Inertia-resonance collaborative optimization control method, system and device for new energy distribution network

Abstract

The present application relates to the technical field of new energy power grid control, and more particularly to a new energy distribution network inertia-resonance collaborative optimization control method, system and device. The method comprises the following steps: a collection unit collects node data to generate a virtual synchronous clock; based on adaptive band-pass filtering, the energy of the frequency band is pre-screened, and the wide frequency impedance spectrum is calculated; according to the constraints of the maximum frequency change rate, the short-circuit ratio and the new energy penetration rate, the minimum virtual inertia safety lower limit of each node is calculated; the node-level virtual inertia and reverse compensation current damping are updated within the safety lower limit and the compensation current boundary; according to the unified risk state, the control mode is switched between steady state, resonance, transient state and conservative damping, and the inverter control instruction after amplitude limiting and slope limiting is output. The present application reduces the influence of distributed sampling phase deviation on impedance identification by using power frequency zero-crossing virtual synchronization and standby inter-harmonic synchronization beacon.

Description

Technical Field

[0001] This invention relates to the field of new energy power grid control technology, and in particular to a method, system and device for inertia-resonance coordinated optimization control of new energy distribution networks. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, energy storage, electric vehicle charging and discharging devices, and flexible interconnection devices into the distribution network, the distribution network exhibits characteristics such as low inertia, weak damping, impedance variation with operating mode, and parallel operation of multiple node controllers. Traditional distribution networks typically rely on the upstream grid and the inertia of synchronous machines to maintain frequency stability. However, if grid-connected inverters in new energy distribution networks still primarily rely on grid-following control, they are prone to wideband oscillations, slow frequency drop recovery, or conflicts in multiple controller commands when voltage phase, impedance peak, and frequency disturbances are coupled.

[0003] Existing resonance identification techniques can obtain the resonant frequency and peak impedance through spectrum analysis, impedance measurement, or injection-based identification; existing virtual synchronizer or virtual inertia control techniques can simulate the inertia and damping of a synchronizer through oscillation equations; existing active damping or virtual impedance control techniques can also suppress oscillations in specific frequency bands. However, these techniques typically handle impedance identification, inertia support, or damping compensation separately, lacking a unified technical link between multi-node sampling synchronization, broadband identification triggering, inertia safety boundaries, and resonant compensation current for new energy distribution networks.

[0004] In distributed acquisition scenarios, misalignment of sampling phases at different nodes can lead to deviations in subsequent impedance spectrum and resonant phase determinations. In scenarios with high renewable energy penetration, continuous full-band precise identification increases the burden on edge computing and may disrupt operation. When resonance and frequency transients overlap, simply increasing virtual inertia may prolong the oscillation energy release process, while simply decreasing inertia may weaken frequency support. Therefore, a collaborative optimization control system that clearly defines the relationships between input, output, state gating, parameter calculation, and anomaly backoff is needed. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a method, system, and device for inertia-resonance coordinated optimization control of new energy distribution networks, reducing the impact of distributed sampling phase deviation on impedance identification.

[0006] This invention provides a method for coordinated optimization control of inertia-resonance in new energy distribution networks, comprising: S1: The acquisition unit collects the voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronization clock based on the voltage and current. S2: Based on adaptive bandpass filtering, the frequency band energy is pre-screened. If the triggering condition is met, FFT (Fast Fourier Transform) is performed in combination with Prony fine identification to calculate the broadband impedance spectrum. If the triggering condition is not met, broadband impedance spectrum is calculated by injecting PRBS (Pseudo-Random Binary Sequence) perturbation and correlation detection. S3: Calculate the minimum virtual inertia safety lower limit for each node based on the constraint of the maximum frequency change rate, the short-circuit ratio, and the penetration rate of new energy sources; S4: Update the node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; S5: Switches between steady-state, resonant, transient, and conservative damping control modes, and outputs inverter control commands after amplitude and slope limiting.

[0007] According to the inertia-resonance coordinated optimization control method for new energy distribution networks provided by this invention, if the acquisition unit detects voltage distortion, low voltage ride-through, or zero-crossing jitter that reduces synchronization quality, the acquisition unit performs virtual synchronization correction and resampling by detecting the abrupt phase change of the beacon through narrowband demodulation. The formula is as follows: in, This represents the synchronization correction amount for the i-th node within the k-th window. To determine the number of zero-crossing points involved in smoothing, For the m-th zero-crossing time under the reference phase of the k-th window, For the k-th window, at the m-th zero-crossing time of the i-th node locally, This is the sampled sequence of the i-th node after synchronous correction. To correct the pre-sampled sequence, The sampling period is The sampling time.

[0008] According to the inertia-resonance collaborative optimization control method for new energy distribution networks provided by the present invention, the triggering condition in step S2 is that the frequency band energy increases relative to the historical reference energy for a duration of not less than three cycles, as shown in the formula: in, Let i be the energy of the b-th frequency band of the i-th node within the k-th window. These are the discrete sampled values ​​after filtering in the b-th frequency band. The number of sampling points for the sliding window. This represents the energy anomaly ratio. As the historical benchmark energy, This is a candidate trigger flag for resonance. Energy trigger threshold, Duration of the energy anomaly It is the power frequency cycle.

[0009] According to the inertia-resonance synergistic optimization control method for new energy distribution networks provided by the present invention, the formula for calculating the broadband impedance spectrum by PRBS disturbance injection and correlation detection in step S2 is as follows: in, This is the estimated broadband impedance spectrum of the i-th node at the angular frequency point. The frequency domain component of the voltage response after PRBS perturbation. The frequency domain component of the PRBS disturbance current. As a quality indicator of impedance spectrum, In response to the reconstruction residual, To allow for an upper limit on residuals, This is the l-th angular frequency point.

[0010] According to the inertia-resonance collaborative optimization control method for new energy distribution networks provided by the present invention, the calculation formula for step S3 is as follows: in, This is the minimum virtual inertia safety lower bound for the i-th node. To predict the active power imbalance, The rated capacity of the inverter at node i is... To allow the maximum rate of change of frequency, Let be the short-circuit ratio of the i-th node. Let i be the penetration rate of new energy at node i. This is the lower bound for virtual inertia. This is the upper limit of virtual inertia. This is the amplitude limiting function.

[0011] According to the inertia-resonance collaborative optimization control method for new energy distribution networks provided by the present invention, the virtual inertia update method in step S4 is as follows: in, Set a value for the virtual inertia of the next window. Assign weights to node inertia. To meet the total inertia requirements of the distribution network, Let i be the communication neighborhood of the i-th node. This is the minimum virtual inertia safety lower bound for neighboring nodes. This represents the upper limit of the node's virtual inertia.

[0012] According to the inertia-resonance collaborative optimization control method for new energy distribution networks provided by the present invention, the reverse compensation current damping update method in step S4 is as follows: in, This is a reverse compensation current damping command. To compensate for the resonant damping gain. This is the resonant current component. The phase of the resonant current. To allow for an upper limit on the compensation current, It is a saturation function.

[0013] According to the inertia-resonance coordinated optimization control method for new energy distribution networks provided by the present invention, step S5 is as follows: in, This is the final inverter control command. For frequency support control components, The gain for switching the resonant component is determined by the risk state. To unify risk state variables, This is the control component for resonant damping. To control the lower limit of instructions, To control the upper limit of instructions.

[0014] This invention also provides a new energy distribution network inertia-resonance coordinated optimization control system, comprising: Multi-source synchronous acquisition unit: The acquisition unit collects voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronous clock based on voltage and current; Wideband Resonance Online Identification Unit: Based on adaptive bandpass filtering to pre-screen band energy, if the triggering condition is met, FFT combined with Prony fine identification is performed to calculate the wideband impedance spectrum; if the triggering condition is not met, the wideband impedance spectrum is calculated through PRBS perturbation injection and correlation detection. Hierarchical virtual inertia control unit: Calculate the minimum virtual inertia safety limit for each node based on the constraint of the maximum frequency change rate, short-circuit ratio, and new energy penetration rate; Collaborative suppression control unit: updates node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; State consistency and backoff unit: Switches control modes between steady state, resonance, transient state and conservative damping, and outputs inverter control commands after amplitude and slope limiting.

[0015] The present invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor is used to execute a computer program to implement the steps of the inertia-resonance collaborative optimization control method for a new energy distribution network as described above.

[0016] This invention provides a new energy distribution network inertia-resonance collaborative optimization control method, system and device, which utilizes power frequency zero-crossing point virtual synchronization and standby interharmonic synchronization beacons to reduce the impact of distributed sampling phase deviation on impedance identification.

[0017] The present invention provides a new energy distribution network inertia-resonance collaborative optimization control method, system and device, which avoids unconditional full-frequency precise identification and unreliable impedance spectrum from entering the control loop through frequency band energy pre-screening and impedance spectrum quality gating.

[0018] The present invention provides a new energy distribution network inertia-resonance collaborative optimization control method, system and device that transmits frequency support constraints to the node-level VSG regulator through the minimum virtual inertia safety lower limit.

[0019] The present invention provides a new energy distribution network inertia-resonance collaborative optimization control method, system and device, which reduces the risk of conflict between resonance suppression command and transient inertia support command by unifying risk state and state consistency and backoff unit.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a new energy distribution network inertia-resonance collaborative optimization control method provided by the present invention.

[0023] Figure 2 This is a schematic diagram of the structure of a new energy distribution network inertia-resonance collaborative optimization control system provided by the present invention.

[0024] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0025] Figure label: 101. Multi-source synchronous acquisition unit; 102. Wideband resonant online identification unit; 103. Hierarchical virtual inertia control unit; 104. Cooperative suppression control unit; 105. State consistency and rollback unit; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0027] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0028] The following is combined with Figures 1 to 3 This invention is described.

[0029] Example like Figure 1 As shown, Figure 1 A flowchart illustrating a new energy distribution network inertia-resonance collaborative optimization control method provided by the present invention includes the following steps: S1: The acquisition unit collects the voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronization clock based on the voltage and current. S2: Based on adaptive bandpass filtering, pre-screen the frequency band energy. If the triggering condition is met, perform FFT combined with Prony fine identification to calculate the broadband impedance spectrum; if the triggering condition is not met, calculate the broadband impedance spectrum through PRBS perturbation injection and correlation detection. S3: Calculate the minimum virtual inertia safety lower limit for each node based on the constraint of the maximum frequency change rate, the short-circuit ratio, and the penetration rate of new energy sources; S4: Update the node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; S5: Switches between steady-state, resonant, transient, and conservative damping control modes, and outputs inverter control commands after amplitude and slope limiting.

[0030] Specifically, if the acquisition unit detects voltage distortion, low-voltage ride-through, or zero-crossing jitter that degrades synchronization quality, the acquisition unit performs virtual synchronization correction and resampling by detecting the abrupt phase change of the beacon using narrowband demodulation. The formula is as follows: in, This represents the synchronization correction amount for the i-th node within the k-th window. To determine the number of zero-crossing points involved in smoothing, For the m-th zero-crossing time under the reference phase of the k-th window, For the k-th window, at the m-th zero-crossing time of the i-th node locally, This is the sampled sequence of the i-th node after synchronous correction. To correct the pre-sampled sequence, The sampling period is The sampling time.

[0031] Specifically, the triggering condition in step S2 is that the frequency band energy increases relative to the historical reference energy for a duration of not less than three cycles, as shown in the formula: in, Let i be the energy of the b-th frequency band of the i-th node within the k-th window. These are the discrete sampled values ​​after filtering in the b-th frequency band. The number of sampling points for the sliding window. This represents the energy anomaly ratio. As the historical benchmark energy, This is a candidate trigger flag for resonance. Energy trigger threshold, Duration of the energy anomaly It is the power frequency cycle.

[0032] Specifically, in step S2, the formula for calculating the broadband impedance spectrum through PRBS perturbation injection and correlation detection is as follows: in, This is the estimated broadband impedance spectrum of the i-th node at the angular frequency point. The frequency domain component of the voltage response after PRBS perturbation. The frequency domain component of the PRBS disturbance current. As a quality indicator of impedance spectrum, In response to the reconstruction residual, To allow for an upper limit on residuals, This is the l-th angular frequency point.

[0033] Specifically, the calculation formula for step S3 is as follows: in, This is the minimum virtual inertia safety lower bound for the i-th node. To predict the active power imbalance, The rated capacity of the inverter at node i is... To allow the maximum rate of change of frequency, Let be the short-circuit ratio of the i-th node. Let i be the penetration rate of new energy at node i. This is the lower bound for virtual inertia. This is the upper limit of virtual inertia. This is the amplitude limiting function.

[0034] Specifically, the virtual inertia update method in step S4 is as follows: in, Set a value for the virtual inertia of the next window. Assign weights to node inertia. To meet the total inertia requirements of the distribution network, Let i be the communication neighborhood of the i-th node. This is the minimum virtual inertia safety lower bound for neighboring nodes. This represents the upper limit of the node's virtual inertia.

[0035] The method for updating the reverse compensation current damping in step S4 is as follows: in, This is a reverse compensation current damping command. To compensate for the resonant damping gain. This is the resonant current component. The phase of the resonant current. To allow for an upper limit on the compensation current, It is a saturation function.

[0036] Specifically, step S5 is as follows: in, This is the final inverter control command. For frequency support control components, The gain for switching the resonant component is determined by the risk state. To unify risk state variables, This is the control component for resonant damping. To control the lower limit of instructions, To control the upper limit of instructions.

[0037] Table 1 is an example data table constructed based on common simulation conditions of new energy distribution networks. It is used to illustrate the correspondence between technical links and evaluation indicators, and does not represent the actual measurement results that have been completed, nor does it limit the protection scope. The example conditions include a 10kV distribution network, multiple distributed photovoltaic and energy storage inverters connected, the node short-circuit ratio changing in the weak network section, and the disturbance frequency band covering the subsynchronous to high frequency bands.

[0038] Table 1. Example data constructed based on common simulation conditions of new energy distribution networks.

[0039] As shown in Table 1, under the same simulation disturbance conditions, pre-screening gating reduces the number of frequency bands entering fine identification; impedance spectrum quality gating avoids unreliable parameters from entering the control; the minimum virtual inertia safety lower limit ensures that the frequency support boundary is still maintained during resonance suppression; and reverse compensation current and damping adjustment work together in the resonant frequency band.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects: First, by utilizing power frequency zero-crossing virtual synchronization and backup interharmonic synchronization beacons, the impact of distributed sampling phase deviation on impedance identification is reduced; Second, by pre-screening of frequency band energy and gated impedance spectrum quality, unconditional full-frequency precise identification and unreliable impedance spectra entering the control loop are avoided; Third, frequency support constraints are transmitted to the node-level VSG regulator through the minimum virtual inertia safety lower limit; Fourth, by unifying risk states and state consistency with the backoff unit, the risk of conflict between resonance suppression commands and transient inertia support commands is reduced.

[0041] like Figure 2 As shown, the following describes a new energy distribution network inertia-resonance collaborative optimization control system provided by the present invention. The new energy distribution network inertia-resonance collaborative optimization control system described below can be referred to in correspondence with the new energy distribution network inertia-resonance collaborative optimization control method described above, and includes the following modules: Multi-source synchronous acquisition unit 101: The acquisition unit acquires the voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronous clock based on the voltage and current. Wideband Resonance Online Identification Unit 102: Based on adaptive bandpass filtering, it pre-screens the frequency band energy. If the triggering condition is met, it performs FFT combined with Prony fine identification to calculate the wideband impedance spectrum. If the triggering condition is not met, it calculates the wideband impedance spectrum through PRBS perturbation injection and correlation detection. Layered virtual inertia control unit 103: Calculates the minimum virtual inertia safety limit for each node based on the constraint of the maximum frequency change rate, short-circuit ratio, and new energy penetration rate; Cooperative suppression control unit 104: updates node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; State Consistency and Backoff Unit 105: Switches control modes between steady state, resonance, transient state and conservative damping, and outputs inverter control commands after amplitude and slope limiting.

[0042] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute a new energy distribution network inertia-resonance coordinated optimization control method, which includes: S1: The acquisition unit collects the voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronization clock based on the voltage and current. S2: Based on adaptive bandpass filtering, pre-screen the frequency band energy. If the triggering condition is met, perform FFT combined with Prony fine identification to calculate the broadband impedance spectrum; if the triggering condition is not met, calculate the broadband impedance spectrum through PRBS perturbation injection and correlation detection. S3: Calculate the minimum virtual inertia safety lower limit for each node based on the constraint of the maximum frequency change rate, the short-circuit ratio, and the penetration rate of new energy sources; S4: Update the node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; S5: Switches between steady-state, resonant, transient, and conservative damping control modes, and outputs inverter control commands after amplitude and slope limiting.

[0043] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0045] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0047] It should be noted that the embodiments of this disclosure can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0048] Furthermore, although the operation of the methods of this disclosure is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps. It should also be noted that the features and functions of two or more devices according to this disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0049] While this disclosure has been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the specific embodiments disclosed. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method for coordinated optimization control of inertia-resonance in a new energy distribution network, characterized in that, Includes the following steps: S1: The acquisition unit collects the voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronization clock based on the voltage and current. S2: Based on adaptive bandpass filtering, pre-screen the frequency band energy. If the triggering condition is met, perform FFT combined with Prony fine identification to calculate the broadband impedance spectrum; if the triggering condition is not met, calculate the broadband impedance spectrum through PRBS perturbation injection and correlation detection. S3: Calculate the minimum virtual inertia safety lower limit for each node based on the constraint of the maximum frequency change rate, the short-circuit ratio, and the penetration rate of new energy sources; S4: Update the node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; S5: Switches between steady-state, resonant, transient, and conservative damping control modes, and outputs inverter control commands after amplitude and slope limiting.

2. The inertia-resonance collaborative optimization control method for a new energy distribution network according to claim 1, characterized in that, If the acquisition unit detects voltage distortion, low-voltage ride-through, or zero-crossing jitter that degrades synchronization quality, the acquisition unit performs virtual synchronization correction and resampling by detecting the beacon phase abrupt change using narrowband demodulation. The formula is as follows: in, This represents the synchronization correction amount for the i-th node within the k-th window. To determine the number of zero-crossing points involved in smoothing, For the m-th zero-crossing time under the reference phase of the k-th window, For the k-th window, at the m-th zero-crossing time of the i-th node locally, This is the sampled sequence of the i-th node after synchronous correction. To correct the pre-sampled sequence, The sampling period is The sampling time.

3. The inertia-resonance coordinated optimization control method for a new energy distribution network according to claim 2, characterized in that, The triggering condition in step S2 is that the frequency band energy increases relative to the historical reference energy for a duration of not less than three cycles. The formula for step S2 is: in, Let i be the energy of the b-th frequency band of the i-th node within the k-th window. These are the discrete sampled values ​​after filtering in the b-th frequency band. The number of sampling points for the sliding window. This represents the energy anomaly ratio. As the historical benchmark energy, This is a candidate trigger flag for resonance. Energy trigger threshold, Duration of the energy anomaly It is the power frequency cycle.

4. The inertia-resonance coordinated optimization control method for a new energy distribution network according to claim 3, characterized in that, In step S2, the formula for calculating the broadband impedance spectrum through PRBS perturbation injection and correlation detection is as follows: in, This is the estimated broadband impedance spectrum of the i-th node at the angular frequency point. The frequency domain component of the voltage response after PRBS perturbation. The frequency domain component of the PRBS disturbance current. As a quality indicator of impedance spectrum, In response to the reconstruction residual, To allow for an upper limit on residuals, This is the l-th angular frequency point.

5. The inertia-resonance coordinated optimization control method for a new energy distribution network according to claim 4, characterized in that, The calculation formula for step S3 is as follows: in, This is the minimum virtual inertia safety lower bound for the i-th node. To predict the active power imbalance, The rated capacity of the inverter at node i is... To allow the maximum rate of change of frequency, Let be the short-circuit ratio of the i-th node. Let i be the penetration rate of new energy at node i. This is the lower bound for virtual inertia. This is the upper limit of virtual inertia. This is the amplitude limiting function.

6. The inertia-resonance coordinated optimization control method for a new energy distribution network according to claim 5, characterized in that, The virtual inertia update method in step S4 is as follows: in, Set a value for the virtual inertia of the next window. Assign weights to node inertia. To meet the total inertia requirements of the distribution network, Let i be the communication neighborhood of the i-th node. This is the minimum virtual inertia safety lower bound for neighboring nodes. This represents the upper limit of the node's virtual inertia.

7. The inertia-resonance collaborative optimization control method for a new energy distribution network according to claim 6, characterized in that, The method for updating the reverse compensation current damping in step S4 is as follows: in, This is a reverse compensation current damping command. To compensate for the resonant damping gain. This is the resonant current component. The phase of the resonant current. To allow for an upper limit on the compensation current, It is a saturation function.

8. The inertia-resonance collaborative optimization control method for a new energy distribution network according to claim 7, characterized in that, Step S5 synthesizes control instructions using the following formula: in, This is the final inverter control command. For frequency support control components, The gain for switching the resonant component is determined by the risk state. To unify risk state variables, This is the control component for resonant damping. To control the lower limit of instructions, To control the upper limit of instructions.

9. A new energy distribution network inertia-resonance collaborative optimization control system, characterized in that, Includes the following modules: Multi-source synchronous acquisition unit: The acquisition unit collects voltage, current, short-circuit ratio, new energy penetration rate, frequency deviation, and frequency change rate of each node, and generates a virtual synchronous clock based on voltage and current; Wideband resonant online identification unit: Based on adaptive bandpass filtering to pre-screen band energy, if the triggering condition is met, FFT combined with Prony fine identification is performed to calculate the wideband impedance spectrum; If the triggering conditions are not met, calculate the broadband impedance spectrum through PRBS perturbation injection and related detection. Hierarchical virtual inertia control unit: Calculate the minimum virtual inertia safety limit for each node based on the constraint of the maximum frequency change rate, short-circuit ratio, and new energy penetration rate; Collaborative suppression control unit: updates node-level virtual inertia and reverse compensation current damping within the safety lower limit and compensation current boundary; State consistency and backoff unit: Switches control modes between steady state, resonance, transient state and conservative damping, and outputs inverter control commands after amplitude and slope limiting.

10. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, characterized in that, When the processor executes the computer program, it implements the steps of the inertia-resonance collaborative optimization control method for a new energy distribution network as described in any one of claims 1 to 8.

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