Method, device and equipment for managing cleaning cycle of photovoltaic module and storage medium

Abstract

The invention belongs to the technical field of photovoltaic module cleaning, and discloses a photovoltaic module cleaning period management method and device, equipment and a storage medium. The method comprises the following steps: acquiring first real-time power generation data of a first group of photovoltaic modules after automatic cleaning according to a preset time interval and second real-time power generation data of a second group of photovoltaic modules which are not cleaned; based on the first real-time generating capacity data and the second real-time generating capacity data, calculating the generating capacity increase rate of the first group of photovoltaic modules compared with the second group of photovoltaic modules; obtaining meteorological prediction data in a preset time window of an area where the photovoltaic power station is located, and determining predicted generating capacity data of the photovoltaic power station in the preset time window; on the basis of the generating capacity increasing rate and the predicted generating capacity data, the cleaning benefit of cleaning the photovoltaic module in the preset time window is evaluated; and generating a cleaning decision instruction in combination with the cleaning benefit and the meteorological prediction data. A more reasonable and efficient cleaning management method can be provided.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic module cleaning, and particularly to a management method, device, equipment and storage medium for the cleaning cycle of photovoltaic modules. Background Art

[0002] Dust pollutants deposited on the surface of photovoltaic modules will affect the power generation efficiency through multiple physical mechanisms: First, the diffuse reflection layer formed by the accumulation of particles will significantly weaken the incident light intensity. According to the measured data, severe dust accumulation can cause the output power of the module to decay by up to 45%; Second, the non-uniform dust accumulation distribution is prone to cause local hot spot effects, resulting in an abnormal increase in the module temperature by more than 20°C, accelerating the aging of the encapsulation material. These combined effects cause the annual power generation loss of the power station to generally remain in the range of 5-15%, and the loss in extreme environmental areas can reach more than 30%.

[0003] At present, there is no unified standard in the industry for whether photovoltaic modules need to be cleaned. The commonly used cleaning decision methods are: First, the subjective judgment method based on visual inspection, which is interfered by factors such as environmental light and the experience of operation and maintenance personnel, and cannot accurately quantify the degree of pollution; Second, the fixed-cycle cleaning strategy (such as twice a year), which is prone to problems such as over-cleaning, increasing operation and maintenance costs, or under-cleaning, resulting in power generation losses. Therefore, there is an urgent need for a more scientific management method for the cleaning cycle of photovoltaic modules. Summary of the Invention

[0004] To this end, the embodiments of the present application provide a management method, device, equipment and storage medium for the cleaning cycle of photovoltaic modules, providing a more reasonable and efficient cleaning management method.

[0005] In a first aspect, the present application provides a management method for the cleaning cycle of photovoltaic modules.

[0006] The present application is achieved through the following technical solutions:

[0007] A management method for the cleaning cycle of photovoltaic modules includes:

[0008] Obtaining the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, wherein the first group of photovoltaic modules is configured to be automatically cleaned at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned;

[0009] Based on the first real-time power generation data and the second real-time power generation data, calculating the power generation improvement rate of the first group of photovoltaic modules after automatic cleaning compared to the second group of photovoltaic modules;

[0010] Obtain the meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, and estimate the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data;

[0011] Based on the power generation increase rate and the predicted power generation data, evaluate the estimated cleaning benefit of the photovoltaic modules during the preset time window;

[0012] Combine the estimated cleaning benefit and the meteorological prediction data to generate a cleaning decision instruction.

[0013] In a preferred example of the present application, it can be further set that, based on the first real-time power generation data and the second real-time power generation data, calculate the power generation increase rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning, including:

[0014] Calculate the power generation difference value between the first real-time power generation data and the second real-time power generation data;

[0015] Calculate the ratio of the power generation difference value to the second real-time power generation data to obtain the initial power generation increase rate;

[0016] Based on the first real-time power generation data of the first group of photovoltaic modules, analyze the power generation attenuation trend of the first group of photovoltaic modules after cleaning, determine the attenuation days corresponding to when the power generation attenuation rate of the first group of photovoltaic modules reaches the preset attenuation rate threshold, and determine the correction factor based on the attenuation days;

[0017] Use the correction factor to correct the initial power generation increase rate to determine the power generation increase rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning.

[0018] In a preferred example of the present application, it can be further set that, obtain the meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, including:

[0019] Obtain the predicted radiation amount data, predicted cloud cover duration data, predicted rainfall data, and predicted sandstorm data within the preset time window in the area where the photovoltaic power station is located.

[0020] In a preferred example of the present application, it can be further set that, based on the meteorological prediction data, estimate the predicted power generation data of the photovoltaic power station within the preset time window, including:

[0021] Based on the predicted cloud cover duration data and predicted radiation amount data within the preset time window, generate a corrected predicted value of the effective radiation amount;

[0022] Based on the predicted value of the effective radiation amount, the installed capacity of the photovoltaic power station, and the system efficiency, calculate the predicted power generation data within the preset time window.

[0023] In a preferred example of the present application, it can be further set that, based on the power generation increase rate and the predicted power generation data, the estimated cleaning benefit of the photovoltaic modules for cleaning within a preset time window is evaluated, including:

[0024] Obtain historical cleaning data and determine the historical cleaning cost;

[0025] Based on the power generation increase rate, the predicted power generation data, and the current on-grid electricity price, calculate the estimated cleaning revenue;

[0026] Subtract the historical cleaning cost from the estimated cleaning revenue to obtain the estimated cleaning benefit of the photovoltaic modules.

[0027] In a preferred example of the present application, it can be further set that, combining the estimated cleaning benefit and the meteorological prediction data, a cleaning decision instruction is generated, including:

[0028] Based on the meteorological prediction data, determine whether the predicted rainfall data within the preset time window is greater than the preset rainfall threshold. If the predicted rainfall data within the preset time window is greater than or equal to the preset rainfall threshold, a cleaning decision instruction not to perform cleaning is generated;

[0029] If the predicted rainfall data within the preset time window is less than the preset rainfall threshold, a cleaning decision instruction is generated based on the predicted sandstorm data and the estimated cleaning benefit within the preset time window.

[0030] In a preferred example of the present application, it can be further set that, if the predicted rainfall data within the preset time window is less than the preset rainfall threshold, a cleaning decision instruction is generated based on the predicted sandstorm data and the estimated cleaning benefit within the preset time window, including:

[0031] If the predicted sandstorm data within the preset time window predicts the existence of a sandstorm weather, a cleaning decision instruction not to perform cleaning is generated;

[0032] If the predicted sandstorm data within the preset time window predicts the non-existence of a sandstorm weather, a cleaning decision instruction is generated based on the estimated cleaning benefit.

[0033] In a second aspect, the present application provides a management device for the cleaning cycle of photovoltaic modules.

[0034] The present application is achieved through the following technical solutions:

[0035] A management device for the cleaning cycle of photovoltaic modules, configured to execute the method described in the first aspect above, includes:

[0036] An automatic spraying module, configured to automatically clean the first group of photovoltaic modules in a photovoltaic power station;

[0037] The power generation increase rate calculation module is used to obtain the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, and calculate the power generation increase rate of the first group of photovoltaic modules compared with the second group of photovoltaic modules after automatic cleaning based on the first real-time power generation data and the second real-time power generation data, wherein the first group of photovoltaic modules is configured to perform automatic cleaning at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned;

[0038] The predicted power generation determination module is used to obtain the meteorological prediction data within a preset time window of the area where the photovoltaic power station is located, and estimate the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data;

[0039] The cleaning benefit evaluation module is used to evaluate the estimated cleaning benefit of the photovoltaic modules when being cleaned within a preset time window based on the power generation increase rate and the predicted power generation data;

[0040] The cleaning decision module is used to generate a cleaning decision instruction by combining the estimated cleaning benefit and the meteorological prediction data.

[0041] In a third aspect, the present application is implemented through the following technical solutions:

[0042] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the above-mentioned management methods for the cleaning cycle of photovoltaic modules are implemented.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium.

[0044] The present application is implemented through the following technical solutions:

[0045] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned management methods for the cleaning cycle of photovoltaic modules are implemented.

[0046] In summary, compared with the prior art, the beneficial effects brought by the technical solutions provided in the embodiments of the present application at least include:

[0047] This application obtains the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules. The first group of photovoltaic modules is configured to be automatically cleaned at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned. Based on the first real-time power generation data and the second real-time power generation data, calculate the power generation improvement rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules. Obtain the meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, and determine the predicted power generation data of the photovoltaic power station within the preset time window. Based on the power generation improvement rate and the predicted power generation data, evaluate the estimated cleaning benefit of cleaning the photovoltaic modules within the preset time window. Combine the estimated cleaning benefit and the meteorological prediction data to generate a cleaning decision instruction. By obtaining the real-time power generation data of two groups of photovoltaic modules, one group is regularly and automatically cleaned, and the other group remains uncleaned, to quantify the improvement effect of cleaning on power generation. Combine the power generation improvement rate and the predicted power generation data to evaluate the cleaning benefit of cleaning within the preset time window. Finally, combine the cleaning benefit and the meteorological prediction data to automatically generate a cleaning decision instruction. This intelligent decision support system can reduce the error of human judgment and improve the scientificity and accuracy of cleaning decisions. Ensure that the photovoltaic modules are cleaned when needed, and avoid over-cleaning or untimely cleaning. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 FIG. is a schematic flowchart of a method for managing the cleaning cycle of photovoltaic modules provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] This specific embodiment is only an interpretation of the present application, and it is not a limitation of the present application. Those skilled in the art can make modifications to this embodiment without creative contributions according to needs after reading this specification, but as long as they are within the scope of the claims of the present application, they are protected by the Patent Law.

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts fall within the scope of protection of the present application.

[0051] In addition, the term "and / or" in the present application is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0052] In this application, terms such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions. It should be understood that there is no logical or chronological dependence between "first", "second", and "nth", nor are the quantity and execution order limited.

[0053] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0054] The embodiments of this application will be further described in detail below with reference to the accompanying drawings of the specification.

[0055] A photovoltaic module is a device that converts solar energy into electrical energy. It is usually composed of multiple photovoltaic cells (silicon-based semiconductors) encapsulated and is the smallest functional unit of a photovoltaic power station. A photovoltaic power station is a complete power generation system composed of a large number of photovoltaic modules, supporting equipment, and infrastructure, which realizes large-scale electrical energy production and connects to the power grid or directly supplies energy.

[0056] As Figure 1 shown, a method for managing the cleaning cycle of a photovoltaic module provided by an embodiment of this application includes:

[0057] S1: Obtain the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, where the first group of photovoltaic modules is configured to be automatically cleaned at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned.

[0058] Obtain the real-time power generation data of the group of photovoltaic modules through the management system of the photovoltaic power station. In this application, the first group of photovoltaic modules includes at least one photovoltaic module, and the second group of photovoltaic modules includes at least one photovoltaic module. To accurately analyze the power generation difference between the two groups of photovoltaic modules, the number of photovoltaic modules in the first group of photovoltaic modules is the same as that in the second group of photovoltaic modules. To compare the differences between cleaning and non-cleaning, an automatic spraying device is used to automatically clean the first group of photovoltaic modules. Specifically, the automatic spraying device includes a water storage cavity, a water outlet pipe, a water pump, a valve, a spraying member, and an automatic spraying system. The automatic spraying system is used to receive a water spraying signal and control the opening of the water pump and the valve in response to the water spraying signal, so that the water in the water storage cavity is sprayed onto the photovoltaic modules through the water outlet pipe and the spraying member. The second group of photovoltaic modules is used as a control group and is not cleaned.

[0059] Set up a group of photovoltaic modules for cleaning, and leave another group of photovoltaic modules uncleaned. This direct comparison can more clearly evaluate the impact of cleaning on the power generation efficiency and performance of the photovoltaic power station, facilitating the evaluation of the effect and decision-making.

[0060] S2: Based on the first real-time power generation data and the second real-time power generation data, calculate the power generation improvement rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning.

[0061] The surface of the first group of photovoltaic modules after cleaning is cleaner, reducing the loss of light reflection and scattering, enabling more sunlight to enter the interior of the photovoltaic cells, and thus improving the efficiency of converting light energy into electrical energy.

[0062] Specifically, the first real-time power generation data of the first group of photovoltaic modules after automatic cleaning is denoted as P1, and the second real-time power generation data of the second group of photovoltaic modules collected within the same time period is denoted as P2. Calculate the difference ΔP between the first real-time power generation data P1 and the second real-time power generation data denoted as P2. The power generation improvement rate R is:

[0063]

[0064] To improve the reliability of the data, the real-time power generation can be measured multiple times at different time points and the average value can be taken for calculation to reduce the influence of accidental factors.

[0065] S3: Obtain the meteorological prediction data within the preset time window of the area where the photovoltaic power station is located, and estimate the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data.

[0066] The meteorological prediction data for the future time period can be obtained through a meteorological service provider or through a meteorological API interface. Extract various key meteorological parameters related to the power generation of the photovoltaic power station from the meteorological prediction data. Evaluate the power generation potential of the photovoltaic power station in the future time period based on these key meteorological parameters, that is, the predicted power generation data. The preset time window is a time period that can be set according to actual needs. For example, it can be 15 days, 30 days, 60 days or other numbers of days.

[0067] Specifically, obtain the meteorological prediction data within the preset time window of the area where the photovoltaic power station is located, and extract the predicted radiation data, predicted cloud cover duration data, predicted rainfall data, and predicted sandstorm data from the meteorological prediction data. Among them, the predicted radiation data is the solar radiation intensity per unit area on the photovoltaic module, the predicted cloud cover duration data is the cloud cover duration (h) between 10:00 and 15:00 in a day, the predicted rainfall data is the daily cumulative rainfall (mm), and the predicted sandstorm data is the daily dust concentration data (μg / m 3 )

[0068] S4: Based on the power generation increase rate and the predicted power generation data, evaluate the estimated cleaning benefit of the photovoltaic modules within a preset time window.

[0069] Specifically, according to the power generation increase rate and the predicted power generation data, calculate the power generation increase value within the preset time window after cleaning, and further calculate the additional revenue brought by cleaning based on the power generation increase value and the grid connection electricity price of the power station. The cleaning benefit is obtained by subtracting the cleaning cost from the additional revenue.

[0070] S5: Combine the estimated cleaning benefit and the meteorological prediction data to generate a cleaning decision instruction.

[0071] The cleaning decision instruction includes cleaning and not cleaning.

[0072] In some embodiments, in step S2, based on the first real-time power generation data and the second real-time power generation data, calculating the power generation increase rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning specifically includes:

[0073] Calculate the power generation difference value ΔP between the first real-time power generation data P1 and the second real-time power generation data P2;

[0074] Calculate the ratio of the power generation difference value ΔP to the second power generation data P2 to obtain the initial power generation increase rate R°;

[0075] Based on the first real-time power generation data P1 of the first group of photovoltaic modules, analyze the power generation decay trend of the first group of photovoltaic modules after cleaning, determine the decay days d corresponding to when the power generation decay rate of the first group of photovoltaic modules reaches the preset decay rate threshold z, and determine the correction factor A based on the decay days d;

[0076] Use the correction factor A to correct the initial power generation increase rate R° to determine the power generation increase rate R of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning.

[0077] Among them, the correction factor A ∈ {a1, a2}, 1 > a1 > a2 > 0. Specifically, compare the decay days d with the preset days threshold D. If the decay days d corresponding to when the power generation decay rate reaches the preset decay rate threshold z are greater than or equal to the preset days threshold D, the correction coefficient is A = a1; if the decay days d corresponding to when the power generation decay rate reaches the preset decay rate threshold z are less than the preset days threshold D, the correction coefficient A = a2. In this embodiment, the preset days threshold D is set to 8. This embodiment further corrects the power generation increase rate using the power generation decay rate of the photovoltaic modules after cleaning, making the result more in line with the actual situation and achieving a more scientific quantitative maintenance effect.

[0078] It should be noted that if the power generation of the first group of photovoltaic modules after cleaning is P1, the solar radiation is G1, the power station installed capacity is M, and the system efficiency is η1, then P1 = G1×M×η1; the data of the first group of photovoltaic modules are measured every other day. If the power generation on the i-th day is P i , the solar radiation is G i , the power station installed capacity is M, and the system efficiency is η i , P i = G i ×M×η i . Then the power generation decay rate z i = (η1 - η i ) / η1. The preset decay rate threshold z is generally set to 7% - 8%, and a1 is generally set to 0.7, which represents the correction factor corresponding to the slower rate of decline to the preset decay rate threshold z; a2 is generally set to 0.4, which represents the correction factor corresponding to the faster rate of decline to the preset decay rate threshold z.

[0079] In some embodiments, in step S3, predicting the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data includes:

[0080] Generating a corrected predicted value of the effective radiation based on the predicted cloud cover duration data and the predicted radiation data within the preset time window;

[0081] Calculating the predicted power generation data within the preset time window based on the predicted value of the effective radiation, the installed capacity of the photovoltaic power station, and the system efficiency parameters.

[0082] Exemplarily, when the preset time window is 15 days, the predicted power generation data P pre for the next 15 days is obtained by multiplying the predicted value of the effective radiation Rad, the installed capacity M of the photovoltaic power station, and the system efficiency η, that is: P pre = Rad×M×η.

[0083] Among them, first, the cloud correction coefficient C is determined according to the predicted cloud cover duration data L in the meteorological prediction data, C ∈ {C1, C2, C3}; the predicted value of the effective radiation Rad is obtained by multiplying the cloud correction coefficient C by the predicted radiation data.

[0084] Specifically, it is determined based on the cloud cover duration L during the period from 10:00 to 15:00 in a day. When the cloud cover duration L < L1, the cloud correction coefficient C takes the value of C1; when L1 ≤ L < L2, the cloud correction coefficient C takes the value of C2; when L ≥ L2, the cloud correction coefficient C takes the value of C3. Among them, L1 is taken as 1.5h, L2 is taken as 3.0h; C1 is taken as 0.85, C2 is taken as 0.725, and C3 is taken as 0.6.

[0085] In some embodiments, based on the power generation increase rate and the predicted power generation data, the cleaning benefit of the photovoltaic module within a preset time window is evaluated, including:

[0086] Obtain historical cleaning data and determine the historical cleaning cost;

[0087] Based on the power generation increase rate, the predicted power generation data, and the current grid connection electricity price, calculate the estimated cleaning revenue;

[0088] Subtract the historical cleaning cost from the estimated cleaning revenue to obtain the estimated cleaning benefit of the photovoltaic module.

[0089] Specifically, the calculation formula for the estimated cleaning benefit is expressed as:

[0090] Y = R × P pre × price - X,

[0091] In the formula, Y represents the estimated cleaning benefit, R represents the power generation increase rate, P pre represents the predicted power generation data, price represents the current grid connection electricity price, and X represents the historical cleaning cost.

[0092] In some embodiments, combining the estimated cleaning benefit and the meteorological prediction data, a cleaning decision instruction is generated, including:

[0093] Based on the meteorological prediction data, determine whether the predicted rainfall data F within the preset time window is greater than the preset rainfall threshold f. If the predicted rainfall data F within the preset time window is greater than or equal to the preset rainfall threshold f, a cleaning decision instruction not to perform cleaning is generated;

[0094] If the predicted rainfall data F within the preset time window is less than the preset rainfall threshold f, a cleaning decision instruction is generated based on the predicted sandstorm data and the estimated cleaning benefit within the preset time window. When the predicted rainfall within the preset time window is sufficient and the rainfall can clean the photovoltaic module, there is no need to arrange for cleaning at this time, which can save cleaning costs; when the predicted rainfall within the preset time window is insufficient, it may cause dust accumulation on the surface of the photovoltaic module. At this time, it is necessary to further determine whether to perform cleaning based on the predicted sandstorm data and the possible benefits brought by cleaning within the preset time window.

[0095] In some embodiments, if the predicted rainfall data within the preset time window is less than the preset rainfall threshold, a cleaning decision instruction is generated based on the predicted sandstorm data and the estimated cleaning benefit within the preset time window, including:

[0096] If the predicted sandstorm data predicts a sandstorm weather within the preset time window, a cleaning decision instruction not to perform cleaning is generated;

[0097] If the predicted sandstorm data within the preset time window predicts the absence of sand and dust weather, a cleaning decision instruction is generated based on the estimated cleaning benefit.

[0098] During actual implementation, when the dust concentration exceeds 500 μg / m 3 it is considered that there is sand and dust weather, and the preset benefit value is set to 0.

[0099] A management device for the cleaning cycle of photovoltaic modules provided by the second exemplary embodiment of the present application specifically includes:

[0100] An automatic spraying module for automatically cleaning the first group of photovoltaic modules in a photovoltaic power station;

[0101] A power generation increase rate calculation module for obtaining the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, and calculating the power generation increase rate of the first group of photovoltaic modules after automatic cleaning compared to the second group of photovoltaic modules based on the first real-time power generation data and the second real-time power generation data, where the first group of photovoltaic modules is configured to be automatically cleaned at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned;

[0102] A predicted power generation determination module for obtaining meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, and estimating the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data;

[0103] A cleaning benefit evaluation module for evaluating the estimated cleaning benefit of the photovoltaic modules when cleaned within a preset time window based on the power generation increase rate and the predicted power generation data;

[0104] A cleaning decision module for generating a cleaning decision instruction by combining the estimated cleaning benefit and the meteorological prediction data.

[0105] The embodiment of the present application provides a computer device, which may include a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the processor executes the steps of the management method for the cleaning cycle of photovoltaic modules in any of the above embodiments.

[0106] For the working process, working details, and technical effects of the computer device provided in this embodiment, reference may be made to the embodiments of the method for managing the cleaning cycle of photovoltaic modules in the foregoing text, which will not be elaborated herein.

[0107] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for managing the cleaning cycle of photovoltaic modules in any of the foregoing embodiments are implemented. Wherein, the computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical discs, hard disks, flash memories, USB flash drives, and / or memory sticks, etc. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices.

[0108] For the working process, working details, and technical effects of the computer-readable storage medium provided in this embodiment, reference may be made to the embodiments of the method for managing the cleaning cycle of photovoltaic modules in the foregoing text, which will not be elaborated herein.

[0109] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application may include non-volatile and / or volatile memories. Non-volatile memories may include read-only memories (ROMs), programmable ROMs (PROMs), electrically programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), or flash memories. Volatile memories may include random access memories (RAMs) or external cache memories. By way of illustration and not limitation, RAMs are available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0111] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system described in this application can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A management method for the cleaning cycle of a photovoltaic module, characterized in that, Including: Obtain the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, where the first group of photovoltaic modules is configured to be automatically cleaned at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned; Based on the first real-time power generation data and the second real-time power generation data, calculate the power generation improvement rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning; Obtain the meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, and estimate the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data; Based on the power generation improvement rate and the predicted power generation data, evaluate the estimated cleaning benefit of cleaning the photovoltaic modules within the preset time window; Combine the estimated cleaning benefit and the meteorological prediction data to generate a cleaning decision instruction.

2. The management method for the cleaning cycle of a photovoltaic module according to claim 1, characterized in that, Based on the first real-time power generation data and the second real-time power generation data, calculating the power generation improvement rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning includes: Calculate the power generation difference value between the first real-time power generation data and the second real-time power generation data; Calculate the ratio of the power generation difference value to the second real-time power generation data to obtain the initial power generation improvement rate; Based on the first real-time power generation data of the first group of photovoltaic modules, analyze the power generation decay trend of the first group of photovoltaic modules after cleaning, determine the decay days corresponding to when the power generation decay rate of the first group of photovoltaic modules reaches a preset decay rate threshold, and determine a correction factor based on the decay days; Use the correction factor to correct the initial power generation improvement rate to determine the power generation improvement rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning.

3. The management method of the cleaning cycle of the photovoltaic module according to claim 1, wherein Obtain the meteorological prediction data within a preset time window in the area where the photovoltaic power station is located, including: Obtain the predicted radiation amount data, predicted cloud cover duration data, predicted rainfall data, and predicted sandstorm data within a preset time window in the area where the photovoltaic power station is located.

4. The management method for the cleaning cycle of a photovoltaic module according to claim 3, characterized in that, Based on the meteorological prediction data, estimating the predicted power generation data of the photovoltaic power station within the preset time window includes: Generate a corrected predicted value of the effective radiation amount based on the predicted cloud cover duration data and the predicted radiation amount data within the preset time window; Calculate the predicted power generation data within the preset time window based on the predicted value of the effective radiation amount, the installed capacity of the photovoltaic power station, and the system efficiency.

5. The management method for the cleaning cycle of a photovoltaic module according to claim 1, characterized in that, Based on the power generation improvement rate and the predicted power generation data, evaluating the estimated cleaning benefit of cleaning the photovoltaic modules within the preset time window includes: Obtain historical cleaning data and determine the historical cleaning cost; Calculate the estimated cleaning income based on the power generation improvement rate, the predicted power generation data, and the current on-grid electricity price; Subtract the historical cleaning cost from the estimated cleaning income to obtain the estimated cleaning benefit of the photovoltaic modules.

6. The management method of the cleaning cycle of the photovoltaic module according to claim 3, characterized in that, Combining the estimated cleaning benefit and the meteorological prediction data to generate a cleaning decision instruction includes: Based on the meteorological prediction data, determine whether the predicted rainfall data within the preset time window is greater than the preset rainfall threshold. If the predicted rainfall data within the preset time window is greater than or equal to the preset rainfall threshold, generate a cleaning decision instruction not to perform cleaning. If the predicted rainfall data within the preset time window is less than the preset rainfall threshold, generate a cleaning decision instruction based on the predicted sandstorm data within the preset time window and the estimated cleaning benefit.

7. The management method for the cleaning cycle of a photovoltaic module according to claim 6, wherein, If the predicted rainfall data within the preset time window is less than the preset rainfall threshold, generate a cleaning decision instruction based on the predicted sandstorm data within the preset time window and the estimated cleaning benefit, including: If the predicted sandstorm data within the preset time window predicts the existence of a sandstorm weather, generate a cleaning decision instruction not to perform cleaning. If the predicted sandstorm data within the preset time window predicts the non-existence of a sandstorm weather, generate a cleaning decision instruction based on the estimated cleaning benefit.

8. A management device for the cleaning cycle of a photovoltaic module, characterized in that, For executing the method according to any one of claims 1 to 7, including: An automatic sprinkler module for automatically cleaning the first group of photovoltaic modules in the photovoltaic power station. A power generation increase rate calculation module for obtaining the first real-time power generation data of the first group of photovoltaic modules and the second real-time power generation data of the second group of photovoltaic modules, and calculating the power generation increase rate of the first group of photovoltaic modules compared to the second group of photovoltaic modules after automatic cleaning based on the first real-time power generation data and the second real-time power generation data, wherein the first group of photovoltaic modules is configured to perform automatic cleaning at a preset time interval, and the second group of photovoltaic modules is configured to remain uncleaned. A predicted power generation determination module for obtaining the meteorological prediction data within the preset time window of the area where the photovoltaic power station is located, and estimating the predicted power generation data of the photovoltaic power station within the preset time window based on the meteorological prediction data. A cleaning benefit evaluation module for evaluating the estimated cleaning benefit of the photovoltaic modules when cleaning within the preset time window based on the power generation increase rate and the predicted power generation data. A cleaning decision module for generating a cleaning decision instruction by combining the estimated cleaning benefit and the meteorological prediction data.

9. A computer device, characterized in that, Including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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