Light storage generator inertia response analysis method and system based on finite element

Abstract

The invention belongs to the technical field of power electronics, and provides a finite element-based optical storage generator inertia response analysis method and system.When basic finite element modeling is carried out, firstly, modeling requirements need to be accurately matched on the basis of practically reflecting all structure partition characteristics of optical storage generator equipment; a complex equipment structure is reasonably divided and marked, and a precise and efficient basic finite element model is effectively constructed in combination with application of a targeted modeling fractal dimension reference value and a correction coefficient; in combination with stability and efficiency indexes of inertia response, the optimal response is focused on the weakest link through layered early warning adjustment, so that the optical storage generator can reach basic safety and performance standards, thereby ensuring that the optical storage generator has higher flexibility and capability of dealing with emergencies. And the overall performance of inertia response of the optical storage generator is effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power electronics, and in particular relates to a method and system for analyzing inertia response of a photovoltaic power generator based on finite elements. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Frequency stability as a key indicator in the design and operation of photovoltaic power generation is required to adjust the configuration of the generator according to the inertia analysis results. Through inertia response analysis, it helps to predict the demand for system inertia due to load changes, thereby improving load management strategies and response measures and optimizing the scheduling strategy and operating parameters of photovoltaic power generation, optimizing load distribution, reducing frequency fluctuations, and achieving optimal performance under different operating modes. At the same time, inertia response analysis of photovoltaic power generation helps to predict and respond to emergencies such as generator failures and drastic load changes, reducing their impact on power supply. In addition, with the advancement of power technology, such as the introduction of smart grids and advanced energy storage systems, inertia response analysis can help the current power storage and supply model better adapt to the challenges and opportunities of new technologies. Inertia response analysis can provide data support for the application of these new technologies, ensuring that the introduction of new technologies will not have a negative impact on grid stability.

[0004] Existing photovoltaic power generation modeling methods often rely on automated output from modeling software. The low level of model refinement leads to large computing requirements and slow simulation speed, making it difficult to perfectly meet actual test requirements. In order to ensure the validity of the results, a large amount of computing resources are often invested. In addition, existing model adjustments generally use static or empirical methods and lack a dynamic adjustment mechanism based on real-time feedback. They are often unable to effectively adapt to changes in actual conditions and are unable to identify the weakest response links in a timely manner, resulting in the dispersion of optimization resources and management energy, making it difficult to effectively improve the overall performance of the inertia response of photovoltaic power generation. Summary of the invention

[0005] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides a method and system for analyzing the inertia response of a photovoltaic power generator based on finite elements, which adopts a dynamic adjustment mechanism based on real-time feedback to effectively adapt to changes in actual conditions, timely identify the weakest link in response, and effectively improve the overall performance of the inertia response of the photovoltaic power generator.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for analyzing the inertia response of a photovoltaic power generator based on finite elements, comprising the following steps: Mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain each device structure partition of the photovoltaic power generator; The modeling fractal dimension reference value of the corresponding device structure partition is calculated by combining the modeling fractal dimension attribute set and the modeling fractal dimension attribute correction coefficient set of the device structure partition of the photovoltaic storage generator; Based on the modeling fractal dimension reference value, the finite element modeling dimension is judged to obtain the basic finite element model; Acquire the coupled multi-physics field of the photovoltaic power generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; A light storage inertia characteristic parameter set is obtained based on the adjusted finite element model, and a light storage inertia response value is calculated based on the light storage inertia characteristic parameter set; Based on the photovoltaic storage inertia response value, the photovoltaic storage generator is given a hierarchical early warning to determine the equipment to be optimized.

[0007] Furthermore, the finite element modeling dimension judgment is performed based on the modeling fractal dimension reference value to obtain the basic finite element model, including: Obtain the modeling dimension judgment value stored in the database, compare the modeling dimension judgment value of each equipment structure partition with the modeling dimension reference value corresponding to each equipment structure partition; if the modeling dimension reference value is greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a two-dimensional finite element model as the basic finite element model; if the modeling dimension reference value is not greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a three-dimensional finite element model as the basic finite element model.

[0008] Furthermore, the step of obtaining the coupled multi-physics field of the photovoltaic power generator is to apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, which specifically includes: Acquire a coupled multi-physics field parameter set based on the coupled multi-physics field of the photovoltaic storage generator, wherein the coupled multi-physics field parameter set is stored in a database and corresponds to the coupled multi-physics field; The coupled multi-physics parameter set is stored as a coupled multi-physics parameter specified tag, and the coupled multi-physics parameter specified tag is compared with the coupled standard set corresponding to each coupled multi-physics parameter specified tag to obtain the coupled standard set corresponding to the coupled multi-physics parameter specified tag; Applying coupled multiphysics to the underlying finite element model to obtain a coupled response set; A coupling response value is obtained based on the coupling standard set and the coupling response set.

[0009] Furthermore, the iterative adjustment of the basic finite element model based on the coupling response value to obtain an adjusted finite element model includes: Get the set coupling response restoration value; Compare the coupling response value with the coupling response restoration value; If the coupling response value is greater than or equal to the coupling response restoration value, the current basic finite element model is marked as the adjusted finite element model; if the coupling response value is less than the coupling response restoration value, the basic finite element model is re-obtained and recorded as the adjusted finite element model.

[0010] Further, the step of obtaining a light-storage inertia characteristic parameter set based on the adjusted finite element model and calculating a light-storage inertia response value based on the light-storage inertia characteristic parameter set includes: Obtaining a first characteristic parameter set of light-storage inertia and a second characteristic parameter set of light-storage inertia based on the adjusted finite element model; Calculate the stability evaluation value of the light storage inertia based on the first characteristic parameter set of the light storage inertia, and calculate the efficiency evaluation value of the light storage inertia based on the second characteristic parameter set of the light storage inertia; The solar energy storage inertia response value is calculated by combining the solar energy storage inertia stability evaluation value and the solar energy storage inertia efficiency evaluation value.

[0011] Furthermore, the light storage inertia response value is calculated by combining the light storage inertia stability evaluation value and the light storage inertia efficiency evaluation value, and specifically includes: Obtaining a stability evaluation value of the photovoltaic storage inertia and an efficiency evaluation value of the photovoltaic storage inertia of each power generation cycle within a plurality of power generation cycles, and performing least squares fitting to calculate a stability evaluation regression coefficient and an efficiency evaluation regression coefficient; Based on the stability evaluation regression coefficient and the efficiency evaluation regression coefficient, weight allocation is performed to calculate the stability weight of the photovoltaic storage inertia and the efficiency weight of the photovoltaic storage inertia; The photoelectric storage inertia response value is calculated based on the photoelectric storage inertia stability weight, the photoelectric storage inertia efficiency weight, the photoelectric storage inertia stability evaluation value and the photoelectric storage inertia efficiency evaluation value.

[0012] Furthermore, the step of performing layered early warning on the photovoltaic power generator based on the photovoltaic power generator inertia response value and determining the equipment to be optimized includes: Obtain the light energy storage inertia warning value, light energy storage inertia stability warning value and light energy storage inertia efficiency warning value stored in the database; Compare the optical storage inertia response value with the optical storage inertia warning value to obtain the qualified judgment result; if the optical storage inertia response value is not less than the optical storage inertia warning value, the qualified judgment result is a qualified device; if the optical storage inertia response value is less than the optical storage inertia warning value, the qualified judgment result is a device to be optimized; Obtain a stability evaluation value of the optical energy storage inertia and an efficiency evaluation value of the optical energy storage inertia of the device to be optimized, compare the stability warning value of the optical energy storage inertia with the stability evaluation value of the optical energy storage inertia, and the efficiency warning value of the optical energy storage inertia with the efficiency evaluation value of the optical energy storage inertia, and obtain a judgment result to be optimized, which includes a stable device to be optimized and an efficient device to be optimized.

[0013] The second aspect of the present invention provides a finite element-based photovoltaic power generation inertia response analysis system, comprising: A device partitioning module is used to mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain each device structure partition of the photovoltaic power generator; The finite element model module is used to calculate the modeling fractal dimension reference value of the corresponding device structure partition by combining the modeling fractal dimension attribute set and the modeling fractal dimension attribute correction coefficient set of the structure partition of the photovoltaic storage generator device; perform finite element modeling dimension judgment based on the modeling fractal dimension reference value to obtain a basic finite element model; A finite element model adjustment module is used to obtain the coupled multi-physics field of the photovoltaic power generation generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; An inertia response value calculation module, which is used to obtain a light storage inertia characteristic parameter set based on the adjusted finite element model, and calculate the light storage inertia response value based on the light storage inertia characteristic parameter set; The hierarchical optimization module is used to carry out hierarchical early warning for the photovoltaic storage generator based on the photovoltaic storage inertia response value and determine the equipment to be optimized.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned finite element-based inertia response analysis method for a photovoltaic power generator.

[0016] A fourth aspect of the present invention provides a computer device.

[0017] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned finite element-based method for analyzing inertia response of a photovoltaic power generator are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. When the present invention performs basic finite element modeling, it is first necessary to accurately match the modeling requirements on the basis of effectively reflecting the characteristics of each structural partition of the photovoltaic power generator equipment. In this process, not only attention is paid to the accuracy of the overall model, and advanced modeling is not blindly pursued, but also the demand for moderate simplification of simple structural parts should be considered. This simplification helps to reduce the complexity of the model. Under the premise of ensuring the effectiveness of the modeling, it greatly saves the required computing power, and thus provides a more efficient solution for subsequent large-scale simulation and analysis. Therefore, by reasonably dividing and marking the complex equipment structure, combined with targeted modeling dimension reference values ​​and the application of correction coefficients, an accurate and efficient basic finite element model is effectively constructed.

[0019] 2. The present invention combines the key performance indicators of stability and efficiency of inertial response, and focuses on the weakest link through hierarchical early warning adjustment to prioritize optimized response, so that the photovoltaic storage generator can first meet the basic safety and performance standards, thereby ensuring that it has greater flexibility and the ability to respond to emergencies, and concentrating resources and management efforts on optimizing one aspect of the weakest response. This can reduce the complex changes within the photovoltaic storage generator caused by simultaneous optimization and avoid interference from interactive effects. In addition, once the shortcomings of the response are successfully optimized, subsequent performance can be evaluated on this basis. If it is subsequently found that stability or efficiency is still insufficient, the optimization path will be adjusted based on the existing successful optimization experience to ensure that each step can effectively promote the improvement of the overall inertial response quality.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 is a flow chart of a method for analyzing inertia response of a photovoltaic power generation device based on finite elements provided in an embodiment of the present invention; Figure 2 It is an image of the evaluation value of the light storage inertia efficiency and the change of the response adjustment frequency provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Explanation of terms Photovoltaic storage generator: Photovoltaic storage generator is a device inertia response analysis that integrates solar photovoltaic power generation and energy storage system; Inertia: refers to the system's resistance to frequency changes, which is mainly provided by the rotating mass (such as the rotor) of the generator set. When the system load changes suddenly, inertia can help maintain the frequency stability of the grid until the generator set can adjust its output to match the new load conditions.

[0027] Finite element: Finite element modeling is a numerical calculation method that divides a complex entity or system into many small, simple, regularly shaped parts (called finite elements).

[0028] Embodiment 1 like Figure 1 As shown, this embodiment provides a finite element-based method for analyzing the inertia response of a photovoltaic power generator, comprising the following steps: Step 1: Mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain the device structure partition of each photovoltaic power generator; In this embodiment, the equipment structure partition diagram of the photovoltaic storage generator is obtained from a database, extracted from the technical specification of the photovoltaic storage generator and stored in the database, and is used to describe the configuration of the main parts of the photovoltaic storage generator, including the photovoltaic module area, the inverter area, the energy storage module area, etc., wherein the photovoltaic module area includes photovoltaic panels and related photovoltaic brackets, the inverter area includes photovoltaic inverters and related electrical equipment, and the energy storage module area includes energy storage batteries (such as lithium batteries) and related management systems (BMS).

[0029] Step 2: Calculate the modeling fractal dimension reference value of the corresponding device structure partition by combining the modeling fractal dimension attribute set and the modeling fractal dimension attribute correction coefficient set of the device structure partition of the photovoltaic storage generator; In this embodiment, the modeling fractal dimension attribute set of any device structure partition includes component connection point density, integration level, and modularity level; Among them, the density of component connection points refers to the ratio of the number of connection points between each component in the device structure partition to the overall volume of the device structure partition, reflecting the proportion of connection points in the structure partition. Integration The degree of integration of different device structure partitions in the system at the functional and physical levels is usually reflected in the complexity of the design and the interaction between components. This parameter can be obtained directly from the database. The degree of modularity reflects the independence and interchangeability of each component in the system and can be obtained directly from the database.

[0030] Acquire a modeling fractal dimension property correction coefficient set stored in a database, wherein the modeling fractal dimension property correction coefficient set includes a component connection point density correction coefficient, an integration correction coefficient, and a modularization degree correction coefficient; The correction coefficients for component connection point density, integration degree, and modularity level used to describe the symmetry of the structural partitions of the photovoltaic power generator are extracted from the database. Based on the established mapping set of historically measured component connection point density, integration degree, and modularity level, and component connection point density correction coefficient, integration degree correction coefficient, and modularity level correction coefficient, the current component connection point density, integration degree, and modularity level are obtained, as well as the corresponding component connection point density correction coefficient, integration degree correction coefficient, and modularity level correction coefficient.

[0031] Specifically, the modeling fractal dimension reference value is calculated by the following formula: , In the formula, is the reference value of modeling fractal dimension, is the component connection point density, For integration, For modularity, is the component connection point density correction coefficient, is the integration correction factor, is the modularity correction factor.

[0032] Using the hyperbolic cosine function cosh makes the effect of the input parameters on the output value nonlinear, that is, small changes in the input do not result in large fluctuations in the output value, which helps to simulate system performance in the real world, where increased complexity does not always lead to linearly increased system benefits or costs.

[0033] Transforming the result of the hyperbolic cosine function by its natural logarithm can compress the output range of the function into an interval that is more manageable and easier to interpret.

[0034] The three parameters of component connection point density, integration and modularity work together to affect the design and performance of the system. The component connection point density affects the physical properties of the structure, the integration affects the functional performance of the system, and the modularity is directly related to the maintainability and flexibility of the system.

[0035] Step 3: Determine the finite element modeling dimension based on the modeling fractal dimension reference value to obtain the basic finite element model; Obtain the modeling dimension judgment value stored in the database, compare the modeling dimension judgment value of each equipment structure partition with the modeling dimension reference value corresponding to each equipment structure partition; if the modeling dimension reference value is greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a two-dimensional finite element model as the basic finite element model; if the modeling dimension reference value is not greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a three-dimensional finite element model as the basic finite element model.

[0036] In a specific embodiment, the modeling fractal dimension reference value is used to comprehensively evaluate the structural status of the photovoltaic power generator. The higher the modeling fractal dimension reference value, the more complex the geometric characteristics of the structural partition corresponding to the modeling fractal dimension reference value, and the more high-dimensional modeling is required for simulation and restoration. The modeling dimension judgment value stored in the database is the standard dividing value of fractal dimension modeling. A value higher than the standard dividing value indicates that the structure requires relatively higher-dimensional modeling. Based on the actual working conditions of the photovoltaic power generator, the higher-dimensional modeling is three-dimensional modeling.

[0037] The two-dimensional finite element model and the three-dimensional finite element model are both obtained from the database, and are two-dimensional restorations and three-dimensional restorations of the photovoltaic power generator.

[0038] It should be noted that when conducting basic finite element modeling, it is first necessary to accurately match the modeling requirements on the basis of effectively reflecting the characteristics of each structural partition of the photovoltaic power generation equipment. In this process, we should not only pay attention to the accuracy of the overall model and not blindly pursue advanced modeling, but also consider the need for moderate simplification of simple structural parts. This simplification helps to reduce the complexity of the model. While ensuring the effectiveness of the modeling, it greatly saves the required computing power, thereby providing a more efficient solution for subsequent large-scale simulation and analysis. Therefore, by reasonably dividing and marking the complex equipment structure, combined with targeted modeling dimension reference values ​​and the application of correction coefficients, a basic finite element model that is both accurate and efficient can be effectively constructed.

[0039] Step 4: Obtain the coupled multi-physics field of the photovoltaic power generation generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; specifically, the following steps are included: Step 401: Obtain the coupled multi-physics field of the photovoltaic power generator from the database, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value. The specific analysis process is as follows: Step 4011, obtaining a coupled multi-physics field parameter set based on the coupled multi-physics field of the photovoltaic power generator, wherein the coupled multi-physics field parameter set is stored in a database and corresponds to the coupled multi-physics field; The coupled multiphysics parameter set includes solar radiation energy, potential gradient density, unit magnetic flux, and sound pressure level. Solar radiation energy refers to the total solar radiation energy directed to a unit area per unit time, and its value is equal to the product of solar radiation intensity and the illuminated area per unit time. Potential gradient density is used to describe the potential change of the electric field per unit length, indicating the rate at which the potential changes with space. Unit magnetic flux refers to the magnetic flux passing through a unit area. Sound pressure level is a measure of the sound intensity level of a sound wave, and its value is calculated by logarithmic comparison relative to the reference sound pressure (the reference sound pressure is 20 µPa).

[0040] Step 4012: storing the coupled multi-physics parameter set as a coupled multi-physics parameter designation tag, comparing the coupled multi-physics parameter designation tag with the coupling standard set corresponding to each coupled multi-physics parameter designation tag stored in the database, and obtaining the coupling standard set corresponding to the coupled multi-physics parameter designation tag; The coupling standard set includes thermal standard displacement, electrothermal standard temperature gradient, magnetic standard deformation, and acoustic standard vibration.

[0041] Step 402, applying coupled multi-physics fields to the basic finite element model to obtain a coupled response set; The calculation of the coupled response set can use specialized multi-physics finite element analysis software, such as ANSYS, COMSOL Multiphysics, etc. The coupled response set includes thermal response displacement (in the finite element model, the thermal response displacement can be simulated by adding thermal expansion material properties and external heat sources (such as temperature boundary conditions). The model will calculate the structural displacement and stress distribution caused by thermal expansion), electrothermal response temperature gradient (in electrothermal coupling analysis, when current passes through a conductive material, Joule heat is generated, causing the material temperature to rise. In the finite element model, this process can be simulated by defining conductivity and current density, and the resulting temperature gradient generated), magnetic response deformation (in the simulation of magnetic response deformation, it is first necessary to define the magnetic permeability and magnetization curve of the material. Then, by applying an external magnetic field and calculating the stress and deformation under the action of the magnetic force. This type of analysis usually involves magneto-mechanical coupling, considering how the magnetic properties of the material affect its mechanical properties), acoustic response vibration (acoustic response vibration can be obtained through acoustic-structural coupling analysis. In this analysis, the pressure field of the sound wave acts on the surface of the structure, causing the structure to vibrate. By setting the sound source and the corresponding boundary conditions (such as fixed boundaries and sound-absorbing boundaries), the model can calculate the structural vibration response caused by the sound wave); Step 403: Comprehensively analyze the coupling standard set and the coupling response set to obtain a coupling response value.

[0042] It should be noted that solar radiation energy, potential gradient density, unit magnetic flux, and sound pressure level are regarded as coupled multi-physics parameters and identified by the labels specified in the database. The label of each parameter will contain physical properties and unit information to facilitate effective identification and retrieval in the database. When the specified label of the coupled multi-physics parameter is entered in the database, the currently input coupled multi-physics parameter label is extracted and compared with the labels in each coupled standard set stored in the database. Once a matching label is found, the corresponding coupled standard set will be output.

[0043] It should be noted that the calculation formula for the coupling response value is as follows: , In the formula, is the coupling response value, is the thermal standard displacement, is the standard temperature gradient of electric heating, is the magnetic standard deformation, is the standard vibration of acoustic force, is the thermal response displacement, is the electrothermal response to the temperature gradient, is the magnetic response deformation, For acoustic force response vibration, is a natural constant.

[0044] It should be noted that It is used to indicate the degree of difference between the actual response effect produced by the coupled multiphysics field and the standard response effect after applying the coupled multiphysics field to the basic finite element model. The smaller it is, the greater the difference between the actual reaction effect and the standard reaction effect. Used to indicate the structural deformation displacement distance caused by thermal expansion. represents the standard deformation displacement length of the structure under thermal expansion, It is used to indicate that the temperature gradient is caused by the thermal effect (such as resistance heating) of electric current passing through the material. Used to indicate the standard temperature gradient under normal thermal effects. It represents the structural deformation caused by the force (such as electromagnetic force) generated by the magnetic field on the conductor part of the material. For the standard deformation of the structure caused by electromagnetic force, It refers to the frequency of structural vibration caused by sound waves. Used to indicate the frequency of structural vibration under normal sound waves.

[0045] Step 403: iteratively adjust the basic finite element modeling based on the coupling response value to obtain an adjusted finite element model. The specific analysis process is as follows: Obtain the coupling response restoration value set by the modeling software, and compare the coupling response value with the coupling response restoration value; if the coupling response value is greater than or equal to the coupling response restoration value, mark the current basic finite element model as the adjusted finite element model; if the coupling response value is less than the coupling response restoration value, re-obtain the basic finite element model and record it as the adjusted finite element model (the basic finite element model re-obtained in this step is a finite element model obtained by relevant modeling personnel based on the actual parameters of the photovoltaic storage generator).

[0046] Step 5: obtaining a light storage inertia characteristic parameter set based on the adjusted finite element model, and calculating a light storage inertia response value based on the light storage inertia characteristic parameter set; comprising the following steps: Step 501: obtaining a first characteristic parameter set of light-energy storage inertia and a second characteristic parameter set of light-energy storage inertia based on the adjusted finite element model; Specifically, the first characteristic parameter set of the photovoltaic storage inertia includes inertia time, charge and discharge efficiency, overshoot and voltage load balance rate; The first characteristic parameter of photovoltaic storage inertia is used to characterize the inertial response stability of photovoltaic storage generator; Among them, the inertia time represents the response ability and delay degree of the photovoltaic generator to disturbances, expressed in seconds; The charging and discharging efficiency refers to the energy efficiency that the photovoltaic power supply can maintain during the charging and discharging process; Overshoot indicates the degree to which the steady-state value is exceeded during the response process. It is used to describe the magnitude of the system response exceeding its final steady-state value. Overshoot is usually expressed as a percentage of the target value or set value. For example, in the voltage or current response, if the system steady-state target is 100V and it reaches 110V during the response process, the overshoot is 10%. The voltage load balance rate measures the voltage stability of the photovoltaic generator when the load changes.

[0047] The second characteristic parameter set of the photoelectric storage inertia includes the response adjustment frequency, the instantaneous power response, the stabilization time and the frequency offset; The second characteristic parameter set of photovoltaic storage inertia is used to characterize the inertial response efficiency of photovoltaic storage generator; Among them, the response regulation frequency indicates the frequency at which the photovoltaic storage generator can be adjusted, reflecting the extent to which the photovoltaic storage generator can quickly respond to frequency changes; the instantaneous power response is the responsiveness to instantaneous disturbances, indicating the extent to which the photovoltaic storage system can quickly provide or absorb power; the stabilization time is the time required for the photovoltaic storage generator to recover to a steady state from the start of the disturbance. The shorter the stabilization time, the higher the response efficiency; the frequency offset refers to the degree of frequency offset when the photovoltaic storage generator encounters a disturbance.

[0048] Step 502: calculating a light energy storage inertia stability evaluation value based on a first light energy storage inertia characteristic parameter set, and calculating a light energy storage inertia efficiency evaluation value based on a second light energy storage inertia characteristic parameter set; Among them, the calculation formula of the stable evaluation value of the optical storage inertia is: ; In the formula, is the stable evaluation value of the optical storage inertia, is the inertia time, is the charge and discharge efficiency, is the overshoot, is the voltage load balance rate.

[0049] It should be noted that Used to indicate the stability level of the inertial response of the photovoltaic generator. The smaller it is, the faster the photovoltaic generator can respond to interference and the stronger its anti-interference ability is. The bigger. The higher This means that the smaller the energy loss, the stronger the ability to stabilize power supply. The bigger. The smaller it is, the smaller the degree of exceeding the steady-state value is, indicating that the response of the photovoltaic storage generator is more stable. The bigger. The higher the value, the stronger the ability of the photovoltaic generator to maintain voltage stability when the load fluctuates. The bigger. The longer This may cause the battery to take more time to adjust to a new equilibrium state during rapid charge and discharge. decline. A higher system loses less energy in response to a disturbance and may reduce ,control Can help maintain voltage stability, especially when the load changes rapidly, When it is too large, it may cause voltage instability and affect , shorter Ability to adapt to load changes faster, buffer voltage fluctuations, and generally help improve .

[0050] The calculation formula of the evaluation value of the photovoltaic energy storage inertia efficiency is: , in, is the evaluation value of the photovoltaic energy storage inertia efficiency, To adjust the frequency in response, is the instantaneous power response, is the stabilization time, is the frequency offset.

[0051] It should be noted that L Used to indicate the efficiency level of the inertial response of the photovoltaic generator. P This means that the photovoltaic power generator can adapt to the fluctuation of the grid frequency more quickly, which directly improves the performance of the photovoltaic power generator under dynamic load changes, and thus L Have a positive impact, H Improve the ability to balance power supply and demand in a short period of time, and be able to quickly provide or absorb power, L Corresponding to the improvement, the shorter J This shows that the photovoltaic power generator can quickly return to normal operation, reducing the impact of frequency fluctuations on the power grid. A This shows that the photovoltaic storage system can effectively control the frequency change within the required range, and the inertial response always remains in a stable and correct range, indicating that it plays a more active role in the frequency regulation process. L Better characterization of higher efficiency levels, higher P This often means the system can adjust power output more quickly, thus improving H When the solar energy storage generator encounters a disturbance, it takes a certain amount of time to adjust its power output to return to a steady state. J The longer the A This means that the power grid may experience large frequency fluctuations during the recovery process. J Can effectively reduce A In addition, the photovoltaic generator can maintain a highP , it can adjust its output power faster, thus shortening J .

[0052] like Figure 2 As shown, the instantaneous power response is set to 2W, the stabilization time is 1s, and the frequency offset is 0.5Hz. P As the frequency changes (the range is 0-10Hz), L also increases.

[0053] Step 503: combining the photovoltaic storage inertia stability evaluation value and the photovoltaic storage inertia efficiency evaluation value, calculating the photovoltaic storage inertia response value; specifically including: Step 5031: obtaining the photovoltaic storage inertia stability evaluation value and photovoltaic storage inertia efficiency evaluation value of each power generation cycle within a plurality of power generation cycles stored in the database and performing least squares fitting to calculate the stability evaluation regression coefficient and the efficiency evaluation regression coefficient; In a specific embodiment, in the process of obtaining the stability evaluation regression coefficient and the efficiency evaluation regression coefficient by least squares fitting: Firstly, define the photovoltaic inertia stability evaluation value and photovoltaic inertia efficiency evaluation value as independent variables, and the photovoltaic inertia response value as dependent variable, collect the historical operation data of the photovoltaic inertia stability evaluation value, photovoltaic inertia efficiency evaluation value, and photovoltaic inertia response value, establish the standard regression equation of the photovoltaic inertia response value on the photovoltaic inertia stability evaluation value and photovoltaic inertia efficiency evaluation value, and obtain the regression coefficients of the photovoltaic inertia stability evaluation value and photovoltaic inertia efficiency evaluation value by fitting the model with the least square method, including the stability evaluation regression coefficient and the efficiency evaluation regression coefficient ; Based on the stable evaluation regression coefficient and the efficiency evaluation regression coefficient To perform weight normalization, first calculate the sum of the absolute values ​​of the regression coefficients. , and then perform weight distribution to obtain the stability weight of the optical storage inertia , light energy storage inertia efficiency weight .

[0054] Step 5032: performing weight allocation based on the stability evaluation regression coefficient and the efficiency evaluation regression coefficient, and calculating the light energy storage inertia stability weight and the light energy storage inertia efficiency weight; Step 5033: Obtain the photovoltaic inertia response value based on the photovoltaic inertia stability weight, the photovoltaic inertia efficiency weight, the photovoltaic inertia stability evaluation value, and the photovoltaic inertia efficiency evaluation value.

[0055] Among them, the specific calculation formula of the light storage inertia response value is: , Among them, G is the light storage inertia response value, is the light storage inertia stability weight, is the weight of the solar energy storage inertia efficiency, D is the stable evaluation value of the solar energy storage inertia, and L is the evaluation value of the solar energy storage inertia efficiency.

[0056] In a specific embodiment, G is used to evaluate the comprehensive quality level of the inertial response of the photovoltaic generator. The larger D is, the higher the inertial response stability of the photovoltaic generator is, and the higher the inertial comprehensive response quality level of the photovoltaic generator is. The larger G is, the larger L is, the higher the inertial response efficiency of the photovoltaic generator is, and the higher the inertial comprehensive response quality level of the photovoltaic generator is. The larger G is, It is used to indicate the influence of the inertial response stability of the photovoltaic power generator on the comprehensive quality level of the inertial response of the photovoltaic power generator. Used to indicate the influence of the inertial response efficiency of the photovoltaic power generation device on the comprehensive quality level of the inertial response of the photovoltaic power generation device.

[0057] Step 6: Performing layered warning on the photovoltaic storage generator based on the photovoltaic storage inertia response value, specifically including the following steps: Step 601, obtaining the light energy storage inertia warning value, light energy storage inertia stability warning value and light energy storage inertia efficiency warning value stored in the database; Step 602, compare the light storage inertia response value with the light storage inertia warning value to obtain a qualified judgment result; if the light storage inertia response value is not less than the light storage inertia warning value, the qualified judgment result is a qualified device; if the light storage inertia response value is less than the light storage inertia warning value, the qualified judgment result is a device to be optimized; Step 603, obtaining a stability evaluation value of the photovoltaic storage inertia and an efficiency evaluation value of the photovoltaic storage inertia of the device to be optimized, comparing the stability warning value of the photovoltaic storage inertia and the stability evaluation value of the photovoltaic storage inertia, the efficiency warning value of the photovoltaic storage inertia and the efficiency evaluation value of the photovoltaic storage inertia to obtain a judgment result to be optimized, wherein the judgment result to be optimized includes a stable device to be optimized and an efficient device to be optimized; Step 604: Perform stratified warning on the photovoltaic power generator based on the judgment result to be optimized.

[0058] The hierarchical warning specifically includes: if the result of the optimization is a stable device to be optimized (the stability warning value of the photovoltaic storage inertia is greater than the stability evaluation value of the photovoltaic storage inertia), an early warning of insufficient stability of the current photovoltaic storage generator is sent to the control center; if the result of the optimization is an efficient device to be optimized (the efficiency warning value of the photovoltaic storage inertia is greater than the efficiency evaluation value of the photovoltaic storage inertia), an early warning of insufficient efficiency of the current photovoltaic storage generator is sent to the control center.

[0059] It should be noted that hierarchical early warning management makes the optimization process more systematic. By obtaining each group of comparison data step by step, the performance of different aspects of the photovoltaic storage generator can be clearly identified, and the advantages and disadvantages of the equipment can be quickly and accurately identified, so that managers can make optimization decisions quickly, save time and resources, and improve the overall optimization and adjustment operation efficiency. Hierarchical management can reduce the risks caused by overall optimization, and gradually optimize under the premise of ensuring a certain level of safety, thereby reducing the potential operational risks caused by insufficient or improper optimization.

[0060] In a specific embodiment, the stability and efficiency of the inertial response are both key performance indicators. By prioritizing the optimization of the response and focusing on the weakest link, the photovoltaic power generator can first meet the basic safety and performance standards, thereby ensuring that it has greater flexibility and the ability to respond to emergencies. Concentrating resources and management attention on optimizing the weakest aspect of the response can reduce the complex changes within the photovoltaic power generator caused by simultaneous optimization and avoid interference from interactive effects. In addition, once the shortcomings of the response are successfully optimized, subsequent performance can be evaluated on this basis. If it is subsequently found that stability or efficiency is still insufficient, the optimization path will be adjusted based on the existing successful optimization experience to ensure that each step can effectively promote the improvement of the overall inertial response quality.

[0061] Embodiment 2 This embodiment provides a finite element-based photovoltaic power generation inertia response analysis system, including: A device partitioning module is used to mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain the device structure partition of each photovoltaic power generator; The finite element model module is used to calculate the modeling fractal dimension reference value of the corresponding device structure partition by combining the modeling fractal dimension attribute set and the modeling fractal dimension attribute correction coefficient set of the structure partition of the photovoltaic storage generator device; perform finite element modeling dimension judgment based on the modeling fractal dimension reference value to obtain a basic finite element model; A finite element model adjustment module is used to obtain the coupled multi-physics field of the photovoltaic power generation generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; An inertia response value calculation module, which is used to obtain a light storage inertia characteristic parameter set based on the adjusted finite element model, and calculate the light storage inertia response value based on the light storage inertia characteristic parameter set; The hierarchical optimization module is used to carry out hierarchical early warning for the photovoltaic storage generator based on the photovoltaic storage inertia response value and determine the equipment to be optimized.

[0062] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the finite element-based photovoltaic generator inertia response analysis method as described above are implemented.

[0063] Embodiment 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the finite element-based photovoltaic generator inertia response analysis method as described above are implemented.

[0064] Embodiment 5 This embodiment provides a program product, which is a computer program product, including a computer program. When the computer program is executed by a processor, the steps in the finite element-based photovoltaic generator inertia response analysis method as described above are implemented.

[0065] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0066] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0067] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0069] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0070] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The finite element-based inertia response analysis method for photovoltaic power generators is characterized by: The steps include: Mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain each device structure partition of the photovoltaic power generator; The modeling fractal dimension reference value of the corresponding device structure partition is calculated by combining the modeling fractal dimension attribute set and the modeling fractal dimension attribute correction coefficient set of the device structure partition of the photovoltaic storage generator; Based on the modeling fractal dimension reference value, the finite element modeling dimension is judged to obtain the basic finite element model; Acquire the coupled multi-physics field of the photovoltaic power generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; A light storage inertia characteristic parameter set is obtained based on the adjusted finite element model, and a light storage inertia response value is calculated based on the light storage inertia characteristic parameter set; Based on the photovoltaic storage inertia response value, the photovoltaic storage generator is given a hierarchical early warning to determine the equipment to be optimized.

2. The finite element-based inertia response analysis method for photovoltaic generators according to claim 1, characterized in that: The method of determining the finite element modeling dimension based on the modeling fractal dimension reference value to obtain the basic finite element model includes: Obtain the modeling dimension judgment value stored in the database, compare the modeling dimension judgment value of each equipment structure partition with the modeling dimension reference value corresponding to each equipment structure partition; if the modeling dimension reference value is greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a two-dimensional finite element model as the basic finite element model; if the modeling dimension reference value is not greater than the number of modeling dimension judgment values, and is greater than the modeling dimension reference value but not greater than the modeling dimension judgment value, then obtain a three-dimensional finite element model as the basic finite element model.

3. The finite element-based inertia response analysis method for photovoltaic generators according to claim 1, characterized in that: The step of obtaining the coupled multi-physics field of the photovoltaic power generator and applying the coupled multi-physics field to the basic finite element model and obtaining the coupled response value specifically includes: Acquire a coupled multi-physics field parameter set based on the coupled multi-physics field of the photovoltaic storage generator, wherein the coupled multi-physics field parameter set is stored in a database and corresponds to the coupled multi-physics field; The coupled multi-physics parameter set is stored as a coupled multi-physics parameter specified tag, and the coupled multi-physics parameter specified tag is compared with the coupled standard set corresponding to each coupled multi-physics parameter specified tag to obtain the coupled standard set corresponding to the coupled multi-physics parameter specified tag; Applying coupled multiphysics to the underlying finite element model to obtain a coupled response set; A coupling response value is obtained based on the coupling standard set and the coupling response set.

4. The finite element-based inertia response analysis method for photovoltaic generators according to claim 1, characterized in that: The iterative adjustment of the basic finite element model based on the coupling response value to obtain an adjusted finite element model includes: Get the set coupling response restoration value; Compare the coupling response value with the coupling response restoration value; If the coupling response value is greater than or equal to the coupling response restoration value, the current basic finite element model is marked as the adjusted finite element model; if the coupling response value is less than the coupling response restoration value, the basic finite element model is re-obtained and recorded as the adjusted finite element model.

5. The finite element-based inertia response analysis method for photovoltaic power generator according to claim 1, characterized in that: The step of obtaining a light storage inertia characteristic parameter set based on the adjusted finite element model, and calculating a light storage inertia response value based on the light storage inertia characteristic parameter set includes: Obtaining a first characteristic parameter set of light-storage inertia and a second characteristic parameter set of light-storage inertia based on the adjusted finite element model; Calculate the stability evaluation value of the light storage inertia based on the first characteristic parameter set of the light storage inertia, and calculate the efficiency evaluation value of the light storage inertia based on the second characteristic parameter set of the light storage inertia; The photoelectric storage inertia response value is calculated by combining the photoelectric storage inertia stability evaluation value and the photoelectric storage inertia efficiency evaluation value.

6. The finite element-based inertia response analysis method for photovoltaic generators according to claim 1, characterized in that: The light storage inertia response value is calculated by combining the light storage inertia stability evaluation value and the light storage inertia efficiency evaluation value, and specifically includes: Obtaining a stability evaluation value of the photovoltaic storage inertia and an efficiency evaluation value of the photovoltaic storage inertia of each power generation cycle within a plurality of power generation cycles, and performing least squares fitting to calculate a stability evaluation regression coefficient and an efficiency evaluation regression coefficient; Based on the stability evaluation regression coefficient and the efficiency evaluation regression coefficient, weight distribution is performed to calculate the stability weight of the photovoltaic storage inertia and the efficiency weight of the photovoltaic storage inertia; The photoelectric storage inertia response value is calculated based on the photoelectric storage inertia stability weight, the photoelectric storage inertia efficiency weight, the photoelectric storage inertia stability evaluation value and the photoelectric storage inertia efficiency evaluation value.

7. The finite element-based inertia response analysis method for photovoltaic generators according to claim 1, characterized in that: The method of performing layered early warning on the photovoltaic power generator based on the photovoltaic power generator inertia response value and determining the equipment to be optimized includes: Obtain the light energy storage inertia warning value, light energy storage inertia stability warning value and light energy storage inertia efficiency warning value stored in the database; Compare the optical storage inertia response value with the optical storage inertia warning value to obtain the qualified judgment result; if the optical storage inertia response value is not less than the optical storage inertia warning value, the qualified judgment result is a qualified device; if the optical storage inertia response value is less than the optical storage inertia warning value, the qualified judgment result is a device to be optimized; Obtain a stability evaluation value of the optical energy storage inertia and an efficiency evaluation value of the optical energy storage inertia of the device to be optimized, compare the stability warning value of the optical energy storage inertia with the stability evaluation value of the optical energy storage inertia, and the efficiency warning value of the optical energy storage inertia with the efficiency evaluation value of the optical energy storage inertia, and obtain a judgment result to be optimized, which includes a stable device to be optimized and an efficient device to be optimized.

8. The finite element-based photovoltaic generator inertia response analysis system is characterized by: include: A device partitioning module is used to mark each device structure partition of the photovoltaic power generator based on the device structure partition diagram to obtain the device structure partition of each photovoltaic power generator; A finite element model module, which is used to calculate a modeling fractal dimension reference value of a corresponding device structure partition by combining a modeling fractal dimension attribute set and a modeling fractal dimension attribute correction coefficient set of a photovoltaic storage generator device structure partition; Based on the modeling fractal dimension reference value, the finite element modeling dimension is judged to obtain the basic finite element model; A finite element model adjustment module is used to obtain the coupled multi-physics field of the photovoltaic power generation generator, apply the coupled multi-physics field to the basic finite element model and obtain the coupled response value, and iteratively adjust the basic finite element model based on the coupled response value to obtain the adjusted finite element model; An inertia response value calculation module, which is used to obtain a light storage inertia characteristic parameter set based on the adjusted finite element model, and calculate the light storage inertia response value based on the light storage inertia characteristic parameter set; The hierarchical optimization module is used to carry out hierarchical early warning for the photovoltaic storage generator based on the photovoltaic storage inertia response value and determine the equipment to be optimized.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the finite element-based inertia response analysis method for a photovoltaic power generator as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the finite element-based inertia response analysis method for a photovoltaic power generator as described in any one of claims 1 to 7 are implemented.

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