Photovoltaic module control method and system based on load feedback

Abstract

The invention discloses a photovoltaic module control method and system based on load feedback, and relates to the technical field related to power grid management, and the method comprises the steps: collecting the construction planning data of a target photovoltaic power station; extracting a load power data set and output power, and performing window equalization analysis; identifying the difference degree between the feedback load power and the output power; performing output power interval identification based on the configuration data; identifying the output power by using an equalization controller; performing fuzzy equilibrium search to generate an abnormal control scheme; and controlling the photovoltaic assembly module set. The technical problems that the existing photovoltaic module control lacks dynamic response to the actual operation environment and cannot obtain the accurate load demand to adjust the output power of the photovoltaic module in real time are solved, the dynamic matching and intelligent adjustment between the photovoltaic module and the load demand are realized, the service life of the photovoltaic module is prolonged, and the power consumption of the photovoltaic module is reduced. And the technical effect of efficient and stable operation of photovoltaic power generation is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic management technology, in particular to a photovoltaic module control method and system based on load feedback. BACKGROUND

[0002] With the transformation of global energy structure and the rapid development of renewable energy, photovoltaic modules as the core components of solar power generation, their operating efficiency and stability have become the key factors determining the performance of solar power generation systems. In photovoltaic systems, the electrical energy output of photovoltaic modules is often affected by various factors such as light intensity, temperature, and shadow blocking. How to achieve intelligent control of photovoltaic modules so that they can maintain optimal working conditions under different environmental conditions is particularly important. However, traditional photovoltaic module control often uses fixed operating points, i.e., photovoltaic modules output electrical energy at fixed voltage and current under specific conditions (such as specific light and ambient temperature), which cannot adapt to these changes in real time, lack intelligent adjustment mechanisms, and cannot adjust the output of photovoltaic modules in real time according to the actual needs of the load, resulting in the output power of photovoltaic modules cannot always remain in the optimal state.

[0003] Therefore, in the current photovoltaic module control related technology, there is a technical problem of relying on fixed operating parameters and preset working modes, lacking dynamic response to actual operating environment, and being unable to obtain accurate load demand to adjust the output power of photovoltaic modules in real time. SUMMARY

[0004] The present application provides a photovoltaic module control method and system based on load feedback, which uses window balancing analysis, load power identification, and balancing controller technology to solve the technical problems of relying on fixed operating parameters and preset working modes, lacking dynamic response to actual operating environment, and being unable to obtain accurate load demand to adjust the output power of photovoltaic modules in real time. The present application realizes dynamic matching and intelligent adjustment between photovoltaic modules and load demand, and achieves the technical effects of prolonging the service life of photovoltaic modules and making photovoltaic power generation operate efficiently and stably.

[0005] The application provides a photovoltaic module control method based on load feedback, which comprises the following steps: collecting construction planning data of a target photovoltaic power station; extracting K load power data sets of K load areas within a preset feedback window, and performing window equalization analysis according to the fluctuation of the K load power data sets to obtain K feedback load powers; traversing and extracting output powers of K photovoltaic module modules within the preset feedback window, and performing window equalization analysis on the K module output power sets to obtain K module output powers; respectively using a consistency identifier to identify the difference between the K feedback load powers and the K module output powers, and obtaining M overloaded photovoltaic module modules and N underloaded photovoltaic module modules according to the identification results; identifying an output power interval based on the K photovoltaic module configuration data to generate K identification results, taking the K identification results as control constraints, combining the K module output powers, the M overloaded power identifiers and the N underloaded power identifiers to perform control analysis on the M overloaded photovoltaic module modules and the N underloaded photovoltaic module modules, and obtaining a conventional control photovoltaic module module set and an abnormal control photovoltaic module module set; taking the power identifier of the conventional control photovoltaic module module set as an equalization control target, using an equalization controller to identify the module output power corresponding to the conventional control photovoltaic module module set to obtain a conventional control parameter set; performing fuzzy equalization search on the abnormal control photovoltaic module module set to generate an abnormal control scheme; and respectively using the conventional control parameter set and the abnormal control scheme to control the conventional control photovoltaic module module set and the abnormal control photovoltaic module module set.

[0006] In a possible implementation, the K load power data sets of the K load areas within the preset feedback window are extracted, and window equalization analysis is performed according to the fluctuation of the K load power data sets to obtain the K feedback load powers, and the following processing is performed: K equalization particle two-dimensional spaces are respectively constructed according to the K module output power sets, wherein the K equalization particle two-dimensional spaces have K particle sets, and each particle corresponds to a module output power; a first equalization particle two-dimensional space is extracted from the K equalization particle two-dimensional spaces, wherein the first equalization particle two-dimensional space has a plurality of first particles; two first particles are extracted from the plurality of first particles in a random extraction manner to construct a first equalization straight line; window equalization analysis is performed in the first equalization particle two-dimensional space based on the first equalization straight line to obtain a first module output power; and window equalization analysis is performed in the K equalization particle two-dimensional spaces to obtain the K feedback load powers.

[0007] In a possible implementation, based on the first equalization straight line, window equalization analysis is performed in the first equalization particle two-dimensional space to obtain a first module output power, and the following processing is performed: a plurality of equalization straight lines are constructed by extracting two first particles from the first equalization particle two-dimensional space in a random manner multiple times; particle aggregation amounts of the first equalization straight line and the plurality of equalization straight lines in a preset bandwidth are extracted, and an equalization straight line corresponding to a maximum particle aggregation amount is taken as a target equalization straight line; the target equalization straight line is taken as a starting point to perform a moving search in the first equalization particle two-dimensional space to obtain an expected equalization straight line; and a plurality of first particles in the preset bandwidth of the expected equalization straight line are weighted and calculated according to distances from the expected equalization straight line to obtain a first feedback load power.

[0008] In a possible implementation, the target equalization straight line is taken as a starting point to perform a moving search in the first equalization particle two-dimensional space to obtain an expected equalization straight line, and the following processing is further performed: after the target equalization straight line is moved by a distance of the preset bandwidth, a first moving straight line is obtained; a particle aggregation amount difference between the target equalization straight line and the first moving straight line is compared to determine whether the particle aggregation amount difference satisfies a preset gain; if not, the first moving straight line is taken as a starting point to perform a moving search to obtain a second moving straight line; and if yes, the target equalization straight line is taken as the expected equalization straight line.

[0009] In a possible implementation, control analysis is performed on the M over-loaded photovoltaic module and the N under-loaded photovoltaic module to obtain a conventional control photovoltaic module set and an abnormal control photovoltaic module set, and the following processing is further performed: module output power and power identifier directionality superposition are performed on the M over-loaded photovoltaic module and the N under-loaded photovoltaic module to obtain L target output powers, L = M+N; the K identification results are taken as control constraints to determine whether the L target output powers satisfy the control constraints; if yes, a corresponding photovoltaic module is added to the conventional control photovoltaic module set; and if not, the corresponding photovoltaic module is added to the abnormal control photovoltaic module set.

[0010] In a possible implementation, the fuzzy balanced search is performed on the set of abnormality control photovoltaic module groups to generate an abnormality control scheme, and the following processing is further performed: extracting a plurality of under-load abnormality control photovoltaic module groups and a plurality of over-load abnormality control photovoltaic module groups from the set of abnormality control photovoltaic module groups; performing fuzzy matching on the plurality of over-load abnormality control photovoltaic module groups respectively indexed by a plurality of under-load power identifiers of the plurality of under-load abnormality control photovoltaic module groups to obtain a plurality of matched over-load module sets, wherein the matched over-load module set is an over-load abnormality control photovoltaic module group having a power difference within a preset adjustable threshold from a corresponding under-load abnormality control photovoltaic module group; and performing abnormality control scheme optimization based on the plurality of under-load abnormality control photovoltaic module groups and the plurality of matched over-load module sets to obtain the abnormality control scheme.

[0011] In a possible implementation, the fuzzy balanced search is performed on the set of abnormality control photovoltaic module groups to generate an abnormality control scheme, and the following processing is further performed: extracting a plurality of under-load abnormality control photovoltaic module groups and a plurality of over-load abnormality control photovoltaic module groups from the set of abnormality control photovoltaic module groups; performing fuzzy matching on the plurality of over-load abnormality control photovoltaic module groups respectively indexed by a plurality of under-load power identifiers of the plurality of under-load abnormality control photovoltaic module groups to obtain a plurality of matched over-load module sets, wherein the matched over-load module set is an over-load abnormality control photovoltaic module group having a power difference within a preset adjustable threshold from a corresponding under-load abnormality control photovoltaic module group; and performing abnormality control scheme optimization based on the plurality of under-load abnormality control photovoltaic module groups and the plurality of matched over-load module sets to obtain the abnormality control scheme.

[0012] In a possible implementation, the photovoltaic module control is performed on the set of normal control photovoltaic module groups and the set of abnormality control photovoltaic module groups, and the following processing is further performed: when the fuzzy matching fails, adding the under-load abnormality control photovoltaic module group into a pre-warning module set; adding the over-load abnormality control photovoltaic module group that is not matched into the pre-warning module set; generating a pre-warning instruction according to the pre-warning module set, and sending the pre-warning instruction to a worker.

[0013] In a possible implementation, the photovoltaic module control method based on load feedback further performs the following processing: monitoring the set of normal control photovoltaic module groups and the set of abnormality control photovoltaic module groups in a preset control feedback period, and generating a pre-warning instruction according to a monitoring result The application further provides a photovoltaic module control system based on load feedback, comprising: A construction planning data acquisition module is configured to acquire construction planning data of a target photovoltaic power station, wherein the construction planning data comprises K photovoltaic module-load area mapping relationships and K photovoltaic module configuration data.

[0014] a load power balance analysis module configured to extract K load power data sets of K load areas within a preset feedback window, and perform window balance analysis according to fluctuations of the K load power data sets to obtain K feedback load powers.

[0015] an output power balance analysis module configured to traverse output powers of the K photovoltaic module within a preset feedback window, and perform window balance analysis on K module output power sets to obtain K module output powers.

[0016] a difference degree identification module configured to identify a difference degree between the K feedback load powers and the K module output powers by using a consistency identifier respectively, and obtain M overloaded photovoltaic modules and N underloaded photovoltaic modules according to an identification result, the M overloaded photovoltaic modules including M overloaded power identifiers, and the N underloaded photovoltaic modules including N underloaded power identifiers.

[0017] an output power interval identification module configured to perform output power interval identification based on the K photovoltaic module configuration data to generate K identification results, and perform control analysis on the M overloaded photovoltaic modules and the N underloaded photovoltaic modules by using the K identification results as control constraints, combining the K module output powers, the M overloaded power identifiers and the N underloaded power identifiers, to obtain a set of regular control photovoltaic modules and a set of abnormal control photovoltaic modules.

[0018] a set of regular control parameters obtaining module configured to use power identifiers of the set of regular control photovoltaic modules as balance control targets, and use a balance controller to identify module output powers corresponding to the set of regular control photovoltaic modules to obtain a set of regular control parameters. an abnormal control scheme generation module configured to perform fuzzy balance search on the set of abnormal control photovoltaic modules to generate an abnormal control scheme.

[0019] a photovoltaic module control module configured to perform photovoltaic module control on the set of regular control photovoltaic modules and the set of abnormal control photovoltaic modules by using the set of regular control parameters and the abnormal control scheme respectively.

[0020] The photovoltaic module control method and system based on load feedback provided in the application collect construction planning data of a target photovoltaic power station, including K photovoltaic module-load area mapping relationships and K photovoltaic module configuration data; extract a load power data set and output power in a preset feedback window and perform window equalization analysis; identify the difference between the feedback load power and the output power, and obtain M overloaded photovoltaic module and N underloaded photovoltaic module according to the identification result; perform output power interval identification based on the configuration data, generate K identification results, perform control analysis on the M overloaded photovoltaic module and the N underloaded photovoltaic module, and obtain a conventional control photovoltaic module set and an abnormal control photovoltaic module set; take the power identifier of the conventional control photovoltaic module set as the equalization control target, and identify the output power by using an equalization controller; perform fuzzy equalization search on the abnormal control photovoltaic module set, and generate an abnormal control scheme; and perform photovoltaic module control on the conventional control photovoltaic module set and the abnormal control photovoltaic module set by using the conventional control parameter set and the abnormal control scheme respectively. The technical problems that the existing photovoltaic module control depends on fixed operation parameters and preset working modes, lacks dynamic response to the actual operation environment, and cannot obtain accurate load demand to adjust the output power of the photovoltaic module in real time are solved, the dynamic matching and intelligent adjustment between the photovoltaic module and the load demand are realized, and the technical effects of prolonging the service life of the photovoltaic module and enabling the photovoltaic power generation to operate efficiently and stably are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0022] Figure 1 A photovoltaic module control method flow diagram based on load feedback is provided for the embodiments of the present application; Figure 2 A photovoltaic module control system structure diagram based on load feedback is provided for the embodiments of the present application.

[0023] Marked with a figure: construction planning data acquisition module 10, load power equalization analysis module 20, output power equalization analysis module 30, difference degree identification module 40, output power interval identification module 50, conventional control parameter set obtaining module 60, abnormal control scheme generation module 70, photovoltaic module control module 80. DETAILED DESCRIPTION

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0025] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0026] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0027] The embodiment of the present application provides a photovoltaic module control method based on load feedback, such as Figure 1 As shown, the method includes: Step S100, collecting construction planning data of the target photovoltaic power station, wherein the construction planning data includes K photovoltaic module-load area mapping relationships and K photovoltaic module module configuration data. The photovoltaic module-load area mapping relationship refers to matching and corresponding each photovoltaic module in the photovoltaic power station with the load area they supply power. In the target photovoltaic power station, there are K photovoltaic module modules distributed in different locations. The electricity generated by each module needs to be supplied to a specific load area, such as a specific factory, industrial park, residential area, etc. The photovoltaic module modules and the load area are mapped based on various factors such as geographical distribution, transmission distance, and load demand to achieve optimal distribution of electricity and reduce transmission losses. The photovoltaic module module configuration data refers to the inverter parameters for configuring the photovoltaic module module and the number of photovoltaic modules in the photovoltaic module module. Based on this data, the power supply range of each photovoltaic module can be determined.

[0028] Step S200, extract K load power data sets of K load areas in the preset feedback window, and perform window equalization analysis according to the fluctuation of the K load power data sets, and obtain K feedback load powers. Specifically, the preset feedback window is a set time range for collecting and analyzing load power data. In this window, the load power data sets of K load areas are extracted, and the fluctuation of these load power data sets is analyzed. Fluctuation refers to the degree and trend of change of load power at different time points, which can understand the stability, periodicity and possible abnormal change of load power. Window equalization analysis is a method of adjusting and optimizing the output power of photovoltaic modules based on the fluctuation of load power data sets. Through equalization analysis, we can determine the average power demand, maximum and minimum power demand, and demand change rate of each load area in the feedback window. Based on these analysis results, we can obtain K feedback load powers. Feedback load power is dynamically adjusted according to the actual demand of the load area and the output capacity of the photovoltaic module, aiming to achieve the best match between the photovoltaic module and the load.

[0029] In a possible implementation, the step S200 further includes a step S210 of constructing K equilibrium particle two-dimensional spaces respectively according to the K module output power sets, where the K equilibrium particle two-dimensional spaces have K particle sets, and each particle corresponds to a module output power. Specifically, the K equilibrium particle two-dimensional spaces are constructed according to the K module output power sets, the equilibrium particle two-dimensional space is an analog space for representing and optimizing the output power of the module, in the equilibrium particle two-dimensional space, the x and y axes respectively represent time and module output power, and each equilibrium particle two-dimensional space has K particle sets, and each particle corresponds to a module output power. The step S200 further includes a step S220 of extracting a first equilibrium particle two-dimensional space from the K equilibrium particle two-dimensional spaces, where the first equilibrium particle two-dimensional space has a plurality of first particles. Specifically, the first equilibrium particle two-dimensional space is any one of the K equilibrium particle two-dimensional spaces. The step S200 further includes a step S230 of extracting two first particles from the plurality of first particles in a random extraction manner to construct a first equilibrium straight line. The two first particles are randomly extracted from the plurality of first particles of the first equilibrium particle space to construct the first equilibrium straight line, and specifically, a straight line is determined by a certain mathematical method (for example, linear interpolation, regression analysis, or the like) based on the two first particles randomly extracted, which represents the equilibrium state of the output power of the photovoltaic module in the equilibrium particle two-dimensional space. The step S200 further includes a step S240 of performing window equilibrium analysis in the first equilibrium particle two-dimensional space based on the first equilibrium straight line to obtain a first module output power. The window equilibrium analysis is performed by using the first equilibrium straight line to evaluate the performance and efficiency of different output power states, and by analyzing the distribution, density change, and relative position relationship with the straight line of the particles near the equilibrium straight line, it can be determined which output power state is closer to the equilibrium state or the optimal solution, and the optimal output power of the module in a given window, that is, the first module output power, is found. The step S200 further includes a step S250 of performing window equilibrium analysis in the K equilibrium particle two-dimensional spaces to obtain K feedback load powers. In the control strategy of the photovoltaic power station, the window equilibrium analysis is performed on the K equilibrium particle two-dimensional spaces corresponding to the K photovoltaic modules, so that K feedback load powers corresponding to the K photovoltaic modules are obtained. The feedback load power is a power value that should be output by the module in a specific window according to the optimization analysis, to meet the actual demand of the load and achieve the optimal operation state of the system, and respectively corresponds to the K photovoltaic modules, and can be used as a basis for adjusting and controlling the output of the photovoltaic module, to ensure the efficient and stable operation of the entire photovoltaic power station.

[0030] In a possible implementation, step S240 further includes step S241 of extracting two first particles from the first equalization particle two-dimensional space by random extraction to construct a plurality of equalization straight lines. Specifically, step S230 is repeated to extract a plurality of first particles to construct a plurality of equalization straight lines. Step S242 of extracting particle aggregation amounts of the first equalization straight line and the plurality of equalization straight lines within a preset bandwidth and taking an equalization straight line corresponding to a maximum particle aggregation amount as a target equalization straight line is further included. The particle aggregation amount is the number of particles with a distance from the first equalization straight line less than or equal to half of the preset bandwidth, the particle aggregation amounts of the plurality of equalization straight lines are compared, and an equalization straight line corresponding to a maximum particle aggregation amount is taken as the target equalization straight line. Step S243 of taking the target equalization straight line as a starting point to perform a moving search in the first equalization particle two-dimensional space to obtain an expected equalization straight line is further included. The moving search is an optimization algorithm that finds a better solution by constantly moving and adjusting in the search space. Specifically, in the first equalization particle two-dimensional space, the algorithm takes the target equalization straight line as a starting point, moves and adjusts step by step according to a certain search rule (such as gradient descent, random walk, heuristic search, etc.), and gradually approaches a better equalization state through continuous movement and search, and finally finds an expected equalization straight line representing a better photovoltaic module output power. Step S244 of performing weighted calculation on a plurality of first particles of the expected equalization straight line within the preset bandwidth according to distances from the expected equalization straight line to obtain a first feedback load power is further included. The plurality of first particles in the equalization two-dimensional space have a certain distance relationship with the expected equalization straight line. According to the distance of each particle from the expected equalization straight line, the particles are assigned corresponding weights and weighted calculation is performed, the distribution of the particles in the two-dimensional space and the relative position of the particles with respect to the expected equalization straight line are considered, and the first feedback load power is obtained.

[0031] In a possible implementation, step S243 further includes step S243-1, obtaining a first moving straight line after moving the target equalization straight line by a distance of the preset bandwidth. The distance between the first moving straight line and the target equalization straight line is the width of the preset bandwidth. Step S243-2 includes comparing the particle aggregation amount difference between the target equalization straight line and the first moving straight line, and determining whether the particle aggregation amount difference satisfies a preset gain. If not, moving from the first moving straight line as a starting point to obtain a second moving straight line. Specifically, if the particle aggregation amount difference between the target equalization straight line and the first moving straight line does not satisfy the preset gain, moving from the first moving straight line as a starting point to obtain a second moving straight line. Step S243-3 includes, if yes, taking the target equalization straight line as an expected equalization straight line. If the particle aggregation amount difference between the two satisfies the preset gain, taking the target equalization straight line as an expected equalization straight line.

[0032] Step S300 includes traversing the output power of K photovoltaic module in a preset feedback window, performing window equalization analysis on the K module output power set, and obtaining K module output power. The output power data of each photovoltaic module in the preset feedback window is obtained, K module output power is obtained, and window equalization analysis is performed on the K module output power set. Based on the equalization analysis result, K module output power is obtained, which reflects the optimal or expected output power of each photovoltaic module under certain conditions.

[0033] Step S400 includes using a consistency identifier to identify the difference between the K feedback load power and the K module output power, obtaining M overloaded photovoltaic modules and N underloaded photovoltaic modules according to the identification result, and M overloaded photovoltaic modules include M overloaded power identifiers and N underloaded photovoltaic modules include N underloaded power identifiers. The consistency identifier is a tool for comparing and analyzing data differences. Here, it is used to compare the differences between the K feedback load power (i.e. expected output power) and the K module output power (i.e. actual output power). By calculating and analyzing these differences, it can be determined whether the running state of each photovoltaic module is consistent with its expected state. Specifically, by comparing the difference or ratio between the feedback load power and the module output power, the identifier can determine which modules are overloaded (i.e. actual output power exceeds expected power) or underloaded (i.e. actual output power is lower than expected power). According to the identification result, M overloaded photovoltaic modules and N underloaded photovoltaic modules are obtained. For each overloaded or underloaded module, the corresponding power identifier, i.e. M overloaded power identifier and N underloaded power identifier, is also obtained.

[0034] Step S500, based on the K photovoltaic module configuration data, the output power interval is identified, K identification results are generated, the K identification results are used as control constraints, the control analysis of the M overload photovoltaic module and the N underload photovoltaic module is carried out by combining the K module output power, the M overload power identifier and the N underload power identifier, the conventional control photovoltaic module set and the abnormal control photovoltaic module set are obtained. Based on the configuration data of K photovoltaic module (including but not limited to component type, size, installation angle, shielding condition, etc.), the output power interval is identified through algorithm model, the possible output power range of each component module under different conditions is predicted or estimated, one or more output power interval identification results are generated for each component module, the obtained output power interval identification result is used as control constraint, then the control analysis of M overload photovoltaic module and N underload photovoltaic module is carried out by combining the obtained K module output power (i.e. actual measured output power data), M overload power identifier and N underload power identifier, whether the working state of them is normal, whether there is abnormality or potential risk is evaluated, based on the analysis result, the photovoltaic module is divided into two categories, the conventional control photovoltaic module set and the abnormal control photovoltaic module set, wherein the conventional control module set includes those photovoltaic modules whose working state is normal, output power meets the expectation and has no overload or underload problem; the abnormal control photovoltaic module set includes those photovoltaic modules which have overload, underload or exceed the control constraint.

[0035] In a possible implementation, the step S500 further includes a step S510 of performing directional superposition of module output power and power identifier on the M over-loaded photovoltaic module and the N under-loaded photovoltaic module to obtain L target output powers, L=M+N. The actual output power and the corresponding power identifier of each over-loaded and under-loaded photovoltaic module are obtained, and directional superposition of module output power and power identifier is performed. Specifically, for the over-loaded module, it is possible to reduce the output power to avoid overheating or damage of the equipment; and for the under-loaded module, it is possible to increase the output power to improve the power generation efficiency of the entire system. In the superposition process, the output power of the module is adjusted according to the direction of the power identifier (i.e., over-loaded or under-loaded) to obtain L target output powers, where the number of target output powers is the sum of the number of over-loaded photovoltaic modules and the number of under-loaded photovoltaic modules. The step S520 further includes judging whether the L target output powers meet the control constraint with the K identification results as the control constraint, and if yes, adding the corresponding photovoltaic module into the set of regularly controlled photovoltaic modules. Specifically, the K identification results are used as the control constraint to judge whether the L target output powers meet the control constraint, and if the target output powers meet the control constraint, the corresponding photovoltaic module is added into the set of regularly controlled photovoltaic modules. The step S530 further includes, if no, adding the corresponding photovoltaic module into the set of abnormally controlled photovoltaic modules. If the target output powers do not meet the control constraint, the corresponding photovoltaic module is added into the set of abnormally controlled photovoltaic modules.

[0036] The step S600 uses the power identifier of the set of regularly controlled photovoltaic modules as the equalization control target, identifies the module output power corresponding to the set of regularly controlled photovoltaic modules by using an equalization controller, and obtains a set of regular control parameters. The power identifier of the set of regularly controlled photovoltaic modules is selected as the equalization control target, representing the expected output power of the module in the normal working state. The output power of the module is identified by using the equalization controller. In the identification process, the equalization controller collects real-time output power data of each module and compares the data with the expected power identifier. Based on the identification result, the equalization controller generates a set of regular control parameters, which is used to adjust and optimize the key information of the working state of the photovoltaic module. The equalization controller is a device for adjusting and balancing the load power output / input of multiple photovoltaic modules, which can monitor and adjust the output power of each module in real time according to the set control strategy, so as to realize power equalization of the entire system.

[0037] In step S700, the set of abnormity control photovoltaic module is subjected to fuzzy equilibrium search to generate an abnormity control scheme. The fuzzy equilibrium search is a search algorithm combining fuzzy logic and equilibrium control thought, which can find the optimal solution or equilibrium point of system state or parameter under incomplete determination or fuzzy information through certain rules and strategies. In the operation and maintenance management of the photovoltaic power station, the fuzzy equilibrium search is used to deal with complex and uncertain abnormal problems caused by multiple factors. Specifically, the set of abnormity control photovoltaic module is subjected to fuzzy equilibrium search to generate an abnormity control scheme for the set of abnormity control photovoltaic module, which includes repair measures, adjustment parameters and optimization operation strategies for specific modules, aiming to eliminate abnormality, improve performance and ensure safe and stable operation of the power station.

[0038] In a possible implementation, step S700 further includes step S710 of extracting a plurality of under-load abnormity control photovoltaic modules and a plurality of over-load abnormity control photovoltaic modules from the set of abnormity control photovoltaic module. Step S720 of respectively performing fuzzy matching on the plurality of over-load abnormity control photovoltaic modules with the plurality of under-load abnormity control photovoltaic modules by taking the plurality of under-load power identifiers of the plurality of under-load abnormity control photovoltaic modules as indexes to obtain a plurality of matched over-load module sets, wherein the matched over-load module set is an over-load abnormity control photovoltaic module having a power difference within a preset adjustable threshold from the corresponding under-load abnormity control photovoltaic module. Specifically, the matched over-load module set provides a potential power balance scheme, that is, the excess power output by the over-load module can compensate for the power deficiency of the under-load module by adjusting the working state or parameters of these modules, so as to realize power balance and optimization of the entire power station. The preset adjustable threshold is set according to the actual situation of the power station, the component characteristics and the operation and maintenance experience, and represents the acceptable range of power matching, which ensures the effectiveness of matching and avoids excessive error. Step S730 of performing abnormity control scheme optimization based on the plurality of under-load abnormity control photovoltaic modules and the plurality of matched over-load module sets to obtain the abnormity control scheme. In the operation and maintenance management of the photovoltaic power station, the optimal control strategy is found by an optimization algorithm for the abnormal conditions of under-load and over-load. Specifically, the abnormity control scheme optimization is performed based on the plurality of under-load abnormity control photovoltaic modules and the plurality of matched over-load module sets, combined with the actual situation of the power station, the component performance, the environmental conditions and the operation and maintenance target, which needs to consider multiple factors such as the power balance effect of the matched module, the cost of adjusting the module and the influence on the overall operation of the power station, to obtain the abnormity control scheme, which lists which over-load module should be matched with which under-load module and how to adjust their parameters and working states to achieve the best power balance and efficiency improvement.

[0039] In a possible implementation, step S730 further includes step S731 of extracting a plurality of overload abnormality control photovoltaic module from the plurality of matching overload module sets respectively, and forming a parallel mapping relationship with the plurality of underload abnormality control photovoltaic modules to generate a plurality of parallel mapping relationship sets. Specifically, a parallel mapping relationship is formed between each overload abnormality control photovoltaic module in the matching overload module set and the plurality of underload abnormality control photovoltaic modules to generate a plurality of parallel mapping relationship sets, each set representing a possible module output power balancing scheme and containing a parallel combination of specific overload and underload modules. Step S732 includes using a bipolar switch to perform module parallel connection according to the plurality of parallel mapping relationship sets to generate a plurality of abnormality control schemes. The bipolar switch is used to perform actual module parallel connection, and the bipolar switch can control the connection and disconnection of photovoltaic module, realize the parallel connection of overload and underload modules, generate a specific abnormality control scheme according to each parallel mapping relationship set, and include the parallel connection mode of the modules and specific measures such as adjusting the parameters of the modules and optimizing the operation strategy. Step S733 includes counting the power loss of the plurality of abnormality control schemes, and taking the minimum power loss as the abnormality control scheme.

[0040] In a possible implementation, step S720 further includes step S721 of adding an underload abnormality control photovoltaic module into a warning module set when the fuzzy matching fails. In photovoltaic module component control of a photovoltaic power station, if a suitable overload abnormality control photovoltaic module and underload abnormality control photovoltaic module cannot be found through fuzzy matching to realize power balancing, the underload modules are regarded as modules with potential risks or problems and are added to a warning module set. The warning module set is a set specially used to store and manage modules with abnormalities or potential risks. Step S722 includes adding an unmatching overload abnormality control photovoltaic module into the warning module set. After the fuzzy matching process, if an overload abnormality control photovoltaic module is not successfully matched to a corresponding underload abnormality control photovoltaic module to realize power balancing, the unmatching overload module is regarded as a module with potential risks or problems and is added to the warning module set. Step S723 includes generating a warning instruction according to the warning module set and sending the warning instruction to a worker. Specifically, the warning instruction for potential risks is automatically generated based on the information of the warning module set, and the instructions are sent to the worker responsible for operation and maintenance in a timely manner, including specific module identification, location information, potential risk type, suggested treatment measures, and the like, to remind the worker to pay attention to the modules with potential risks and take corresponding measures to prevent potential problems from turning into actual faults or accidents.

[0041] Step S800, respectively using the conventional control parameter set and the abnormal control scheme to control the photovoltaic module set and the abnormal control photovoltaic module set. According to the different states and needs of photovoltaic module, different control strategy and parameter are adopted for accurate control, in particular, for the conventional control photovoltaic module, the conventional control parameter set is used for control, to ensure that the photovoltaic module runs efficiently and stably under normal working conditions; for the abnormal control photovoltaic module, the generated abnormal control scheme is used for control, aiming to restore the normal operation of the module as soon as possible or prevent the problem from further deterioration. By respectively using the conventional control parameter set and the abnormal control scheme to control the photovoltaic module set of different types, the service life of the photovoltaic module can be prolonged, the fine management and optimized operation of the photovoltaic power station can be realized, and the stability and power generation efficiency of the photovoltaic power station can be improved.

[0042] Further, step S800 further comprises, in a preset control feedback period, monitoring the conventional control photovoltaic module set and the abnormal control photovoltaic module set, and generating a warning instruction according to the monitoring result.

[0043] Preferably, the preset control feedback period refers to a pre-set fixed monitoring period (such as 15 minutes, 1 hour, etc.), or a period dynamically adjusted according to the illumination and load fluctuation characteristics, which is used for periodic collection of operation data to monitor the conventional control photovoltaic module set and the abnormal control photovoltaic module set, including monitoring the electrical parameters of each module such as output voltage, current, power, power factor, real-time deviation of module output power and corresponding load area demand (such as overload power difference, underload power difference), component temperature, inverter efficiency, cable temperature rise and other operating states, and whether the parameters of the conventional control module (such as voltage regulation coefficient, power distribution ratio) continuously meet the balance target, and whether the scheme of the abnormal control module (such as power compensation strategy) is effectively executed.

[0044] Preferably, according to the monitoring result, a warning instruction is generated, and the warning is triggered when the monitoring data exceeds the preset threshold, for example, the module output power exceeds 110% of the rated value (overload warning), the load matching deviation exceeds 20% and the duration exceeds 10 minutes (matching imbalance warning), the component temperature exceeds 85℃ (overheating warning); even if the current data does not exceed the threshold, but through historical data fitting, it is found that the parameter shows a sharp rising / falling trend (such as power fluctuation slope exceeding 5% / minute), the early warning is given when it is predicted that the abnormality may occur, and the warning level and instruction type data are as shown in Table 1: Table 1 Warning level and instruction type data table In the foregoing, with reference to Figure 1The load feedback-based photovoltaic module control method according to the embodiment of the present application is described in detail. Next, the load feedback-based photovoltaic module control system according to the embodiment of the present application will be described with reference to Figure 2 The load feedback-based photovoltaic module control system according to the embodiment of the present application is described.

[0045] The load feedback-based photovoltaic module control system according to the embodiment of the present application is used to solve the technical problem that the existing photovoltaic module control relies on fixed operating parameters and preset working modes, lacks dynamic response to actual operating environment, and cannot obtain accurate load demand to adjust the output power of the photovoltaic module in real time, realizes dynamic matching and intelligent adjustment between the photovoltaic module and the load demand, and achieves the technical effects of prolonging the service life of the photovoltaic module and enabling the photovoltaic power generation to operate efficiently and stably. The load feedback-based photovoltaic module control system comprises a construction planning data acquisition module 10, a load power balance analysis module 20, an output power balance analysis module 30, a difference degree identification module 40, an output power interval identification module 50, a conventional control parameter set obtaining module 60, an abnormal control scheme generation module 70, and a photovoltaic module control module 80.

[0046] The construction planning data acquisition module 10 is used to acquire construction planning data of a target photovoltaic power station, wherein the construction planning data comprises K photovoltaic module-load area mapping relationships and K photovoltaic module configuration data.

[0047] The load power balance analysis module 20 is used to extract K load power data sets of K load areas in a preset feedback window, and perform window balance analysis according to the fluctuation of the K load power data sets to obtain K feedback load powers.

[0048] The output power balance analysis module 30 is used to traverse and extract output powers of K photovoltaic module in a preset feedback window, and perform window balance analysis on the K module output power sets to obtain K module output powers.

[0049] The difference degree identification module 40 is used to identify the difference degree between the K feedback load powers and the K module output powers by using a consistency identifier respectively, and obtain M overloaded photovoltaic modules and N underloaded photovoltaic modules according to the identification results, wherein the M overloaded photovoltaic modules comprise M overloaded power identifiers, and the N underloaded photovoltaic modules comprise N underloaded power identifiers.

[0050] An output power interval identification module 50 is configured to identify output power intervals based on the K photovoltaic module configuration data, generate K identification results, and use the K identification results as control constraints to analyze the control of the M overloaded photovoltaic modules and the N underloaded photovoltaic modules in combination with the K module output powers, the M overloaded power identifiers, and the N underloaded power identifiers, to obtain a set of regularly controlled photovoltaic modules and a set of abnormally controlled photovoltaic modules.

[0051] A regularly controlled parameter set obtaining module 60 is configured to use the power identifiers of the set of regularly controlled photovoltaic modules as a balanced control target, use a balanced controller to identify the module output powers corresponding to the set of regularly controlled photovoltaic modules, and obtain a regularly controlled parameter set.

[0052] An abnormal control scheme generating module 70 is configured to perform fuzzy balanced search on the set of abnormally controlled photovoltaic modules, and generate an abnormal control scheme.

[0053] A photovoltaic module control module 80 is configured to use the regularly controlled parameter set and the abnormal control scheme to control the set of regularly controlled photovoltaic modules and the set of abnormally controlled photovoltaic modules, respectively.

[0054] In the following, the specific configuration of the load power balanced analysis module 20 will be described in detail. The load power balanced analysis module 20 can further include: K balanced particle two-dimensional spaces are constructed according to the K set of module output powers, respectively, wherein the K balanced particle two-dimensional spaces have K particle sets, and each particle corresponds to a module output power; a first balanced particle two-dimensional space is extracted from the K balanced particle two-dimensional spaces, wherein the first balanced particle two-dimensional space has a plurality of first particles; two first particles are extracted from the plurality of first particles in a random extraction manner, and a first balanced straight line is constructed; a window balanced analysis is performed in the first balanced particle two-dimensional space based on the first balanced straight line, and a first module output power is obtained; a window balanced analysis is performed in the K balanced particle two-dimensional spaces, and K feedback load powers are obtained.

[0055] Below, the specific configuration of the load power balance analysis module 20 will be described in detail. The load power balance analysis module 20 further comprises: extracting two first particles from the first balance particle two-dimensional space in a random manner to construct a plurality of balance straight lines; extracting the particle aggregation amount of the first balance straight line and the plurality of balance straight lines within the preset bandwidth, and taking the balance straight line corresponding to the maximum particle aggregation amount as the target balance straight line; taking the target balance straight line as the starting point to search in the first balance particle two-dimensional space, obtaining an expected balance straight line; and performing weighted calculation on the plurality of first particles within the preset bandwidth of the expected balance straight line according to the distance from the expected balance straight line, obtaining the first feedback load power.

[0056] Below, the specific configuration of the load power balance analysis module 20 will be described in detail. The load power balance analysis module 20 can further comprise: obtaining a first moving straight line after moving the target balance straight line by a distance of the preset bandwidth; comparing the particle aggregation amount difference of the target balance straight line and the first moving straight line to determine whether the particle aggregation amount difference satisfies a preset gain, if not, taking the first moving straight line as the starting point to move and obtaining a second moving straight line; and if yes, taking the target balance straight line as the expected balance straight line.

[0057] Below, the specific configuration of the output power interval identification module 40 will be described in detail. The output power interval identification module 40 further comprises: traversing the M over-loaded photovoltaic module and the N under-loaded photovoltaic module to perform directional superposition of module output power and power identifier, obtaining L target output powers, L = M + N; taking the K identification results as control constraints to determine whether the L target output powers satisfy the control constraints, if yes, adding the corresponding photovoltaic module into the regular control photovoltaic module set; and if not, adding the corresponding photovoltaic module into the abnormal control photovoltaic module set.

[0058] Below, the specific configuration of the abnormal control scheme generation module 60 will be described in detail. The abnormal control scheme generation module 60 further comprises: extracting a plurality of under-loaded abnormal control photovoltaic modules and a plurality of over-loaded abnormal control photovoltaic modules from the abnormal control photovoltaic module set; respectively taking the under-loaded power identifiers of the plurality of under-loaded abnormal control photovoltaic modules as indexes and performing fuzzy matching with the plurality of over-loaded abnormal control photovoltaic modules to obtain a plurality of matched over-loaded module sets, wherein the matched over-loaded module set is an over-loaded abnormal control photovoltaic module whose power difference with the corresponding under-loaded abnormal control photovoltaic module is within a preset adjustable threshold; and performing abnormal control scheme optimization based on the plurality of under-loaded abnormal control photovoltaic modules and the plurality of matched over-loaded module sets to obtain the abnormal control scheme.

[0059] Next, the specific configuration of the abnormality control scheme generation module 60 will be described in detail. The abnormality control scheme generation module 60 can further comprise: repeatedly extracting an overload abnormality control photovoltaic module from the plurality of matched overload module sets, respectively, and generating a plurality of parallel mapping relationship sets by forming parallel mapping relationships with the plurality of underload abnormality control photovoltaic modules; generating a plurality of abnormality control schemes by performing module parallel according to the plurality of parallel mapping relationship sets using a bipolar switch; and counting the power loss of the plurality of abnormality control schemes, and taking the minimum power loss as the abnormality control scheme.

[0060] Next, the specific configuration of the photovoltaic module control module 70 will be described in detail. The photovoltaic module control module 70 can further comprise: adding an underload abnormality control photovoltaic module into a warning module set when fuzzy matching fails; adding an un-matched overload abnormality control photovoltaic module into the warning module set; generating a warning instruction according to the warning module set, and sending the warning instruction to a worker.

[0061] The photovoltaic module control system based on load feedback provided by the embodiments of the present application can execute the photovoltaic module control method based on load feedback provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0062] 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, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy mutual differentiation, and do not limit the protection scope of the present application.

[0063] 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 should be included in the protection scope of the present application.

Claims

1. A photovoltaic module control method based on load feedback, characterized in that: The method comprises: Collecting construction planning data of a target photovoltaic power station, wherein the construction planning data includes K photovoltaic module-load area mapping relationships and K photovoltaic module configuration data; Extracting K load power data sets from K load areas within a preset feedback window, and performing window balancing analysis based on the fluctuation of the K load power data sets to obtain K feedback load powers; Traverse and extract the output power of K photovoltaic modules within the preset feedback window, and perform window balance analysis on the K module output power set to obtain the K module output power; Using consistency identifiers, respectively, to identify the degree of difference between the K feedback load powers and the K module output powers, and obtaining M overload photovoltaic assembly modules and N underload photovoltaic assembly modules according to the identification results, wherein the M overload photovoltaic assembly modules include M overload power identifiers, and the N underload photovoltaic assembly modules include N underload power identifiers; Based on the K photovoltaic assembly module configuration data, output power interval identification is performed to generate K identification results. The K identification results are used as control constraints. In combination with the K module output powers, the M overload power identifiers, and the N underload power identifiers, control analysis of the M overloaded photovoltaic assembly modules and the N underloaded photovoltaic assembly modules is performed to obtain a set of normally controlled photovoltaic assembly modules and a set of abnormally controlled photovoltaic assembly modules. Taking the power identifier of the conventionally controlled photovoltaic assembly module set as a balancing control target, using a balancing controller to identify the module output power corresponding to the conventionally controlled photovoltaic assembly module set to obtain a conventional control parameter set; Performing a fuzzy equilibrium search on the abnormal control photovoltaic component module set to generate an abnormal control solution; Photovoltaic assembly control is performed on the normal control parameter set and the abnormal control scheme respectively for the normal control photovoltaic assembly module set and the abnormal control photovoltaic assembly module set.

2. The photovoltaic module control method based on load feedback according to claim 1, characterized in that: Extracting K load power data sets from K load areas within a preset feedback window, and performing window balancing analysis based on fluctuations of the K load power data sets to obtain K feedback load powers, the method comprising: Constructing K balanced particle two-dimensional spaces according to the K module output power sets respectively, wherein the K balanced particle two-dimensional spaces have K particle sets, and each particle corresponds to one module output power; Extracting a first balanced particle two-dimensional space from the K balanced particle two-dimensional spaces, wherein the first balanced particle two-dimensional space has a plurality of first particles; extracting two first particles from the plurality of first particles in a random sampling manner to construct a first equilibrium straight line; Performing window balance analysis in the two-dimensional space of the first balance particle based on the first balance straight line to obtain the output power of the first module; Window balancing analysis is performed in the two-dimensional space of the K balancing particles to obtain K feedback load powers.

3. The photovoltaic module control method based on load feedback according to claim 2, characterized in that: Performing window balance analysis in the first balance particle two-dimensional space based on the first balance straight line to obtain the output power of the first module, the method comprising: extracting two first particles from the first equilibrium particle two-dimensional space in a random sampling manner multiple times to construct multiple equilibrium straight lines; Extracting the particle aggregation amounts of the first equilibrium line and the plurality of equilibrium lines within a preset bandwidth, and taking the equilibrium line corresponding to the maximum particle aggregation amount as the target equilibrium line; Taking the target equilibrium line as a starting point, a moving search is performed in the first equilibrium particle two-dimensional space to obtain a desired equilibrium line; A weighted calculation is performed on a plurality of first particles within the preset bandwidth according to their distance from the expected equilibrium line to obtain a first feedback load power.

4. The photovoltaic module control method based on load feedback according to claim 3, characterized in that: Taking the target equilibrium line as a starting point, a mobile search is performed in the first equilibrium particle two-dimensional space to obtain a desired equilibrium line, the method comprising: After moving the target balancing straight line by the distance of the preset bandwidth, a first moving straight line is obtained; Comparing the difference in particle aggregation between the target equilibrium line and the first moving line, determining whether the difference in particle aggregation satisfies a preset gain, and if not, moving with the first moving line as a starting point to obtain a second moving line; If so, the target equilibrium line is taken as the expected equilibrium line.

5. The photovoltaic module control method based on load feedback according to claim 1, characterized in that: The method comprises: Traversing the M overloaded photovoltaic assembly modules and the N underloaded photovoltaic assembly modules, performing directional superposition of module output power and power identifiers, and obtaining L target output powers, where L=M+N; Taking the K identification results as control constraints, determining whether the L target output powers meet the control constraints, and if so, adding the corresponding photovoltaic assembly modules to the conventional control photovoltaic assembly module set; If not, the corresponding photovoltaic assembly module is added to the abnormal control photovoltaic assembly module set.

6. The photovoltaic module control method based on load feedback according to claim 1, characterized in that: The method comprises: Extracting a plurality of underload abnormally controlled photovoltaic component modules and a plurality of overload abnormally controlled photovoltaic component modules from the abnormally controlled photovoltaic component module set; Performing fuzzy matching with the multiple overload abnormality control photovoltaic module modules using the multiple underload power identifiers of the multiple underload abnormality control photovoltaic module modules as indexes to obtain multiple matching overload module sets, wherein the matching overload module sets are overload abnormality control photovoltaic module modules whose power difference with the corresponding underload abnormality control photovoltaic module module is within a preset adjustable threshold; An abnormal control solution is optimized based on the multiple underload abnormal control photovoltaic component modules and the multiple matching overload module sets to obtain the abnormal control solution.

7. The photovoltaic module control method based on load feedback according to claim 6, characterized in that: The method comprises: extracting an overload abnormality control photovoltaic component module from the plurality of matching overload module sets multiple times, and forming a parallel mapping relationship with the plurality of underload abnormality control photovoltaic component modules to generate a plurality of parallel mapping relationship sets; Utilizing bipolar switches to connect modules in parallel according to the plurality of parallel mapping relationship sets to generate a plurality of abnormal control schemes; The power losses of the multiple abnormal control schemes are counted, and a minimum power loss value is used as the abnormal control scheme.

8. The photovoltaic module control method based on load feedback according to claim 6, characterized in that: The method comprises: When fuzzy matching fails, the underload abnormality control photovoltaic component module is added to the early warning module set; Adding unmatched overload abnormality control photovoltaic component modules into the early warning module set; Generate an early warning instruction based on the early warning module set, and send the early warning instruction to the staff.

9. The photovoltaic module control method based on load feedback according to claim 1, characterized in that: During a preset control feedback period, the normally controlled photovoltaic assembly module set and the abnormally controlled photovoltaic assembly module set are monitored, and an early warning instruction is generated according to the monitoring results.

10. A photovoltaic module control system based on load feedback, characterized in that: The system is used to implement the photovoltaic assembly control method based on load feedback according to any one of claims 1 to 9, and the system includes: A construction planning data acquisition module, wherein the construction planning data acquisition module is used to collect construction planning data of the target photovoltaic power station, wherein the construction planning data includes K photovoltaic module-load area mapping relationships and K photovoltaic module configuration data; A load power balancing analysis module, which is used to extract K load power data sets from K load areas within a preset feedback window, and perform window balancing analysis based on the fluctuation of the K load power data sets to obtain K feedback load powers; An output power balance analysis module, which is used to traverse and extract the output power of K photovoltaic modules within a preset feedback window, and perform window balance analysis on the K module output power set to obtain the K module output power; A difference degree identification module is used to respectively identify the difference degrees between the K feedback load powers and the K module output powers using a consistency identifier, and obtain M overload photovoltaic component modules and N underload photovoltaic component modules according to the identification results, wherein the M overload photovoltaic component modules include M overload power identifiers, and the N underload photovoltaic component modules include N underload power identifiers; an output power interval identification module, the output power interval identification module being configured to perform output power interval identification based on the K photovoltaic module module configuration data, generate K identification results, use the K identification results as control constraints, and perform control analysis of the M overloaded photovoltaic module modules and the N underloaded photovoltaic module modules in combination with the K module output powers, the M overload power identifiers, and the N underload power identifiers to obtain a set of normally controlled photovoltaic module modules and a set of abnormally controlled photovoltaic module modules; a conventional control parameter set acquisition module, the conventional control parameter set acquisition module being configured to use the power identifier of the conventional control photovoltaic assembly module set as a balancing control target, identify the module output power corresponding to the conventional control photovoltaic assembly module set using a balancing controller, and obtain a conventional control parameter set; An abnormal control solution generation module, the abnormal control solution generation module is used to perform fuzzy equilibrium search on the abnormal control photovoltaic component module set to generate an abnormal control solution; A photovoltaic assembly control module is configured to perform photovoltaic assembly control on the conventional control photovoltaic assembly module set and the abnormal control photovoltaic assembly module set using the conventional control parameter set and the abnormal control scheme respectively.

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