Photovoltaic module control method based on solar energy tracking analysis

Abstract

The present invention relates to a photovoltaic module control method based on solar energy tracking analysis, belonging to the field of photovoltaic module control technology. The present invention evaluates solar energy supply data within a current preset period, obtains abnormal solar energy supply data or normal solar energy supply data, and finally introduces a genetic algorithm, and optimizes the solar tracking angle of the photovoltaic module based on the genetic algorithm and the abnormal solar energy supply data, obtains the final solar tracking angle of the photovoltaic module, and controls the photovoltaic module according to the final solar tracking angle of the photovoltaic module. The present invention optimizes the solar tracking angle of the photovoltaic module through a genetic algorithm based on the solar energy supply data generated within a preset time, can respond quickly, adjust the solar tracking angle of the photovoltaic module, so that the power generation power of the photovoltaic module meets the predetermined requirements, and realizes adaptive control.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic assembly control, and in particular to a photovoltaic assembly control method based on solar energy tracking analysis. Background Art

[0002] Photovoltaic power generation is a technology that uses the photovoltaic effect of semiconductor interfaces to directly convert light energy into electrical energy. It is mainly composed of four parts: solar panels, controllers, photovoltaic panel angle control devices, and inverters, and the main components are composed of electronic components. After the solar cells are connected in series and packaged for protection, they can form a large-area solar cell module, and then combined with power controllers and other components to form a photovoltaic power generation device. Photovoltaic modules (also called solar panels) are the core part of the solar power generation system and the most important part of the solar power generation system. However, the power generation of photovoltaic modules is closely related to parameters such as the sun's light intensity, the real-time sun position, and the tracking angle of the photovoltaic modules. When the photovoltaic modules are facing away from the sun or the environment changes, the power generation generated at this time does not meet the requirements. In the prior art, the real-time light intensity of the photovoltaic modules is often used to dynamically adjust the tracking angle of the photovoltaic modules to the sun. However, when the instrument for obtaining the real-time light intensity fails, the photovoltaic modules cannot continue to be controlled, resulting in the photovoltaic modules being unable to generate electricity normally. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a photovoltaic component control method based on solar energy tracking analysis.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a photovoltaic assembly control method based on solar tracking analysis, which specifically comprises the following steps:

[0006] Obtaining solar energy supply data generated by the photovoltaic components at each timestamp, and building a solar energy supply data tracking model based on the solar energy supply data generated by the photovoltaic components at each timestamp,

[0007] The solar energy supply data in the current preset period is identified and analyzed by the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period;

[0008] By evaluating the solar energy supply data in the current preset time period, abnormal solar energy supply data or normal solar energy supply data is obtained;

[0009] A genetic algorithm is introduced, and the solar tracking angle of the photovoltaic module is optimized based on the genetic algorithm and abnormal solar energy supply data, a final solar tracking angle of the photovoltaic module is obtained, and the photovoltaic module is controlled according to the final solar tracking angle of the photovoltaic module.

[0010] Furthermore, in this method, the solar energy supply data generated by the photovoltaic module in each timestamp is obtained, specifically:

[0011] Obtain the initial solar energy supply data generated by the photovoltaic module at each timestamp and use it as sample data, obtain the data collection delay of each sample data when it is collected, and set a data collection delay threshold;

[0012] Determine whether the data collection delay of the sample data when it is collected is greater than the data collection delay threshold;

[0013] If the data collection delay of the sample data during collection is greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being greater than the data collection delay threshold is eliminated;

[0014] If the data collection delay of the sample data during collection is not greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being no greater than the data collection delay threshold is output as the solar energy supply data generated by the photovoltaic components in each timestamp.

[0015] Furthermore, in this method, a solar energy supply data tracking model is constructed according to the solar energy supply data generated by the photovoltaic components in each timestamp, specifically:

[0016] A solar energy supply data tracking model is constructed based on a deep neural network, a Markov transfer matrix method is introduced, and a solar energy supply data matrix based on a time series is constructed according to the solar energy supply data generated by the photovoltaic components in each timestamp;

[0017] Taking the solar energy supply data generated by the photovoltaic components at each time stamp in the solar energy supply data matrix based on the time series as a state vector, and calculating the transition probability value of the state vector transferring to another state vector based on the Markov transfer matrix method;

[0018] Preset a transition probability threshold, determine whether the transition probability value is greater than the transition probability threshold, and when the transition probability value is not greater than the transition probability threshold, maintain the current state vector unchanged, and input the solar energy supply data matrix based on the time series into the solar energy supply data tracking model for encoding learning;

[0019] When the transition probability value is greater than the transition probability threshold, the state vector is updated to another state vector, and the time series-based solar energy supply data matrix is ​​updated, and the updated time series-based solar energy supply data matrix is ​​input into the solar energy supply data tracking model for encoding learning.

[0020] Furthermore, in this method, the solar energy supply data in the current preset period is identified and analyzed by the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period, specifically:

[0021] Acquire solar energy supply data generated by each photovoltaic component at a timestamp within a preset time, and input the solar energy supply data generated by each photovoltaic component at a timestamp within the preset time into the solar energy supply data tracking model;

[0022] The solar energy supply data generated by each photovoltaic component at each timestamp within the preset time is updated by the solar energy supply data tracking model to obtain the updated solar energy supply data generated by each photovoltaic component at each timestamp within the preset time;

[0023] The solar energy supply data generated by the photovoltaic assembly at each timestamp within the updated preset time is counted to obtain the solar energy supply data within the current preset time period.

[0024] Furthermore, in the present method, abnormal solar energy supply data or normal solar energy supply data is obtained by evaluating the solar energy supply data in the current preset time period, specifically:

[0025] Preset a solar energy supply data threshold evaluation index, and determine whether the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index;

[0026] When the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as normal solar energy supply data;

[0027] When the solar energy supply data in the current preset time period is not greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as abnormal solar energy supply data.

[0028] Furthermore, in this method, a genetic algorithm is introduced, and the solar tracking angle of the photovoltaic module is optimized based on the genetic algorithm and abnormal solar energy supply data, the final solar tracking angle of the photovoltaic module is obtained, and the photovoltaic module is controlled according to the final solar tracking angle of the photovoltaic module, specifically:

[0029] Introducing a genetic algorithm, and setting a genetic algebra based on the genetic algorithm, obtaining communication protocol information of the photovoltaic assembly corresponding to the abnormal solar energy supply data, and initializing the solar tracking angle of the photovoltaic assembly corresponding to the abnormal solar energy supply data;

[0030] Setting a test period, and testing the abnormal photovoltaic component based on the sun tracking angle of the photovoltaic component corresponding to the abnormal solar energy supply data and the test period, and obtaining the solar energy supply data generated by the photovoltaic component during the test period;

[0031] When the solar energy supply data generated by the photovoltaic assembly during the test period is greater than the solar energy supply data threshold evaluation index, the control terminal communicates with the photovoltaic assembly based on the communication protocol information as the final solar tracking angle of the photovoltaic assembly, and controls the photovoltaic assembly according to the final solar tracking angle of the photovoltaic assembly;

[0032] When the solar energy supply data generated by the photovoltaic component during the test period is not greater than the solar energy supply data threshold evaluation index, inheritance is performed based on the genetic algebra to adjust the solar tracking angle of the photovoltaic component corresponding to the abnormal solar energy supply data until the solar energy supply data generated by the photovoltaic component during the test period is greater than the solar energy supply data threshold evaluation index.

[0033] A second aspect of the present invention provides a photovoltaic component control system based on solar tracking analysis, the control system includes a memory and a processor, the memory includes a photovoltaic component control method program based on solar tracking analysis, and when the photovoltaic component control method program based on solar tracking analysis is executed by the processor, the steps of the photovoltaic component control method based on solar tracking analysis are implemented.

[0034] A third aspect of the present invention provides a computer-readable storage medium, which includes a photovoltaic component control method program based on solar tracking analysis. When the photovoltaic component control method program based on solar tracking analysis is executed by a processor, the steps of the photovoltaic component control method based on solar tracking analysis are implemented.

[0035] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0036] The present invention obtains the solar energy supply data generated by the photovoltaic module in each timestamp, and constructs a solar energy supply data tracking model according to the solar energy supply data generated by the photovoltaic module in each timestamp, and then identifies and analyzes the solar energy supply data in the current preset period through the solar energy supply data tracking model, obtains the solar energy supply data in the current preset period, and thereby obtains abnormal solar energy supply data or normal solar energy supply data by evaluating the solar energy supply data in the current preset period, and finally introduces a genetic algorithm, and optimizes the solar tracking angle of the photovoltaic module based on the genetic algorithm and the abnormal solar energy supply data, obtains the final solar tracking angle of the photovoltaic module, and controls the photovoltaic module according to the final solar tracking angle of the photovoltaic module. The present invention optimizes the solar tracking angle of the photovoltaic module through the genetic algorithm for the solar energy supply data generated within the preset time, can respond quickly, adjust the solar tracking angle of the photovoltaic module, so that the power generation power of the photovoltaic module meets the predetermined requirements, and realizes adaptive control. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0038] Figure 1 The overall flow chart of the photovoltaic module control method based on solar energy tracking analysis is shown;

[0039] Figure 2 A partial method flow chart of a photovoltaic assembly control method based on solar tracking analysis is shown;

[0040] Figure 3 A system block diagram of a photovoltaic module control system based on solar tracking analysis is shown; DETAILED DESCRIPTION

[0041] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below. Example 1

[0043] like Figure 1 As shown, the first aspect of the present invention provides a photovoltaic assembly control method based on solar energy tracking analysis, which specifically includes the following steps:

[0044] S102: Obtain solar energy supply data generated by the photovoltaic components in each timestamp, and build a solar energy supply data tracking model according to the solar energy supply data generated by the photovoltaic components in each timestamp;

[0045] S104: Identifying and analyzing the solar energy supply data in the current preset period through the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period;

[0046] S106: Obtaining abnormal solar energy supply data or normal solar energy supply data by evaluating the solar energy supply data in the current preset time period;

[0047] S108: Introduce a genetic algorithm, and optimize the solar tracking angle of the photovoltaic module based on the genetic algorithm and abnormal solar energy supply data, obtain the final solar tracking angle of the photovoltaic module, and control the photovoltaic module according to the final solar tracking angle of the photovoltaic module.

[0048] It should be noted that the present invention optimizes the solar tracking angle of the photovoltaic module by using a genetic algorithm based on the solar energy supply data generated within a preset time, and can respond quickly and adjust the solar tracking angle of the photovoltaic module so that the power generation power of the photovoltaic module meets the predetermined requirements.

[0049] Furthermore, in this method, the solar energy supply data generated by the photovoltaic module in each timestamp is obtained, specifically:

[0050] Obtain the initial solar energy supply data generated by the photovoltaic module at each timestamp and use it as sample data, obtain the data collection delay of each sample data when it is collected, and set a data collection delay threshold;

[0051] Determine whether the data collection delay of the sample data when it is collected is greater than the data collection delay threshold;

[0052] If the data collection delay of the sample data during collection is greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being greater than the data collection delay threshold is eliminated;

[0053] If the data collection delay of the sample data during collection is not greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being no greater than the data collection delay threshold is output as the solar energy supply data generated by the photovoltaic components in each timestamp.

[0054] It should be noted that the working parameters of the photovoltaic module can be transmitted in real time through the communication equipment, including the solar energy supply data, which includes the energy generated per unit time, the power generated per unit time and other data. If the data collection delay of the sample data during collection is greater than the data collection delay threshold, it means that the data is not real-time data. This method can improve the rationality of controlling the solar tracking angle of the photovoltaic module.

[0055] It should be noted that, regarding the meaning of "timestamp", a timestamp is usually a character sequence that uniquely identifies a certain moment in time. Since data acquisition is data acquired at a certain moment in time.

[0056] Furthermore, in this method, a solar energy supply data tracking model is constructed according to the solar energy supply data generated by the photovoltaic components in each timestamp, specifically:

[0057] A solar energy supply data tracking model is built based on a deep neural network, the Markov transfer matrix method is introduced, and a solar energy supply data matrix based on a time series is constructed according to the solar energy supply data generated by the photovoltaic modules in each timestamp;

[0058] The solar energy supply data generated by the photovoltaic module at each time stamp in the solar energy supply data matrix based on the time series is taken as a state vector, and the transition probability value of the state vector transferring to another state vector is calculated based on the Markov transition matrix method;

[0059] A transfer probability threshold is preset, and it is determined whether the transfer probability value is greater than the transfer probability threshold. When the transfer probability value is not greater than the transfer probability threshold, the current state vector is maintained unchanged, and the solar energy supply data matrix based on the time series is input into the solar energy supply data tracking model for encoding learning;

[0060] When the transition probability value is greater than the transition probability threshold, the state vector is updated to another state vector, and the solar energy supply data matrix based on the time series is updated, and the updated solar energy supply data matrix based on the time series is input into the solar energy supply data tracking model for encoding learning.

[0061] It should be noted that the Markov transfer matrix method is a method for studying the trend of random events. In fact, in the process of data collection, a time series of data is often collected to extract the most reliable data. For example, if the data collection frequency is 25ms, 40 sample data can be collected in 1 second. In this time series, the solar energy supply data may be transferred, that is, from one solar energy supply data state to another solar energy supply data state. The most suitable sample data is selected as the sample data of the current timestamp. This method can improve the state recognition of solar energy supply data.

[0062] Furthermore, in this method, the solar energy supply data in the current preset period is identified and analyzed by the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period, specifically:

[0063] Obtaining solar energy supply data generated by the photovoltaic assembly at each timestamp within a preset time, and inputting the solar energy supply data generated by the photovoltaic assembly at each timestamp within the preset time into a solar energy supply data tracking model;

[0064] The solar energy supply data generated by the photovoltaic component at each timestamp within the preset time is updated through the solar energy supply data tracking model, and the solar energy supply data generated by the photovoltaic component at each timestamp within the preset time after the update is obtained;

[0065] The solar energy supply data generated by the photovoltaic components at each timestamp within the updated preset time is counted to obtain the solar energy supply data within the current preset time period.

[0066] It should be noted that the solar energy supply data generated by each timestamp photovoltaic component within the preset time after the update can be statistically analyzed by the averaging method, and the average of the solar energy supply data generated by each timestamp photovoltaic component within the preset time can be obtained as the solar energy supply data within the current preset time period. The present invention is not limited to the solar energy supply data generated by each timestamp photovoltaic component within the preset time after the update by the averaging method, and statistics can also be performed based on the frequency of occurrence of the data. The present invention can also be implemented by other methods, not just the method mentioned in this embodiment.

[0067] Furthermore, in this method, by evaluating the solar energy supply data in the current preset time period, abnormal solar energy supply data or normal solar energy supply data is obtained, specifically:

[0068] Preset a solar energy supply data threshold evaluation index, and determine whether the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index;

[0069] When the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as normal solar energy supply data;

[0070] When the solar energy supply data in the current preset time period is not greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as abnormal solar energy supply data.

[0071] like Figure 2 As shown, further, in this method, a genetic algorithm is introduced, and the solar tracking angle of the photovoltaic module is optimized based on the genetic algorithm and abnormal solar energy supply data, the final solar tracking angle of the photovoltaic module is obtained, and the photovoltaic module is controlled according to the final solar tracking angle of the photovoltaic module, specifically:

[0072] S202: introducing a genetic algorithm, and setting a genetic algebra based on the genetic algorithm, obtaining communication protocol information of the photovoltaic assembly corresponding to the abnormal solar energy supply data, and initializing the sun tracking angle of the photovoltaic assembly corresponding to the abnormal solar energy supply data;

[0073] S204: setting a test period, and testing the abnormal photovoltaic assembly based on the sun tracking angle of the photovoltaic assembly corresponding to the abnormal solar energy supply data and the test period, and obtaining the solar energy supply data generated by the photovoltaic assembly during the test period;

[0074] S206: When the solar energy supply data generated by the photovoltaic assembly during the test period is greater than the solar energy supply data threshold evaluation index, the control terminal communicates with the photovoltaic assembly based on the communication protocol information as the final solar tracking angle of the photovoltaic assembly, and controls the photovoltaic assembly according to the final solar tracking angle of the photovoltaic assembly;

[0075] S208: When the solar energy supply data generated by the photovoltaic component during the test period is not greater than the solar energy supply data threshold evaluation index, inheritance is performed based on genetic algebra to adjust the solar tracking angle of the photovoltaic component corresponding to the abnormal solar energy supply data until the solar energy supply data generated by the photovoltaic component during the test period is greater than the solar energy supply data threshold evaluation index.

[0076] It should be noted that the solar tracking angle of the photovoltaic module can be improved by this method, so that the solar energy supply data generated by the photovoltaic module during the test period is greater than the solar energy supply data threshold evaluation index, and the power generation of the photovoltaic module is kept within a predetermined range. The solar tracking angle includes the rotation angle of the photovoltaic module support bracket and the rotation angle of the rotating mechanism in the photovoltaic module.

[0077] In addition, the method may further include:

[0078] After inheritance through genetic algebra, if the solar energy supply data generated by the photovoltaic components during the test period is not greater than the solar energy supply data threshold evaluation index, then the power generation demand information in the target area is obtained;

[0079] Acquire photovoltaic components corresponding to evaluation indicators whose solar energy supply data is not greater than a solar energy supply data threshold, initialize photovoltaic components for power generation according to power generation demand information in the target area, and randomly select photovoltaic components for power generation;

[0080] Setting a constraint condition based on the photovoltaic component corresponding to the solar energy supply data being no greater than a solar energy supply data threshold evaluation index, and selecting the photovoltaic component for power generation based on the constraint condition;

[0081] When there is no photovoltaic component corresponding to the solar energy supply data not greater than the solar energy supply data threshold evaluation index among the photovoltaic components for power generation, outputting the photovoltaic components for power generation, and generating power for the target area based on the photovoltaic components for power generation;

[0082] When there is a photovoltaic component whose solar energy supply data is not greater than the solar energy supply data threshold evaluation index among the photovoltaic components for power generation, the photovoltaic components for power generation are reselected, and power generation is performed for the target area based on the photovoltaic components for power generation.

[0083] It should be noted that the rationality of power generation can be further improved through this method.

[0084] In addition, the method may further include:

[0085] Obtaining geographical location information of each photovoltaic module, and searching through big data based on the geographical location information of each photovoltaic module, obtaining environmental characteristic data of the geographical location of each photovoltaic module, and building a solar energy supply data prediction model;

[0086] Obtaining solar energy supply data under various environmental characteristics through big data, and inputting the solar energy supply data under various environmental characteristics into the solar energy supply data prediction model for training, thereby obtaining a trained solar energy supply data prediction model;

[0087] Inputting the environmental characteristic data of the geographical location of each photovoltaic component into the trained solar energy supply data prediction model for prediction, and obtaining the solar energy supply data under the environmental characteristic data of the geographical location of each photovoltaic component;

[0088] Acquire photovoltaic assemblies corresponding to evaluation indicators whose solar energy supply data is greater than a solar energy supply data threshold, and use the photovoltaic assemblies corresponding to the evaluation indicators whose solar energy supply data is greater than the solar energy supply data threshold as controllable photovoltaic assemblies, and display the controllable photovoltaic assemblies in a prediction manner;

[0089] The photovoltaic component group corresponding to the solar energy supply data being no greater than the solar energy supply data threshold evaluation index is regarded as an uncontrollable photovoltaic component, and the uncontrollable photovoltaic component is displayed in a predicted manner.

[0090] It should be noted that environmental characteristic data include temperature, humidity, visibility, light intensity, etc. Since solar power generation is closely related to environmental characteristic data, due to the influence of the environment, no matter how the angle is adjusted, the solar energy supply data cannot be greater than the solar energy supply data threshold evaluation index. This method can further improve the rationality of photovoltaic module control. Example 2

[0091] like Figure 3 As shown, the second aspect of the present invention provides a photovoltaic component control system 4 based on solar tracking analysis. The control system 4 includes a memory 41 and a processor 42. The memory 41 includes a photovoltaic component control method program based on solar tracking analysis. When the photovoltaic component control method program based on solar tracking analysis is executed by the processor 42, the solution of Example 1 is implemented. Example 3

[0092] A third aspect of the present invention provides a computer-readable storage medium, which includes a photovoltaic component control method program based on solar tracking analysis. When the photovoltaic component control method program based on solar tracking analysis is executed by a processor, the solution of Example 1 is implemented.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0094] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0095] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0096] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0097] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0098] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A photovoltaic module control method based on solar energy tracking analysis, characterized in that: The specific steps include: Obtaining solar energy supply data generated by the photovoltaic components at each timestamp, and building a solar energy supply data tracking model based on the solar energy supply data generated by the photovoltaic components at each timestamp, The solar energy supply data in the current preset period is identified and analyzed by the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period; A solar energy supply data tracking model is constructed based on the solar energy supply data generated by the photovoltaic components in each timestamp, specifically: A solar energy supply data tracking model is constructed based on a deep neural network, a Markov transfer matrix method is introduced, and a solar energy supply data matrix based on a time series is constructed according to the solar energy supply data generated by the photovoltaic components in each timestamp; Taking the solar energy supply data generated by the photovoltaic components at each time stamp in the solar energy supply data matrix based on the time series as a state vector, and calculating the transition probability value of the state vector transferring to another state vector based on the Markov transfer matrix method; Preset a transition probability threshold, determine whether the transition probability value is greater than the transition probability threshold, and when the transition probability value is not greater than the transition probability threshold, maintain the current state vector unchanged, and input the solar energy supply data matrix based on the time series into the solar energy supply data tracking model for encoding learning; When the transition probability value is greater than the transition probability threshold, the state vector is updated to another state vector, and the solar energy supply data matrix based on the time series is updated, and the updated solar energy supply data matrix based on the time series is input into the solar energy supply data tracking model for encoding learning; By evaluating the solar energy supply data in the current preset time period, abnormal solar energy supply data or normal solar energy supply data is obtained; A genetic algorithm is introduced, and the solar tracking angle of the photovoltaic module is optimized based on the genetic algorithm and abnormal solar energy supply data, a final solar tracking angle of the photovoltaic module is obtained, and the photovoltaic module is controlled according to the final solar tracking angle of the photovoltaic module.

2. The photovoltaic assembly control method based on solar energy tracking analysis according to claim 1, characterized in that: Get the solar energy supply data generated by the photovoltaic module at each timestamp, specifically: Obtain the initial solar energy supply data generated by the photovoltaic module at each timestamp and use it as sample data, obtain the data collection delay of each sample data when it is collected, and set a data collection delay threshold; Determine whether the data collection delay of the sample data when it is collected is greater than the data collection delay threshold; If the data collection delay of the sample data during collection is greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being greater than the data collection delay threshold is eliminated; If the data collection delay of the sample data during collection is not greater than the data collection delay threshold, the sample data corresponding to the data collection delay of the sample data during collection being no greater than the data collection delay threshold is output as the solar energy supply data generated by the photovoltaic components in each timestamp.

3. The photovoltaic assembly control method based on solar energy tracking analysis according to claim 1, characterized in that: The solar energy supply data in the current preset period is identified and analyzed by the solar energy supply data tracking model to obtain the solar energy supply data in the current preset period, specifically: Acquire solar energy supply data generated by each photovoltaic component at a timestamp within a preset time, and input the solar energy supply data generated by each photovoltaic component at a timestamp within the preset time into the solar energy supply data tracking model; The solar energy supply data generated by each photovoltaic component at each timestamp within the preset time is updated by the solar energy supply data tracking model to obtain the updated solar energy supply data generated by each photovoltaic component at each timestamp within the preset time; The solar energy supply data generated by the photovoltaic assembly at each timestamp within the updated preset time is counted to obtain the solar energy supply data within the current preset time period.

4. The photovoltaic assembly control method based on solar tracking analysis according to claim 1, characterized in that: By evaluating the solar energy supply data in the current preset time period, abnormal solar energy supply data or normal solar energy supply data is obtained, specifically: Preset a solar energy supply data threshold evaluation index, and determine whether the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index; When the solar energy supply data in the current preset time period is greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as normal solar energy supply data; When the solar energy supply data in the current preset time period is not greater than the solar energy supply data threshold evaluation index, the corresponding solar energy supply data is regarded as abnormal solar energy supply data.

5. The photovoltaic assembly control method based on solar energy tracking analysis according to claim 1, characterized in that: A genetic algorithm is introduced, and the solar tracking angle of the photovoltaic module is optimized based on the genetic algorithm and abnormal solar energy supply data, a final solar tracking angle of the photovoltaic module is obtained, and the photovoltaic module is controlled according to the final solar tracking angle of the photovoltaic module, specifically: Introducing a genetic algorithm, and setting a genetic algebra based on the genetic algorithm, obtaining communication protocol information of the photovoltaic assembly corresponding to the abnormal solar energy supply data, and initializing the solar tracking angle of the photovoltaic assembly corresponding to the abnormal solar energy supply data; Setting a test period, and testing the abnormal photovoltaic component based on the sun tracking angle of the photovoltaic component corresponding to the abnormal solar energy supply data and the test period, and obtaining the solar energy supply data generated by the photovoltaic component during the test period; When the solar energy supply data generated by the photovoltaic assembly during the test period is greater than the solar energy supply data threshold evaluation index, the control terminal communicates with the photovoltaic assembly based on the communication protocol information as the final solar tracking angle of the photovoltaic assembly, and controls the photovoltaic assembly according to the final solar tracking angle of the photovoltaic assembly; When the solar energy supply data generated by the photovoltaic component during the test period is not greater than the solar energy supply data threshold evaluation index, inheritance is performed based on the genetic algebra to adjust the solar tracking angle of the photovoltaic component corresponding to the abnormal solar energy supply data until the solar energy supply data generated by the photovoltaic component during the test period is greater than the solar energy supply data threshold evaluation index.

6. A photovoltaic module control system based on solar tracking analysis, characterized in that: The control system includes a memory and a processor, wherein the memory includes a photovoltaic component control method program based on solar tracking analysis, and when the photovoltaic component control method program based on solar tracking analysis is executed by the processor, the steps of the photovoltaic component control method based on solar tracking analysis as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a photovoltaic component control method program based on solar tracking analysis. When the photovoltaic component control method program based on solar tracking analysis is executed by a processor, the steps of the photovoltaic component control method based on solar tracking analysis as described in any one of claims 1 to 5 are implemented.

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