System stability margin evaluation method, device, control device and readable storage medium

Abstract

The present application provides a system stability margin assessment method, device, control equipment and readable storage medium, which relates to the field of renewable energy power generation technology. The method includes: sending disturbance instructions corresponding to different frequency points to the target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, the target photovoltaic inverter is a grid-connected photovoltaic inverter in the photovoltaic inverter system, and the disturbance response information includes amplitude and phase; according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity at each frequency point, the closed-loop frequency response of the target physical quantity is calculated; according to the closed-loop frequency response of the target physical quantity, the target information is calculated, and the target information includes the first dominant characteristic root corresponding to the target physical quantity; the system stability margin is determined according to the target information. In this way, the system stability margin can be evaluated.

Description

Technical Field

[0001] The present application relates to the technical field of renewable energy power generation, and in particular to a system stability margin assessment method, device, control equipment and readable storage medium. Background Art

[0002] Photovoltaic inverters are important equipment for achieving high power factor and highly controllable grid connection of photovoltaic energy. In order to enable new energy to support traditional power generation methods, photovoltaic power generation is currently moving towards scale and integration, so medium and high power three-phase photovoltaic inverters are occupying a dominant position. However, while providing powerful new energy grid connection capabilities, three-phase photovoltaic inverters also introduce low damping and low inertia dynamic behaviors to the power grid, making the system tend to have low-frequency oscillations of tens to hundreds of hertz. Therefore, how to evaluate the stability margin of the system to determine whether the system has an oscillation risk has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0003] The embodiments of the present application provide a system stability margin assessment method, apparatus, control device and readable storage medium, which do not require advance knowledge of specific system parameters, nor do they require connection of additional equipment to the actual system to complete disturbance-observation based system information identification. The overall dominant characteristic root of the system can be estimated using only the communication interface of the photovoltaic inverter, thereby evaluating the grid-connected stability margin of the photovoltaic inverter on-site.

[0004] The embodiments of the present application can be implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a system stability margin assessment method, which is applied to a control device in a photovoltaic inverter system, and the method includes:

[0006] Sending disturbance instructions corresponding to different frequency points to a target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, wherein the target photovoltaic inverter is a grid-connected photovoltaic inverter in a photovoltaic inverter system, and the disturbance response information includes amplitude and phase;

[0007] Calculate the closed-loop frequency response of the target physical quantity according to the disturbance command corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point;

[0008] Calculating target information according to the closed-loop frequency response of the target physical quantity, wherein the target information includes a first dominant characteristic root corresponding to the target physical quantity;

[0009] The system stability margin is determined based on the target information.

[0010] In a second aspect, an embodiment of the present application provides a system stability margin assessment device, which is applied to a control device in a photovoltaic inverter system, and the device includes:

[0011] A data acquisition module, used for sending disturbance instructions corresponding to different frequency points to a target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, wherein the target photovoltaic inverter is a grid-connected photovoltaic inverter in a photovoltaic inverter system, and the disturbance response information includes amplitude and phase;

[0012] A first calculation module, used to calculate the closed-loop frequency response of the target physical quantity according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point;

[0013] A second calculation module is used to calculate target information according to the closed-loop frequency response of the target physical quantity, wherein the target information includes a first dominant characteristic root corresponding to the target physical quantity;

[0014] The processing module is used for determining a system stability margin according to the target information.

[0015] In a third aspect, an embodiment of the present application provides a control device, including a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor can execute the machine executable instructions to implement the system stability margin assessment method described in the aforementioned embodiment.

[0016] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the system stability margin assessment method as described in the aforementioned embodiment is implemented.

[0017] The system stability margin assessment method, device, control device and readable storage medium provided in the embodiment of the present application, the control device first sends disturbance instructions corresponding to different frequency points to a photovoltaic inverter in the photovoltaic inverter system as a target photovoltaic inverter, and obtains the disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, and the disturbance response information includes amplitude and phase; then, according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point, the closed-loop frequency response of the target physical quantity is calculated, and then the target information is calculated according to the closed-loop frequency response of the target physical quantity, and finally the system stability margin is determined according to the target information, and the above target information includes the first dominant characteristic root corresponding to the target physical quantity. In this way, there is no need to know the specific parameters of the system in advance, nor is there any need to connect additional equipment to the actual system to complete the system information identification based on disturbance-observation, and the overall dominant characteristic root of the system can be estimated by using only the communication interface of the photovoltaic inverter, so as to evaluate the grid-connected stability margin of the photovoltaic inverter on the spot. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is a schematic diagram of the oscillation case of a photovoltaic grid-connected inverter;

[0020] Figure 2 This is a schematic diagram of a multi-PV inverter grid-connected scenario;

[0021] Figure 3 A block diagram of a control device provided in an embodiment of the present application;

[0022] Figure 4 One of the flow charts of the system stability margin evaluation method provided in the embodiment of the present application;

[0023] Figure 5 A schematic diagram of a photovoltaic inverter system provided in an embodiment of the present application;

[0024] Figure 6 for Figure 4 One of the flowchart diagrams of the sub-steps included in step S140;

[0025] Figure 7 for Figure 4 2 is a flow chart of the sub-steps included in step S140;

[0026] Figure 8 A schematic diagram of a second characteristic root distribution diagram provided in an embodiment of the present application;

[0027] Fig. 9 A second flow chart of a method for evaluating system stability margin provided in an embodiment of the present application;

[0028] Fig.10 A schematic diagram of a Bode diagram provided for an embodiment of the present application;

[0029] Fig.11 A third flow chart of the system stability margin assessment method provided in an embodiment of the present application;

[0030] Fig.12 A schematic diagram of the changing law of the dominant characteristic roots of the system when the three parameters provided in the embodiment of the present application change;

[0031] Fig.13 A block diagram of a system stability margin assessment device provided in an embodiment of the present application.

[0032] Icon: 100 - control device; 110 - memory; 120 - processor; 130 - communication unit; 200 - system stability margin assessment device; 210 - data acquisition module; 220 - first calculation module; 230 - second calculation module; 240 - processing module. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0035] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0036] While providing powerful new energy grid-connected capabilities, three-phase photovoltaic inverters also introduce low-damping, low-inertia dynamic behavior into the grid, causing the system to tend to experience low-frequency oscillations of tens to hundreds of hertz. Figure 1 Figure 2 shows the generation process of the above low-frequency oscillation, where: Figure 1 The waveforms in the figure are from top to bottom: AC bus voltage, VSC1 DC voltage, VSC1 output current, and VSC2 output current.

[0037] The scenario of connecting multiple photovoltaic inverters to the grid can be as follows: Figure 2 As shown in Figure 1. Multiple three-phase photovoltaic inverters are connected to an AC bus. Each photovoltaic inverter uses DC voltage stabilization vector control. There is an equivalent reactance between the AC bus and the secondary side of the medium voltage transformer. The capacitors connected to the AC bus include: external reactive compensation capacitors, line parasitic capacitors, and the inverter's own XY filter capacitors.

[0038] The mainstream method for evaluating the stability margin under the dynamic interaction between the inverter and the power grid is to treat all the capacitors on the AC bus and the reactance connected to the secondary side of the transformer as a whole, and the grid-side impedance Z g (s), and consider each inverter as an equivalent port admittance matrix Y k (s), and then use the positive and negative sequence impedance criterion or the dq series impedance criterion to analyze the system stability to find out whether there is an oscillation risk in the interaction between the inverter and the grid.

[0039] The above method can theoretically accurately determine the stability of a specific photovoltaic inverter grid-connected system, and can be used to determine the technical solution and system parameters in the early stage of the project. However, in the later stage of the photovoltaic grid-connected power generation project, when it is necessary to conduct system motion modal analysis based on the actual situation, the project executor often finds it difficult to effectively analyze the dynamic interaction between the photovoltaic inverter and the power grid because the system parameters (such as the grid-side impedance Zg(s), the inverter's own parameters, etc.) are unclear and inaccurate. This makes it difficult for the photovoltaic inverter to be put into operation without sufficient field evaluation, which will bring potential risks and easily cause unexpected oscillation phenomena, causing delays in engineering projects.

[0040] Although Z g (s) and Y k (s) can be detected by injecting external disturbances (e.g. Figure 2 The introduction of this disturbance device not only increases the system cost, but also requires additional access permits, which is not conducive to the implementation of photovoltaic grid-connected projects.

[0041] In summary, in order to conduct on-site assessment of the risk of dynamic interactive oscillation between medium and large power three-phase photovoltaic grid-connected inverters (groups) and the power grid, an engineering assessment method is needed. The implementation of this method does not require system parameters as input, nor can it rely on other equipment other than the photovoltaic inverter itself.

[0042] In response to the above situation, the embodiments of the present application provide a system stability margin assessment method, apparatus, control device and readable storage medium. It does not require advance knowledge of the specific system parameters, nor does it require connection of additional equipment to the actual system to complete disturbance-observation based system information identification. The overall dominant characteristic root of the system can be estimated by only using the communication interface of the photovoltaic inverter, thereby evaluating the grid-connected stability margin of the photovoltaic inverter on-site.

[0043] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0044] In this embodiment, the photovoltaic inverter system that needs to perform stability margin evaluation includes a control device and at least one photovoltaic inverter. The control device is communicatively connected to the at least one photovoltaic inverter mentioned above. Through data interaction and data processing with the photovoltaic inverter, the grid-connected stability margin of the photovoltaic inverter can be evaluated on-site, wherein the photovoltaic inverter in the photovoltaic inverter system is a grid-connected photovoltaic inverter.

[0045] Please refer to Figure 3 , Figure 3A block diagram of a control device 100 provided in an embodiment of the present application. The control device 100 may be, but is not limited to, a computer, a server, etc. The control device 100 may include a memory 110, a processor 120, and a communication unit 130. The memory 110, the processor 120, and the communication unit 130 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines.

[0046] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0047] The processor 120 is used to read / write data or programs stored in the memory 110 and execute corresponding functions. For example, the memory 110 stores a system stability margin assessment device 200, which includes at least one software function module that can be stored in the memory 110 in the form of software or firmware. The processor 120 executes various functional applications and data processing by running software programs and modules stored in the memory 110, such as the system stability margin assessment device 200 in the embodiment of the present application, that is, realizing the system stability margin assessment method in the embodiment of the present application.

[0048] The communication unit 130 is used to establish a communication connection between the control device 100 and other communication terminals through a network, and to send and receive data through the network.

[0049] It should be understood that Figure 3 The structure shown is only a schematic diagram of the structure of the control device 100. The control device 100 may also include Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown. Figure 3 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0050] Please refer to Figure 4 , Figure 4One of the flow diagrams of the system stability margin evaluation method provided in the embodiment of the present application. The method is applied to a control device in a photovoltaic inverter system. The specific process of the system stability margin evaluation method is described in detail below. In this embodiment, the method may include steps S110 to S140.

[0051] Step S110, sending disturbance instructions corresponding to different frequency points to the target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent.

[0052] Since the overall dominant characteristic root (motion mode) of the dynamic system is theoretically independent of the observation method, when multiple inverters are connected to the grid at the same time and place, only one of the inverters needs to be operated to evaluate the stability margin of the overall system. Therefore, in this embodiment, when the photovoltaic inverter system includes multiple photovoltaic inverters, one photovoltaic inverter can be determined from the multiple photovoltaic inverters as the target photovoltaic inverter for operation. Optionally, the target photovoltaic inverter can be manually determined from the multiple photovoltaic inverters, or one photovoltaic inverter can be randomly selected from the multiple photovoltaic inverters as the target photovoltaic inverter.

[0053] like Figure 5 As shown in the figure, the photovoltaic inverter system includes inverter 1, inverter 2, inverter 3 and PC (Personal Computer). Inverter 1, inverter 2, inverter 3 are multiple photovoltaic inverters connected to the grid at the same time and place, and PC is the above control device. Figure 3 Inverter 1 in the example is used as the target PV inverter, and the stability margin is evaluated through interaction and data processing.

[0054] It can be understood that, when the photovoltaic inverter system includes only one photovoltaic inverter, the photovoltaic inverter is directly used as the target photovoltaic inverter.

[0055] The target photovoltaic inverter has a built-in communication interface, which may be a 485 interface or even a wireless communication interface. The control device can achieve communication connection with the target photovoltaic inverter through the built-in communication interface of the target photovoltaic inverter.

[0056] The control device can perform a frequency sweep process for the target photovoltaic inverter. In this process, the control device sends a disturbance instruction corresponding to a frequency point to the target photovoltaic inverter; the target photovoltaic inverter performs self-disturbance according to the received disturbance instruction, and the control device also obtains the disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance through interaction with the target photovoltaic inverter, and the disturbance response information includes amplitude and phase; then, the above process is repeated based on the disturbance instruction corresponding to another frequency point until the disturbance instructions corresponding to all frequency points are sent to the target photovoltaic inverter, and the disturbance response information of the target physical quantity corresponding to each frequency point is obtained. Among them, the frequency point targeted by the frequency sweep process can be determined in combination with actual needs, and no specific limitation is made here. The frequency sweep order in the frequency sweep process can be determined in combination with actual needs, and no specific limitation is made here. For example, based on a random order, disturbance instructions corresponding to different frequency points are sent to the target photovoltaic inverter in sequence.

[0057] As a possible implementation method, an appropriate number of frequency points can be selected as the frequency points for the frequency sweep process within a frequency range of tens to hundreds of Hz (such as 30 Hz to 300 Hz, which basically covers the frequency bands where low-frequency oscillations may occur). Among them, the optional frequency band range used when selecting the frequency point (i.e., selecting the frequency point from the optional frequency band range) can be determined in combination with actual needs. For example, in the current scenario, low-frequency oscillations of about 100 Hz may occur due to the excessively high control bandwidth of the phase-locked loop, and low-frequency oscillations of about 200 Hz may occur due to the excessively high bandwidth of the DC voltage control loop. It can be considered that the frequency band where low-frequency oscillations may occur is 100 Hz to 200 Hz, and 30 Hz to 300 Hz can be used as the optional frequency band range. The frequency points do not have to be selected evenly. For example, they can be selected relatively densely near the underdamped convergent oscillation frequency to ensure that the characteristic roots of the low stability margin are identified. Too few frequency points cannot guarantee the accuracy of characteristic root calculation, and too many frequency points will make the frequency scanning process take too long. In this embodiment, the number of frequency points used in the scanning process is within 50~100, so as to ensure the calculation accuracy and avoid the frequency scanning process taking too long.

[0058] Among them, the target physical quantity is the main state quantity that needs to be observed, which may be one or more. The specific main state quantity and the number of main state quantities can be determined in combination with actual needs. The target physical quantity is the physical quantity that can be observed by the target photovoltaic inverter.

[0059] Step S120, calculating the closed-loop frequency response of the target physical quantity according to the disturbance command corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point.

[0060] After the frequency sweep process is completed, the control device can calculate the closed-loop frequency response of the target physical quantity based on the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point. Among them, the frequency response refers to the magnitude and phase relationship of the output response generated by the system for input signals of different frequencies.

[0061] Step S130, calculating target information according to the closed-loop frequency response of the target physical quantity.

[0062] The target information can be obtained by vector fitting according to the closed-loop frequency response of the obtained target physical quantity. The target information includes the first dominant characteristic root corresponding to the target physical quantity. The first dominant characteristic root is the dominant characteristic root of the system estimated by the above process (i.e., step S110 to step S130).

[0063] Step S140: determining a system stability margin according to the target information.

[0064] In the case of obtaining target information, the first dominant characteristic root can be directly used as the final estimated system dominant characteristic root, and then the system stability margin can be directly determined based on the first dominant characteristic root. The obtained first dominant characteristic root can also be further processed to update the system dominant characteristic root estimated by steps S110 to S130, and the system stability margin can be determined based on the updated system dominant characteristic root.

[0065] Among them, the system stability margin can be determined based on the estimated system dominant characteristic root in any combination. For example, the damping coefficient of each estimated system dominant characteristic root can be calculated for each estimated system dominant characteristic root. Among them, the damping coefficient of an estimated system dominant characteristic root is the result of dividing the real part of the estimated system dominant characteristic root by the modulus of the estimated system dominant characteristic root and then negating it. The larger the damping coefficient, the higher and more stable the stability margin of the estimated system dominant characteristic root (motion mode). The damping coefficient can be directly used as the stability margin represented by the corresponding estimated system dominant characteristic root. The system stability margin can include the damping coefficient of each estimated system dominant characteristic root, or it can only include the average value of the damping coefficient based on each estimated system dominant characteristic root.

[0066] Alternatively, a stability margin may be determined for each estimated system dominant characteristic root according to the real part of the estimated system dominant characteristic root. The distance between the real part and the imaginary axis corresponds to the stability margin, and the closer the real part is to the imaginary axis, the lower the stability margin. The system stability margin may include each stability margin determined in the above manner.

[0067] It can be understood that the above method of determining the system stability margin based on the target information is only an example, and other methods can also be used to determine the system stability margin.

[0068] In this way, there is no need to know the specific system parameters in advance, nor is there any need to connect additional equipment to the actual system to complete the disturbance-observation based system information identification. The overall dominant characteristic root of the system can be estimated by only using the communication interface of the photovoltaic inverter, thereby evaluating the grid-connected stability margin of the photovoltaic inverter on site.

[0069] In this embodiment, during the frequency sweep process, if Figure 5 As shown, the control device can send a disturbance instruction to the target photovoltaic inverter, and the disturbance instruction can include a disturbance amplitude and a disturbance frequency. The controller of the target photovoltaic inverter generates a real-time disturbance signal according to the disturbance instruction, adds the disturbance signal to the q-axis current instruction of the controller to perform self-disturbance, and maintains the second preset duration. Among them, the disturbance signal is a sinusoidal signal (i.e., a disturbance sinusoidal signal), the amplitude of the disturbance sinusoidal signal is the disturbance amplitude in the disturbance instruction, and the frequency of the disturbance sinusoidal signal is the disturbance frequency in the disturbance instruction. After the target photovoltaic inverter starts self-disturbance for a preset duration, the controller of the target photovoltaic inverter can sample and record the target physical quantity to obtain a sampling sequence of the target physical quantity until the self-disturbance ends.

[0070] That is, in the process of self-disturbance based on a disturbance instruction of one frequency point, the duration of the self-disturbance is the second preset duration. After the self-disturbance starts for the first preset duration, the sampling is performed in the end time period of the self-disturbance (the duration of the end time period is the difference between the second preset duration and the first preset duration) until the end of the self-disturbance, and a sampling sequence of at least one target physical quantity can be obtained. The first preset duration and the second preset duration can be set according to actual needs. Assuming that the second preset duration is 1s and the first preset duration is 0.9s, at the end of 1.0 seconds, the target photovoltaic inverter samples and records the last 0.1s of the sampling sequence for the target physical quantity.

[0071] Optionally, the target photovoltaic inverter may send the obtained sampling sequence to the control device, and the control device calculates the disturbance response information based on the sampling sequence.

[0072] Alternatively, the target photovoltaic inverter calculates disturbance response information based on the sampling sequence obtained by its own sampling, and sends the disturbance response information to the control device. In this way, the calculation amount of the control device can be reduced.

[0073] When the inverter operates normally to the steady-state operating point, the q-axis current command is 0, so that the power factor of the inverter is close to 1, and the d-axis current command is given by the DC voltage controller, so that the power output of the inverter is equal to the power of the MPPT link on the input side. In this case, a sinusoidal small disturbance signal is added to the q-axis current command (the amplitude and frequency of the disturbance signal are given by the host computer), and then under the action of the dynamic coupling mechanism of the system itself, small response signals with the same frequency, different amplitudes and different phases will appear in the target physical quantity. The amplitude and phase relationship between the above-mentioned small response signal and the added q-axis disturbance signal is the closed-loop frequency response (also called closed-loop frequency response characteristics) in step S120.

[0074] The above-mentioned small response signal contains transient components and steady-state components, and the "closed-loop frequency response" is the relationship between the steady-state components of the small response signals at various locations and the injected q-axis current command small disturbance signal. Therefore, before detecting the small response signals at various locations, it is necessary to reserve a period of time (that is, the first preset time mentioned above) to allow the transient component to fully decay. After starting the self-disturbance for a preset time, the inverter can start collecting small response data from various locations, and consider these data to be the steady-state components of the small response. The collected data is the sampling sequence of the target physical quantity mentioned above. It can be seen that by sampling to obtain a sampling sequence of at least one target physical quantity after starting the first preset time of self-disturbance, the accuracy of the subsequent closed-loop frequency response can be guaranteed.

[0075] The target physical quantity may be at least one of the DC voltage, d-axis AC voltage, d-axis AC current, q-axis AC voltage, and q-axis AC current of the target photovoltaic inverter, which may be determined in combination with actual needs. As a possible implementation, the target physical quantity may be the DC voltage, d-axis AC voltage, d-axis AC current, q-axis AC voltage, and q-axis AC current of the target photovoltaic inverter. Correspondingly, the disturbance response amplitude and phase of the above five target physical quantities at the frequency point may be obtained based on the disturbance instruction at one frequency point.

[0076] After obtaining the disturbance response amplitude and phase at one frequency point, the above process can be repeated to obtain the disturbance response amplitude and phase at other frequency points, thereby obtaining the disturbance response information of multiple frequency points. Among them, the disturbance amplitude in the disturbance command corresponding to different frequency points is the same, and the disturbance amplitude can be much smaller than the effective value of the inverter output current.

[0077] After the frequency sweep is completed, the closed-loop frequency response of at least one target physical quantity can be calculated according to the disturbance response information and disturbance instruction of at least one target physical quantity corresponding to each of the multiple frequency points by dividing the response signal phasor by the disturbance signal phasor.

[0078] When the five target physical quantities are set as above, five closed-loop frequency responses (Φid (jω), Φ iq (jω), Φ vd (jω), Φ vq (jω), Φ vdc (jω)) of the measurement results. Among them, , , , , , represents the AC d-axis current i d The q-axis current command disturbance i qP The frequency response of represents the AC q-axis current i q The q-axis current command disturbance i qP The frequency response of Indicates the AC d-axis voltage V d The q-axis current command disturbance i qP The frequency response of Indicates the AC q-axis voltage V q The q-axis current command disturbance i qP The frequency response of Indicates DC voltage V dc The q-axis current command disturbance i qP frequency response.

[0079] Afterwards, vector fitting can be performed based on the obtained closed-loop frequency response of the target physical quantity to obtain at least one group of first dominant characteristic roots and corresponding residues. Among them, one group of first dominant characteristic roots can be obtained based on the closed-loop frequency response of one target physical quantity. One group of first dominant characteristic roots can include two pairs of first dominant characteristic roots, and one pair of first dominant characteristic roots includes two first dominant characteristic roots. One pair of first dominant characteristic roots is a pair of conjugate complex roots. One group of first dominant characteristic roots and corresponding residues can represent the corresponding closed-loop frequency response function.

[0080] Optionally, as a possible implementation manner, vector fitting may be performed in the following manner.

[0081] When the five target physical quantities are set as above, five closed-loop frequency responses (Φ id (jω), Φ iq (jω), Φ vd (jω), Φ vq (jω), Φ vdc (jω)). Assuming there are N frequency sweep points in total, the frequency ω belongs to the frequency sweep sequence {ω k}, (k=1,2,…,N). Vector fitting is to approximate the five closed-loop responses respectively through partial fraction polynomials.

[0082] Φ id (jω) as an example, vector fitting is to find a set of extreme points {p m} and the residue {R m}, so that a partial fraction polynomial is related to Φ id The second norm of the error between (jω) is the smallest, as shown in the following formula (1):

[0083] (1)

[0084] The value of m ranges from 1 to the number of poles P.

[0085] Three-phase grid-connected inverters usually have two pairs of dominant poles. In order to make the polynomial approximate the frequency response better, the number of initial poles P for vector fitting can be taken as 6 (m takes the value of 1, 2, …, 6). If the approximation shown in (1) is achieved, this set of poles {p m} is from Φ id (jω) is the dominant characteristic root of the system identified, and the residue {R m} indicates that the corresponding pole is in Φ id Observability in (jω).

[0086] The above approximation is achieved through iteration, and each iteration step contains two steps: residue optimization and pole rearrangement.

[0087] The residue optimization process is shown below.

[0088] Solve the overdetermined linear equations as shown in the following formula (2). The vector adjacent to the left side of the equal sign in formula (2) is the output result of this step. Let the coefficient matrix be A. Formula (2) is as follows:

[0089] (2)

[0090] Where H represents the closed-loop frequency response and r is the auxiliary residue. The above overdetermined linear equations are solved by matrix singular value decomposition. The coefficient matrix A in formula (2) has a unique singular value decomposition , where U and V are both unitary matrices, and D is a real diagonal matrix. Take several larger singular values ​​in D (screening condition: not less than 1e-5 times the largest singular value), reduce the order of the singular value decomposition result to obtain U s , D s and V s , the result is shown in the following formula: .

[0091] The pole redistribution process is shown below.

[0092] According to the auxiliary residues {r1,…,r P} to rearrange the poles so that the poles of the partial fraction polynomial are closer to the actual characteristic roots of the system (that is, the actual dominant characteristic roots of the system). The rearranged poles are the zeros of the auxiliary partial fraction polynomial σ(jω), and the expression of this auxiliary partial fraction is as follows: .

[0093] By the closed-loop frequency response Φ id (jω) can be iterated 5 to 10 times by performing the above “residue optimization-pole rearrangement”, and a sufficiently accurate estimate of the system characteristic root and the corresponding residue can be obtained. During the iteration process, the maximum residue max{r m} is the accuracy indicator of the iterative calculation result of this step, max{r m Only when} is much smaller than 1 (the threshold can be set to 0.01), the poles and residues can be considered accurate enough.

[0094] The above iterative process is the vector fitting algorithm. id (jω), Φ iq (jω), Φ vd (jω), Φ vq (jω), Φ vdc (jω)) are fitted by vectors to obtain five groups of poles (i.e., five groups of first dominant characteristic roots) and residues.

[0095] As a possible implementation, there are multiple target physical quantities, for example, the above five target physical quantities or two or more of the above five target physical quantities. The target information also includes the residue corresponding to the first dominant characteristic root. In this case, Figure 6 The system stability margin is determined in the manner shown to improve accuracy. Figure 6 , Figure 6 for Figure 4 One of the flowcharts of the sub-steps included in step S140. In this embodiment, step S140 may include sub-steps S141 and S142.

[0096] Sub-step S141 , grouping the first dominant characteristic roots of each target physical quantity according to the position of the first dominant characteristic root of each target physical quantity in the first characteristic root distribution diagram, to obtain at least one first dominant characteristic root group.

[0097] Sub-step S142, calculating the second dominant characteristic root corresponding to each first dominant characteristic root group by weighted combination according to the first dominant characteristic roots in each first dominant characteristic root group and the residues corresponding to each first dominant characteristic root.

[0098] In this embodiment, a first characteristic root distribution diagram may be generated according to the first dominant characteristic roots of each target physical quantity. In the first characteristic root distribution diagram, the horizontal axis is a real axis and the vertical axis is an imaginary axis, so each first dominant characteristic root may be plotted in the first characteristic root distribution diagram. The first dominant characteristic roots obtained may be grouped according to the positions where each first dominant characteristic root is plotted in the first characteristic root distribution diagram, thereby obtaining at least one first dominant characteristic root grouping.

[0099] Optionally, the first dominant characteristic roots of each target physical quantity may be grouped with the first dominant characteristic root group including one first dominant characteristic root of each target physical quantity and the sum of the distances between the positions of the first dominant characteristic roots in the first dominant characteristic root group in the first characteristic root distribution diagram being minimized, thereby obtaining at least one first dominant characteristic root group. For example, if there are 5 target physical quantities, the 5 first dominant characteristic roots are divided into one group, and the 5 first dominant characteristic roots correspond to 5 target physical quantities; that is, the 5 first dominant characteristic roots corresponding to the 5 target physical quantities that are closest to each other are divided into one group.

[0100] After the grouping is completed, a weighted sum operation can be performed for each first dominant characteristic root group based on the first dominant characteristic root included in the first dominant characteristic root group and the residue corresponding to the first dominant characteristic root included in the group, and the operation result is used as the second dominant characteristic root corresponding to the first dominant characteristic root group. In the weighted sum operation, the weighting coefficient is the modulus value of the corresponding residue. The second dominant characteristic root is the currently estimated system dominant characteristic root, which is used to indicate the system stability margin.

[0101] As a possible implementation, Figure 7 As shown, after sub-step S142, step S140 may further include sub-step S143.

[0102] Sub-step S143, generating and displaying a second dominant characteristic root distribution graph according to the second dominant characteristic roots corresponding to each first dominant characteristic root group.

[0103] In this embodiment, a second characteristic root distribution graph may be generated according to the second dominant characteristic root corresponding to each first dominant characteristic root group, and the second characteristic root distribution graph may be displayed. In the second characteristic root distribution graph, the horizontal axis is the real axis and the vertical axis is the imaginary axis, and the second characteristic root distribution graph is used to indicate the system stability margin. The distance between the real part of the second dominant characteristic root and the imaginary axis in the second characteristic root distribution graph indicates the system stability margin. In this way, the user can intuitively understand the system stability margin based on the second characteristic root distribution graph.

[0104] Optionally, the second characteristic root distribution diagram may be as follows Figure 8As shown, the second characteristic root distribution diagram includes the first dominant characteristic roots represented by gray * (ie, each group of characteristic roots output by vector fitting) and the second dominant characteristic roots represented by black * (ie, the characteristic roots obtained by weighted combination).

[0105] Please refer to Fig. 9 , Fig. 9 The second flowchart of the system stability margin evaluation method provided in the embodiment of the present application is as follows: As a possible implementation, the system stability margin evaluation method may further include step S150 , which may be performed after step 130 .

[0106] Step S150 , generating and displaying a Bode diagram according to the closed-loop frequency response of the target physical quantity and the target information.

[0107] In this embodiment, a closed-loop frequency response function can be obtained by vector fitting according to the closed-loop frequency response of the obtained target physical quantity. The closed-loop frequency response function can be represented by a first dominant characteristic root and a corresponding residue, and the target information may include the first dominant characteristic root and the corresponding residue. A Bode diagram can be generated according to the above closed-loop frequency response function and the closed-loop frequency response of the obtained target physical quantity. That is, the Bode diagram includes the closed-loop frequency response of the obtained target physical quantity and the lines corresponding to the closed-loop frequency response function.

[0108] The generated Bode diagram can be Fig.10 shown. Fig.10 In represents the closed-loop frequency response obtained by frequency sweeping, and the line represents the approximate partial fraction of the vector fitting output (i.e., the closed-loop frequency response function). Fig.10 The gain in is the ratio of the amplitude of the small response signal at each location to the injected q-axis current command small disturbance signal, and the “phase” refers to the phase difference between the small response signal at each location and the injected q-axis current command small disturbance signal.

[0109] The accuracy of the eigenvalue estimates can be evaluated by comparing the vector fit results (approximate partial fraction polynomials of the frequency responses) with the measured closed-loop response of the system. Fig.10 As shown, the partial fraction polynomial approximation of each closed-loop response (represented by a line) is consistent with the measured data of the closed-loop response itself (represented by ' ' indicates) are very close, which means that the system dominant characteristic root (i.e., the first dominant characteristic root) estimated by bright vector fitting is very close to the actual situation of the system.

[0110] The generated first characteristic root distribution diagram may be displayed to demonstrate the evaluation accuracy of the present solution. Optionally, if the partial fraction polynomial approximation of each closed-loop response is not very close to the measured data of the closed-loop response itself, the problem may be troubleshooted and then the system stability margin evaluation may be performed again.

[0111] Please refer to Fig.11 , Fig.11 The third flow chart of the system stability margin evaluation method provided in the embodiment of the present application is as follows: As a possible implementation, after step S140, the system stability margin evaluation method may further include step S160.

[0112] Step S160, when the stability margin needs to be adjusted, at least one parameter of the AC voltage feedforward low-pass filter bandwidth, the DC voltage control proportional gain, and the phase-locked loop proportional gain of the photovoltaic inverter in the photovoltaic inverter system is adjusted to improve the system stability margin.

[0113] In this embodiment, the generated second characteristic root distribution diagram can be displayed. If the user is not satisfied with the stability margin of some second dominant characteristic roots, the user can manually determine whether the stability margin needs to be adjusted. It is also possible to determine whether the stability margin needs to be adjusted by other methods, such as combining threshold judgment, and the specific judgment method for whether the stability margin needs to be adjusted is not limited here.

[0114] The inventor of the present application has found through research that among the control parameters of the three-phase photovoltaic grid-connected inverter, there are three parameters that have a more obvious influence on the dominant characteristic root: AC voltage feedforward low-pass filter bandwidth ω ff , DC voltage control proportional gain K dcp , PLL proportional gain K plp The above three parameters can be used to improve the stability margin (i.e., increase the damping ratio of the dominant characteristic root of the system). When it is determined that the stability margin needs to be adjusted, the AC voltage feedforward low-pass filter bandwidth ω of the photovoltaic inverter in the photovoltaic inverter system can be adjusted. ff , DC voltage control proportional gain K dcp , PLL proportional gain K plp At least one parameter in the control system is adjusted to improve the system stability margin.

[0115] The inventor of the present application has also found through research that if Fig.12 As shown, when the AC voltage feedforward low-pass filter bandwidth ω is reduced ff When the two pairs of dominant characteristic roots are shifted to the left, the DC voltage control proportional gain K can be reduced. dcp It can increase the dominant characteristic root damping ratio in the higher frequency band, but reduce the characteristic root damping ratio in the lower frequency band, and reduce the proportional gain K of the phase-locked loop. plp The role of reducing the DC voltage control proportional gain K dcp Just the opposite.

[0116] Based on the above findings, in this embodiment, the system stability margin is first improved by reducing the AC voltage feedforward low-pass filter bandwidth; when the system stability margin after reducing the AC voltage feedforward low-pass filter bandwidth does not meet the requirements, the DC voltage control proportional gain and / or the phase-locked loop proportional gain are reduced to improve the system stability margin. That is, when improving the system stability margin, due to reducing the AC voltage feedforward low-pass filter bandwidth ω ff The two pairs of dominant characteristic roots can be shifted to the left, so it is preferred to reduce the AC voltage feedforward low-pass filter bandwidth ω ff ; Secondly, consider reducing the DC voltage control proportional gain K dcp Or the phase-locked loop proportional gain K plp .

[0117] After the adjustment, the above steps S110 to S140 may be re-executed to evaluate the effectiveness of the parameter adjustment until the adjustment is satisfactory.

[0118] The above-mentioned system stability margin assessment method provided in this embodiment is a method for assessing the grid-connected stability margin of photovoltaic inverters through self-disturbance, which can be used to assess the stability margin of photovoltaic inverters (groups) on site and predict the risk of oscillation; no additional power circuit hardware is required, and all steps can be completed through a control device and the communication interface of the photovoltaic inverter; and there is no requirement for real-time communication. If the target photovoltaic inverter has a wireless communication function, all steps can be performed remotely; when the above-mentioned method is used for assessment, the photovoltaic inverter's own control system only needs to add a disturbance injection program and a response component detection program. These programs have no real-time requirements, so there is no need to interrupt the normal control process of the controller, and it is easy to implement in common control chips (C2000 series or Cortex-M4 series). In the above method, after obtaining the frequency response characteristics through "disturbance-observation", the system's dominant characteristic roots can be estimated only through vector fitting and characteristic root weighted combination, eliminating the common complex theoretical process based on impedance-admittance analysis. The second characteristic root distribution diagram outputted in the above method is independent of the model and the observation process and is a basic characteristic of the inverter grid-connected system. Therefore, the evaluation has high credibility and is easy to understand.

[0119] In order to execute the corresponding steps in the above embodiments and various possible methods, a method for implementing the system stability margin assessment device 200 is given below. Optionally, the system stability margin assessment device 200 can adopt the above Figure 3 The device structure of the control device 100 shown in FIG. Fig.13 , Fig.13A block diagram of a system stability margin assessment device 200 provided in an embodiment of the present application. It should be noted that the basic principle and technical effect of the system stability margin assessment device 200 provided in this embodiment are the same as those of the above-mentioned embodiments. For the sake of brief description, for parts not mentioned in this embodiment, reference can be made to the corresponding contents in the above-mentioned embodiments. In this embodiment, the system stability margin assessment device 200 is applied to a control device in a photovoltaic inverter system. The system stability margin assessment device 200 may include: a data acquisition module 210, a first calculation module 220, a second calculation module 230 and a processing module 240.

[0120] The data acquisition module 210 is used to send disturbance instructions corresponding to different frequency points to the target photovoltaic inverter, and obtain disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent. The target photovoltaic inverter is a grid-connected photovoltaic inverter in the photovoltaic inverter system, and the disturbance response information includes amplitude and phase.

[0121] The first calculation module 220 is used to calculate the closed-loop frequency response of the target physical quantity according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point.

[0122] The second calculation module 230 is used to calculate target information according to the closed-loop frequency response of the target physical quantity, wherein the target information includes the first dominant characteristic root corresponding to the target physical quantity.

[0123] The processing module 240 is used to determine the system stability margin according to the target information.

[0124] Optionally, in this embodiment, the processing module 240 is also used to: generate and display a Bode diagram based on the closed-loop frequency response of the target physical quantity and the target information, wherein the Bode diagram includes the closed-loop frequency response of the target physical quantity and a closed-loop frequency response function represented by the target information obtained by vector fitting, and the target information also includes the residue corresponding to the first dominant characteristic root.

[0125] Optionally, in this embodiment, the processing module 240 is also used to: when the stability margin needs to be adjusted, adjust at least one parameter of the AC voltage feedforward low-pass filter bandwidth, the DC voltage control proportional gain, and the phase-locked loop proportional gain of the photovoltaic inverter in the photovoltaic inverter system to improve the system stability margin.

[0126] Optionally, the above modules can be stored in the form of software or firmware. Figure 3 The memory 110 shown in FIG. 100 may be fixed in the operating system (OS) of the control device 100 and may be Figure 3 Meanwhile, the data and program codes required for executing the above modules may be stored in the memory 110.

[0127] An embodiment of the present application also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned system stability margin assessment method is implemented.

[0128] In summary, the embodiment of the present application provides a system stability margin assessment method, device, control device and readable storage medium. The control device first sends disturbance instructions corresponding to different frequency points to a photovoltaic inverter in the photovoltaic inverter system as a target photovoltaic inverter, and obtains the disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent. The disturbance response information includes amplitude and phase; then, according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point, the closed-loop frequency response of the target physical quantity is calculated, and then the target information is calculated according to the closed-loop frequency response of the target physical quantity, and finally the system stability margin is determined according to the target information. The above target information includes the first dominant characteristic root corresponding to the target physical quantity. In this way, there is no need to know the specific parameters of the system in advance, nor is there any need to connect additional equipment to the actual system to complete the system information identification based on disturbance-observation. The overall dominant characteristic root of the system can be estimated by using only the communication interface of the photovoltaic inverter, so as to evaluate the grid-connected stability margin of the photovoltaic inverter in the field.

[0129] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0130] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0131] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0132] The above description is only an optional embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A system stability margin assessment method, characterized in that: A control device applied to a photovoltaic inverter system, the method comprising: Sending disturbance instructions corresponding to different frequency points to a target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, wherein the target photovoltaic inverter is a grid-connected photovoltaic inverter in the photovoltaic inverter system, and the disturbance response information includes amplitude and phase; Calculate the closed-loop frequency response of the target physical quantity according to the disturbance command corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point; Calculating target information according to a closed-loop frequency response of the target physical quantity, wherein the target information includes a first dominant characteristic root corresponding to the target physical quantity; determining a system stability margin according to the target information; There are multiple target physical quantities, and the target information also includes the residue corresponding to the first dominant characteristic root. Determining the system stability margin according to the target information includes: Grouping the first dominant characteristic roots of each target physical quantity according to the position of the first dominant characteristic root of each target physical quantity in the first characteristic root distribution map to obtain at least one first dominant characteristic root group; According to the first dominant characteristic roots in each first dominant characteristic root group and the residues corresponding to each first dominant characteristic root, the second dominant characteristic root corresponding to the first dominant characteristic root group is calculated by weighted combination, wherein the second dominant characteristic root corresponding to each first dominant characteristic root group is used to indicate the system stability margin.

2. The method according to claim 1, characterized in that The determining of the system stability margin according to the target information further includes: A second characteristic root distribution graph is generated and displayed according to the second dominant characteristic roots corresponding to each first dominant characteristic root group, wherein the second characteristic root distribution graph is used to indicate a system stability margin.

3. The method according to claim 1, characterized in that The first dominant characteristic roots of each target physical quantity are grouped according to the positions of the first dominant characteristic roots of each target physical quantity in the first characteristic root distribution map to obtain at least one first dominant characteristic root group, including: With the goal of including one first dominant characteristic root of each target physical quantity in the first dominant characteristic root group and minimizing the sum of distances between the positions of the first dominant characteristic roots in the first dominant characteristic root group in the first characteristic root distribution diagram, the first dominant characteristic roots of each target physical quantity are grouped to obtain at least one first dominant characteristic root group.

4. The method according to any one of claims 1 to 3, characterized in that: The target physical quantity is at least one of a DC voltage, a d-axis AC voltage, a d-axis AC current, a q-axis AC voltage, and a q-axis AC current of the target photovoltaic inverter; and / or, The obtaining, after each disturbance instruction is sent, disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction, comprises: The disturbance response information is received by the target photovoltaic inverter, which is calculated based on a sampling sequence of at least one target physical quantity, wherein the target photovoltaic inverter performs self-disturbance by adding a disturbance signal generated based on a received disturbance command to a q-axis current command, and the sampling sequence is obtained by sampling the target photovoltaic inverter after starting self-disturbance for a first preset time period.

5. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: A Bode diagram is generated and displayed based on the closed-loop frequency response of the target physical quantity and the target information, wherein the Bode diagram includes the closed-loop frequency response of the target physical quantity and a closed-loop frequency response function represented by the target information obtained by vector fitting, and the target information also includes the residue corresponding to the first dominant characteristic root.

6. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: When the stability margin needs to be adjusted, at least one parameter of the AC voltage feedforward low-pass filter bandwidth, the DC voltage control proportional gain, and the phase-locked loop proportional gain of the photovoltaic inverter in the photovoltaic inverter system is adjusted to improve the system stability margin; The step of adjusting at least one parameter of the AC voltage feedforward low-pass filter bandwidth, the DC voltage control proportional gain, and the phase-locked loop proportional gain of the photovoltaic inverter in the photovoltaic inverter system to improve the system stability margin includes: Improve the system stability margin by reducing the AC voltage feedforward low-pass filter bandwidth; When the system stability margin does not meet the requirement after reducing the AC voltage feedforward low-pass filter bandwidth, the DC voltage control proportional gain and / or the phase-locked loop proportional gain are reduced to improve the system stability margin.

7. A system stability margin assessment device, characterized in that: A control device applied to a photovoltaic inverter system, the device comprising: A data acquisition module, used for sending disturbance instructions corresponding to different frequency points to a target photovoltaic inverter, and obtaining disturbance response information of at least one target physical quantity of the target photovoltaic inverter when the target photovoltaic inverter performs self-disturbance based on the received disturbance instruction after each disturbance instruction is sent, wherein the target photovoltaic inverter is a grid-connected photovoltaic inverter in the photovoltaic inverter system, and the disturbance response information includes amplitude and phase; A first calculation module, used to calculate the closed-loop frequency response of the target physical quantity according to the disturbance instructions corresponding to each frequency point and the disturbance response information of the target physical quantity corresponding to each frequency point; A second calculation module is used to calculate target information according to the closed-loop frequency response of the target physical quantity, wherein the target information includes a first dominant characteristic root corresponding to the target physical quantity; A processing module, used for determining a system stability margin according to the target information; Among them, there are multiple target physical quantities, and the target information also includes the residue corresponding to the first dominant characteristic root. The processing module is specifically used to: group the first dominant characteristic roots of each target physical quantity according to the position of the first dominant characteristic root of each target physical quantity in the first characteristic root distribution diagram to obtain at least one first dominant characteristic root grouping; calculate the second dominant characteristic root corresponding to the first dominant characteristic root grouping through weighted combination according to the first dominant characteristic root in each first dominant characteristic root grouping and the residue corresponding to each first dominant characteristic root, wherein the second dominant characteristic root corresponding to each first dominant characteristic root grouping is used to indicate the system stability margin.

8. A control device, characterized in that: It comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor can execute the machine executable instructions to implement the system stability margin assessment method according to any one of claims 1 to 6.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the system stability margin evaluation method according to any one of claims 1 to 6 is implemented.

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