Photovoltaic module control strategy optimization method and system based on power deviation

Abstract

The invention discloses a photovoltaic module control strategy optimization method and system based on power deviation, and relates to the technical field of intelligent operation and maintenance of photovoltaic power stations, and the method comprises the steps: collecting real-time monitoring data through an interactive photovoltaic module monitoring collection module, and constructing a multi-level photovoltaic power structure; performing power deviation operation to obtain a relative power deviation value of each component; establishing a power transmission path matrix and mapping a relative power deviation value, and analyzing and identifying a target optimization node and a mismatched node; and the working state of the assembly is adjusted by taking the maximum photovoltaic effective summary power as a target optimization strategy. According to the method, the technical problems of low power convergence efficiency and strategy adjustment lagging caused by the fact that a traditional control strategy optimization method does not consider a multi-level power structure and deviation conduction and is difficult to accurately identify key nodes and adapt to dynamic working conditions are solved, and the technical effects of accurately regulating and controlling the power of the photovoltaic system and improving the power generation efficiency and strategy adaptability are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent operation and maintenance of photovoltaic power stations, and particularly relates to a photovoltaic module control strategy optimization method and system based on power deviation. BACKGROUND

[0002] In a photovoltaic power station, the performance of components deviates due to performance differences and environmental changes, which affects power generation efficiency and system stability, so control strategy optimization is crucial. Existing technologies mostly use conventional control methods, which are effective in simple scenarios, but as the scale of the power station expands and the environment becomes more complex, the deficiencies are exposed.

[0003] Traditional control strategies do not consider multi-level power structure and deviation conduction, making it difficult to accurately identify key nodes and adapt to dynamic working conditions, resulting in inefficient power aggregation and delayed strategy adjustment, which cannot meet the efficient operation requirements of photovoltaic systems. Therefore, innovative strategy optimization methods are needed to improve control accuracy and power generation efficiency. SUMMARY

[0004] The present application provides a photovoltaic module control strategy optimization method and system based on power deviation, which solves the technical problem that traditional control strategy optimization methods do not consider multi-level power structure and deviation conduction, making it difficult to accurately identify key nodes and adapt to dynamic working conditions, which in turn leads to inefficient power aggregation and delayed strategy adjustment.

[0005] The first aspect of the present application provides a photovoltaic module control strategy optimization method based on power deviation, which includes: an interactive photovoltaic module monitoring and collecting module that acquires real-time monitoring data of each photovoltaic module, including real-time output power, and constructs a multi-level photovoltaic power structure; performs power deviation operation on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation of each component; establishes a power transmission path matrix of the photovoltaic power station, maps the relative power deviation of each component to the power transmission path matrix, performs path response analysis, and identifies the target optimization node; identifies mismatched nodes that have an inhibitory effect on path power transmission according to the response radiation area of the target optimization node in the power transmission path matrix; performs strategy optimization based on the regulation and control influence of the target optimization node and mismatched nodes to obtain a photovoltaic control strategy for adjusting the working state of the photovoltaic module.

[0006] In a second aspect of the present application, a photovoltaic module control strategy optimization system based on power deviation is provided, which comprises: an interactive photovoltaic module monitoring and collecting module for obtaining 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 amount obtaining module for performing power deviation operation on each photovoltaic module based on the multi-level photovoltaic power structure to obtain the relative power deviation amount of each module; a target optimization node identifying module for establishing a power transmission path matrix of a photovoltaic power station, mapping the relative power deviation amount of each module to the power transmission path matrix, performing path response analysis, and identifying a target optimization node; a mismatch node identifying module for identifying a mismatch node having an inhibitory effect on path power transmission according to the response radiation area of the target optimization node in the power transmission path matrix; and a photovoltaic module working state adjusting module for taking the maximum effective photovoltaic power as a target, performing strategy optimization based on the regulation and control influence of the target optimization node and the mismatch node, obtaining a photovoltaic control strategy, and adjusting the working state of the photovoltaic module.

[0007] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] In the present application, a multi-level photovoltaic power structure is constructed, the module power data is collected and processed through mapping projection and deviation operation, the target and mismatch nodes are identified in combination with the power transmission path matrix, the control strategy is optimized based on dynamic threshold screening and target function, and then the control strategy is corrected through safety verification, so as to realize accurate power regulation of the photovoltaic system, improve the power generation efficiency and stability, make the photovoltaic module control strategy more adaptive to complex working conditions, and make the optimization effect more reliable, thereby achieving the technical effects of accurate power regulation of the photovoltaic system and improvement of the power generation efficiency and strategy adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

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

[0011] Figure 2 is a structural schematic diagram of the photovoltaic module control strategy optimization system based on power deviation provided by the embodiments of the present 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 collecting module collects the voltage and current data of the photovoltaic module in real time by deploying high-precision current and voltage sensors on each photovoltaic module with a sampling period of 100 ms. The sensor converts the analog signals collected into digital signals through a 24-bit ADC and transmits them to the data aggregation unit through an RS485 bus or a LoRa wireless communication network. The data aggregation unit pre-processes the raw data, removes outliers, and obtains the real-time output power of the module by calculating the product of the voltage and the current. For example, if a certain module collects a voltage of 30V and a current of 8A, the real-time power is calculated to be 240W.

[0020] Next, the geographic distribution position and connection relationship of the photovoltaic modules in the photovoltaic power station are obtained to identify the power partition hierarchy at the module level, string level, array level, and system level, and then a photovoltaic hierarchical structure is constructed and a mapping association relationship between each node and the monitoring and collecting device is established. Real-time monitoring data is projected into the hierarchical structure according to this relationship to obtain a photovoltaic power structure containing multiple hierarchical power nodes and data mappings. The specific steps are described in detail in A110-A130.

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

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

[0023] In the embodiments of the present application, the power deviation refers to the deviation of the real-time output power of the photovoltaic module relative to the average power of its power partition hierarchy.

[0024] Optionally, based on the multi-level photovoltaic power structure (module level, string level, array level, system level) constructed above, first, the real-time power data of each module and the average power reference of the corresponding level are obtained from the database of the photovoltaic hierarchical structure. Taking a certain string S001 as an example, it has 10 modules C001-C010 under its jurisdiction, and the real-time powers are 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, the power deviation operation is performed on each component: the component relative power deviation amount = (component real-time power - module average power) / module average power x 100%. Taking the power 240W of component C005 as an example, the deviation amount is (240-249) / 249x100%≈-3.61%, and the deviation amount of the power 258W of component C006 is (258-249) / 249x100%≈3.62%. Through the operation, the power deviation degree of each component relative to the module can be quantified.

[0026] Further, it can be extended to array level or system level benchmark comparison. For example, if array A001 contains 10 modules, the average module power is 2500W, and the module S001 power is 2490W, the relative deviation from the array average is (2490-2500) / 2500x100%≈-0.4%, and the contribution of the -3.61% deviation of component C005 to the module deviation can be traced through the hierarchical mapping relationship.

[0027] Through the hierarchical data support of the multi-level power structure, the power deviation quantification operation from the component level to the system level is realized, the fuzzy alarm at the module level in the traditional scheme is converted into accurate deviation positioning at the component level, and a quantitative basis is provided for subsequent target optimization node identification and strategy adjustment, thereby improving the tracing efficiency and optimization accuracy of the power mismatch problem of the photovoltaic system.

[0028] Step A300: establishing a power transmission path matrix of the photovoltaic power station, mapping the relative power deviation amount of each component to the power transmission path matrix, performing path response analysis, and identifying a target optimization node.

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

[0030] In one embodiment of the application, to establish the power transmission path matrix of the photovoltaic power station, the transmission units in the power transmission path are first identified and numbered as a node set, then directed edges between the nodes are established according to the electrical connection relationship, edge weights are configured based on cable parameters, and the constructed weighted directed graph is converted into an adjacency matrix. The specific steps are described in detail in A310-A340.

[0031] When identifying the target optimization node, a vector is constructed according to the relative power deviation amount of each component, multiplied by the power transmission path matrix to obtain a path response weight vector, and nodes with a response weight greater than a threshold value are screened out. The specific steps are described in detail in A350-A370.

[0032] Step A400: identifying mismatched nodes that have inhibitory effects on path power transmission according to the response radiation area of the target optimization node in the power transmission path matrix.

[0033] In the embodiments of the present application, the response radiation area is a set of nodes that have a power transmission influence relationship with the target optimization node. The mismatched node is a node that has an inhibitory effect on path power transmission, and the node inhibition coefficient exceeds a preset threshold.

[0034] Specifically, to identify the mismatched node, the response radiation area of the target optimization node needs to be determined based on the power transmission path matrix, the I-V characteristic curve of the components in the area is collected, the inflection point voltage and other characteristic quantities are extracted, the inhibition coefficient is calculated by weighting and fusing the edge weight, and the nodes exceeding the preset threshold are screened. The specific steps are described in detail in A410-A450.

[0035] Step A500: performing strategy optimization based on the regulation and influence of the target optimization node and the mismatched node to maximize the effective aggregate power of the photovoltaic, obtaining a photovoltaic control strategy for adjusting the working state of the photovoltaic component.

[0036] Specifically, to perform strategy optimization with the goal of maximizing the effective aggregate power of the photovoltaic, the response relationship of each level path needs to be analyzed according to the power transmission path matrix, the objective function of maximizing the system output power is constructed, the node control parameters are adjusted under the constraint of component parameters, and the optimal control strategy is selected by the objective function. The specific steps are described in detail in A510-A520.

[0037] Further, the method provided in the embodiments of the present application includes:

[0038] A110: acquiring the geographical distribution position and the connection relationship of the photovoltaic components in the photovoltaic power station, identifying the power partition level in the power station, including the component level, the string level, the array level and the system level.

[0039] A120: constructing a photovoltaic hierarchical structure according to the power partition level, and establishing a mapping and association relationship between each node in the photovoltaic hierarchical structure and the monitoring and collecting device.

[0040] A130: projecting the real-time monitoring data of the photovoltaic components to the photovoltaic hierarchical structure according to the mapping and association relationship, obtaining the multi-level photovoltaic power structure, including the multi-level power node and the data mapping photovoltaic power structure.

[0041] Specifically, first, the geographic distribution coordinates (such as north latitude 39.9°, east longitude 116.4°) and electrical connection relationship diagram of the photovoltaic components are obtained by using the power station design drawings and the GIS geographic information system, and the group string attribution and array division rules of each component are determined. Taking a 10MW photovoltaic power station as an example, it contains 2000 photovoltaic components, which are connected in series according to the rule of 10 components per group string (a total of 200 group strings), and 10 group strings per array (a total of 20 arrays). The system level is the global power aggregation level of the entire photovoltaic power station, which naturally covers all arrays (the above 20 arrays), that is, the system level power is equal to the sum of the powers of all arrays, such as 20 array total power aggregation as system level power, and finally the component level, group string level, array level and system level four-level power partition level are identified.

[0042] Then, according to the power partition level, the hierarchical partition rule of component attribution, group string division and array composition is obtained, and based on the rule, the relationship function of the lower node power merging to the upper node is obtained, and then the photovoltaic hierarchical structure is built according to the partition rule and power aggregation relationship in the tree structure, and the specific steps are described in detail in A121-A123.

[0043] Secondly, after the tree-shaped photovoltaic hierarchical structure is constructed based on the above partition level, the mapping association relationship between the hierarchical nodes and the monitoring and collecting devices is established by using the database table structure. For example, a unique ID (such as C001-C2000) is allocated to each component, corresponding to group string ID (S001-S200), array ID (A001-A020) and system level node. The component level current voltage sensor (sampling accuracy ±0.5%) and group string level busbar box monitoring module (sampling period 100ms) and other devices are physically connected and mapped to the hierarchical nodes through RS485 bus, forming a four-level data interaction link of component-group string-array-system.

[0044] Finally, when the monitoring module collects the real-time power data of the components, such as the output of component C001 is 295W, the data is projected to the corresponding hierarchical node through the mapping relationship: the power of group string S001 is the sum of the powers of the 10 components under its jurisdiction, such as 2950W, the power of array A001 is the sum of the powers of the 10 group strings, such as 29500W, and the system level power is the sum of the powers of all arrays, such as 590000W. Thus, a database of photovoltaic power structure containing multiple hierarchical power nodes and real-time data mapping is formed, in which each hierarchical node stores corresponding power data and time stamp, such as 2025-06-18-10:30:00, system level power 590kW.

[0045] Through hierarchical identification of geographical distribution and connection relationship, mapping construction of monitoring equipment and hierarchical nodes, and hierarchical projection of real-time data, a multi-level power data system including component level to system level is formed, which provides accurate structured data support for subsequent cross-level operation and path conduction analysis of power deviation, thereby realizing hierarchical visual management and deep tracing of power state of the photovoltaic system.

[0046] Further, step A120 in the method provided by the embodiment of the application comprises:

[0047] A121: Obtain inter-level partition rules of the power partition hierarchy, including component attribution relationship, group string division logic and array composition specification.

[0048] A122: Obtain hierarchical power summary relationship based on the inter-level partition rules, including a merging relationship function of power of a lower-level node to an upper-level node.

[0049] A123: Build a hierarchical structure according to a tree structure based on the inter-level partition rules and the hierarchical power summary relationship, and obtain the photovoltaic hierarchical structure.

[0050] Optionally, first, inter-level partition rules of the power partition hierarchy are obtained. Taking a typical photovoltaic power station as an example, the component attribution relationship is defined as 10 adjacent components in series as 1 group string, the group string division logic follows the same orientation and same inclination of group strings in parallel as 1 array, and the array composition specification is 10 group strings as 1 array. Through the rules, 2000 components can be divided into 200 group strings (S001-S200) and 20 arrays (A001-A020), forming a four-level partition of component level→group string level→array level→system level.

[0051] Secondly, the hierarchical power summary relationship is constructed based on the partition rules. The merging relationship function of power of a lower-level node to an upper-level node is: group string power = Σ(component power), array power = Σ(group string power), and system power = Σ(array power).

[0052] Finally, the photovoltaic hierarchical structure is built according to a tree structure: taking the system level as a root node, each array level node is hung under the system level as a child node, the group string level node is a child node of the array level, and the component level node is a child node of the group string level. When a database ER model is used for storage, a hierarchical node table is established to record node ID, type (component / group string / array / system), parent node ID and power value. For example, the parent node of node A001 is the system level, contains child nodes S001-S010, and the real-time power is 25000W.

[0053] By defining the inter-level partitioning rules, building the power merging function and tree structure, a clear logical relationship photovoltaic hierarchical structure is formed, realizing the structured management and cross-level aggregation of power data from the component level to the system level, providing a standardized data model for subsequent hierarchical conduction analysis and path response calculation of power deviation, thereby improving the efficiency and accuracy of photovoltaic system power anomaly tracing.

[0054] Further, the step A300 in the method provided by the embodiment of the application comprises:

[0055] A310: identifying a power transmission path, numbering transmission units in the power transmission path as nodes to obtain a power node set.

[0056] A320: establishing a directed connection edge between nodes in the power node set according to an electrical connection relationship of the photovoltaic power station, and configuring an edge weight value based on a cable parameter, a connection resistance and a historical running attenuation characteristic.

[0057] A330: constructing a weighted directed graph structure according to the node set and the edge set.

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

[0059] In the embodiment of the application, the electrical connection relationship is an actual connection mode between power transmission units (such as components, strings, arrays, etc.) in the photovoltaic power station, including physical connection relationships such as series connection and parallel connection. The weighted directed graph structure is a graph model constructed according to the power node set and the directed connection edge set, wherein the node represents the power transmission unit, the directed edge represents the power transmission direction, and the edge weight value reflects the loss characteristic of the transmission path.

[0060] Specifically, first, the transmission units in the power transmission path are identified and numbered as nodes. Taking a 10MW power station as an example, the transmission units thereof include 2000 components (C001-C2000), 200 strings (S001-S200), 20 arrays (A001-A020) and combiner boxes, inverters, etc., a total of 2221 nodes. Each node is assigned a unique ID to form a power node set N={component level, string level, array level, system level}.

[0061] Secondly, the cross-level directed edges are established according to the electrical connection relationship: the components C001-C010 are connected in series to form the string S001, the edge direction is C001→S001, C002→S001, etc.; the strings S001-S010 are connected in parallel to form the array A001, the edge direction is S001→A001, etc. The configuration of the edge weight value needs to be determined in steps by comprehensively considering the cable parameter, the connection resistance and the historical attenuation characteristic, as shown in Table 1:

[0062] Component level → group string level: First, determine the basic weight value based on the cable physical parameters, such as the resistance of a 50-meter cable is 0.05Ω, and the basic weight value is directly taken as 0.05; when the joint resistance is 0.01Ω, the corresponding weight value is set as 0.01. Second, add the cable resistance weight value and the joint resistance weight value to obtain the initial edge weight value, for example, the cable resistance weight value of component C001 to group string S001 is 0.05, and the joint resistance weight value is 0.01, and the initial edge weight value is 0.06. Finally, according to the historical running attenuation characteristics, the initial weight value is corrected, if the cable running for 3 years has a decay rate of 5%, then the initial weight value is multiplied by (1+decay rate), that is, 0.06*1.05=0.063, to obtain the final edge weight value, which dynamically represents the influence of cable aging on power transmission.

[0063] Group string level → array level: 10 group strings S001-S010 are connected in parallel to form array A001, the edge direction is S001→A001, S002→A001, etc., and the edge weight value includes the cable resistance (0.07Ω) from group string to array and the busbar resistance (0.01Ω), and the cumulative value is 0.08, reflecting the transmission loss from group string to array.

[0064] Array level → system level: all arrays are connected in parallel to the system level node, the edge direction is A001→system level, A002→system level, etc., and the edge weight value is configured according to the cable parameters from array to inverter (such as 0.09Ω).

[0065] Then, the nodes and directed edges across layers are combined to form a tree-shaped weighted directed graph: the bottom layer component nodes converge to the middle layer group string nodes through directed edges, the group string nodes converge to the high layer array nodes through directed edges, and finally all array nodes converge to the system level node. The weight value 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 the visualization modeling of the power transmission path from the component level to the system level.

[0066] Finally, the weighted directed graph is converted into an adjacency matrix M, and the matrix dimension is 2221*2221. The matrix element M[i][j] represents the edge weight value from node i to j, and is 0 when there is no connection. For example, M[C001][S001]=0.063, M[S001][A001]=0.08, and other non-connected positions are 0, thereby obtaining a path matrix that accurately represents the power transmission characteristics.

[0067] By incorporating cable parameters, connection resistance and historical attenuation characteristics, the actual power transmission network is converted into an accurate mathematical matrix model, which provides a dynamic and high-precision operation basis for subsequent power deviation mapping, path response analysis and target node identification, thereby realizing accurate modeling and quantitative analysis of the power transmission characteristics of the photovoltaic power station.

[0068] Table 1: Power transmission path edge weight value configuration and hierarchical connection relationship table of photovoltaic power station

[0069]

[0070]

[0071] Further, the step A300 in the method provided by the embodiment of the application comprises:

[0072] A350: constructing a power deviation vector according to the relative power deviation amount of each component.

[0073] A360: performing matrix multiplication on the power deviation vector and the power transmission path matrix to obtain a path response weight vector.

[0074] A370: identifying a node with a response weight greater than a threshold value according to the path response weight vector to obtain the target optimization node.

[0075] Specifically, first, a power deviation vector is constructed according to the relative power deviation amount of each component. Taking 2000 components of a 10 MW power station as an example, if component C001 deviates by -5%, C002 deviates by +3%, and the remaining components deviate by 0, then the deviation vector V = [-0.05, 0.03,..., 0], with a dimension of 2000x1, and the vector elements correspond to the deviation amount of each component.

[0076] Second, the vector is multiplied by the power transmission path matrix M (2221x2221) to obtain a path response weight vector W (1x2221). During matrix operation, the edge weight value (such as 0.063) of the component to the string in the matrix M is multiplied by the deviation amount, and the response strength of each transmission node is reflected after accumulation.

[0077] Finally, a threshold value (such as 0.05) is set to screen the nodes with a weight greater than the threshold value in the weight vector to determine the target optimization node. If the array A001 weight 0.065>0.05, then it is listed as a target node and its transmission path needs to be optimized first.

[0078] By constructing the mathematical mapping model of the power deviation vector and the transmission matrix, the component-level deviation amount is converted into the full-path response weight distribution, realizing the quantitative analysis from local deviation to global key node, providing accurate node positioning basis for subsequent strategy optimization, and thus improving the targeting and efficiency of photovoltaic system power optimization.

[0079] Further, the step A400 in the method provided by the embodiment of the application comprises:

[0080] A410: obtaining a response radiation region of the target optimization node based on the power transmission path matrix, the response radiation region being a node set having a power transmission influence relationship with the target optimization node.

[0081] A420: synchronously collecting I-V characteristic curve data of the photovoltaic components in the response radiation region.

[0082] A430: extracting an inflection point voltage, a voltage decay rate, and a fill factor in the curve as mismatch characteristic quantities based on the I-V characteristic curve data.

[0083] A440: weighting and fusing the mismatch characteristic quantities and the edge weight values in the power transmission path matrix to calculate an inhibition coefficient of each node.

[0084] A450: screening nodes whose inhibition coefficients exceed a preset threshold to obtain mismatch nodes.

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

[0086] Second, I-V characteristic curve data of components in the radiation region are synchronously collected. A person skilled in the art uses a data collection device with a sampling rate of 100 Hz to obtain the curve under standard test conditions of 25°C, 1000 W / m 2 For example, the I-V curve of a certain component shows that the inflection point voltage decreases from 28 V to 25 V, the voltage decay rate is 10% (the standard value is ≤5%), and the fill factor decreases from 0.78 to 0.72.

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

[0088] Next, the inhibition coefficient is calculated by weighting and fusing the mismatch characteristic quantities and the edge weight values. Assuming that the edge weight value of the component C001 to the string node S001 is 0.063, and the weight distribution is 0.4 for the inflection point voltage, 0.3 for the voltage decay rate, and 0.3 for the fill factor, then the inhibition coefficient = 0.4x10.7% + 0.3x5% + 0.3x7.7% = 7.69%.

[0089] Finally, the nodes with an inhibition coefficient exceeding a preset threshold are screened as mismatched nodes. The preset threshold is obtained by dynamic calculation, and the specific steps are described in detail in A451-A453.

[0090] Through radiation area analysis of the power transmission path matrix, multi-dimensional I-V characteristic quantity extraction, and dynamic threshold weighted fusion, upgrading from single parameter alarm to path correlation inhibition analysis is realized, accurate positioning of nodes with significant inhibition effect on power transmission is achieved, targeted regulation objects are provided for subsequent strategy optimization, thereby improving the power aggregation efficiency and operation stability of the photovoltaic system.

[0091] Further, the step A450 in the method provided by the embodiment of the application comprises:

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

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

[0094] A453: Establish the weight relationship of the fill factor standard deviation, the light ratio coefficient, and the dynamic threshold, perform weighted calculation, and obtain the preset threshold.

[0095] In one embodiment, first, the historical fill factor data of the target photovoltaic module is extracted. Taking a certain module in a string as an example, the fill factors of the module in the past 30 standard test days (light intensity 1000 W / m 2 , 25℃) are collected: 0.77, 0.78, 0.76, etc., and the average value is calculated as 0.775, and the standard deviation is calculated as 0.0075. This value reflects the consistency of the performance of the module itself - the larger the standard deviation, the more intense the historical fluctuation of the module, and a more tolerant judgment space needs to be reserved in the threshold.

[0096] At the same time, the current environmental light intensity is collected in real time. Assuming that the standard test light S0 = 1000 W / m 2 , the actual light S = 800 W / m 2 , the light ratio coefficient k = S / S0 = 0.8 is calculated. In low light (such as k = 0.5, corresponding to light intensity 500 W / m 2 ), environmental interference (such as cloud cover) makes the system more unstable, and the threshold needs to be increased to avoid misjudgment; in high light (such as k = 0.95, corresponding to light intensity 950 W / m 2 ), the environment is more stable, and the mismatch characteristics of the module itself can be focused.

[0097] Subsequently, the influence of the two is fused through a weight formula: Tdynamic = 0.4 x (1 + e -k ) + 0.6 x σ PP . Substituting k = 0.8, σ pp = 0.015, we get T dynamic = 0.4 x (1 + e -0.8 ) + 0.6 x 0.015 ≈ 0.5886. If k = 0.5 (low light), superimposed σ pp = 0.02 (greater component fluctuation), then T dynamic = 0.4 x (1 + e -0.5 ) + 0.6 x 0.02 ≈ 0.6548, the threshold value increases synchronously with the environment instability and component fluctuation; if k = 0.95 (high light), superimposed σ pp = 0.01 (stable component), then T dynamic = 0.4 x (1 + e -0.95 ) + 0.6 x 0.01 ≈ 0.5548, the preset threshold value decreases synchronously with the environment stability and component consistency.

[0098] By quantifying the component historical consistency first, then depicting the current environment stability, and finally constructing the dynamic threshold value by weighting, the method breaks through the limitations of the traditional fixed threshold value: in low light, the threshold value is increased by amplifying the environmental impact to resist noise interference; in high light, the threshold value is reduced by focusing on the component difference to capture implicit mismatches, thereby achieving the balance between the accuracy and robustness of mismatch node screening under different working conditions.

[0099] Further, the step A500 in the method provided by the embodiment of the application comprises:

[0100] A510: Analyzing the path response relationship of each photovoltaic level to the power station output power according to the power transmission path matrix, and constructing a target function with the maximum system output power as the target.

[0101] A520: Under the constraint of the component working parameter adjustment, based on the regulation and control influence of the target optimization node and the mismatch node, adjusting the control parameters of multiple nodes, and performing strategy evaluation and screening through the target function to obtain the photovoltaic control strategy with the maximum output power.

[0102] Optionally, first, the response relationship of each level is analyzed based on the power transmission path matrix. Taking a 10MW power station as an example, the row vector [0.08, 0.07,..., 0] of the array level node A001 in the matrix M (2221 x 2221) represents the transmission weight of A001 with the subordinate 200 string nodes, and the contribution coefficient of A001 to the system power is 0.15 through matrix row operation, that is, the system power is theoretically increased by 0.15kW when the power of A001 is increased by 1kW. The target function F = Σ (α i x Pi ), wherein a i is the system response coefficient of node i, such as component level a = 0.001, string level a = 0.01, and array level a = 0.1; P i is the real-time power of the node, realizing multi-level power mapping from the component to the system.

[0103] Secondly, the strategy adjustment is performed under the constraint of the component working parameter. Taking the target optimization node S001 (a certain string) and the mismatch node C005 (a component in the string) as examples, the adjustable range of the component voltage is set to 25-35V and the current is set to 5-8A: when the C005 inflection point voltage is increased from 25V to 28V, the adjustment amount is 12%, and the power contribution increment of S001 is calculated as 0.063x(28-25)x5 = 0.945W according to the foregoing edge weight 0.063. At the same time, the junction box impedance of S001 needs to be adjusted from 0.08Ω to 0.07Ω, and the combined strategy is calculated through the target function to make the system power prediction increase by 1.2kW, which is better than the 0.5kW increase effect of adjusting C005 alone.

[0104] By combining the path response analysis with the dynamic strategy adjustment, the method breaks through the limitation of the traditional single optimization, effectively improves the system aggregate power, and realizes the precise regulation and control from the local node optimization to the global power optimization.

[0105] Further, the step A530 in the method provided by the embodiment of the application comprises:

[0106] A531: converting the photovoltaic control strategy into node injection amounts of the power transmission path matrix, and analyzing the power aggregation relationship and the control feedback path of each node injection amount among the component level, the string level and the array level based on the multi-level structure.

[0107] A532: constructing a system power distribution model based on the power aggregation relationship and the control feedback path, calculating a global power redistribution result, predicting voltage response values of each level key node, and determining voltage offset amounts of each key node relative to a stable running state.

[0108] A533: when any node in the voltage offset amount exceeds a safety threshold set for the corresponding level, generating a strategy correction instruction, and adjusting a related control parameter in the photovoltaic control strategy or blocking the execution.

[0109] In one embodiment, first, the photovoltaic control strategy is converted into the node injection amount of the power transmission path matrix. For example, a certain strategy requires that the power of component C001 be increased by 10%, from 240W to 264W, which is converted into the node injection amount +24W in the matrix. Based on the edge weight value of the matrix, the C001→S001 edge weight value is 0.063, and it is analyzed that the power of string S001 will increase by 24*0.063≈1.51W, and the power of array A001 increases by 1.51*0.08≈0.12W (S001→A001 edge weight), forming a power convergence conduction chain of component→string→array. At the same time, the control feedback path shows that the voltage feedback of A001 will affect the subsequent adjustment of C001, and a closed-loop control logic is constructed.

[0110] Secondly, based on the power convergence relationship, a system power distribution model is constructed. 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 between the component level, string level and array level are analyzed based on the multi-level structure; then, based on the above power convergence relationship and control feedback path, a system power distribution model is constructed. After inputting the node injection amount, the model calculates the full network power redistribution result: for example, if the total power of string S001 increases from 2490W to 2491.51W after C001 adjustment, and the total power of array A001 increases from 25000W to 25000.12W, the total power of the system increases from 500000W to 500000.12W. The key node voltage response is simultaneously predicted: the voltage of S001 increases from 24V to 24.02V, with a deviation of 0.08%, and the voltage of A001 increases from 380V to 380.05V, with a deviation of 0.013%, both of which are within the safety threshold. If another strategy causes the voltage of an array to deviate by 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 instruction. For example, a certain strategy causes the voltage of string S005 to deviate by 6% after component C050 is adjusted, and 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 be 4.8%, which meets the requirements, and then issues the instruction; if the threshold is still exceeded, the execution is blocked. For example, the voltage of array A010 deviates by 7% after the strategy adjustment, and the system still fails to meet the requirements after three consecutive parameter corrections, and finally blocks the strategy to avoid equipment damage.

[0112] By converting the strategy into node injection amount and analyzing the power conduction path, combined with real-time voltage deviation prediction and dynamic correction mechanism, the safety verification of the control strategy from generation to execution is realized, ensuring that the photovoltaic system always maintains voltage stability during the strategy optimization process, avoiding the risk of overload caused by strategy adjustment, thereby improving the safety of power station 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] Obtain the geographical distribution position of the photovoltaic components in the photovoltaic power station and the connection relationship thereof, identify the power partition level in the power station, including the component level, the string level, the array level and the system level; according to the power partition level, construct the photovoltaic hierarchical structure, and establish the mapping and association relationship between each node in the photovoltaic hierarchical structure and the monitoring and collecting device; project the real-time monitoring data of the photovoltaic components into the photovoltaic hierarchical structure according to the mapping and association relationship, obtain the multi-level photovoltaic power structure, including the multi-level power node and the data mapping photovoltaic power structure.

[0123] Further, the interactive photovoltaic component monitoring and collecting module 1 is used to perform the following steps:

[0124] Obtain the inter-level partition rule of the power partition level, including the component attribution relationship, the string division logic and the array composition specification; based on the inter-level partition rule, obtain the hierarchical power summary relationship, including the merging relationship function of the power of the lower level node to the upper level node; according to the inter-level partition rule and the hierarchical power summary relationship, build the hierarchical structure according to the tree structure, and obtain the photovoltaic hierarchical structure.

[0125] Further, 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; according to the electrical connection relationship of the photovoltaic power station, establish the directed connection edge between the nodes in the power node set, and configure the edge weight value based on the cable parameter, the connection resistance and the historical operation attenuation characteristics; according to the node set and the edge set, construct a weighted directed graph structure; convert the weighted directed graph structure into an adjacency matrix to obtain the power transmission path matrix of the photovoltaic power station.

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

[0128] According to the relative power deviation amount of each component, construct a power deviation vector; perform matrix multiplication operation on the power deviation vector and the power transmission path matrix to obtain a path response weight vector; according to the path response weight vector, identify the nodes with response weight greater than a threshold value to obtain the target optimization node.

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

[0130] Based on the power transmission path matrix, a response radiation area of the target optimization node is obtained, the response radiation area being a node set having a power transmission influence relationship with the target optimization node; I-V characteristic curve data of photovoltaic components in the response radiation area are synchronously collected; based on the I-V characteristic curve data, an inflection point voltage, a voltage decay rate, and a fill factor in the curve are extracted as mismatch characteristic quantities; the mismatch characteristic quantities are weighted and fused with edge weights in the power transmission path matrix to calculate a node suppression coefficient; nodes whose node suppression coefficients exceed a preset threshold are screened to obtain the mismatch nodes.

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

[0132] A historical fill factor of a target photovoltaic component under standard test conditions is obtained, and a fill factor standard deviation is calculated; an actual light intensity value under a current environmental light is obtained, and a ratio of the actual light intensity value to a light intensity under the standard test conditions is calculated to obtain a light ratio coefficient; a weight relationship of the fill factor standard deviation, the light ratio coefficient, and a dynamic threshold is established, and a weighted calculation is performed to obtain the preset threshold.

[0133] Further, the photovoltaic component working state adjustment module 5 is configured to perform the following steps:

[0134] According to the power transmission path matrix, a path response relationship of each photovoltaic level to power station output power is analyzed, and a target function with a maximum system output power as a target is constructed; under a constraint of adjustable component working parameters, based on regulation and control influence of the target optimization node and the mismatch node, control parameters of multiple nodes are adjusted, strategy evaluation and screening are performed through the target function, and a photovoltaic control strategy with maximum output power is obtained.

[0135] Further, the photovoltaic component working state adjustment module 5 is configured to perform the following steps:

[0136] The photovoltaic control strategy is converted into a node injection amount of the power transmission path matrix, a power convergence relationship and a control feedback path of each node injection amount between a component level, a string level, and an array level are analyzed based on a multi-level structure; based on the power convergence relationship and the control feedback path, a system power distribution model is constructed, a full-network power redistribution result is calculated, voltage response values of key nodes at each level are predicted, and voltage offsets of the key nodes relative to a stable operating state are determined; when the voltage offset of any node exceeds a safety threshold set at a corresponding level, a strategy correction instruction is generated, and related control parameters in the photovoltaic control strategy are adjusted or execution is blocked.

[0137] The power deviation based photovoltaic module control strategy optimization system provided by the embodiments of the present application can execute the power deviation based photovoltaic module control strategy optimization method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0138] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules are only divided according to the functional logic, and are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual differentiation, and do not limit the protection scope of the present application.

[0139] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 the present application shall be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be executed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A method for optimizing a control strategy for a photovoltaic module based on power deviation, characterized in that, The method comprises the following steps: An interactive photovoltaic module monitoring and collecting module acquires real-time monitoring data of each photovoltaic module, including real-time output power, and constructs a multi-level photovoltaic power structure; Power deviation operations are performed on each photovoltaic module based on the multi-level photovoltaic power structure to obtain relative power deviation amounts of each module; A power transmission path matrix of a photovoltaic power station is established, the relative power deviation amounts of the modules are mapped into the power transmission path matrix, path response analysis is performed, and a target optimization node is identified; According to the response radiation area of the target optimization node in the power transmission path matrix, a mismatch node that has an inhibitory effect on path power transmission is identified; A photovoltaic control strategy is obtained by performing strategy optimization based on the regulation and control influence of the target optimization node and the mismatch node, with the goal of maximizing the effective aggregate power of photovoltaic power, and is used to adjust the working state of the photovoltaic module.

2. The power offset based photovoltaic module control strategy optimization method of claim 1, wherein, The multi-level photovoltaic power structure is constructed, comprising: The geographical distribution positions and connection relationships of photovoltaic modules in a photovoltaic power station are acquired, power partition levels in the power station are identified, including the module level, the string level, the array level, and the system level; According to the power partition levels, a photovoltaic hierarchical structure is constructed, and a mapping and association relationship between each node in the photovoltaic hierarchical structure and a monitoring and collecting device is established; Real-time monitoring data of the photovoltaic modules are projected into the photovoltaic hierarchical structure according to the mapping and association relationship to obtain the multi-level photovoltaic power structure, which contains multi-level power nodes and data-mapped photovoltaic power structures.

3. The method of claim 2, wherein, According to the power partition levels, a photovoltaic hierarchical structure is constructed, comprising: The inter-level partition rules of the power partition levels are acquired, including module attribution relationships, string division logic, and array composition specifications; Based on the inter-level partition rules, a hierarchical power aggregation relationship is obtained, including a merging relationship function of lower-level node power to upper-level nodes; According to the inter-level partition rules and the hierarchical power aggregation relationship, a hierarchical structure is built in a tree structure to obtain the photovoltaic hierarchical structure.

4. The power offset based photovoltaic array control strategy optimization method of claim 1, wherein, The power transmission path matrix of the photovoltaic power station is established, comprising: Power transmission paths are identified, transmission units in the power transmission paths are numbered as nodes to obtain a power node set; Based on the electrical connection relationship of the photovoltaic power station, directed connection edges between nodes in the power node set are established, and edge weights are configured based on cable parameters, connection resistance, and historical operation attenuation characteristics; Based on the node set and the edge set, a weighted directed graph structure is constructed; The weighted directed graph structure is converted into an adjacency matrix to obtain the power transmission path matrix of the photovoltaic power station.

5. The method of claim 4, wherein, The target optimization node is identified, comprising: A power deviation vector is constructed according to the relative power deviation amounts of the modules; A matrix multiplication operation is performed on the power deviation vector and the power transmission path matrix to obtain a path response weight vector; According to the path response weight vector, nodes with a response weight greater than a threshold value are identified to obtain the target optimization node.

6. The method of claim 5, wherein, According to the response radiation area of the target optimization node in the power transmission path matrix, a mismatch node that has an inhibitory effect on path power transmission is identified, comprising: Based on the power transmission path matrix, a response radiation area of the target optimization node is obtained, and the response radiation area is a node set having a power transmission influence relationship with the target optimization node; I-V characteristic curve data of the photovoltaic components in the response radiation area are synchronously collected; Based on the I-V characteristic curve data, inflection point voltage, voltage attenuation rate, and fill factor in the curve are extracted as mismatch characteristic quantities; The mismatch characteristic quantities are weighted and fused with edge weights in the power transmission path matrix to calculate a node suppression coefficient of each node; The nodes whose node suppression coefficients exceed a preset threshold are screened to obtain the mismatch nodes.

7. The power offset based photovoltaic array control strategy optimization method of claim 6, wherein, Screening the nodes whose node suppression coefficients exceed a preset threshold includes: A historical fill factor of a target photovoltaic component under standard test conditions is obtained, and a fill factor standard deviation is calculated; An actual light intensity value under current environmental light is obtained, and a light ratio coefficient is obtained by ratio calculation of the actual light intensity value and the light intensity under the standard test conditions; A weight relationship of the fill factor standard deviation, the light ratio coefficient, and a dynamic threshold is established, and a preset threshold is obtained by weighted calculation.

8. The power offset based photovoltaic array control strategy optimization method of claim 1, wherein, A photovoltaic control strategy is obtained by performing strategy optimization based on the regulation and control influence of the target optimization node and the mismatch nodes, with the maximum effective aggregate power of photovoltaic as the target, for adjusting the working state of the photovoltaic components, including: According to the power transmission path matrix, the path response relationship of each photovoltaic level to the power station output power is analyzed, and a target function with the maximum system output power as the target is constructed; Under the constraint of adjustable component working parameters, based on the regulation and control influence of the target optimization node and the mismatch nodes, the control parameters of multiple nodes are adjusted, the strategy is evaluated and screened through the target function, and a photovoltaic control strategy with the maximum output power is obtained.

9. The method of claim 8, wherein, Further comprising: The photovoltaic control strategy is converted into node injection amounts of the power transmission path matrix, and based on the multi-level structure, the power aggregation relationship and control feedback path of each node injection amount between the component level, the string level, and the array level are analyzed; Based on the power aggregation relationship and the control feedback path, a system power distribution model is constructed, the full-network power redistribution result is calculated, the voltage response value of each level key node is predicted, and the voltage offset of each key node relative to the stable running state is determined; When any node in the voltage offset exceeds the safety threshold set for the corresponding level, a strategy correction instruction is generated to adjust the related control parameters in the photovoltaic control strategy or block the execution.

10. A power deviation based photovoltaic module control strategy optimization system, characterized in that, The system is used to implement the photovoltaic component control strategy optimization method based on power deviation according to any one of claims 1-9, and the system comprises: An interactive photovoltaic component monitoring and collecting module is configured to obtain real-time monitoring data of each photovoltaic component, including real-time output power, and construct a multi-level photovoltaic power structure; A relative power deviation amount obtaining module is configured to perform power deviation operation on each photovoltaic component based on the multi-level photovoltaic power structure to obtain a relative power deviation amount of each component; The target optimization node identification module is configured to establish a power transmission path matrix of the photovoltaic power station, map the relative power deviation of each component into the power transmission path matrix, perform path response analysis, and identify a target optimization node. The mismatch node identification module is configured to identify a mismatch node that has an inhibitory effect on path power transmission according to a response radiation area of the target optimization node in the power transmission path matrix. The photovoltaic component working state adjustment module is configured to take the maximum effective aggregate power of the photovoltaic power station as a target, perform strategy optimization based on the regulation and control influence of the target optimization node and the mismatch node, obtain a photovoltaic control strategy, and adjust the working state of the photovoltaic component.

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