A photovoltaic grid-connected power generation control method and related device based on cloud platform

Abstract

The present invention discloses a photovoltaic grid-connected power generation control method and related devices based on a cloud platform, and relates to the field of photovoltaic power generation technology. The method includes: photovoltaic steady-state power generation analysis based on real-time environmental data; line operation status analysis of the power grid based on a power grid flow model; node matching of the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result to establish its grid connection; collecting real-time grid-connected power generation data and transmitting it to the cloud platform; performing resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data to generate a resonance modulation scheme; generating a fundamental voltage expected value based on the real-time grid-connected power generation data using the Jacobian matrix; adjusting the parameters of the modular inverter based on the resonance modulation scheme, and switching the modules of the modular inverter based on the fundamental voltage expected value. The present invention improves the reliability of photovoltaic grid-connected power generation, and enables photovoltaic grid-connected power generation control to achieve a more ideal effect.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic grid-connected power generation control method and related devices based on a cloud platform. Background Art

[0002] With the concept of sustainable development, solar photovoltaic power generation has been widely used as a clean and renewable energy source. In this regard, the grid connection of photovoltaic power generation has become a research focus. In the current photovoltaic grid-connected power generation control, the steady-state impact of environmental data on photovoltaic power generation is rarely considered, resulting in photovoltaic power generation being greatly affected by environmental conditions, making photovoltaic power generation intermittent and unstable. For the grid connection of photovoltaic power generation systems, the line operation status of the power grid is usually analyzed. However, the current line operation status analysis of the power grid usually only considers the node load of the power grid, but does not consider the node flow of the power grid, resulting in an incomplete analysis of the line operation status of the power grid, which cannot accurately reflect the state of the power grid, and then leads to low reliability of the matched power generation nodes, affecting the stability of photovoltaic grid-connected power generation. After the grid connection of the photovoltaic power generation system is established, it is necessary to collect power generation data in real time for analysis, and resonance analysis is a very important part of it. The current resonance analysis is mainly carried out through system modeling, but this method has the problems of low analysis efficiency and low accuracy of resonance analysis results. At the same time, the current photovoltaic power generation grid-connected control does not take into account the impact of the expected value of the fundamental voltage on the regulation of the inverter in the photovoltaic grid-connected power generation. The lack of analysis of the expected value of the fundamental voltage makes the regulation reliability of the inverter insufficient, and the power quality cannot be effectively improved, resulting in the photovoltaic grid-connected power generation control failing to achieve the expected effect. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a photovoltaic grid-connected power generation control method and related devices based on a cloud platform to improve the reliability of photovoltaic grid-connected power generation and achieve a more ideal effect of photovoltaic grid-connected power generation control.

[0004] In order to solve the above technical problems, the present invention provides a photovoltaic grid-connected power generation control method based on a cloud platform, the method comprising:

[0005] Acquire real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results;

[0006] Constructing a power grid flow model, and performing line operation status analysis of the power grid based on the power grid flow model to obtain line operation status analysis results;

[0007] Based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result, the photovoltaic power generation system is node matched to obtain the target power generation node, and the photovoltaic power generation system is connected to the grid based on the target power generation node;

[0008] After the photovoltaic power generation system is connected to the grid, real-time grid-connected power generation data is collected based on the data acquisition device, and the real-time grid-connected power generation data is transmitted to the cloud platform;

[0009] The cloud platform performs resonance analysis using the real-time grid-connected power generation data based on the state space matrix and the admittance matrix to obtain a resonance analysis result, and generates a resonance modulation scheme based on the resonance analysis result;

[0010] Generate a fundamental voltage expected value using a Jacobian matrix based on the real-time grid-connected power generation data;

[0011] The cloud platform generates a resonant modulation control instruction based on the resonant modulation scheme, adjusts parameters of the modular inverter based on the resonant modulation control instruction, generates a module switching instruction based on the fundamental voltage expected value, and switches modules of the modular inverter based on the module switching instruction.

[0012] Optionally, performing photovoltaic steady-state power generation analysis based on the real-time environmental data to obtain photovoltaic steady-state power generation analysis results includes:

[0013] Acquiring a photovoltaic discharge impact record data set, and performing numerical processing on the photovoltaic discharge impact record data set to obtain a numerical photovoltaic discharge impact record data set;

[0014] Constructing a characteristic matrix based on the digitized photovoltaic discharge impact record data set, and acquiring a light source timing characteristic vector and a temperature timing characteristic vector based on the characteristic matrix;

[0015] Generate a number of photovoltaic power generation environmental impact factors based on the light source time series characteristic vector and the temperature time series characteristic vector, and evaluate the number of photovoltaic power generation environmental impact factors to obtain corresponding photovoltaic power generation environmental impact characteristic values;

[0016] A steady-state analysis network is constructed based on several photovoltaic power generation environmental impact factors and corresponding photovoltaic power generation environmental impact characteristic values;

[0017] Based on the steady-state analysis network, the real-time environmental data is used to predict light source changes and temperature rise degrees, and light source change prediction information and temperature rise degree prediction information are obtained. Based on the light source change prediction information and temperature rise degree prediction information, photovoltaic steady-state power generation analysis is performed to obtain photovoltaic steady-state power generation analysis results.

[0018] Optionally, constructing a power grid flow model, performing a line operation status analysis of a power grid based on the power grid flow model, and obtaining a line operation status analysis result includes:

[0019] Acquire a target power grid architecture, generate a power flow topology structure based on the target power grid architecture, and construct a power grid flow model based on the power flow topology structure;

[0020] Based on the power grid flow model, a node load matrix is ​​generated using the total number of power grid nodes and the node loads, and based on the power grid flow model, a node flow matrix is ​​generated using the line flow;

[0021] A grid element timing conversion diagram is obtained, and based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to perform line operation status analysis of the grid to obtain a line operation status analysis result.

[0022] Optionally, performing node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result to obtain a target power generation node includes:

[0023] Generate a node planning model based on the line operation status analysis results and the photovoltaic steady-state power generation analysis results;

[0024] Generate a node planning scheme using the node planning model based on an optimization algorithm, generate a corresponding game tree based on the node planning scheme, and determine a corresponding optimization path based on the game tree;

[0025] Constructing an evaluation fitness function, and performing optimization evaluation of the node planning scheme for shunt charging and grid connection based on the evaluation fitness function, to obtain optimization evaluation results for shunt charging and grid connection;

[0026] The node planning scheme is optimized based on the optimization path, the optimization evaluation results of the shunt charging and the optimization evaluation results of the grid connection to obtain an optimized node planning scheme, and the photovoltaic power generation system is node matched based on the optimized node planning scheme to obtain a target power generation node.

[0027] Optionally, performing resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data to obtain a resonance analysis result includes:

[0028] Obtaining the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter;

[0029] Constructing a state space matrix and an admittance matrix based on the operation link parameters of the grid-connected connection of the photovoltaic power generation system and the operation parameters of the modular inverter;

[0030] Calculating a left eigenvector and a right eigenvector of the state space matrix, and obtaining a resonance participation factor based on the left eigenvector and the right eigenvector;

[0031] Calculating the eigenvalues ​​and eigenvectors of the admittance matrix, and generating a modal impedance curve based on the eigenvalues ​​and eigenvectors of the admittance matrix;

[0032] A resonance analysis is performed using the real-time grid-connected power generation data based on the resonance participation factor and the modal impedance curve to obtain a resonance analysis result.

[0033] Optionally, the generating the fundamental voltage expected value by using the Jacobian matrix based on the real-time grid-connected power generation data includes:

[0034] Calculating a Jacobian matrix of a photovoltaic power generation system, and calculating a gain matrix spectrum based on the Jacobian matrix;

[0035] Calculating the shaft current component based on the real-time grid-connected power generation data, calculating a first difference between the shaft current component and an active current reference value, and calculating a second difference between the shaft current component and a reactive current reference value;

[0036] The fundamental voltage expected value is determined using the gain matrix spectrum based on the first difference and the second difference.

[0037] Optionally, the adjusting parameters of the modular inverter based on the resonance modulation control instruction includes:

[0038] The virtual resistance, equivalent virtual inductance and virtual capacitance of the modular inverter are adjusted based on the resonance modulation control instruction.

[0039] In addition, the present invention also provides a photovoltaic grid-connected power generation control device based on a cloud platform, the device comprising:

[0040] Photovoltaic steady-state power generation analysis module: used to obtain real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results;

[0041] Line operation status analysis module: used to construct a power grid flow model, perform line operation status analysis of the power grid based on the power grid flow model, and obtain line operation status analysis results;

[0042] Node matching module: used for performing node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result, obtaining the target power generation node, and establishing the grid connection of the photovoltaic power generation system based on the target power generation node;

[0043] Data collection and transmission module: used to collect real-time grid-connected power generation data based on data collection equipment after the grid-connected connection of the photovoltaic power generation system is established, and transmit the real-time grid-connected power generation data to the cloud platform;

[0044] Resonance analysis module: used for the cloud platform to perform resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data, obtain resonance analysis results, and generate a resonance modulation scheme based on the resonance analysis results;

[0045] A fundamental voltage expected value generation module is used to generate a fundamental voltage expected value based on the real-time grid-connected power generation data using a Jacobian matrix;

[0046] Modular inverter adjustment module: used for the cloud platform to generate a resonant modulation control instruction based on the resonant modulation scheme, adjust the parameters of the modular inverter based on the resonant modulation control instruction, and generate a module switching instruction based on the fundamental voltage expected value, and switch the modules of the modular inverter based on the module switching instruction.

[0047] In addition, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned cloud platform-based photovoltaic grid-connected power generation control method.

[0048] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned cloud platform-based photovoltaic grid-connected power generation control method.

[0049] In an embodiment of the present invention, based on the steady-state analysis network, real-time environmental data is used to predict light source changes and temperature rise to perform photovoltaic steady-state power generation analysis, and the light source changes and temperature changes of the environment are added to the photovoltaic steady-state power generation analysis, which greatly reduces the impact of environmental conditions on photovoltaic power generation and improves the stability of photovoltaic power generation. Based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to analyze the line operation state of the power grid, which can more comprehensively and accurately analyze the line operation state of the power grid and more accurately reflect the state of the power grid. Based on the optimized node planning scheme generated by the optimization path, the optimal evaluation results of the shunt charging and the optimal evaluation results of the grid connection, the photovoltaic power generation system is node matched, the accuracy of the power generation node matching is improved, and the grid connection of the established photovoltaic power generation system is more reliable. The cloud platform uses real-time grid-connected power generation data to perform resonance analysis based on the resonance participation factor generated by the state space matrix and the modal impedance curve generated by the admittance matrix, which improves the efficiency of the resonance analysis and the accuracy of the resonance analysis, so that the resonance analysis results are closer to the actual situation, and thus the parameters of the modular inverter can be better adjusted to further improve the stability of photovoltaic power generation. Based on the shaft current component calculated from the real-time grid-connected power generation data, the gain matrix spectrum is used to determine the expected value of the fundamental voltage. The modules of the modular inverter are switched according to the expected value of the fundamental voltage. The power quality can be effectively improved so that its output can meet the power quality requirements of the power grid, thereby achieving a more ideal effect of photovoltaic grid-connected power generation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a flow chart of a photovoltaic grid-connected power generation control method based on a cloud platform in an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of the structure of a photovoltaic grid-connected power generation control device based on a cloud platform in an embodiment of the present invention;

[0053] Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1

[0056] See also Figure 1 , Figure 1 : is a flow chart of a photovoltaic grid-connected power generation control method based on a cloud platform in an embodiment of the present invention, the method comprising:

[0057] S11: Acquire real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results;

[0058] In the specific implementation process of the present invention, the photovoltaic steady-state power generation analysis is performed based on the real-time environmental data to obtain the photovoltaic steady-state power generation analysis result, including: obtaining a photovoltaic discharge impact record data set, and digitizing the photovoltaic discharge impact record data set to obtain a digitized photovoltaic discharge impact record data set; constructing a feature matrix based on the digitized photovoltaic discharge impact record data set, and obtaining a light source timing feature vector and a temperature timing feature vector based on the feature matrix; generating a number of photovoltaic power generation environmental impact factors based on the light source timing feature vector and the temperature timing feature vector, and evaluating a number of photovoltaic power generation environmental impact factors to obtain corresponding photovoltaic power generation environmental impact characteristic values; constructing a steady-state analysis network based on a number of photovoltaic power generation environmental impact factors and corresponding photovoltaic power generation environmental impact characteristic values; based on the steady-state analysis network, using the real-time environmental data to perform light source change prediction and temperature rise degree prediction, obtain light source change prediction information and temperature rise degree prediction information, and performing photovoltaic steady-state power generation analysis based on the light source change prediction information and temperature rise degree prediction information to obtain photovoltaic steady-state power generation analysis results.

[0059] Specifically, real-time environmental data is obtained through a combination of environmental sensors, and the real-time environmental data includes temperature data and light source data, and the light source data includes data such as illumination angle and illumination intensity. A photovoltaic discharge impact record data set is obtained from a database, and the photovoltaic discharge impact record data set is a record of changes in the impact of temperature data and light source data at different time points on photovoltaic grid-connected discharge; the photovoltaic discharge impact record data set is numerically processed, and before data analysis, non-numerical data in the data set needs to be converted into numerical data for subsequent analysis and processing, that is, numerical processing, and label coding can be used to convert the category data in the photovoltaic discharge impact record data set into continuous integer coding, thereby realizing numerical conversion in the data set and obtaining a numerical photovoltaic discharge impact record data set. Based on the digitized photovoltaic discharge impact record data set, a feature matrix is ​​constructed, and a feature data set is obtained by using a feature extraction algorithm through the digitized photovoltaic discharge impact record data set. Each feature data in the feature data set is decentralized to obtain a number of decentralized feature data, and a feature average value is calculated for the several decentralized feature data. A new feature data set is constructed according to the feature average value, and a covariance matrix is ​​calculated according to the new feature data set. The eigenvalues ​​and eigenvectors of the covariance matrix are calculated, and a feature data matrix is ​​constructed according to the eigenvalues ​​and eigenvectors of the covariance matrix, which is a feature matrix. Based on the feature matrix, a light source timing feature vector and a temperature timing feature vector are obtained. The elements in the feature matrix include the light source timing feature vector and the temperature timing feature vector. By analyzing the elements of the feature matrix, the light source timing feature vector and the temperature timing feature vector can be obtained. Based on the light source timing feature vector and the temperature timing feature vector, several photovoltaic power generation environmental impact factors are generated, based on the light source timing feature vector, a light source-power generation interaction feature vector is generated, based on the temperature timing feature vector, a temperature-power generation interaction feature vector is generated, and based on the light source-power generation interaction feature vector and the temperature-power generation interaction feature vector, several photovoltaic power generation environmental impact factors are generated by using the feature interaction of the attention mechanism, and the photovoltaic power generation environmental impact factors include light source impact factors and temperature impact factors. Several photovoltaic power generation environmental impact factors are evaluated, and the evaluation coefficient of photovoltaic power generation environmental impact is used to obtain the corresponding photovoltaic power generation environmental impact characteristic value. A steady-state analysis network is constructed based on several photovoltaic power generation environmental impact factors and the corresponding photovoltaic power generation environmental impact characteristic values, and several photovoltaic power generation environmental impact factors are used as nodes, and the corresponding photovoltaic power generation environmental impact characteristic values ​​are used as the strength between nodes, and a steady-state analysis network is constructed through the connection of nodes. Based on the steady-state analysis network, the real-time environmental data is used to predict the light source change and the temperature rise degree, and the real-time environmental data is input into the steady-state analysis network. The steady-state analysis network contains light source change information and temperature rise change information at different time points, and the light source change prediction information and the temperature rise degree prediction information can be obtained.Photovoltaic steady-state power generation analysis is performed based on the light source change prediction information and the temperature rise prediction information, that is, the steady-state power generation power at different time points is predicted according to the light source change prediction information and the temperature rise prediction information. Light source changes will affect the quality of photovoltaic power generation, and temperature changes will affect the output power of photovoltaic power supply equipment. Under the same irradiation conditions, the output power will decrease with the increase of ambient temperature. Therefore, the photovoltaic steady-state power generation analysis results obtained by performing photovoltaic steady-state power generation analysis based on the light source change prediction information and the temperature rise prediction information can more comprehensively reflect the impact of environmental information on photovoltaic power generation.

[0060] S12: constructing a power grid flow model, and performing line operation status analysis of the power grid based on the power grid flow model to obtain line operation status analysis results;

[0061] In the specific implementation process of the present invention, the construction of the power grid flow model, the line operation status analysis of the power grid based on the power grid flow model, and the acquisition of the line operation status analysis results include: obtaining the target power grid architecture, generating a power flow topology structure based on the target power grid architecture, and constructing the power grid flow model based on the power flow topology structure; generating a node load matrix based on the power grid flow model using the total number of power grid nodes and node loads, and generating a node flow matrix based on the power grid flow model using the line flow; obtaining a power grid element timing conversion diagram, and performing the line operation status analysis of the power grid using the node load matrix and the node flow matrix based on the power grid element timing conversion diagram to obtain the line operation status analysis results.

[0062] Specifically, the target power grid architecture is obtained from the database. The target power grid architecture may include the operation mode of the high and medium voltage side busbars, the high and medium voltage side grid-connected power supply, and the high and medium voltage side line flow, etc. A power grid model is generated according to the target power grid architecture, and the power grid model is simplified into a power flow topology structure. A power grid flow model is constructed based on the power flow topology structure, and the operation nodes are determined according to the power flow topology structure. A power grid flow model is constructed according to the operation nodes and the power flow topology structure. A node load matrix is ​​generated based on the power grid flow model using the total number of power grid nodes and the node load, and the load mounting is determined according to the power grid flow model using the total number of power grid nodes and the node load. Matrix elements are formed according to the load mounting and the node load, and a node load matrix is ​​generated according to the matrix elements. A node flow matrix is ​​generated based on the power grid flow model using the line flow, and the active power flow and the flow direction of the active power flow are obtained using the line flow according to the power grid flow model. The matrix nodes are determined according to the active power flow, and the branches between the nodes are determined according to the flow direction of the active power flow. The node flow matrix is ​​generated according to the matrix nodes and the branches between the nodes. The node flow matrix and the node load matrix can more comprehensively and specifically reflect the power grid state. Obtain a grid element timing conversion diagram, set the initial state of each element in the grid, the initial state is a normal state, obtain the continuous operation time of each element in the initial state and the duration of each conversion process, analyze the continuous operation time of each element in the initial state and the duration of each conversion process, obtain the element state change process, arrange and combine the element state change process of each element in a preset order, obtain a grid element timing conversion diagram, and the grid element timing conversion diagram can reflect the timing change of the grid element state. Based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to analyze the line operation state of the grid, and the node power flow matrix and the node power diversion coefficient matrix are generated according to the node load matrix and the node flow matrix. According to the node power flow matrix and the node power diversion coefficient matrix combined with the grid element timing conversion diagram, the Bayesian model is used to perform photovoltaic access and line probability flow analysis of grid state uncertainty, thereby obtaining the line operation state analysis result.

[0063] S13: performing node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result to obtain a target power generation node, and establishing a grid-connected connection of the photovoltaic power generation system based on the target power generation node;

[0064] In the specific implementation process of the present invention, the node matching of the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result to obtain the target power generation node includes: generating a node planning model based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result; generating a node planning scheme based on the optimization algorithm using the node planning model, generating a corresponding game tree based on the node planning scheme, and determining the corresponding optimization path based on the game tree; constructing an evaluation fitness function, and performing optimization evaluation of shunt charging and grid connection on the node planning scheme based on the evaluation fitness function to obtain the optimization evaluation result of shunt charging and the optimization evaluation result of grid connection; optimizing the node planning scheme based on the optimization path, the optimization evaluation result of shunt charging and the optimization evaluation result of grid connection to obtain the optimized node planning scheme, and performing node matching of the photovoltaic power generation system based on the optimized node planning scheme to obtain the target power generation node.

[0065] Specifically, a node planning model is generated based on the line operation status analysis results and the photovoltaic steady-state power generation analysis results, node constraints are generated based on the line operation status analysis results and the photovoltaic steady-state power generation analysis results, and a node planning model is generated based on the node constraints. A node planning scheme is generated using the node planning model based on an optimization algorithm. The optimization algorithm can use a particle swarm algorithm to perform optimization processing on the particle group generated by the node planning model until a preset number of iterations is reached to obtain the final target solution, thereby obtaining a node planning scheme. A corresponding game tree is generated based on the node planning scheme, with the node planning scheme as the root node, and a number of child nodes corresponding to the root node are generated. The target child node is determined by the prior probability and evaluation value of each child node, and the target child node is used as the parent node. New child nodes are repeatedly generated through the parent node, and new target child nodes are also determined by the prior probability and evaluation value of the new child nodes. The corresponding game path is obtained by connecting the new target child node and the root node, and the importance value of the game path is calculated. Iterative expansion is performed through the importance value of the game path to obtain the corresponding game tree, and the corresponding optimization path is determined based on the game tree. The importance value of the child node corresponding to the root node in the game tree is calculated, and the child node with the largest importance value is used as the optimized child node. The importance value of the child node corresponding to the optimized child node is calculated, and the child node with the largest importance value is used as the new optimized child node. The above process is repeated until the leaf node of the game tree is reached, and the root node is connected with all the optimized child nodes in sequence to obtain the optimized path. Construct an evaluation fitness function, and perform an optimization evaluation of the node planning scheme for shunt charging and grid connection based on the evaluation fitness function, that is, perform a steady-state evaluation of shunt charging and grid connection on the node planning scheme according to the evaluation fitness function, and obtain steady-state evaluation feedback data of shunt charging and grid connection, that is, obtain the optimization evaluation results of shunt charging and the optimization evaluation results of grid connection. Optimize the node planning scheme based on the optimization path, the optimization evaluation results of shunt charging, and the optimization evaluation results of grid connection, generate scheme improvement point data based on the optimization path, the optimization evaluation results of shunt charging, and the optimization evaluation results of grid connection, optimize the scheme based on the scheme improvement point data, and obtain an optimized node planning scheme. The optimized node planning scheme includes the time node of grid connection and the line node of the photovoltaic power generation system connected to the power grid. Perform node matching on the photovoltaic power generation system based on the optimized node planning scheme, match the required access time node and line node, that is, obtain the target power generation node, and establish the grid connection of the photovoltaic power generation system based on the target power generation node.

[0066] S14: after establishing the grid connection of the photovoltaic power generation system, collecting real-time grid-connected power generation data based on the data acquisition device, and transmitting the real-time grid-connected power generation data to the cloud platform;

[0067] In the specific implementation process of the present invention, after the grid-connected connection of the photovoltaic power generation system is established, real-time grid-connected power generation data is collected based on the data acquisition equipment. The data acquisition equipment is a sensor combination. The real-time grid-connected power generation data includes the output voltage and current of the photovoltaic power generation system, etc. The real-time grid-connected power generation data is transmitted to the cloud platform through the corresponding data transmission channel.

[0068] S15: the cloud platform performs resonance analysis using the real-time grid-connected power generation data based on the state space matrix and the admittance matrix to obtain a resonance analysis result, and generates a resonance modulation scheme based on the resonance analysis result;

[0069] In the specific implementation process of the present invention, the resonance analysis is performed using the real-time grid-connected power generation data based on the state space matrix and the admittance matrix to obtain the resonance analysis result, including: obtaining the grid-connected connection operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter; constructing the state space matrix and the admittance matrix based on the grid-connected connection operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter; calculating the left eigenvector and the right eigenvector of the state space matrix, and obtaining the resonance participation factor based on the left eigenvector and the right eigenvector; calculating the eigenvalues ​​and eigenvectors of the admittance matrix, and generating a modal impedance curve based on the eigenvalues ​​and eigenvectors of the admittance matrix; performing the resonance analysis using the real-time grid-connected power generation data based on the resonance participation factor and the modal impedance curve to obtain the resonance analysis result.

[0070] Specifically, the cloud platform receives real-time grid-connected power generation data, and obtains the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter. The modular inverter is used to convert the DC power generated by the solar photovoltaic system into AC power so as to feed the grid or supply it to the power load. The grid-connected operation link parameters include cascade operation link parameters and parallel operation link parameters, etc. The operation parameters of the modular inverter include the number of modules and distribution positions, etc. Based on the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter, a state space matrix and an admittance matrix are constructed, and a coefficient matrix is ​​generated using a state space strategy according to the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter, and a state space matrix is ​​constructed according to the coefficient matrix; an aggregate equivalent impedance model is generated according to the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter, and a reduced-order admittance matrix is ​​generated according to the aggregate equivalent impedance model, and the relationship between the node aggregate admittance and the frequency is obtained, and an admittance matrix is ​​generated according to the reduced-order admittance matrix combined with the relationship between the node aggregate admittance and the frequency. Calculate the left eigenvector and right eigenvector of the state space matrix, calculate the eigenroot in the state space matrix, determine the left eigenvector and right eigenvector according to the eigenroot, obtain the resonance participation factor based on the left eigenvector and the right eigenvector, combine the left eigenvector and the right eigenvector to obtain the participation matrix, obtain the resonance participation factor according to the participation matrix, and the elements in the participation matrix are the resonance participation factors. Calculate the eigenvalues ​​and eigenvectors of the admittance matrix, perform eigendecomposition on the admittance matrix to obtain the diagonal eigenmatrix, calculate the left eigenvector matrix and the right eigenvector matrix of the diagonal eigenmatrix, calculate the modal impedance eigenvalue according to the diagonal eigenmatrix, the left eigenvector matrix and the right eigenvector matrix, which are the eigenvalues ​​of the admittance matrix, perform eigendecomposition on the admittance matrix based on the modal analysis method, obtain the eigenvector of the admittance matrix, generate the modal impedance curve based on the eigenvalues ​​and eigenvectors of the admittance matrix, and draw the modal impedance curve according to the eigenvectors and modal impedance eigenvalues ​​of the admittance matrix. Based on the resonance participation factor and the modal impedance curve, the real-time grid-connected power generation data is used to perform resonance analysis, and the resonance influence law analysis is performed based on the real-time grid-connected power generation data using the resonance participation factor. The resonance center point and the corresponding resonance sensitivity are determined based on the real-time grid-connected power generation data using the modal impedance curve, and the resonance analysis result is obtained. Based on the resonance analysis result, a resonance modulation scheme is generated, that is, the corresponding adjustment parameters are determined according to the analyzed resonance influence law, resonance center and resonance sensitivity, and a resonance modulation scheme can be generated.

[0071] S16: Generate a fundamental voltage expected value using a Jacobian matrix based on the real-time grid-connected power generation data;

[0072] In the specific implementation process of the present invention, the fundamental voltage expectation value is generated based on the real-time grid-connected power generation data using the Jacobian matrix, including: calculating the Jacobian matrix of the photovoltaic power generation system, and calculating the gain matrix spectrum based on the Jacobian matrix; calculating the shaft current component based on the real-time grid-connected power generation data, and calculating the first difference between the shaft current component and the active current reference value, and calculating the second difference between the shaft current component and the reactive current reference value; based on the first difference and the second difference, the fundamental voltage expectation value is determined using the gain matrix spectrum.

[0073] Specifically, the Jacobian matrix of the photovoltaic power generation system is calculated, the dynamic equations of the components, the network equations and the interface equations between the components and the network of the photovoltaic power generation system are calculated, the partial derivatives of the dynamic equations of the components, the network equations and the interface equations between the components and the network are calculated, and the Jacobian matrix is ​​calculated using the partial derivatives of the dynamic equations of the components, the network equations and the interface equations between the components and the network according to the chain derivation method. The gain matrix spectrum is calculated based on the Jacobian matrix, the Jacobian matrix is ​​decomposed, the gain function is calculated using the decomposed Jacobian matrix according to the preset static input-output characteristics, the matrix spectrum of the gain function is calculated, and the gain matrix spectrum is obtained. The shaft current component is calculated based on the real-time grid-connected power generation data, and the AC data in the real-time grid-connected power generation data is converted into an electrical quantity in a two-phase rotating coordinate system using the Park transformation to obtain the shaft current component. The first difference between the shaft current component and the active current reference value is calculated, and the second difference between the shaft current component and the reactive current reference value is calculated. The active current reference value and the reactive current reference value are pre-stored in the database. Based on the first difference and the second difference, the gain matrix spectrum is used to determine the expected value of the fundamental voltage, the radius of the gain matrix spectrum is obtained, proportional integral adjustment is performed according to the first difference and the second difference to obtain a correction amount, a coupling operation is performed according to the correction amount using the radius of the gain matrix spectrum to obtain a coupling operation result, the coupling operation result is subjected to coordinate transformation to obtain the expected value of the fundamental voltage.

[0074] S17: The cloud platform generates a resonant modulation control instruction based on the resonant modulation scheme, adjusts parameters of the modular inverter based on the resonant modulation control instruction, generates a module switching instruction based on the fundamental voltage expected value, and switches modules of the modular inverter based on the module switching instruction.

[0075] In a specific implementation of the present invention, the parameter adjustment of the modular inverter based on the resonant modulation control instruction includes: adjusting the virtual resistance, equivalent virtual inductance and virtual capacitance of the modular inverter based on the resonant modulation control instruction.

[0076] Specifically, the cloud platform generates a resonant modulation control instruction based on the resonant modulation scheme, transmits the resonant modulation control instruction to the modular inverter, and adjusts the parameters of the modular inverter based on the resonant modulation control instruction, that is, adjusts the virtual resistance, equivalent virtual inductance and virtual capacitance of the modular inverter. The adjustment of the virtual resistance, equivalent virtual inductance and virtual capacitance can enhance the inverter body damping, suppress the parallel resonance of the modular inverter, and improve stability. Generate a module switching instruction based on the fundamental voltage expected value, and switch the module of the modular inverter based on the module switching instruction. The modular inverter includes several submodules, each of which includes an insulated gate bipolar transistor, a reverse diode and a DC capacitor. The capacitor voltage value of each submodule is calculated, and the number of submodules in the modular inverter is determined according to the fundamental voltage expected value combined with the capacitor voltage value. The switching of the submodules of the modular inverter can improve the solar energy utilization rate of the photovoltaic power generation system, so that its output can meet the power quality requirements of the power grid, thereby achieving a more ideal effect of photovoltaic grid-connected power generation control.

[0077] In an embodiment of the present invention, based on the steady-state analysis network, real-time environmental data is used to predict light source changes and temperature rise to perform photovoltaic steady-state power generation analysis, and the light source changes and temperature changes of the environment are added to the photovoltaic steady-state power generation analysis, which greatly reduces the impact of environmental conditions on photovoltaic power generation and improves the stability of photovoltaic power generation. Based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to analyze the line operation state of the power grid, which can more comprehensively and accurately analyze the line operation state of the power grid and more accurately reflect the state of the power grid. Based on the optimized node planning scheme generated by the optimization path, the optimal evaluation results of the shunt charging and the optimal evaluation results of the grid connection, the photovoltaic power generation system is node matched, the accuracy of the power generation node matching is improved, and the grid connection of the established photovoltaic power generation system is more reliable. The cloud platform uses real-time grid-connected power generation data to perform resonance analysis based on the resonance participation factor generated by the state space matrix and the modal impedance curve generated by the admittance matrix, which improves the efficiency of the resonance analysis and the accuracy of the resonance analysis, so that the resonance analysis results are closer to the actual situation, and thus the parameters of the modular inverter can be better adjusted to further improve the stability of photovoltaic power generation. Based on the shaft current component calculated from the real-time grid-connected power generation data, the gain matrix spectrum is used to determine the expected value of the fundamental voltage. The modules of the modular inverter are switched according to the expected value of the fundamental voltage. The power quality can be effectively improved so that its output can meet the power quality requirements of the power grid, thereby achieving a more ideal effect of photovoltaic grid-connected power generation control.

[0078] Embodiment 2

[0079] See also Figure 2 , Figure 2: is a schematic diagram of the structure of a photovoltaic grid-connected power generation control device based on a cloud platform in an embodiment of the present invention, the device comprises:

[0080] Photovoltaic steady-state power generation analysis module 21: used to obtain real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results;

[0081] The line operation status analysis module 22 is used to construct a power grid flow model, perform line operation status analysis of the power grid based on the power grid flow model, and obtain line operation status analysis results;

[0082] Node matching module 23: used to perform node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result, obtain the target power generation node, and establish the grid connection of the photovoltaic power generation system based on the target power generation node;

[0083] The data collection and transmission module 24 is used to collect real-time grid-connected power generation data based on the data collection device after the grid-connected connection of the photovoltaic power generation system is established, and transmit the real-time grid-connected power generation data to the cloud platform;

[0084] Resonance analysis module 25: used for the cloud platform to perform resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data, obtain resonance analysis results, and generate a resonance modulation scheme based on the resonance analysis results;

[0085] A fundamental voltage expected value generating module 26 is used to generate a fundamental voltage expected value based on the real-time grid-connected power generation data using a Jacobian matrix;

[0086] Modular inverter adjustment module 27: used for the cloud platform to generate a resonant modulation control instruction based on the resonant modulation scheme, adjust the parameters of the modular inverter based on the resonant modulation control instruction, generate a module switching instruction based on the fundamental voltage expected value, and switch the modules of the modular inverter based on the module switching instruction.

[0087] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0088] In an embodiment of the present invention, based on the steady-state analysis network, real-time environmental data is used to predict light source changes and temperature rise to perform photovoltaic steady-state power generation analysis, and the light source changes and temperature changes of the environment are added to the photovoltaic steady-state power generation analysis, which greatly reduces the impact of environmental conditions on photovoltaic power generation and improves the stability of photovoltaic power generation. Based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to analyze the line operation state of the power grid, which can more comprehensively and accurately analyze the line operation state of the power grid and more accurately reflect the state of the power grid. Based on the optimized node planning scheme generated by the optimization path, the optimal evaluation results of the shunt charging and the optimal evaluation results of the grid connection, the photovoltaic power generation system is node matched, the accuracy of the power generation node matching is improved, and the grid connection of the established photovoltaic power generation system is more reliable. The cloud platform uses real-time grid-connected power generation data to perform resonance analysis based on the resonance participation factor generated by the state space matrix and the modal impedance curve generated by the admittance matrix, which improves the efficiency of the resonance analysis and the accuracy of the resonance analysis, so that the resonance analysis results are closer to the actual situation, and thus the parameters of the modular inverter can be better adjusted to further improve the stability of photovoltaic power generation. Based on the shaft current component calculated from the real-time grid-connected power generation data, the gain matrix spectrum is used to determine the expected value of the fundamental voltage. The modules of the modular inverter are switched according to the expected value of the fundamental voltage. The power quality can be effectively improved so that its output can meet the power quality requirements of the power grid, thereby achieving a more ideal effect of photovoltaic grid-connected power generation control.

[0089] A computer-readable storage medium is provided in an embodiment of the present invention, wherein a computer program is stored on the computer-readable storage medium, and when the program is executed by a processor, a photovoltaic grid-connected power generation control method based on a cloud platform in any of the above embodiments is implemented. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0090] Embodiment 3

[0091] See also Figure 3 , Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0092] The embodiment of the present invention further provides an electronic device, such as Figure 3 As shown, the electronic device includes a memory 31, a processor 33, and a computer program 32 stored in the memory 31 and executable on the processor 33. Those skilled in the art will appreciate that Figure 3 The electronic device shown does not constitute a limitation on all devices, and may include more or fewer components than shown, or combine certain components. The memory 31 can be used to store the computer program 32 and various functional modules, and the processor 33 runs the computer program 32 stored in the memory 31, thereby executing various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include both internal and external memories. The internal memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, or a random access memory. The external memory can include a hard disk, a floppy disk, a ZIP disk, a U disk, a magnetic tape, etc. The processor 33 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general processor may be a microprocessor, a single chip microcomputer or the processor 33 may also be any conventional processor, etc. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0093] As an embodiment, the electronic device includes: one or more processors 33, a memory 31, and one or more computer programs 32, wherein the one or more computer programs 32 are stored in the memory 31 and are configured to be executed by the one or more processors 33, and the one or more computer programs 32 are configured to execute the cloud platform-based photovoltaic grid-connected power generation control method in any of the above-mentioned embodiments. For the specific implementation process, please refer to the above-mentioned embodiments and will not be repeated here.

[0094] In an embodiment of the present invention, based on the steady-state analysis network, real-time environmental data is used to predict light source changes and temperature rise to perform photovoltaic steady-state power generation analysis, and the light source changes and temperature changes of the environment are added to the photovoltaic steady-state power generation analysis, which greatly reduces the impact of environmental conditions on photovoltaic power generation and improves the stability of photovoltaic power generation. Based on the grid element timing conversion diagram, the node load matrix and the node flow matrix are used to analyze the line operation state of the power grid, which can more comprehensively and accurately analyze the line operation state of the power grid and more accurately reflect the state of the power grid. Based on the optimized node planning scheme generated by the optimization path, the optimal evaluation results of the shunt charging and the optimal evaluation results of the grid connection, the photovoltaic power generation system is node matched, the accuracy of the power generation node matching is improved, and the grid connection of the established photovoltaic power generation system is more reliable. The cloud platform uses real-time grid-connected power generation data to perform resonance analysis based on the resonance participation factor generated by the state space matrix and the modal impedance curve generated by the admittance matrix, which improves the efficiency of the resonance analysis and the accuracy of the resonance analysis, so that the resonance analysis results are closer to the actual situation, and thus the parameters of the modular inverter can be better adjusted to further improve the stability of photovoltaic power generation. Based on the shaft current component calculated from the real-time grid-connected power generation data, the gain matrix spectrum is used to determine the expected value of the fundamental voltage. The modules of the modular inverter are switched according to the expected value of the fundamental voltage. The power quality can be effectively improved so that its output can meet the power quality requirements of the power grid, thereby achieving a more ideal effect of photovoltaic grid-connected power generation control.

[0095] In addition, the above is a detailed introduction to a photovoltaic grid-connected power generation control method and related devices based on a cloud platform provided in an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A photovoltaic grid-connected power generation control method based on a cloud platform, characterized in that: The method comprises: Acquire real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results; Constructing a power grid flow model, and performing line operation status analysis of the power grid based on the power grid flow model to obtain line operation status analysis results; Based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result, the photovoltaic power generation system is node matched to obtain the target power generation node, and the photovoltaic power generation system is connected to the grid based on the target power generation node; After the photovoltaic power generation system is connected to the grid, real-time grid-connected power generation data is collected based on the data acquisition device, and the real-time grid-connected power generation data is transmitted to the cloud platform; The cloud platform performs resonance analysis using the real-time grid-connected power generation data based on the state space matrix and the admittance matrix to obtain a resonance analysis result, and generates a resonance modulation scheme based on the resonance analysis result; Generate a fundamental voltage expected value using a Jacobian matrix based on the real-time grid-connected power generation data; The cloud platform generates a resonant modulation control instruction based on the resonant modulation scheme, adjusts parameters of the modular inverter based on the resonant modulation control instruction, generates a module switching instruction based on the fundamental voltage expected value, and switches modules of the modular inverter based on the module switching instruction; Wherein, the photovoltaic steady-state power generation analysis based on the real-time environmental data to obtain the photovoltaic steady-state power generation analysis result includes: obtaining a photovoltaic discharge impact record data set, and digitizing the photovoltaic discharge impact record data set to obtain a digitized photovoltaic discharge impact record data set; constructing a feature matrix based on the digitized photovoltaic discharge impact record data set, and obtaining a light source timing feature vector and a temperature timing feature vector based on the feature matrix; generating a number of photovoltaic power generation environmental impact factors based on the light source timing feature vector and the temperature timing feature vector, and evaluating a number of photovoltaic power generation environmental impact factors to obtain corresponding photovoltaic power generation environmental impact characteristic values; constructing a steady-state analysis network based on a number of photovoltaic power generation environmental impact factors and corresponding photovoltaic power generation environmental impact characteristic values; based on the steady-state analysis network, using the real-time environmental data to predict light source changes and temperature rise degrees, obtaining light source change prediction information and temperature rise degree prediction information, and performing photovoltaic steady-state power generation analysis based on the light source change prediction information and temperature rise degree prediction information to obtain photovoltaic steady-state power generation analysis results; The method of constructing a power grid flow model, analyzing the line operation status of the power grid based on the power grid flow model, and obtaining the line operation status analysis results includes: obtaining a target power grid architecture, generating a power flow topology structure based on the target power grid architecture, and constructing a power grid flow model based on the power flow topology structure; generating a node load matrix based on the power grid flow model using the total number of power grid nodes and node loads, and generating a node flow matrix based on the power grid flow model using line flow; obtaining a power grid element timing conversion diagram, and analyzing the line operation status of the power grid using the node load matrix and the node flow matrix based on the power grid element timing conversion diagram to obtain the line operation status analysis results.

2. The photovoltaic grid-connected power generation control method based on a cloud platform according to claim 1, characterized in that: The performing node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result to obtain the target power generation node includes: Generate a node planning model based on the line operation status analysis results and the photovoltaic steady-state power generation analysis results; Generate a node planning scheme using the node planning model based on an optimization algorithm, generate a corresponding game tree based on the node planning scheme, and determine a corresponding optimization path based on the game tree; Constructing an evaluation fitness function, and performing optimization evaluation of the node planning scheme for shunt charging and grid connection based on the evaluation fitness function, to obtain optimization evaluation results for shunt charging and grid connection; The node planning scheme is optimized based on the optimization path, the optimization evaluation results of the shunt charging and the optimization evaluation results of the grid connection to obtain an optimized node planning scheme, and the photovoltaic power generation system is node matched based on the optimized node planning scheme to obtain a target power generation node.

3. The photovoltaic grid-connected power generation control method based on a cloud platform according to claim 1, characterized in that: The step of performing resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data to obtain a resonance analysis result includes: Obtaining the grid-connected operation link parameters of the photovoltaic power generation system and the operation parameters of the modular inverter; Constructing a state space matrix and an admittance matrix based on the operation link parameters of the grid-connected connection of the photovoltaic power generation system and the operation parameters of the modular inverter; Calculating a left eigenvector and a right eigenvector of the state space matrix, and obtaining a resonance participation factor based on the left eigenvector and the right eigenvector; Calculating the eigenvalues ​​and eigenvectors of the admittance matrix, and generating a modal impedance curve based on the eigenvalues ​​and eigenvectors of the admittance matrix; A resonance analysis is performed using the real-time grid-connected power generation data based on the resonance participation factor and the modal impedance curve to obtain a resonance analysis result.

4. The photovoltaic grid-connected power generation control method based on a cloud platform according to claim 1, characterized in that: The method of generating a fundamental voltage expected value based on the real-time grid-connected power generation data using a Jacobian matrix includes: Calculating a Jacobian matrix of a photovoltaic power generation system, and calculating a gain matrix spectrum based on the Jacobian matrix; Calculating the shaft current component based on the real-time grid-connected power generation data, calculating a first difference between the shaft current component and an active current reference value, and calculating a second difference between the shaft current component and a reactive current reference value; The fundamental voltage expected value is determined using the gain matrix spectrum based on the first difference and the second difference.

5. The photovoltaic grid-connected power generation control method based on a cloud platform according to claim 1, characterized in that: The step of adjusting the parameters of the modular inverter based on the resonance modulation control instruction includes: The virtual resistance, equivalent virtual inductance and virtual capacitance of the modular inverter are adjusted based on the resonance modulation control instruction.

6. A photovoltaic grid-connected power generation control device based on a cloud platform, characterized in that: The device comprises: Photovoltaic steady-state power generation analysis module: used to obtain real-time environmental data, perform photovoltaic steady-state power generation analysis based on the real-time environmental data, and obtain photovoltaic steady-state power generation analysis results; Line operation status analysis module: used to construct a power grid flow model, perform line operation status analysis of the power grid based on the power grid flow model, and obtain line operation status analysis results; Node matching module: used for performing node matching on the photovoltaic power generation system based on the line operation status analysis result and the photovoltaic steady-state power generation analysis result, obtaining the target power generation node, and establishing the grid connection of the photovoltaic power generation system based on the target power generation node; Data collection and transmission module: used to collect real-time grid-connected power generation data based on data collection equipment after the grid-connected connection of the photovoltaic power generation system is established, and transmit the real-time grid-connected power generation data to the cloud platform; Resonance analysis module: used for the cloud platform to perform resonance analysis based on the state space matrix and the admittance matrix using the real-time grid-connected power generation data, obtain resonance analysis results, and generate a resonance modulation scheme based on the resonance analysis results; A fundamental voltage expected value generation module is used to generate a fundamental voltage expected value based on the real-time grid-connected power generation data using a Jacobian matrix; Modular inverter adjustment module: used for the cloud platform to generate a resonant modulation control instruction based on the resonant modulation scheme, adjust the parameters of the modular inverter based on the resonant modulation control instruction, generate a module switching instruction based on the fundamental voltage expected value, and switch the modules of the modular inverter based on the module switching instruction; Wherein, the photovoltaic steady-state power generation analysis based on the real-time environmental data to obtain the photovoltaic steady-state power generation analysis result includes: obtaining a photovoltaic discharge impact record data set, and digitizing the photovoltaic discharge impact record data set to obtain a digitized photovoltaic discharge impact record data set; constructing a feature matrix based on the digitized photovoltaic discharge impact record data set, and obtaining a light source timing feature vector and a temperature timing feature vector based on the feature matrix; generating a number of photovoltaic power generation environmental impact factors based on the light source timing feature vector and the temperature timing feature vector, and evaluating a number of photovoltaic power generation environmental impact factors to obtain corresponding photovoltaic power generation environmental impact characteristic values; constructing a steady-state analysis network based on a number of photovoltaic power generation environmental impact factors and corresponding photovoltaic power generation environmental impact characteristic values; based on the steady-state analysis network, using the real-time environmental data to predict light source changes and temperature rise degrees, obtaining light source change prediction information and temperature rise degree prediction information, and performing photovoltaic steady-state power generation analysis based on the light source change prediction information and temperature rise degree prediction information to obtain photovoltaic steady-state power generation analysis results; The method of constructing a power grid flow model, analyzing the line operation status of the power grid based on the power grid flow model, and obtaining the line operation status analysis results includes: obtaining a target power grid architecture, generating a power flow topology structure based on the target power grid architecture, and constructing a power grid flow model based on the power flow topology structure; generating a node load matrix based on the power grid flow model using the total number of power grid nodes and node loads, and generating a node flow matrix based on the power grid flow model using line flow; obtaining a power grid element timing conversion diagram, and analyzing the line operation status of the power grid based on the power grid element timing conversion diagram using the node load matrix and the node flow matrix to obtain the line operation status analysis results.

7. An electronic device, comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the photovoltaic grid-connected power generation control method based on a cloud platform as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the photovoltaic grid-connected power generation control method based on a cloud platform as described in any one of claims 1 to 5.

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