Stability analysis method and system for micro photovoltaic grid-connected inverter based on multi-time scale

Abstract

The application discloses a kind of micro photovoltaic grid-connected inverter stability analysis method and system based on multi-time scale, comprising: 1) the multi-time scale dynamic model of micro photovoltaic grid-connected inverter is constructed;2) based on the multi-time scale dynamic model, the system eigenvalue distribution is analyzed in complex frequency domain, and the criterion for quantitatively evaluating the stability margin of system under different time scales is established;3) for the system matrix of the multi-time scale dynamic model, the eigenvalue and participation factor thereof are calculated, the influence degree of control parameter and circuit parameter on the stability of different oscillation modes is analyzed, and the analysis result is obtained;4) according to the analysis result, the sensitive parameter affecting the stability of system is adjusted, and the criterion is used as constraint or optimization target, to obtain the parameter combination that makes the system have better stability in the whole time scale range, which can realize comprehensive and accurate stability analysis and system parameter optimization.

Description

Technical Field

[0001] This invention belongs to the field of power system stability analysis and power electronic device design, and relates to a stability analysis method and system for micro photovoltaic grid-connected inverters based on multiple time scales. Background Technology

[0002] With the continuous growth of global demand for renewable energy, photovoltaic (PV) power generation technology has been widely applied and developed rapidly. As the core equipment of a PV power generation system, the stability of the micro PV grid-connected inverter directly affects the safe and reliable operation of the entire system. Micro PV grid-connected inverter systems exhibit complex multi-timescale dynamic characteristics, involving multiple timescales from fast dynamics at the switching frequency level to slow dynamics such as maximum power point tracking (MPPT). These dynamic behaviors at different timescales are coupled with each other, making system stability analysis extremely complex.

[0003] Currently, there are some research results on the stability analysis of grid-connected photovoltaic inverters. Chinese patent CN115276103B discloses a grid stability analysis method for new energy units containing multiple grid-connected converters. This method takes into account the multi-time-scale coupling characteristics of new energy units, constructs a control system based on the Hamiltonian model, and derives a stability criterion based on passive theory. Chinese patent CN110233503A discloses a method for optimizing control parameters of grid-connected photovoltaic inverters. This method establishes a small-signal analysis model of the photovoltaic power generation system, uses eigenvalue analysis and sensitivity analysis to determine the main factors affecting system oscillations, and employs a genetic algorithm for parameter optimization. Chinese patent CN115329593A discloses a power system stability analysis method based on the nonlinear characteristics of new energy generator units. This method constructs a port Hamiltonian model for a grid-connected converter system containing a high proportion of new energy generator units. Chinese patent CN115291520B discloses a model order reduction method in microgrid group analysis and control. It establishes the boundary layer of the microgrid group system through the classical singular perturbation method and achieves model order reduction under dual time scales. Chinese patent CN118508502A discloses a modeling and stability analysis method for a grid-connected inverter of a photovoltaic power generation system integrating energy storage, which uses the singular perturbation method to reduce the order of the inverter's state-space equations.

[0004] However, existing technologies still have many shortcomings in the stability analysis of micro-photovoltaic grid-connected inverters: 1. Oversimplification of models: Most methods adopt a single time-scale assumption or decouple fast dynamics (such as switching ripple) from slow dynamics (such as MPPT) and analyze them separately, ignoring the fundamental cause of novel instability—cross-scale dynamic coupling. For example, phase-locked loop dynamics may couple into the current loop through coordinate transformation, exciting medium-frequency oscillations under weak power grid conditions.

[0005] 2. Limited analytical dimensions: While traditional eigenvalue analysis can determine system stability, it does not systematically classify oscillation modes according to their physical causes and time scales, making it difficult for designers to intuitively understand "where the instability originates" and resulting in a disconnect between "diagnosis" and "prescription".

[0006] 3. One-sided optimization objectives: Parameter optimization often only targets the phase margin at a certain frequency point or the damping of a certain mode, lacking a perspective of coordinated optimization across the entire time-scale spectrum. This may lead to a situation where "pressing down one gourd causes another to float up"—improving the stability of one frequency band but worsening another.

[0007] 4. Insufficient analysis of adaptability to weak power grids: Existing methods often treat the power grid as an ideal voltage source and do not include the power grid impedance as a variable in the model, which makes it impossible to effectively analyze and predict the stability boundary of the inverter in a real weak power grid environment.

[0008] Therefore, there is an urgent need for an analytical method that can uniformly model multi-scale dynamics, diagnose stability according to physical time scales, and guide global collaborative optimization of parameters. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for stability analysis of micro photovoltaic grid-connected inverters based on multiple time scales. This method and system can achieve comprehensive and accurate stability analysis and optimize system parameters.

[0010] To achieve the above objectives, this invention discloses a stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales, comprising: 1) Construct a multi-timescale dynamic model of a micro-photovoltaic grid-connected inverter; 2) Based on the multi-timescale dynamic model, the distribution of system eigenvalues ​​is analyzed in the complex frequency domain, and a criterion for quantitatively evaluating the stability margin of the system at different timescales is established. 3) For the system matrix of the multi-timescale dynamic model, calculate its eigenvalues ​​and participation factors, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results; 4) Based on the analysis results, adjust the sensitive parameters that affect system stability, and use the criterion as a constraint or optimization target to obtain a parameter combination that makes the system have better stability over the entire time scale.

[0011] Furthermore, the specific operation of step 1) is as follows: A mathematical model of the photovoltaic array and DC / DC conversion stage is established, and a continuous-time model of the full-bridge inverter and LCL filter stage is established based on the state-space averaging method. Mathematical models of the MPPT control loop, DC voltage control loop, phase-locked loop and grid-connected current control loop are established respectively. The power circuit model is coupled and linearized with the multi-loop control system model to obtain a unified linear state-space equation describing the small-signal behavior of the system. The equation contains multi-time-scale state variables characterizing the dynamics of the switching frequency stage, the control loop stage and the power outer loop stage.

[0012] Furthermore, by introducing state variables that describe the dynamics of the phase-locked loop, the impact of grid voltage phase disturbances on the system is explicitly included in the linear state-space equations.

[0013] Furthermore, the criteria include: an attenuation rate index based on the real part of the eigenvalues, used to evaluate the damping level of each oscillation mode; and a frequency domain stability margin index calculated based on the Nyquist criterion or amplitude-phase margin, used to evaluate the relative stability of a specific control loop.

[0014] Furthermore, step 2) also includes: grouping the system eigenvalues ​​according to the magnitude of their real absolute values ​​or the corresponding oscillation frequencies, mapping them to different physical time scales, and calculating the comprehensive stability margin for each time scale group.

[0015] Furthermore, the specific operation of step 3) is as follows: Calculate all eigenvalues ​​λ in the system matrix of the linear state-space equation. i and its corresponding left and right eigenvectors; based on participation factor analysis, identify the eigenvalues ​​λ and their corresponding left and right eigenvectors. i The state variable with the strongest correlation is used to determine the main physical cause of this oscillation mode; the eigenvalue λ is calculated. i Sensitivity to a specific system parameter p λ i / p quantifies the direction and intensity of the influence of the parameter p on the stability of a specific mode, and sorts the specific system parameters according to the sensitivity of each specific system parameter p.

[0016] Furthermore, the specific system parameter p includes: current loop PI controller parameters (K) p , K i ), PLL bandwidth ω pll DC bus capacitor C dc LCL filter parameters (L f C f , L g ) and the maximum power point voltage V of the photovoltaic array mpp Small-signal impedance characteristics in the vicinity.

[0017] Further, step 4) involves the following steps: based on the sorting results, the top k parameters that have the greatest impact on the weakly damped or negatively damped modes are selected as parameters to be optimized; with the goal of improving the worst-case mode damping ratio or expanding the overall stability region across the entire time scale, a parameter optimization problem is constructed; an optimization algorithm is used to solve the parameter optimization problem to obtain an optimized parameter set that enables the system to meet stability requirements within typical operating conditions.

[0018] This invention discloses a stability analysis system for micro-photovoltaic grid-connected inverters based on multiple time scales, comprising: The parametric model library module is used to build multi-timescale dynamic models of micro photovoltaic grid-connected inverters; A construction module is used to analyze the distribution of system eigenvalues ​​in the complex frequency domain based on the multi-timescale dynamic model, and to establish a criterion for quantitatively evaluating the stability margin of the system at different timescales. The analysis module is used to calculate the eigenvalues ​​and participation factors of the system matrix of the multi-timescale dynamic model, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results. The optimization module is used to adjust the sensitive parameters affecting system stability based on the analysis results, and to obtain a parameter combination that makes the system more stable across the entire time scale, using the criterion as a constraint or optimization target.

[0019] Furthermore, mathematical models of the photovoltaic array and DC / DC conversion stage are established, and continuous-time models of the full-bridge inverter and LCL filter stage are established based on the state-space averaging method. Mathematical models of the MPPT control loop, DC voltage control loop, phase-locked loop, and grid-connected current control loop are established respectively. The power circuit model is coupled and linearized with the multi-loop control system model to obtain a unified linear state-space equation describing the small-signal behavior of the system. The equation contains multi-time-scale state variables characterizing the dynamics of the switching frequency stage, control loop stage, and power outer loop stage.

[0020] The present invention has the following beneficial effects: The stability analysis method and system for micro-photovoltaic grid-connected inverters based on multiple time scales described in this invention establishes a model that explicitly includes full-scale dynamics from switching dynamics to MPPT and grid impedance, enabling a more realistic reflection of system characteristics. It is particularly adept at analyzing interactive instability issues under weak grid conditions. Through eigenvalue clustering and participation factor analysis, unstable oscillation modes can be clearly traced back to specific physical components, providing a clear direction for subsequent rectification. Furthermore, this invention employs cross-time scale collaborative optimization, providing engineers with a set of robust parameters that perform well across all frequency bands. This avoids the hidden risks that may arise from traditional single-point optimization, automating the "modeling-analysis-optimization" process and significantly improving the efficiency of inverter R&D and grid-connected commissioning. Attached Figure Description

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

[0022] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0027] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0028] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0031] Example 1 The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales described in this invention includes: 1) Construct a multi-timescale dynamic model of a micro-photovoltaic grid-connected inverter; Step 1) involves the following steps: establishing mathematical models for the photovoltaic array and DC / DC conversion stage, and establishing continuous-time models for the full-bridge inverter and LCL filter stage based on the state-space averaging method; establishing mathematical models for the MPPT control loop, DC voltage control loop, phase-locked loop, and grid-connected current control loop, respectively; coupling and linearizing the power circuit model with the multi-loop control system model to obtain a unified linear state-space equation describing the small-signal behavior of the system. This equation includes multi-time-scale state variables characterizing the dynamics of the switching frequency level, control loop level, and power outer loop level. By introducing state variables describing the dynamics of the phase-locked loop, the impact of grid voltage phase disturbances on the system is explicitly included in the linear state-space equation.

[0032] 2) Based on the multi-timescale dynamic model, the distribution of system eigenvalues ​​is analyzed in the complex frequency domain, and a criterion for quantitatively evaluating the stability margin of the system at different timescales is established. The criteria include: a decay rate index based on the real part of the eigenvalues, used to evaluate the damping level of each oscillation mode; and a frequency domain stability margin index calculated based on the Nyquist criterion or amplitude-phase margin, used to evaluate the relative stability of a specific control loop.

[0033] Step 2) also includes: grouping the system eigenvalues ​​according to the magnitude of their real absolute values ​​or the corresponding oscillation frequencies, mapping them to different physical time scales, and calculating the comprehensive stability margin for each time scale group.

[0034] 3) For the system matrix of the multi-timescale dynamic model, calculate its eigenvalues ​​and participation factors, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results; The specific operation of step 3) is as follows: Calculate all eigenvalues ​​λ in the system matrix of the linear state-space equation. i and its corresponding left and right eigenvectors; based on participation factor analysis, identify the eigenvalues ​​λ and their corresponding left and right eigenvectors. i The state variable with the strongest correlation is used to determine the main physical cause of this oscillation mode; the eigenvalue λ is calculated. i Sensitivity to a specific system parameter p λ i / p quantifies the direction and intensity of the influence of the parameter p on the stability of a specific mode, and sorts the specific system parameters according to the sensitivity of each specific system parameter p.

[0035] The specific system parameter p includes: current loop PI controller parameters (K) p , K i ), PLL bandwidth ω pll DC bus capacitor C dc LCL filter parameters (L f C f , L g ) and the maximum power point voltage V of the photovoltaic array mpp Small-signal impedance characteristics in the vicinity.

[0036] 4) Based on the analysis results, adjust the sensitive parameters that affect system stability, and use the criterion as a constraint or optimization target to obtain a parameter combination that makes the system have better stability over the entire time scale.

[0037] Step 4) involves the following steps: Based on the sorting results, select the top k parameters that have the greatest impact on the weakly damped or negatively damped modes as the parameters to be optimized; with the goal of improving the worst-case mode damping ratio or expanding the overall stability region across the entire time scale, construct a parameter optimization problem; use an optimization algorithm to solve the parameter optimization problem and obtain an optimized parameter set that ensures the system meets stability requirements within typical operating conditions.

[0038] Example 2 The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales described in this invention includes the following steps: 1) Unified modeling across multiple time scales; Taking a single-phase full-bridge microinverter with a rated power of 5kW as an example, first, a detailed simulation model is built in MATLAB / Simulink or PLECS. Then, the following sub-steps are performed to build a small-signal model for stability analysis: 11) Photovoltaic module model: Using a single diode model, calculate its output current I at typical illumination and temperature operating points (S=1000W / m², T=25℃). pv For voltage V pv The differential dI pv / dV pv The small-signal conductance G at the operating point is obtained. pv .

[0039] 12) Power Circuit Averaging Model: State-space averaging is used for the DC / DC Boost circuit and the DC / AC H4 bridge arm. The LCL filter parameters are: L1 = 1.5mH, C f =10μF, L2=0.5mH. The derivation yields the inductor current i L1 i L2 and capacitor voltage v CfCircuit state equation for state variables.

[0040] 13) Control system model: Inner current loop: PI control in the synchronous rotating coordinate system, Kp i = 0.5, Ki i = 100.

[0041] Phase - locked loop (PLL): Adopt a typical second - order structure based on SRF - PLL, bandwidth ω pll Set to 31.4 rad / s (5Hz).

[0042] Outer DC voltage loop: PI control, Kp v = 0.1, Ki v = 5.

[0043] MPPT: Adopt the perturbation and observation method, and its dynamics can be simplified to a low - frequency power reference value perturbation.

[0044] 14) Linearization and interconnection: Linearize all the above - mentioned sub - models at the steady - state operating point (such as V dc = 400V, I grid = 5kW / 220V). The key step: Introduce the dynamic equation of the PLL (with phase error as the state), and regard the phase perturbation θ g of the grid voltage as an input variable. At the same time, connect a variable impedance in series at the inverter output to simulate a weak grid. Finally, obtain a high - dimensional linear state - space model in the form of . The state vector contains [i L1 , v Cf , i L2 ,..., integrator states, PLL states] and other 15 - 20 states, and its dynamic response time ranges from dozens of microseconds to several seconds.

[0045] 2) Establish a stability criterion based on mode clustering; Calculate all the eigenvalues of the system matrix A .

[0046] Mode clustering: High - frequency cluster (HF): Screen the eigenvalues with f = ω / (2π)>1 kHz. In this embodiment, there may be a pair of conjugate poles related to the resonance frequency of the LCL filter (about 1.8 kHz). Calculate its damping ratio ζ = -σ / |λ|.

[0047] Medium - frequency cluster (MF): Screen the eigenvalues with 10 Hz < f < 1 kHz. Usually contains modes dominated by the current loop and PLL. Check when the grid inductance Lg When the damping ratio increases from 0 (strong grid) to 3 mH (weak grid), does the damping ratio of any mode in the cluster decrease significantly or even become positive in the real part?

[0048] Low-frequency cluster (LF): Screens for eigenvalues ​​with f < 10 Hz. Primarily corresponds to DC voltage loop and MPPT dynamics.

[0049] Criterion evaluation: Set a stability threshold: the damping ratio ζ of all modes in each cluster is greater than 5%, and the real part of the rightmost eigenvalue σ max <-10. In L g When = 2 mH, it may be found that the damping ratio of one mode of the intermediate frequency cluster drops to 3% (σ ≈ -5), the system is in a weakly damped state, and it is determined that there is a stability risk under this weak power grid condition.

[0050] 3) Parameter sensitivity and dominant causative factor analysis; For the weakly damped mid-frequency mode λ identified in step 2), weak Calculate its sensitivity to each parameter, for example, find that... λ weak / ω pll and λ weak / Kp i The largest magnitude indicates that the PLL bandwidth and current loop proportional gain are the most sensitive parameters affecting this mode.

[0051] Calculate the pattern λ weak The participating factors. It was found that the state variable θ of the PLL... err and q-axis current i q The participation rate is the highest. Therefore, it is diagnosed that this weakly damped mode is essentially an interactive oscillation between the PLL dynamics and the q-axis channel of the current loop.

[0052] 4) Collaborative optimization of parameters across time scales; Problem Construction: Using ω pll Kp i Ki i Kp v To optimize variables, the objective function is set as follows: This means maximizing the damping ratio of the worst mode in the three time-scale clusters. The constraints are within the physical range of the parameters (e.g., ω). pll ∈ [2π*3, 2π*15] rad / s) and steady-state performance requirements (such as current tracking error).

[0053] Optimization Solution: The NSGA-II multi-objective genetic algorithm is used for the solution. After iteration, a set of Pareto optimal solutions is obtained. An equilibrium solution is selected from these, for example: ωpll * = 4 Hz, Kp i * = 0.35. This solution sacrifices extremely fast PLL tracking speed in exchange for improving the damping ratio of the mid-frequency cluster mode from 3% to 12%, without significantly deteriorating the damping ratio of the high-frequency and low-frequency clusters.

[0054] Verification: Substituting the optimized parameters into the original time-domain simulation model and applying grid voltage dips and illumination step disturbances, the system was able to recover smoothly, verifying the effectiveness of the optimized parameters.

[0055] Example 3 The stability analysis system for micro-photovoltaic grid-connected inverters based on multiple time scales described in this invention includes: The parametric model library module is used to build multi-timescale dynamic models of micro photovoltaic grid-connected inverters; A construction module is used to analyze the distribution of system eigenvalues ​​in the complex frequency domain based on the multi-timescale dynamic model, and to establish a criterion for quantitatively evaluating the stability margin of the system at different timescales. The analysis module is used to calculate the eigenvalues ​​and participation factors of the system matrix of the multi-timescale dynamic model, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results. The optimization module is used to adjust the sensitive parameters affecting system stability based on the analysis results, and to obtain a parameter combination that makes the system more stable across the entire time scale, using the criterion as a constraint or optimization target.

[0056] In this embodiment, a mathematical model of the photovoltaic array and DC / DC conversion stage is established, and a continuous-time model of the full-bridge inverter and LCL filter stage is established based on the state-space averaging method. Mathematical models of the MPPT control loop, DC voltage control loop, phase-locked loop and grid-connected current control loop are established respectively. The power circuit model is coupled and linearized with the multi-loop control system model to obtain a unified linear state-space equation describing the small-signal behavior of the system. The equation contains multi-time-scale state variables characterizing the dynamics of the switching frequency level, control loop level and power outer loop level.

[0057] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0058] Example 4 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the stability analysis method for a micro-photovoltaic grid-connected inverter based on multiple time scales. For example, the steps include: 1) constructing a multi-time-scale dynamic model of the micro-photovoltaic grid-connected inverter; 2) based on the multi-time-scale dynamic model, analyzing the distribution of system eigenvalues ​​in the complex frequency domain and establishing a criterion for quantitatively evaluating the stability margin of the system at different time scales; 3) for the system matrix of the multi-time-scale dynamic model, calculating its eigenvalues ​​and participation factors, analyzing the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtaining analysis results; 4) based on the analysis results, adjusting the sensitive parameters affecting system stability, and using the criterion as a constraint or optimization objective, obtaining a parameter combination that gives the system better stability across the entire time scale. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0059] Example 5 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the stability analysis method for a micro-photovoltaic grid-connected inverter based on multiple time scales. For example, the method includes: 1) constructing a multi-time-scale dynamic model of the micro-photovoltaic grid-connected inverter; 2) based on the multi-time-scale dynamic model, analyzing the distribution of system eigenvalues ​​in the complex frequency domain, and establishing a criterion for quantitatively evaluating the stability margin of the system at different time scales; 3) for the system matrix of the multi-time-scale dynamic model, calculating its eigenvalues ​​and participation factors, analyzing the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtaining analysis results; 4) based on the analysis results, adjusting the sensitive parameters affecting system stability, and using the criterion as a constraint or optimization objective, obtaining a parameter combination that gives the system better stability across the entire time scale. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0060] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0065] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0066] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales, characterized in that, include: 1) Construct a multi-timescale dynamic model of a micro-photovoltaic grid-connected inverter; 2) Based on the multi-timescale dynamic model, the distribution of system eigenvalues ​​is analyzed in the complex frequency domain, and a criterion for quantitatively evaluating the stability margin of the system at different timescales is established. 3) For the system matrix of the multi-timescale dynamic model, calculate its eigenvalues ​​and participation factors, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results; 4) Based on the analysis results, adjust the sensitive parameters that affect system stability, and use the criterion as a constraint or optimization target to obtain a parameter combination that makes the system have better stability over the entire time scale.

2. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 1, characterized in that, The specific operation of step 1) is as follows: Mathematical models of photovoltaic arrays and DC / DC conversion stages are established, and continuous-time models of full-bridge inverters and LCL filter stages are established based on the state-space averaging method; mathematical models of MPPT control loop, DC voltage control loop, phase-locked loop and grid-connected current control loop are established respectively. By coupling and linearizing the power circuit model with the multi-loop control system model, a unified linear state-space equation describing the small-signal behavior of the system is obtained. The equation contains multi-time-scale state variables characterizing the dynamics of the switching frequency level, the control loop level, and the power outer loop level.

3. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 2, characterized in that, By introducing state variables that describe the dynamics of the phase-locked loop, the impact of grid voltage phase disturbances on the system is explicitly included in the linear state-space equations.

4. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 1, characterized in that, The criteria include: a decay rate index based on the real part of the eigenvalues, used to evaluate the damping level of each oscillation mode; and a frequency domain stability margin index calculated based on the Nyquist criterion or amplitude-phase margin, used to evaluate the relative stability of a specific control loop.

5. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 1, characterized in that, Step 2) also includes: grouping the system eigenvalues ​​according to the magnitude of their real absolute values ​​or the corresponding oscillation frequencies, mapping them to different physical time scales, and calculating the comprehensive stability margin for each time scale group.

6. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 1, characterized in that, The specific operation of step 3) is as follows: Calculate all eigenvalues ​​λ in the system matrix of the linear state-space equation. i and its corresponding left and right eigenvectors; based on participation factor analysis, identify the eigenvalues ​​λ and their corresponding left and right eigenvectors. i The state variable with the strongest correlation is used to determine the main physical cause of this oscillation mode; the eigenvalue λ is calculated. i Sensitivity to a specific system parameter p λ i / p quantifies the direction and intensity of the influence of the parameter p on the stability of a specific mode, and sorts the specific system parameters according to the sensitivity of each specific system parameter p.

7. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 6, characterized in that, The specific system parameter p includes: current loop PI controller parameters (K) p , K i ), PLL bandwidth ω pll DC bus capacitor C dc LCL filter parameters (L f C f , L g ) and the maximum power point voltage V of the photovoltaic array mpp Small-signal impedance characteristics in the vicinity.

8. The stability analysis method for micro-photovoltaic grid-connected inverters based on multiple time scales according to claim 6, characterized in that, Step 4) involves the following steps: Based on the sorting results, select the top k parameters that have the greatest impact on the weakly damped or negatively damped modes as the parameters to be optimized; with the goal of improving the worst-case mode damping ratio or expanding the overall stability region across the entire time scale, construct a parameter optimization problem; use an optimization algorithm to solve the parameter optimization problem and obtain an optimized parameter set that ensures the system meets stability requirements within typical operating conditions.

9. A stability analysis system for micro-photovoltaic grid-connected inverters based on multiple time scales, characterized in that, include: The parametric model library module is used to build multi-timescale dynamic models of micro photovoltaic grid-connected inverters; A construction module is used to analyze the distribution of system eigenvalues ​​in the complex frequency domain based on the multi-timescale dynamic model, and to establish a criterion for quantitatively evaluating the stability margin of the system at different timescales. The analysis module is used to calculate the eigenvalues ​​and participation factors of the system matrix of the multi-timescale dynamic model, analyze the influence of control parameters and circuit parameters on the stability of different oscillation modes, and obtain the analysis results. The optimization module is used to adjust the sensitive parameters affecting system stability based on the analysis results, and to obtain a parameter combination that makes the system more stable across the entire time scale, using the criterion as a constraint or optimization target.

10. The stability analysis system for micro photovoltaic grid-connected inverters based on multiple time scales according to claim 9, characterized in that, The specific operations of the parametric model library module are as follows: Mathematical models of photovoltaic arrays and DC / DC conversion stages are established, and continuous-time models of full-bridge inverters and LCL filter stages are established based on the state-space averaging method; mathematical models of MPPT control loop, DC voltage control loop, phase-locked loop and grid-connected current control loop are established respectively. By coupling and linearizing the power circuit model with the multi-loop control system model, a unified linear state-space equation describing the small-signal behavior of the system is obtained. The equation contains multi-time-scale state variables characterizing the dynamics of the switching frequency level, the control loop level, and the power outer loop level.

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