Power deviation-based photovoltaic module control strategy optimization method and system

Abstract

The application discloses a photovoltaic module control strategy optimization method and system based on power deviation, relates to the intelligent operation and maintenance technical field of a photovoltaic power station, and comprises the following steps: constructing a multilevel photovoltaic power structure by collecting real-time monitoring data through an interactive photovoltaic module monitoring and collecting module; performing power deviation operation to obtain the relative power deviation amount of each module; establishing a power transmission path matrix and mapping the relative power deviation amount, and analyzing and identifying target optimization nodes and mismatched nodes; and adjusting the working state of the module by taking the maximum effective aggregate power of the photovoltaic module as the target optimization strategy. The application solves the technical problems that the traditional control strategy optimization method does not consider the multilevel power structure and deviation conduction, it is difficult to accurately identify key nodes and adapt to dynamic working conditions, and thus power aggregation is inefficient and strategy adjustment is lagged, and achieves the technical effects of accurate power regulation of the photovoltaic system, improved power generation efficiency and strategy adaptability.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for photovoltaic power plants, and in particular to a method and system for optimizing photovoltaic module control strategies based on power deviation. Background Technology

[0002] In photovoltaic power plants, deviations in module power output due to performance differences and environmental changes affect power generation efficiency and system stability, making control strategy optimization crucial. Existing technologies mostly employ conventional control methods, which are effective in simple scenarios, but their shortcomings become apparent as the scale of the power plant increases and the environment becomes more complex.

[0003] Traditional control strategies do not consider multi-level power structures and deviation transmission, making it difficult to accurately identify key nodes and adapt to dynamic operating conditions. This results in inefficient power aggregation and lagging strategy adjustments, failing to meet the high-efficiency operation requirements of photovoltaic systems. Therefore, it is necessary to innovate strategy optimization methods to improve control accuracy and power generation efficiency. Summary of the Invention

[0004] This application provides a photovoltaic module control strategy optimization method and system based on power deviation, which solves the technical problems of traditional control strategy optimization methods not considering multi-level power structure and deviation transmission, making it difficult to accurately identify key nodes and adapt to dynamic operating conditions, thus leading to inefficient power aggregation and lagging strategy adjustment.

[0005] The first aspect of this application provides a photovoltaic module control strategy optimization method based on power deviation. The method includes: an interactive photovoltaic module monitoring and acquisition module to acquire real-time monitoring data of each photovoltaic module, including real-time output power, and construct a multi-level photovoltaic power structure; performing power deviation calculations on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation of each module; establishing a power transmission path matrix of the photovoltaic power station, mapping the relative power deviation of each module to the power transmission path matrix, performing path response analysis, and identifying target optimization nodes; identifying mismatch nodes that inhibit path power transmission based on the response radiation area of ​​the target optimization node in the power transmission path matrix; and performing strategy optimization with the goal of maximizing the effective total photovoltaic power, based on the regulatory influence of the target optimization node and the mismatch nodes, to obtain a photovoltaic control strategy for adjusting the operating state of the photovoltaic modules.

[0006] A second aspect of this application provides a photovoltaic module control strategy optimization system based on power deviation. The system includes: an interactive photovoltaic module monitoring and acquisition module for acquiring real-time monitoring data of each photovoltaic module, including real-time output power, and constructing a multi-level photovoltaic power structure; a relative power deviation acquisition module for performing power deviation calculations on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation of each module; a target optimization node identification module for establishing a power transmission path matrix of the photovoltaic power station, mapping the relative power deviation of each module to the power transmission path matrix, performing path response analysis, and identifying target optimization nodes; a mismatch node identification module for identifying mismatch nodes that inhibit path power transmission based on the response radiation area of ​​the target optimization node in the power transmission path matrix; and a photovoltaic module operating state adjustment module for performing strategy optimization based on the control effects of the target optimization node and mismatch nodes, with the goal of maximizing the effective total photovoltaic power, to obtain a photovoltaic control strategy for adjusting the operating state of the photovoltaic modules.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This application constructs a multi-level photovoltaic power structure, collects module power data and processes it through mapping, projection, and deviation calculation. It then identifies targets and mismatched nodes by combining the power transmission path matrix, optimizes the control strategy based on dynamic threshold screening and objective function, and finally corrects it through safety verification. This achieves precise power control of the photovoltaic system, improves power generation efficiency and stability, makes the photovoltaic module control strategy more adaptable to complex operating conditions, and makes the optimization effect more reliable. The application achieves the technical effect of precise power control of the photovoltaic system, improving power generation efficiency and strategy adaptability. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the photovoltaic module control strategy optimization method based on power deviation provided in the embodiments of this application.

[0011] Figure 2 This is a schematic diagram of the structure of the photovoltaic module control strategy optimization system based on power deviation provided in the embodiments of this application.

[0012] Figure labeling: 1. Interactive photovoltaic module monitoring and acquisition module; 2. Relative power deviation acquisition module; 3. Target optimization node identification module; 4. Mismatch node identification module; 5. Photovoltaic module working status adjustment module. Detailed Implementation

[0013] This application provides a photovoltaic module control strategy optimization method and system based on power deviation, which solves the technical problems of traditional control strategy optimization methods not considering multi-level power structure and deviation transmission, making it difficult to accurately identify key nodes and adapt to dynamic operating conditions, thus leading to inefficient power aggregation and lagging strategy adjustment.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the term "first" in the specification and the above-mentioned figures of this application is used in a different way.

[0016] The terms "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, a photovoltaic module control strategy optimization method based on power deviation is described, wherein the method includes:

[0018] Step A100: Interact with the photovoltaic module monitoring and acquisition module to obtain real-time monitoring data of each photovoltaic module, including real-time output power, and construct a multi-level photovoltaic power structure.

[0019] Specifically, the interactive photovoltaic module monitoring and acquisition module deploys high-precision current and voltage sensors on each photovoltaic module to collect voltage and current data in real time with a sampling period of 100ms. The sensors convert the collected analog signals into digital signals via a 24-bit ADC and transmit them to the data aggregation unit via an RS485 bus or LoRa wireless communication network. The data aggregation unit preprocesses the raw data, removes outliers, and calculates the real-time output power of the module by multiplying the voltage and current. For example, if a module collects a voltage of 30V and a current of 8A, its real-time power is calculated to be 240W.

[0020] Next, the geographical distribution of photovoltaic modules and their connection relationships within the photovoltaic power plant are obtained to identify the power partitioning levels at the module, string, array, and system levels. Then, a photovoltaic hierarchical structure is constructed and a mapping relationship between each node and the monitoring and acquisition equipment is established. Real-time monitoring data is then projected into the hierarchical structure according to this relationship to obtain a photovoltaic power structure containing multiple levels of power nodes and data mappings. The specific steps are explained in detail in A110-A130.

[0021] By deploying high-precision sensors at the component level to collect voltage and current data in real time, and through signal conversion, preprocessing and hierarchical mapping, a real-time database containing multi-level power nodes is constructed, providing accurate data support for subsequent power deviation calculation and path analysis, thereby realizing comprehensive perception and hierarchical management of the power status of the photovoltaic system.

[0022] Step A200: Based on the multi-level photovoltaic power structure, perform power deviation calculation on each photovoltaic module to obtain the relative power deviation of each module.

[0023] In this embodiment of the application, power deviation refers to the degree of deviation of the real-time output power of the photovoltaic module from the average power of its power partition level.

[0024] Optionally, based on the aforementioned multi-level photovoltaic power structure (module level, string level, array level, system level), the real-time power data of each module and the average power benchmark of the corresponding level are first obtained from the photovoltaic hierarchical structure database. Taking a certain string S001 as an example, it has 10 modules C001-C010, with real-time powers of 245W, 252W, 248W, 255W, 240W, 258W, 242W, 250W, 247W, and 253W respectively. The total power of the string is 2490W, and the average power of the string is 249W.

[0025] Secondly, a power deviation calculation is performed on each component: Component relative power deviation = (Component real-time power - String average power) / String average power × 100%. Taking component C005 with a power of 240W as an example, its deviation is (240-249) / 249×100% ≈ -3.61%; the deviation of component C006 with a power of 258W is (258-249) / 249×100% ≈ 3.62%. Through this calculation, the degree of power deviation of each component relative to its string can be quantified.

[0026] Furthermore, this can be extended to array-level or system-level benchmark comparisons. For example, array A001 contains 10 strings with an average string power of 2500W. The deviation of string S001's 2490W relative to the array's average is (2490-2500) / 2500×100%≈-0.4%, while the contribution of component C005's -3.61% deviation to the string deviation can be traced through hierarchical mapping relationships.

[0027] With the support of hierarchical data from a multi-level power structure, power deviation quantification calculations from the component level to the system level are realized. This transforms the string-level fuzzy alarm in traditional solutions into precise deviation positioning at the component level, providing a quantitative basis for subsequent target optimization node identification and strategy adjustment. This improves the source tracing efficiency and optimization accuracy of photovoltaic system power mismatch problems.

[0028] Step A300: Establish the power transmission path matrix of the photovoltaic power station, map the relative power deviation of each component to the power transmission path matrix, perform path response analysis, and identify the target optimization node.

[0029] In this embodiment, the power transmission path matrix is ​​formed by identifying the transmission units in the power transmission path of the photovoltaic power station and numbering them as nodes, establishing directed connection edges between nodes based on electrical connection relationships, and configuring edge weights based on cable parameters, connection resistance, and historical operation attenuation characteristics, thereby converting the constructed weighted directed graph structure into an adjacency matrix.

[0030] In one embodiment of this application, to establish a power transmission path matrix for a photovoltaic power station, it is necessary to first identify the power transmission path and number the transmission units into a set of nodes, then establish directed edges between nodes according to electrical connection relationships, configure edge weights based on cable parameters, etc., construct a weighted directed graph and convert it into an adjacency matrix. The specific steps are described in detail in A310-A340.

[0031] When identifying target optimization nodes, a vector is constructed based on the relative power deviation of each component. This vector is then multiplied by the power transmission path matrix to obtain the path response weight vector. Nodes with response weights greater than a threshold are selected. The specific steps are detailed in A350-A370.

[0032] Step A400: Based on the response radiation region of the target optimization node in the power transmission path matrix, identify the mismatched node that inhibits path power transmission.

[0033] In this embodiment, the response radiation region is a set of nodes that have a power transmission influence relationship with the target optimization node. A mismatched node is a node that inhibits path power transmission and whose inhibition coefficient exceeds a preset threshold.

[0034] Specifically, identifying mismatched nodes requires first determining the response radiation area of ​​the target optimized node based on the power transmission path matrix, collecting IV characteristic curves of components within the area, extracting features such as inflection point voltage, and weighting and fusing them with edge weights to calculate the suppression coefficient, and then screening nodes that exceed the preset threshold. The specific steps are explained in detail in A410-A450.

[0035] Step A500: With the goal of maximizing the effective total photovoltaic power, and based on the control effects of the target optimization node and the mismatch node, perform strategy optimization to obtain a photovoltaic control strategy for adjusting the working state of the photovoltaic module.

[0036] Specifically, to optimize the strategy with the goal of maximizing the effective total power of photovoltaics, it is necessary to analyze the path response relationship of each level based on the power transmission path matrix, construct the objective function that maximizes the system output power, adjust the node control parameters under the constraint of adjustable component parameters, and select the optimal control strategy through the objective function. The specific steps are explained in detail in A510-A520.

[0037] Furthermore, step A100 in the method provided in this application embodiment includes:

[0038] A110: Obtain the geographical distribution and connection relationship of photovoltaic modules within a photovoltaic power station, and identify the power zoning levels within the power station, including module level, string level, array level, and system level.

[0039] A120: Based on the power partitioning hierarchy, construct a photovoltaic hierarchy structure and establish a mapping relationship between each node in the photovoltaic hierarchy structure and the monitoring and acquisition equipment.

[0040] A130: Real-time monitoring data of photovoltaic modules are projected into the photovoltaic hierarchical structure according to the mapping relationship to obtain the multi-level photovoltaic power structure, which includes a photovoltaic power structure with multi-level power nodes and data mapping.

[0041] Specifically, firstly, the geographical distribution coordinates (e.g., 39.9°N, 116.4°E) and electrical connection diagrams of the photovoltaic modules are obtained using power plant design drawings and a GIS geographic information system, clarifying the string affiliation and array division rules for each module. Taking a 10MW photovoltaic power plant as an example, it contains 2000 photovoltaic modules. Following the rule that 10 modules are connected in series to form one string (200 strings in total), and 10 strings are connected in parallel to form one array (20 arrays in total), the system level is the global power aggregation level for the entire photovoltaic power plant. Its scope naturally covers all arrays (the aforementioned 20 arrays), meaning the system-level power equals the sum of the power of all arrays. The total power of the 20 arrays is then summed to form the system-level power. Finally, four power zoning levels are identified: module level, string level, array level, and system level.

[0042] Next, based on the power partitioning hierarchy, the partitioning rules between the levels of component affiliation, string division, and array composition are obtained. Based on these rules, the relationship function of the lower-level node power merging to the upper-level node is derived. Then, a photovoltaic hierarchical structure is built according to the partitioning rules and power aggregation relationship in a tree structure. The specific steps are explained in detail in A121-A123.

[0043] Secondly, after constructing a tree-shaped photovoltaic hierarchy based on the aforementioned partitioning hierarchy, a database table structure is used to establish the mapping relationship between the hierarchical nodes and the monitoring and acquisition devices. For example, a unique ID (such as C001-C2000) is assigned to each module, corresponding to the string ID (S001-S200), array ID (A001-A020), and system-level node. Through an RS485 bus, physical connections are established between the module-level current and voltage sensors (sampling accuracy ±0.5%), string-level combiner box monitoring modules (sampling period 100ms), and other devices and the hierarchical nodes, forming a four-level data interaction link: module-string-array-system.

[0044] Finally, when the monitoring module collects real-time power data of the components, such as component C001 outputting 295W, the data is projected to the corresponding level nodes through a mapping relationship: the power of string S001 is the sum of the power of its 10 subordinate components, such as 2950W; the power of array A001 is the sum of the power of its 10 strings, such as 29500W; and the system-level power is the sum of the power of all arrays, such as 590000W. This forms a database of photovoltaic power structure containing multi-level power nodes and real-time data mapping, where each level node stores the corresponding power data and timestamp, such as 2025-06-18-10:30:00, system-level power 590kW.

[0045] By hierarchically identifying geographical distribution and connectivity, mapping monitoring equipment to hierarchical nodes, and projecting real-time data hierarchically, a multi-level power data system from the component level to the system level is formed. This provides accurate structured data support for subsequent cross-level calculations of power deviation and path transmission analysis, thereby realizing hierarchical visualization management and in-depth traceability of the power status of photovoltaic systems.

[0046] Furthermore, step A120 in the method provided in this application embodiment includes:

[0047] A121: Obtain the inter-level partitioning rules of the power partitioning hierarchy, including component affiliation, string division logic, and array composition specifications.

[0048] A122: Based on the inter-level partitioning rules, obtain the hierarchical power aggregation relationship, including the merging relationship function of the power of lower-level nodes to the power of higher-level nodes.

[0049] A123: Based on the inter-level partitioning rules and the hierarchical power aggregation relationship, the hierarchical structure is constructed according to the tree structure to obtain the photovoltaic hierarchical structure.

[0050] Optionally, firstly, obtain the inter-level partitioning rules for the power partitioning hierarchy. Taking a typical photovoltaic power station as an example, its component affiliation is defined as follows: every 10 adjacent components connected in series form a string. The string partitioning logic follows that strings with the same orientation and tilt angle are connected in parallel to form an array. The array composition specification is that every 10 strings constitute one array. Through this rule, 2000 components can be divided into 200 strings (S001-S200) and 20 arrays (A001-A020), forming a four-level partitioning system: component level → string level → array level → system level.

[0051] Secondly, a hierarchical power aggregation relationship is constructed based on partitioning rules. The merging relationship function between the power of lower-level nodes and higher-level nodes is: string power = Σ(component power), array power = Σ(string power), system power = Σ(array power).

[0052] Finally, a hierarchical photovoltaic structure is constructed using a tree structure: the system level is the root node, each array-level node is a child node attached to the system level, string-level nodes are array-level child nodes, and module-level nodes are string-level child nodes. When using an ER database model for storage, a hierarchical node table is created to record the node ID, type (module / string / array / system), parent node ID, and power value. For example, the parent node of node A001 is the system level, containing child nodes S001-S010, with a real-time power of 25000W.

[0053] By clarifying the partitioning rules between levels, constructing the power merging function, and building the tree structure, a photovoltaic hierarchical structure with clear logical relationships was formed. This enabled structured management and cross-level aggregation of power data from the module level to the system level, providing a standardized data model for subsequent hierarchical transmission analysis of power deviations and path response calculations, thereby improving the efficiency and accuracy of power anomaly tracing in photovoltaic systems.

[0054] Furthermore, step A300 in the method provided in this application embodiment includes:

[0055] A310: Identify the power transmission path, number the transmission units in the power transmission path as nodes, and obtain the power node set.

[0056] A320: Based on the electrical connection relationship of the photovoltaic power station, establish directed connection edges between nodes in the power node set, and configure edge weights based on cable parameters, connection resistance and historical operation attenuation characteristics.

[0057] A330: Construct a weighted directed graph structure based on the set of nodes and the set of edges.

[0058] A340: Convert the weighted directed graph structure into an adjacency matrix to obtain the power transmission path matrix of the photovoltaic power station.

[0059] In this embodiment, the electrical connection relationship refers to the actual connection method between various power transmission units (such as modules, strings, arrays, etc.) within the photovoltaic power station, including physical connection relationships such as series and parallel connections. The weighted directed graph structure is a graph model constructed based on the set of power nodes and the set of directed edges, where nodes represent power transmission units, directed edges represent the power transmission direction, and edge weights reflect the loss characteristics of the transmission path.

[0060] Specifically, firstly, the transmission units in the power transmission path are identified and assigned as node numbers. Taking a 10MW power plant as an example, its transmission units include 2000 modules (C001-C2000), 200 strings (S001-S200), 20 arrays (A001-A020), combiner boxes, inverters, etc., totaling 2221 nodes. Each node is assigned a unique ID, forming a power node set N = {module level, string level, array level, system level}.

[0061] Secondly, cross-level directed edges are established based on electrical connection relationships: components C001-C010 are connected in series to form string S001, with edge directions of C001→S001, C002→S001, etc.; strings S001-S010 are connected in parallel to form array A001, with edge directions of S001→A001, etc.

[0062] Component-level → String-level: First, determine the basic weights based on the cable's physical parameters. For example, if the resistance of a 50-meter cable is 0.05Ω, its basic weight is directly set to 0.05; when the joint impedance is 0.01Ω, the corresponding weight is set to 0.01. Second, sum the cable resistance weight and the joint impedance weight to obtain the initial edge weight. For example, the cable resistance weight of 0.05 from component C001 to string S001 is summed with the joint impedance weight of 0.01, resulting in an initial edge weight of 0.06. Finally, correct the initial weights based on historical operating attenuation characteristics. If the cable attenuation rate is 5% after 3 years of operation, multiply the initial weight by (1 + attenuation rate), i.e., 0.06 × 1.05 = 0.063, to obtain the final edge weight. This dynamically characterizes the impact of cable aging on power transmission.

[0063] String-level → Array-level: 10 strings S001-S010 are connected in parallel to form array A001, with side directions such as S001→A001, S002→A001, etc. The side weights include the cable resistance from string to array (0.07Ω) and the combiner box impedance (0.01Ω), which are accumulated to 0.08, reflecting the transmission loss from string to array.

[0064] Array level → System level: All arrays are connected in parallel to the system level node, with the edge direction being A001 → system level, A002 → system level, etc. The edge weight is configured according to the cable parameters from the array to the inverter (e.g., 0.09Ω).

[0065] Then, nodes across different levels are combined with directed edges to form a tree-like weighted directed graph: bottom-level component nodes converge to mid-level string nodes through directed edges, string nodes then converge to higher-level array nodes through directed edges, and finally all array nodes converge to the system-level node. The weight of each edge in the weighted directed graph dynamically represents the power loss characteristics of the corresponding transmission path, thereby constructing a complete hierarchical power transmission network and realizing visualized modeling of power transmission paths from the component level to the system level.

[0066] Finally, the weighted directed graph is transformed into an adjacency matrix M with dimensions 2221×2221. Matrix element M[i][j] represents the edge weight from node i to node j, which is 0 when there is no connection. For example, M[C001][S001] = 0.063, M[S001][A001] = 0.08, and other unconnected positions are 0, thus obtaining a path matrix that accurately characterizes the power transmission properties.

[0067] By incorporating cable parameters, connection resistance, and historical attenuation characteristics, the actual power transmission network is transformed into an accurate mathematical matrix model, providing a dynamic and high-precision computational foundation for subsequent power deviation mapping, path response analysis, and target node identification. This enables precise modeling and quantitative analysis of the power transmission characteristics of photovoltaic power plants.

[0068] Table 1: Power Transmission Path Edge Weight Configuration and Hierarchical Connection Relationship of Photovoltaic Power Plants

[0069]

[0070]

[0071] Furthermore, step A300 in the method provided in this application embodiment includes:

[0072] A350: Construct a power deviation vector based on the relative power deviation of each component.

[0073] A360: Perform matrix multiplication between the power deviation vector and the power transmission path matrix to obtain the path response weight vector.

[0074] A370: Based on the path response weight vector, identify nodes with response weights greater than a threshold, and obtain the target optimization node.

[0075] Specifically, firstly, a power deviation vector is constructed based on the relative power deviation of each component. Taking a 10MW power plant with 2000 components as an example, if component C001 has a deviation of -5%, C002 has a deviation of +3%, and the deviation of the remaining components is 0, then the deviation vector V = [-0.05, 0.03, ..., 0], with a dimension of 2000 × 1, and the vector elements correspond to the deviation of each component.

[0076] Secondly, this vector is multiplied by the power transmission path matrix M (2221×2221) to obtain the path response weight vector W (1×2221). During the matrix operation, the edge weights from the components to the string in M ​​(e.g., 0.063) are multiplied by the deviation, and the sum reflects the response strength of each transmission node.

[0077] Finally, a threshold (e.g., 0.05) is set to filter nodes in the weight vector that are greater than the threshold, and these nodes are identified as target optimization nodes. If the weight of array A001 is 0.065 > 0.05, it is listed as a target node, and its transmission path needs to be optimized first.

[0078] By constructing a mathematical mapping model between the power deviation vector and the transmission matrix, the component-level deviation is transformed into a full-path response weight distribution, realizing the quantitative analysis from local deviations to global key nodes. This provides a precise node location basis for subsequent strategy optimization, thereby improving the targeting and efficiency of photovoltaic system power optimization.

[0079] Furthermore, step A400 in the method provided in this application embodiment includes:

[0080] A410: Based on the power transmission path matrix, obtain the response radiation region of the target optimization node, wherein the response radiation region is a set of nodes that have a power transmission influence relationship with the target optimization node.

[0081] A420: Simultaneously collect IV characteristic curve data for photovoltaic modules within the aforementioned response radiation region.

[0082] A430: Based on the IV characteristic curve data, extract the inflection point voltage, voltage decay rate, and fill factor from the curve as mismatch characteristic quantities.

[0083] A440: The mismatch feature is weighted and fused with the edge weights in the power transmission path matrix to calculate the suppression coefficient of each node.

[0084] A450: Filter the nodes whose node suppression coefficient exceeds a preset threshold to obtain the mismatched nodes.

[0085] Specifically, firstly, the response radiation area of ​​the target optimization node is determined based on the power transmission path matrix. Taking a string node S001 as an example, its response radiation area includes all component nodes C001-C010 and array nodes A001 that are connected to S001 by directed edges. These node sets are extracted through matrix adjacency relationships to form a path network affected by the power changes of string node S001.

[0086] Secondly, IV characteristic curve data of the components within the radiation area are collected synchronously. Those skilled in the art use a data acquisition device with a sampling rate of 100Hz at 25℃ and 1000W / m². 2 Curves are obtained under standard test conditions. For example, the IV curve of a component shows that the inflection point voltage drops from 28V to 25V, the voltage decay rate is 10% (standard value ≤5%), and the fill factor drops from 0.78 to 0.72.

[0087] Then, the inflection point voltage, voltage decay rate, and fill factor of the IV characteristic curve data are extracted as mismatch characteristics. The standard inflection point voltage is set to 28V, the voltage decay rate threshold is 5%, and the standard fill factor value is 0.78. The deviation of each characteristic is calculated as follows: Inflection point voltage deviation = (28-25) / 28 ≈ 10.7%, voltage decay rate deviation = 10% - 5% = 5%, fill factor deviation = (0.78-0.72) / 0.78 ≈ 7.7%.

[0088] Next, the mismatch characteristics are weighted and fused with the edge weights to calculate the suppression coefficient. Assuming the edge weight from component C001 to S001 is 0.063, and the weights are allocated as follows: inflection point voltage 0.4, voltage decay rate 0.3, and fill factor 0.3, then the suppression coefficient = 0.4 × 10.7% + 0.3 × 5% + 0.3 × 7.7% = 7.69%.

[0089] Finally, nodes whose inhibition coefficients exceed a preset threshold are identified as mismatched nodes. The preset threshold is obtained through dynamic calculation, and the specific steps are explained in detail in A451-A453.

[0090] By analyzing the radiation area of ​​the power transmission path matrix, extracting multi-dimensional IV features, and using dynamic threshold weighted fusion, an upgrade from single-parameter alarm to path association suppression analysis has been achieved. This accurately locates nodes that have a significant inhibitory effect on power transmission, providing targeted control objects for subsequent strategy optimization, thereby improving the power aggregation efficiency and operational stability of the photovoltaic system.

[0091] Furthermore, step A450 in the method provided in this application embodiment includes:

[0092] A451: Obtain the historical fill factor of the target photovoltaic module under standard test conditions and calculate the standard deviation of the fill factor.

[0093] A452: Obtain the actual light intensity value under the current ambient light, calculate the ratio of the actual light intensity value to the light intensity under standard test conditions, and obtain the light ratio coefficient.

[0094] A453: Establish the weighted relationship between the fill factor standard deviation, the illumination ratio coefficient, and the dynamic threshold, perform weighted calculations, and obtain the preset threshold.

[0095] In one embodiment, firstly, historical fill factor data of the target photovoltaic module is extracted. Taking a module within a string as an example, its data for the past 30 standard test days (1000W / m² irradiance) is collected. 2 The fill factors for (at 25℃) are 0.77, 0.78, 0.76, etc., and the calculated average is 0.775, with a standard deviation of [missing value]. This value reflects the consistency of the component's own performance—the larger the standard deviation, the more drastic the historical fluctuations of the component, and a more lenient judgment space needs to be reserved in the threshold.

[0096] Simultaneously, the current ambient light intensity is collected in real time. Assume the standard test light intensity S0 = 1000 W / m². 2 Actual illumination S = 800 W / m 2 The illuminance ratio coefficient k = S / S0 = 0.8 is calculated. Under low light conditions (e.g., k = 0.5, corresponding to illuminance of 500 W / m²), the illuminance is... 2 Environmental interference (such as cloud cover) makes the system more unstable, requiring a higher threshold to avoid false alarms; under high light conditions (e.g., k = 0.95, corresponding to 950 W / m² light intensity), the system is more unstable. 2 The environment is more stable, allowing us to focus on the mismatch characteristics of the components themselves.

[0097] Subsequently, the influence of both is combined using a weighting formula: Tdynamic =0.4×(1+e -k )+0.6×σ PP Substituting k = 0.8 and σ... pp =0.015, T is calculated dynamic =0.4×(1+e -0.8 ) + 0.6 × 0.015 ≈ 0.5886. If k = 0.5 (low illumination), then σ is added. pp =0.02 (component fluctuations are greater), then T dynamic =0.4×(1+e -0.5 ) + 0.6 × 0.02 ≈ 0.6548, the threshold increases synchronously with environmental instability and component fluctuations; if k = 0.95 (high light intensity), superimposed σ pp =0.01 (component stable), then T dynamic =0.4×(1+e -0.95 )+0.6×0.01≈0.5548, the preset threshold decreases synchronously as the environment stabilizes and the components are consistent.

[0098] By first quantifying the historical consistency of components, then characterizing the stability of the current environment, and finally constructing a weighted dynamic threshold, this method overcomes the limitations of traditional fixed thresholds: under low light conditions, the threshold is increased by amplifying the environmental influence to resist noise interference; under high light conditions, the threshold is reduced by focusing on component differences to capture latent mismatches, thereby achieving a balance between the accuracy and robustness of mismatch node screening under different operating conditions.

[0099] Furthermore, step A500 in the method provided in this application embodiment includes:

[0100] A510: Based on the power transmission path matrix, analyze the path response relationship of each photovoltaic level to the power output of the power station, and construct an objective function with the goal of maximizing the system output power.

[0101] A520: Under the constraint of adjustable component operating parameters, based on the regulation influence of the target optimization node and mismatch node, the control parameters of multiple nodes are adjusted by strategy, and the strategy is evaluated and screened through the objective function to obtain the photovoltaic control strategy with the maximum output power.

[0102] Optionally, firstly, the response relationships at each level are analyzed based on the power transmission path matrix. Taking a 10MW power plant as an example, the row vector [0.08, 0.07, ..., 0] of array-level node A001 in matrix M (2221×2221) represents its transmission weight with its 200 subordinate string nodes. Through matrix row operations, the contribution coefficient of A001 to the system power can be analyzed as 0.15, that is, for every 1kW increase in the power of A001, the theoretical system power increases by 0.15kW. Based on this, the objective function F = Σ(α i ×Pi ), where α i Let be the system response coefficient for node i, such as α = 0.001 at the component level, α = 0.01 at the string level, and α = 0.1 at the array level; P i Real-time power for nodes enables multi-level power mapping from components to the system.

[0103] Secondly, strategy adjustments are performed under the constraints of component operating parameters. The adjustable range of component voltage is set to 25-35V and current to 5-8A. Taking the target optimization node S001 (a certain string) and the mismatched node C005 (a component within that string) as an example: when the inflection point voltage of C005 increases from 25V to 28V, the adjustment amount is 12%. Combined with the aforementioned edge weight of 0.063, its power contribution increment to S001 is calculated to be 0.063×(28-25)×5=0.945W. Simultaneously, the combiner box impedance of S001 needs to be adjusted synchronously from 0.08Ω to 0.07Ω. Calculations using the objective function show that this combined strategy improves the system power prediction by 1.2kW, which is better than the 0.5kW improvement effect of adjusting C005 alone.

[0104] By combining path response analysis with dynamic strategy adjustment, this method breaks through the limitations of traditional single optimization, enabling the system to effectively aggregate power improvement and achieve precise control from local node optimization to network-wide power optimization.

[0105] Furthermore, step A530 in the method provided in this application embodiment includes:

[0106] A531: The photovoltaic control strategy is converted into the node injection amount of the power transmission path matrix, and the power convergence relationship and control feedback path of each node injection amount at the module level, string level and array level are analyzed based on the multi-level structure.

[0107] A532: Based on power convergence relationship and control feedback path, construct system power distribution model, calculate the power redistribution results of the whole network, predict the voltage response value of key nodes at each level, and determine the voltage offset of each key node relative to the stable operating state.

[0108] A533: When any node of the voltage offset exceeds the safety threshold set at the corresponding level, a strategy correction instruction is generated to adjust the relevant control parameters in the photovoltaic control strategy or to block execution.

[0109] In one embodiment, the photovoltaic control strategy is first converted into node injection amounts in the power transmission path matrix. For example, a strategy requires increasing the power of module C001 by 10%, from 240W to 264W, which translates to an injection amount of +24W at node C001 in the matrix. Based on the matrix edge weights, the edge weight of C001→S001 is 0.063. This indicates that the power of string S001 will increase by 24 × 0.063 ≈ 1.51W, and the power of array A001 will increase by 1.51 × 0.08 ≈ 0.12W (S001→A001 edge weight), forming a power convergence and conduction chain from module to string to array. Simultaneously, the control feedback path shows that the voltage feedback of A001 will affect the subsequent adjustment of C001, constructing a closed-loop control logic.

[0110] Secondly, a system power distribution model is constructed based on power convergence relationships. The photovoltaic control strategy is converted into node injection quantities in the power transmission path matrix. Based on a multi-level structure, the power convergence relationships and control feedback paths between the node injection quantities at the module, string, and array levels are analyzed. Then, based on these power convergence relationships and control feedback paths, a system power distribution model is constructed. After inputting the node injection quantities, the model calculates the power redistribution results for the entire network: For example, if C001 is adjusted, the total power of string S001 increases from 2490W to 2491.51W, array A001 increases from 25000W to 25000.12W, and the total system power increases from 500000W to 500000.12W. The voltage response of key nodes is predicted synchronously: the S001 voltage increases from 24V to 24.02V, with an offset of 0.08%, and the A001 voltage increases from 380V to 380.05V, with an offset of 0.013%, both within the safe threshold. If another strategy causes an array voltage deviation of 5.2% (exceeding the 5% threshold), the model will immediately issue a warning.

[0111] When the voltage deviation exceeds the threshold, the system automatically generates a correction command. For example, if a certain strategy causes the voltage deviation of string S005 to reach 6% after component C050 is adjusted, the system first attempts to reduce the injection amount of C050 to 80% of the original strategy (i.e., +19.2W), recalculates the deviation to 4.8%, and issues the command if it meets the requirements; if it still exceeds the threshold, execution is blocked. For example, if the strategy adjustment causes the voltage deviation of array A010 to be 7%, and the system still fails to meet the standard after three consecutive parameter corrections, the strategy is ultimately blocked to prevent equipment damage.

[0112] By converting the strategy into node injection quantities and parsing the power conduction path, and combining real-time voltage offset prediction and dynamic correction mechanisms, the entire link security verification of the control strategy from generation to execution is realized. This ensures that the photovoltaic system maintains voltage stability throughout the strategy optimization process, avoids overload risks caused by strategy adjustments, and thus improves the safety of power plant operation and the reliability of power regulation.

[0113] In summary, the photovoltaic module control strategy optimization method based on power deviation provided in this application has the following technical effects:

[0114] This application acquires real-time monitoring data (including real-time output power) of each photovoltaic module through an interactive photovoltaic module monitoring and acquisition module, constructs a multi-level photovoltaic power structure, obtains the relative power deviation of each module through power deviation calculation, maps it to the power transmission path matrix for path response analysis to identify target optimization nodes, and then identifies mismatch nodes that inhibit path power transmission based on the response radiation area of ​​the target optimization nodes. Finally, with the goal of maximizing the effective total photovoltaic power, the application optimizes the execution strategy based on the control influence of the target optimization nodes and mismatch nodes to obtain a control strategy for adjusting the working state of photovoltaic modules. This makes the power control of the photovoltaic power station more precise and efficient, achieving the technical effect of precise power regulation of the photovoltaic system, improving power generation efficiency and strategy adaptability.

[0115] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a photovoltaic module control strategy optimization system based on power deviation, the system comprising:

[0116] Interactive photovoltaic module monitoring and acquisition module 1 is used to acquire real-time monitoring data of each photovoltaic module, including real-time output power, and to construct a multi-level photovoltaic power structure.

[0117] The relative power deviation acquisition module 2 performs power deviation calculations on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation of each module.

[0118] The target optimization node identification module 3 is used to establish the power transmission path matrix of the photovoltaic power station, map the relative power deviation of each component to the power transmission path matrix, perform path response analysis, and identify the target optimization node.

[0119] The mismatch node identification module 4 is used to identify mismatch nodes that inhibit path power transmission based on the response radiation region of the target optimized node in the power transmission path matrix.

[0120] The photovoltaic module operating status adjustment module 5 is used to optimize the photovoltaic module operating status by performing strategy optimization based on the control effects of the target optimization node and mismatch node, with the goal of maximizing the effective total photovoltaic power.

[0121] Furthermore, the interactive photovoltaic module monitoring and acquisition module 1 is used to perform the following steps:

[0122] The geographical distribution and connection relationships of photovoltaic modules within a photovoltaic power station are obtained, and the power zoning levels within the power station are identified, including module level, string level, array level, and system level. Based on the power zoning levels, a photovoltaic hierarchical structure is constructed, and a mapping relationship between each node in the photovoltaic hierarchical structure and the monitoring and acquisition equipment is established. Real-time monitoring data of the photovoltaic modules are projected onto the photovoltaic hierarchical structure according to the mapping relationship to obtain the multi-level photovoltaic power structure, which includes a photovoltaic power structure with multi-level power nodes and data mapping.

[0123] Furthermore, the interactive photovoltaic module monitoring and acquisition module 1 is used to perform the following steps:

[0124] The hierarchical partitioning rules of the power partitioning hierarchy are obtained, including component affiliation, string partitioning logic, and array composition specifications; based on the hierarchical partitioning rules, the hierarchical power aggregation relationship is obtained, including the merging relationship function of lower-level node power to upper-level node; according to the hierarchical partitioning rules and the hierarchical power aggregation relationship, the hierarchical structure is constructed according to the tree structure to obtain the photovoltaic hierarchical structure.

[0125] Furthermore, the target optimization node identification module 3 is used to perform the following steps:

[0126] Identify the power transmission path, number the transmission units in the power transmission path as nodes to obtain a power node set; establish directed connection edges between nodes in the power node set according to the electrical connection relationship of the photovoltaic power station, and configure edge weights based on cable parameters, connection impedance and historical operation attenuation characteristics; construct a weighted directed graph structure based on the node set and edge set; convert the weighted directed graph structure into an adjacency matrix to obtain the power transmission path matrix of the photovoltaic power station.

[0127] Furthermore, the target optimization node identification module 3 is used to perform the following steps:

[0128] Based on the relative power deviation of each component, a power deviation vector is constructed; the power deviation vector is multiplied by the power transmission path matrix to obtain a path response weight vector; based on the path response weight vector, nodes with response weights greater than a threshold are identified to obtain the target optimization node.

[0129] Furthermore, the mismatch node identification module 4 is used to perform the following steps:

[0130] Based on the power transmission path matrix, the response radiation region of the target optimization node is obtained, where the response radiation region is a set of nodes that have a power transmission influence relationship with the target optimization node. For photovoltaic modules within the response radiation region, IV characteristic curve data are simultaneously collected. Based on the IV characteristic curve data, the inflection point voltage, voltage decay rate, and fill factor are extracted as mismatch features. The mismatch features are weighted and fused with the edge weights in the power transmission path matrix to calculate the suppression coefficient of each node. Nodes whose suppression coefficients exceed a preset threshold are selected to obtain the mismatched nodes.

[0131] Furthermore, the mismatch node identification module 4 is used to perform the following steps:

[0132] Obtain the historical fill factor of the target photovoltaic module under standard test conditions and calculate the standard deviation of the fill factor; obtain the actual illuminance value under the current ambient light, calculate the ratio of the actual illuminance value to the illuminance under the standard test conditions, and obtain the illuminance ratio coefficient; establish the weight relationship between the fill factor standard deviation, the illuminance ratio coefficient and the dynamic threshold, perform weighted calculation, and obtain the preset threshold.

[0133] Furthermore, the photovoltaic module operating status adjustment module 5 is used to perform the following steps:

[0134] Based on the power transmission path matrix, the path response relationship of each photovoltaic level to the power output of the power station is analyzed, and an objective function with the goal of maximizing the system output power is constructed. Under the constraint of adjustable component operating parameters, the control parameters of multiple nodes are adjusted according to the regulation influence of the target optimization node and mismatch node. The strategy is evaluated and screened through the objective function to obtain the photovoltaic control strategy with the maximum output power.

[0135] Furthermore, the photovoltaic module operating status adjustment module 5 is used to perform the following steps:

[0136] The photovoltaic control strategy is converted into node injection quantities in the power transmission path matrix. Based on the multi-level structure, the power convergence relationship and control feedback path of each node injection quantity at the module level, string level, and array level are analyzed. Based on the power convergence relationship and control feedback path, a system power distribution model is constructed, the power redistribution result of the entire network is calculated, the voltage response value of each key node at each level is predicted, and the voltage offset of each key node relative to the stable operating state is determined. When any node in the voltage offset exceeds the safety threshold set at the corresponding level, a strategy correction instruction is generated to adjust the relevant control parameters in the photovoltaic control strategy or block its execution.

[0137] The photovoltaic module control strategy optimization system based on power deviation provided in this embodiment of the invention can execute the photovoltaic module control strategy optimization method based on power deviation provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0138] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A photovoltaic module control strategy optimization method based on power deviation, characterized in that, include: The interactive photovoltaic module monitoring and acquisition module acquires real-time monitoring data of each photovoltaic module, including real-time output power, and constructs a multi-level photovoltaic power structure. Based on the multi-level photovoltaic power structure, power deviation calculation is performed on each photovoltaic module. Real-time power data of each module and the average power benchmark of the corresponding level are obtained from the database of photovoltaic hierarchical structure. Power deviation calculation is performed on each module to obtain the relative power deviation of each module. The power deviation refers to the degree of deviation of the real-time output power of the photovoltaic module from the average power of its power partition level. A power transmission path matrix for a photovoltaic power plant is established, and the relative power deviation of each component is mapped to the power transmission path matrix. Path response analysis is then performed to identify target optimization nodes. Based on the response radiation region of the target optimization node in the power transmission path matrix, identify the mismatch node that inhibits the path power transmission; With the goal of maximizing the effective total photovoltaic power, and based on the regulatory influence of the target optimization node and the mismatch node, strategy optimization is performed to obtain a photovoltaic control strategy, which is used to adjust the working state of the photovoltaic module; Constructing a multi-level photovoltaic power structure includes: Obtain the geographical distribution and connection relationship of photovoltaic modules within the photovoltaic power station, and identify the power zoning levels within the power station, including module level, string level, array level and system level; Based on the power partitioning hierarchy, a photovoltaic hierarchical structure is constructed, and a mapping relationship between each node in the photovoltaic hierarchical structure and the monitoring and acquisition equipment is established. According to the mapping relationship, the real-time monitoring data of the photovoltaic module is projected into the photovoltaic hierarchical structure to obtain the multi-level photovoltaic power structure, which includes a photovoltaic power structure with multi-level power nodes and data mapping; Establish the power transmission path matrix of the photovoltaic power plant, including: Identify the power transmission path, number the transmission units in the power transmission path as nodes, and obtain the power node set. Based on the electrical connection relationship of the photovoltaic power station, directed connection edges are established between nodes in the power node set, and edge weights are configured based on cable parameters, connection resistance and historical operation attenuation characteristics. Construct a weighted directed graph structure based on the set of nodes and the set of edges; The weighted directed graph structure is converted into an adjacency matrix to obtain the power transmission path matrix of the photovoltaic power station; The target identification optimization node includes: Based on the relative power deviation of each component, a power deviation vector is constructed; The path response weight vector is obtained by performing a matrix multiplication operation between the power deviation vector and the power transmission path matrix. Based on the path response weight vector, nodes with response weights greater than a threshold are identified, and the target optimization node is obtained.

2. The photovoltaic module control strategy optimization method based on power deviation according to claim 1, characterized in that, Based on the power partitioning hierarchy, a photovoltaic hierarchical structure is constructed, including: Obtain the inter-level partitioning rules of the power partitioning hierarchy, including component affiliation, string division logic, and array composition specifications; Based on the inter-level partitioning rules, the hierarchical power aggregation relationship is obtained, including the merging relationship function of the power of the lower-level nodes to the power of the upper-level nodes; Based on the partitioning rules between the levels and the power aggregation relationship of the levels, the hierarchical structure is constructed according to the tree structure to obtain the photovoltaic hierarchical structure.

3. The photovoltaic module control strategy optimization method based on power deviation according to claim 1, characterized in that, Based on the response radiation region of the target optimization node in the power transmission path matrix, mismatched nodes that inhibit path power transmission are identified, including: Based on the power transmission path matrix, the response radiation region of the target optimization node is obtained, and the response radiation region is a set of nodes that have a power transmission influence relationship with the target optimization node. For the photovoltaic modules within the aforementioned response radiation region, IV characteristic curve data are collected simultaneously; Based on the IV characteristic curve data, the inflection point voltage, voltage decay rate, and fill factor in the curve are extracted as mismatch characteristic quantities. The mismatch feature is weighted and fused with the edge weights in the power transmission path matrix to calculate the suppression coefficient of each node. Nodes whose suppression coefficient exceeds a preset threshold are selected to obtain the mismatched nodes.

4. The photovoltaic module control strategy optimization method based on power deviation according to claim 3, characterized in that, Nodes whose suppression coefficient exceeds a preset threshold are selected, prior to which the following steps are taken: Obtain the historical fill factor of the target photovoltaic module under standard test conditions and calculate the standard deviation of the fill factor. Obtain the actual light intensity value under the current ambient light, calculate the ratio of the actual light intensity value to the light intensity under standard test conditions, and obtain the light ratio coefficient. Establish the weighted relationship between the fill factor standard deviation, the illumination ratio coefficient, and the dynamic threshold, and perform weighted calculations to obtain the preset threshold.

5. The photovoltaic module control strategy optimization method based on power deviation according to claim 1, characterized in that, With the goal of maximizing the effective total photovoltaic power, and based on the regulatory influence of the target optimization node and the mismatch node, strategy optimization is performed to obtain a photovoltaic control strategy for adjusting the operating state of the photovoltaic modules, including: Based on the power transmission path matrix, the path response relationship of each photovoltaic level to the power output of the power station is analyzed, and an objective function with the goal of maximizing the system output power is constructed. Under the constraint of adjustable component operating parameters, based on the regulation influence of the target optimization node and mismatched node, the control parameters of multiple nodes are adjusted by strategy. The strategy is evaluated and screened through the objective function to obtain the photovoltaic control strategy with the maximum output power.

6. The photovoltaic module control strategy optimization method based on power deviation according to claim 5, characterized in that, Also includes: The photovoltaic control strategy is converted into the node injection amount of the power transmission path matrix. Based on the multi-level structure, the power convergence relationship and control feedback path of each node injection amount between the module level, string level and array level are analyzed. Based on the power convergence relationship and control feedback path, a system power distribution model is constructed, the power redistribution results of the entire network are calculated, the voltage response values ​​of key nodes at each level are predicted, and the voltage offset of each key node relative to the stable operating state is determined. When any node of the voltage offset exceeds the safety threshold set at the corresponding level, a strategy correction instruction is generated to adjust the relevant control parameters in the photovoltaic control strategy or to block execution.

7. A photovoltaic module control strategy optimization system based on power deviation, characterized in that, The system is used to implement the photovoltaic module control strategy optimization method based on power deviation as described in any one of claims 1-6, the system comprising: The interactive photovoltaic module monitoring and acquisition module is used to acquire real-time monitoring data of each photovoltaic module, including real-time output power, and to construct a multi-level photovoltaic power structure. The relative power deviation acquisition module performs power deviation calculations on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation of each module. The target optimization node identification module is used to establish the power transmission path matrix of the photovoltaic power station, map the relative power deviation of each component to the power transmission path matrix, perform path response analysis, and identify the target optimization node. The mismatch node identification module is used to identify mismatch nodes that inhibit path power transmission based on the response radiation region of the target optimized node in the power transmission path matrix. The photovoltaic module operating status adjustment module is used to optimize the photovoltaic control strategy based on the control effects of the target optimization node and the mismatch node, with the goal of maximizing the effective total photovoltaic power, and to adjust the operating status of the photovoltaic module.

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