S HCC These are the rated capacities of grid-connected new energy power stations and grid-connected virtual parallel SVG, respectively; Y IBR (s) represents the small-signal complex frequency domain admittance transfer function matrix of a grid-type renewable energy power station; Establish the closed-loop characteristic equation of the feed system, specifically as follows: ; where det{·} represents the determinant of the matrix; I2 represents the second-order identity matrix.

9. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 8, characterized in that, When the grid strength changes, the virtual component ratio coefficient of the grid-connected virtual parallel SVG is adjusted in real time based on the small-disturbance stability assessment framework to enhance the small-disturbance stability of the feedin system when the grid strength changes. This includes: when dealing with broadband oscillation problems dominated by grid-connected renewable energy plants and grid-connected virtual parallel SVG, the short-circuit ratio and its critical value are established as follows: When dealing with broadband oscillation problems dominated by grid-connected renewable energy plants, considering the current source characteristics of the virtual grid-connected converter and the voltage source characteristics of the virtual grid-connected converter in the grid-connected virtual parallel SVG, the feedin system closed-loop characteristic equation, the short-circuit ratio correction value SCR1, and the expression of its critical value CSCR1 are as follows: ； Where arg{·} is the solution to the equation, j is the imaginary unit; CSCR1 is the short-circuit ratio value corresponding to the characteristic root of the oscillation problem dominated by the grid-connected new energy power station, which is exactly located on the imaginary axis of the complex plane, and ω1 is the oscillation angular frequency at this time; when considering the broadband oscillation problem dominated by the grid-connected virtual parallel SVG, the current source characteristics of the virtual grid-connected converter in the new energy power station and the grid-connected virtual parallel SVG are considered, and the expressions of the system closed-loop characteristic equation, the short-circuit ratio correction value SCR2 and its critical value CSCR2 are as follows: ； ; where arg{·} is the solution to the equation, j is the imaginary unit; CSCR2 is the short-circuit ratio value corresponding to the characteristic root of the oscillation problem dominated by the virtual parallel SVG (mainly the synchronization link of the virtual grid converter) which is located exactly on the imaginary axis of the complex plane, and ω2 is the oscillation angular frequency at this time; the proportional coefficients α and β of the virtual grid converter and the virtual grid converter are adaptively adjusted according to the short-circuit ratio SCR1 / SCR2 and its critical value CSCR1 / CSCR2.

10. The adaptive grid strength variation-based virtual parallel SVG control method as described in claim 9, characterized in that, The adaptive adjustment of the proportional coefficients α and β of the virtual grid converter and the virtual mesh converter based on the short-circuit ratio SCR1 / SCR2 and its critical value CSCR1 / CSCR2 includes: assuming the equivalent admittance of the virtual mesh converter is B. eq The critical short-circuit ratios are CSCR1 and CSCR2, respectively. Let S be the capacity of the grid-connected renewable energy power station at time t. IBR,t The capacity of the virtual parallel SVG structure is S. HCC,t The virtual component proportion coefficient is α t and β t If at this time the admittance B0 or / and B g If a change occurs, regulation begins: Step A: Based on α at this time t and β t Calculate the short-circuit ratio SCR2 using parameters. If δ=SCR2-(1-n%)CSCR2<0, where n% is the preset small disturbance stability margin for the broadband oscillation problem dominated by the virtual parallel SVG of the grid-connected system, then proceed to step B; otherwise, if β t If the value is greater than or equal to 1, then the virtual network converter will stop operating, and β will be set to 1. t If δ = 0 and Flag = 1, proceed to step B; otherwise, adjust α in real time according to δ. t and β t Repeat step A; Step B: Based on α at this time t and β t Calculate the short-circuit ratio SCR1 using parameters such as ε=(1+m%)CSCR1-SCR1<0, where m% is the preset small disturbance stability margin for broadband oscillation problems dominated by grid-connected renewable energy power plants. Then update the system's operating state, such as α. t and β t Parameters; otherwise, if β t If β = 1 or Flag = 1, the system's active support capability is severely insufficient. Output generator and load shedding actions are initiated to maintain system stability under small disturbances, and Flag = 0, returning to step A; otherwise, proceed to step C; Step C: If β t If ε = 0, then the virtual network converter will start running, and α will be adjusted in real time according to ε. t and β t If necessary, return to step A; otherwise, adjust α in real time according to ε. t and β t Return to step B; after updating the system's operating status, if admittance B0 or / and Bg change, return to step A.

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