A photovoltaic intelligent cleaning method, device and storage medium based on power generation prediction

Abstract

The application relates to the field of photovoltaic cleaning technology, in particular to a photovoltaic intelligent cleaning method and device based on power generation prediction and a storage medium. The method comprises the following steps: in a target detection area of a solar cell panel, according to a dust deposition light transmittance variable, an influence degree variable of non-dust deposition dirt on light absorption, and current environmental characteristics and parameters of a cleaned solar cell panel group string, predicting a predicted power generation of each group string in each period; after iteration of multiple periods is completed, the dust deposition light transmittance variable of the multiple periods and the influence degree variable of the multiple periods are summarized; if there are a set number of dust deposition light transmittance variables greater than a set threshold, it is determined that the corresponding group string is of a dust deposition type; according to a change trend, it is determined that the corresponding group string is of an easy-to-clean type or a difficult-to-clean type; and the group string is cleaned according to the dust deposition type, the easy-to-clean type or the difficult-to-clean type of the group string. The application can determine different dirt types and cleaning methods.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic cleaning, in particular to a photovoltaic intelligent cleaning method based on power generation prediction, equipment and storage medium. BACKGROUND

[0002] Solar panels are devices that convert solar energy into electricity, mainly made of semiconductor materials such as silicon. When sunlight shines on the surface of the panel, photons excite electrons to generate current, thereby realizing photoelectric conversion. It is necessary to clean the solar panels to ensure the normal operation of the panels. Dust, dirt, etc. can block sunlight and reduce the power generation efficiency of the panels. Regular cleaning can ensure that the panels fully absorb sunlight and maintain high efficiency. Dirt can corrode the surface of the panel and affect its life. Cleaning can reduce corrosion and extend the service life of the equipment. Accumulation of dirt can cause some areas to overheat, forming hot spots and damaging the panel. Cleaning can avoid this problem.

[0003] At present, due to the large coverage of solar panels, some areas arrange cameras around the solar panels to determine whether the panels need to be cleaned through image detection technology. When cleaning is needed, a robot or personnel is sent to clean the panels.

[0004] However, the existing image detection technology can only determine whether the panels need to be cleaned, but it is difficult to determine the degree of pollution and the way to clean. In view of this, the present application provides a photovoltaic intelligent cleaning scheme relying on power generation. SUMMARY

[0005] The purpose of the present application is to provide a photovoltaic intelligent cleaning method based on power generation prediction, equipment and storage medium to make up for the defects of image detection.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme:

[0007] In the first aspect, the present application provides a photovoltaic intelligent cleaning method based on power generation prediction, comprising:

[0008] In the target detection area of the solar panel, according to the dust deposition light transmission rate variable, the influence degree variable of non-dust deposition dirt on light absorption, and the current environmental characteristics and the parameters of the cleaned solar panel string, the predicted power generation of each string in each period is predicted;

[0009] The actual power generation of each string in each period is obtained;

[0010] The soot transmittance variable and the influence degree variable are iterated in each time period to minimize the difference between the predicted power generation and the actual power generation of each group string, and the soot transmittance variable and the influence degree variable in each time period are obtained; in the same time period iteration process, the difference between the soot transmittance variables of different group strings in the same area is less than a set value; in the adjacent time period iteration process, the soot transmittance of the same group string in the next time period is less than the soot transmittance in the previous time period;

[0011] After the iteration of multiple time periods is completed, the soot transmittance variables of multiple time periods and the influence degree variables of multiple time periods are summarized;

[0012] If a set number of soot transmittance variables in multiple time periods are greater than a set threshold value, it is determined that the corresponding group string is of a soot type;

[0013] According to the chronological order, the change trend of the influence degree variable of each group string is determined, and according to the change trend, it is determined that the corresponding group string is of an easy-to-clean type or a difficult-to-clean type;

[0014] According to the soot type, the easy-to-clean type or the difficult-to-clean type of the group string, the group string is cleaned.

[0015] Optionally, according to the soot transmittance variable, the influence degree variable of the non-soot dirt on light absorption, and the current environmental characteristics and the parameters of the cleaned solar cell panel group string, the predicted power generation of each group string in each time period is predicted, including:

[0016] The predicted power generation P of each group string in each time period is calculated according to the following formula:

[0017]

[0018] Wherein, G is the light intensity, A is the panel group string area, η is the photoelectric conversion efficiency, the range is 0-1; t represents the time length of the time period, R represents the original reflectivity of the panel surface, the range is 0-1; σ is the soot transmittance variable, the range is 0-1; τ is the soot area ratio, which is a constant value 1; δ is the influence degree variable of the non-soot dirt on light absorption, which represents the sum of the product of the reciprocal of the transmittance of each non-soot dirt and the area ratio.

[0019] Optionally, according to the chronological order, the change trend of the influence degree variable of each group string is determined, and according to the change trend, it is determined that the corresponding group string is of an easy-to-clean type or a difficult-to-clean type, including:

[0020] If the influence degree variable of the same group string increases over time, it is determined that the group string is of a difficult-to-clean type;

[0021] If the influence degree variable of the same group string fluctuates or decreases over time, it is determined that the group string is of an easy-to-clean type.

[0022] Optionally, the group string is cleaned according to the dusting type, easy-to-clean type or difficult-to-clean type of the group string, including:

[0023] If the group string is of the dusting type, the group string is cleaned with clean water;

[0024] If the group string is of the easy-to-clean type, the group string is cleaned with a scraper and clean water;

[0025] If the group string is of the difficult-to-clean type, the group string is cleaned with clean water, a cleaning agent and a flexible brush.

[0026] Optionally, after the group string is cleaned, the method further includes returning to the operation of predicting the predicted power generation of each group string in each time period.

[0027] In a second aspect, the present application provides an electronic device, including:

[0028] at least one processor, and a memory connected to the at least one processor in communication;

[0029] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any of the photovoltaic intelligent cleaning methods based on power generation prediction.

[0030] In a third aspect, the present application provides a computer-readable storage medium, the medium storing computer instructions, the computer instructions being used to make the computer execute any of the photovoltaic intelligent cleaning methods based on power generation prediction.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] In the present application, the dusting occupying the entire area of the battery panel is divided out, and the non-dusting dirt occupying part of the area of the battery panel is divided out according to the different areas occupied by the dirt, and a power generation prediction method is constructed. Then, the influence degree variable of the non-dusting dirt and the dusting light transmittance variable are iterated by approaching the actual power generation through the predicted power generation. In this way, the change trend of the dusting on the battery panel and the change trend of the non-dusting dirt can be obtained respectively, so as to determine different types and cleaning methods. When the dusting light transmittance variable is iterated, the present application adopts a space constraint condition and a time constraint condition, i.e., the dusting light transmittance variables of different group strings are not greatly different, and the dusting of the same group string becomes thicker with time, and the dusting light transmittance becomes smaller and smaller. This iteration method is closer to the actual situation, and the change law of the non-dusting dirt can also be successfully determined with the aid of the change law of the dusting. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art of the present application, the drawings needed in the description of the specific embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0034] Figure 1 is a flowchart of a photovoltaic intelligent cleaning method based on power generation prediction provided by an embodiment of the present application.

[0035] Figure 2 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0036] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to help understanding. They should be considered only as exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures.

[0037] The scheme provided by the embodiments of the present application is used to determine the dirt type of a solar cell panel and the cleaning difficulty according to the power generation prediction technology, so as to adapt to different cleaning modes. The embodiments only need to perform power generation prediction, and use the big data of multiple panel groups to distinguish the dust accumulation type, easy-to-clean type or difficult-to-clean type of the groups, and clean the groups without the participation of cameras and other sensors.

[0038] Figure 1 is a flowchart of a photovoltaic intelligent cleaning method based on power generation prediction provided by an embodiment of the present application. The method can be executed by an electronic device. As shown in Figure 1 , the method comprises the following steps:

[0039] S110, in a target detection area of a solar cell panel, according to a dust accumulation light transmittance variable, a non-dust accumulation dirt light absorption influence degree variable, and current environmental characteristics and parameters for cleaning a solar cell panel group, predicting the predicted power generation of each group in each time period.

[0040] The target detection area can be the entire geographical area occupied by the solar cell panel, and there are multiple solar cell panel groups (hereinafter referred to as groups) in the target detection area. The power generation prediction is performed for each group as a unit.

[0041] Optionally, the predicted power generation P of each group in each time period is calculated according to the following formula:

[0042]

[0043] Wherein, G is the light intensity, A is the panel string area, η is the photoelectric conversion efficiency, the range is 0-1; t represents the length of the time period (for example, half an hour or 1 hour), R represents the original reflectivity of the panel surface, the range is 0-1; σ is the soot transmittance variable, the range is 0-1, the more serious the soot, the lower the soot transmittance, the lower the power generation; τ is the soot area ratio, which is a fixed value 1; δ is the influence degree variable of non-soot dirt on light absorption, which represents the sum of the reciprocal of the transmittance of each non-soot dirt and the area ratio. For example, there is the following formula:

[0044]

[0045] Wherein, σ n represents the transmittance of the nth non-soot dirt, τ n is the area ratio of the nth non-soot dirt on the panel. This embodiment does not distinguish the type and area ratio of non-soot dirt, but only fuses the above formula (2) of various non-soot dirt and its area ratio to obtain the influence degree variable of non-soot dirt on light absorption, which is a value of 0-1, the larger the value, the lower the power generation. Based on this, the σ in the above formula (1) represents the influence on the power generation of the solar panel with soot and non-soot dirt compared with the clean panel, the value is between 0-1, the smaller the value, the lower the power generation.

[0046] S120, obtaining the actual power generation of each string in each time period.

[0047] Each string is connected with ammeter, voltmeter and power meter, etc., then the actual power generation of each string in each time period is obtained according to the readings of these instruments.

[0048] S130, iterating the soot transmittance variable and the influence degree variable in each time period to minimize the difference between the predicted power generation and the actual power generation of each string, and obtaining the soot transmittance variable and the influence degree variable in each time period.

[0049] Before the first iteration, it should be ensured that the solar cell has been cleaned and is relatively clean. Then a larger value is given to the soot transmittance variable and a smaller value is given to the influence degree variable. A predicted power generation and an actual power generation are obtained for each string in each time period. If the difference between the predicted power generation and the actual power generation is greater than a set value, for example, 50, the soot transmittance variable and the influence degree variable need to be updated until the difference between the predicted power generation and the actual power generation is less than the set value.

[0050] In the same period iteration process, the following iteration condition should be met: the difference between the dust deposition transmittance variables of different groups of strings in the same area is less than a set value. Here, the area is the different terrain areas divided in the target detection area, for example, different areas are divided according to the height of the terrain, slope, etc. The dust distribution in different areas may have some differences, while the dust distribution in the same area is considered uniform, and the dust deposition rate is considered similar, so the difference between the dust deposition transmittance variables of different groups of strings in the same area in the same period should be small; while the difference between the dust deposition transmittance variables of groups of strings in different areas in the same period is allowed to be a larger value.

[0051] In the adjacent period iteration process, the following iteration condition should be met: the dust deposition transmittance of the same group of strings in the next period is less than that in the previous period. For any group of strings, as time goes on, the dust deposition should gradually thicken, and the dust deposition transmittance should gradually decrease (this embodiment does not consider extreme weather such as strong wind, rain and snow, in fact, the solar panels should be closed in extreme weather to ensure safety, and the power generation is 0).

[0052] Each group of strings in each period needs to be iterated multiple times to find the appropriate dust deposition transmittance and influence degree variable.

[0053] S140, after completing the iteration of multiple periods, the dust deposition transmittance variables of multiple periods and the influence degree variables of multiple periods are summarized.

[0054] After the foregoing operation, the dust deposition transmittance and influence degree variable of each group of strings in each period are obtained. For each group of strings, a time sequence of the dust deposition transmittance variable and a time sequence of the influence degree variable are formed in chronological order.

[0055] S150, if there are a set number of dust deposition transmittance variables greater than a set threshold value in the dust deposition transmittance variables of multiple periods, it is determined that the corresponding group of strings is of the dust deposition type.

[0056] The set number can be determined empirically, for example, if 30% of the dust deposition transmittance variables of the periods are greater than the set threshold value, it is determined that the corresponding group of strings is of the dust deposition type. That is, there is dust deposition on the group of strings.

[0057] S160, the change trend of the influence degree variable of each group of strings is determined according to the chronological order, and the corresponding group of strings is determined to be of the easy-to-clean type or the difficult-to-clean type according to the change trend.

[0058] If the influence degree variable of the same group of strings increases over time, it is determined that the group of strings is of the difficult-to-clean type. That is, as time goes on, the non-dust deposition dirt area becomes larger and larger, and the transmittance becomes lower and lower, which is not affected by drying, moisture and microbial decomposition, and is of the difficult-to-clean type, such as bird droppings.

[0059] If the influence degree variable of the same group of strings fluctuates up and down or decreases over time, the group of strings is determined to be of the easy-to-clean type. That is, over time, some non-deposited dirt will be reduced in area or thickness due to the effects of wind, moisture and microbial decomposition, such as leaves and the like.

[0060] It should be noted that if the influence degree variable of the same group of strings is less than the specified threshold value, it is a small value, and it is considered that there is no non-deposited dirt. For the influence degree variable greater than the specified threshold value, it is determined to be of the easy-to-clean type or the difficult-to-clean type according to the change trend.

[0061] In summary, a group of strings can be of the deposition type + difficult-to-clean type, the deposition type + easy-to-clean type, the difficult-to-clean type, the easy-to-clean type or the deposition type.

[0062] S170, according to the deposition type, easy-to-clean type or difficult-to-clean type of the group of strings, the group of strings is cleaned.

[0063] If the group of strings is of the deposition type, clean water is used to clean the group of strings, and the clean water is sufficient to remove the deposition on the surface of the panel. If the group of strings is of the easy-to-clean type, a scraper and clean water are used for cleaning. For example, first, the panel surface is washed with clean water, then the dirt is scraped off with a scraper, and finally, the panel surface is washed again with clean water. If the group of strings is of the difficult-to-clean type, clean water, cleaning agent and flexible brush are used for cleaning. For example, the cleaning agent is mixed with clean water to wash the surface of the panel, and the flexible brush is used for brushing during the washing process to achieve deep cleaning.

[0064] The embodiment does not limit the cleaning method, which can be robot cleaning or cleaning device provided by the panel.

[0065] Optionally, after cleaning the group of strings, the operation of predicting the predicted power generation of each group of strings at each time period is returned to perform the next round of power generation prediction, variable iteration and cleaning operation.

[0066] The embodiment of the present application divides the dust accumulation occupying the whole area of the battery panel and the non-dust accumulation occupying the partial area of the battery panel according to the different areas occupied by the dirt, and constructs a power generation prediction method. Then, the influence degree variable of the dust accumulation light transmittance variable and the non-dust accumulation are iterated by predicting the power generation to approach the actual power generation. In this way, the change trend of the dust accumulation on the battery panel and the change trend of the non-dust accumulation can be obtained respectively, so as to determine different types and cleaning methods. When the dust accumulation light transmittance variable is iterated, the embodiment adopts the space constraint condition and the time constraint condition, that is, the dust accumulation light transmittance variables of different groups of strings are not much different, and the dust accumulation of the same group of strings becomes thicker with time, and the dust accumulation light transmittance becomes smaller and smaller. This iteration method is closer to the actual situation, and the change rule of the non-dust accumulation can also be successfully determined with the aid of the change rule of the dust accumulation.

[0067] The embodiment of the present application provides an electronic device, referring to Figure 2 , comprising at least one processor 301 and a memory 302 connected with the at least one processor 301;

[0068] The memory 302 stores instructions executable by the at least one processor 301, and the instructions are executed by the at least one processor 301 to enable the at least one processor 301 to perform the above-mentioned photovoltaic intelligent cleaning method based on power generation prediction, thus having at least the same advantages as the above-mentioned method.

[0069] Optionally, the electronic device further includes an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are connected with each other by different buses, and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed in the electronic device, including graphical information stored in the memory or on the memory to display a GUI (Graphical User Interface) on an external input / output device, such as a display device coupled to the interface. In other embodiments, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories, if necessary. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), each device providing part of the necessary operations.

[0070] The memory 302 as a kind of computer readable storage medium can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the photovoltaic intelligent cleaning method based on power generation prediction in the embodiment of the present application. The processor 301 executes the various functions of the device and data processing by running the software programs, instructions and modules stored in the memory 301, that is, the photovoltaic intelligent cleaning method based on power generation prediction is realized.

[0071] The memory 301 can include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application program required for at least one function, and the data storage area can store data created according to the use of the terminal, etc. In addition, the memory 302 can include a high-speed random access memory, and can further include a non-volatile memory such as at least one of a magnetic disk storage device, a flash memory device, or other non-volatile solid state storage device. In some examples, the memory 302 can further include a memory remotely located with respect to the processor, which can be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0072] The electronic device further includes an input device 303 and an output device 304. The processor 301, the memory 301, the input device 303, and the output device 304 can be connected through a bus or other means.

[0073] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., an LED), a haptic feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0074] It should be understood that the various forms of flow shown above can be reordered, added, or deleted steps. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present application can be achieved, which are not limited herein.

[0075] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A photovoltaic intelligent cleaning method based on power generation prediction, characterized in that, The method comprises the following steps: In a target detection area of a solar panel, according to a soiling light transmittance variable, a non-soiling dirt light absorption influence degree variable, and current environmental characteristics and parameters for cleaning a solar panel string, a predicted power generation of each string in each period is predicted; An actual power generation of each string in each period is obtained; In each period, the soiling light transmittance variable and the influence degree variable are iterated to minimize the difference between the predicted power generation and the actual power generation of each string, so as to obtain the soiling light transmittance variable and the influence degree variable in each period; In the same period iteration process, the difference between the soiling light transmittance variables of different strings in the same area is less than a set value; in the adjacent period iteration process, the soiling light transmittance of the same string in the next period is less than that in the previous period; After the iteration of multiple periods is completed, the soiling light transmittance variables of multiple periods and the influence degree variables of multiple periods are summarized; If a set number of soiling light transmittance variables in the soiling light transmittance variables of multiple periods are greater than a set threshold, it is determined that the corresponding string is of a soiling type; According to the chronological order, a change trend of the influence degree variable of each string is determined, and according to the change trend, it is determined that the corresponding string is of an easy-to-clean type or a difficult-to-clean type; According to the soiling type, the easy-to-clean type or the difficult-to-clean type of the string, the string is cleaned; The predicted power generation of each string in each period is predicted according to the soiling light transmittance variable, the non-soiling dirt light absorption influence degree variable, and the current environmental characteristics and the parameters for cleaning the solar panel string, comprising: The predicted power generation P of each string in each period is calculated according to the following formula: ； ； Wherein, G is the light intensity, A is the area of the panel string, is the photoelectric conversion efficiency, the range is 0~1; t represents the length of the time period, R represents the original reflectivity of the panel surface, the range is 0~1; is the soot transmittance variable, the range is 0~1; is the soot area proportion, the value is 1; is the non-soot dirt light absorption influence degree variable, which represents the product of the reciprocal of the transmittance of each non-soot dirt and the area proportion, and then the result is added; represents the transmittance of the nth non-soot dirt, is the area proportion of the nth non-soot dirt on the panel.

2. The method of claim 1, wherein, According to the chronological order, a change trend of the influence degree variable of each string is determined, and according to the change trend, it is determined that the corresponding string is of an easy-to-clean type or a difficult-to-clean type, comprising: If the influence degree variable of the same string increases over time, it is determined that the string is of a difficult-to-clean type; If the influence degree variable of the same string fluctuates or decreases over time, it is determined that the string is of an easy-to-clean type.

3. The method of claim 2, wherein, According to the soiling type, the easy-to-clean type or the difficult-to-clean type of the string, the string is cleaned, comprising: If the string is of a soiling type, the string is cleaned with water; If the string is of an easy-to-clean type, the string is cleaned with a scraper and water; If the string is of a difficult-to-clean type, the string is cleaned with water, a cleaning agent and a flexible brush.

4. The method of claim 3, wherein, After the string is cleaned, the operation of predicting the predicted power generation of each string in each period is returned.

5. An electronic device, comprising: The method comprises the following steps: At least one processor and a memory connected in communication with the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the photovoltaic intelligent cleaning method based on power generation prediction according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The medium stores computer instructions, and the computer instructions are used to enable the computer to perform the photovoltaic intelligent cleaning method based on power generation prediction according to any one of claims 1-4.

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