Photovoltaic module cleaning optimization system and method based on pollution monitoring

Abstract

The invention relates to the technical field of photovoltaic power stations, and discloses a photovoltaic module cleaning optimization system based on pollution monitoring, and the system comprises a monitoring unit which is provided with a sensor module at an equipment point, and the sensor module is used for collecting the operation data of the equipment point; the central control unit is used for setting a dust accumulation model and a prediction time period, and setting a dust accumulation amount according to the environment data and predicted weather data in the prediction time period; the central control unit comprises a first processing module which is used for setting a soil texture scene for each monitoring sub-region; the second processing module is used for calculating a first dust accumulation amount according to the predicted wind power data and the soil scene; the third processing module is used for generating a soil texture variation coefficient according to the predicted rainfall data; the sweeping unit is used for generating sweeping time according to the second dust accumulation amount and executing a sweeping strategy according to the sweeping time; and the fourth processing module is used for generating first cleaning time according to the second dust accumulation amount, adjusting the photovoltaic module according to the first cleaning time and a preset condition, and generating cleaning time.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power plant technology, and in particular to a photovoltaic module cleaning optimization system and method based on pollution monitoring. Background Technology

[0002] Photovoltaic power generation, as a clean and renewable energy source, is receiving increasing attention and importance. Depending on the application scenario, photovoltaic power generation technology is mainly divided into two categories: centralized photovoltaic power plants and distributed photovoltaic power plants. Centralized photovoltaic power plants concentrate large-scale photovoltaic power generation facilities in areas with abundant solar resources, such as deserts and Gobi deserts. These power plants convert solar energy into electrical energy through large-scale photovoltaic panel arrays and transmit the electricity to load centers far from the power generation center through high-voltage transmission networks, meeting the electricity demand of a wide area.

[0003] In existing technologies, a fixed cleaning cycle is generally used when cleaning photovoltaic modules, which cannot be adapted to different environments and climates by setting flexible cleaning cycles. Existing technologies generally only consider the washing effect of rainwater on the dust on the photovoltaic panels, without considering the different changes caused by different rainfall amounts and different dust compositions. When designing cleaning schemes, existing technologies do not consider that when building photovoltaic power stations in extremely remote or inaccessible areas, it is impossible to adjust the dust accumulation rate to achieve a one-time cleaning in the design of the cleaning scheme, thus resulting in a waste of manpower and resources. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a photovoltaic module cleaning optimization system based on pollution monitoring. The aim is to obtain a technical solution that allows for flexible cleaning cycles to be set according to different environments and climates, thereby greatly improving cleaning efficiency and power plant reliability.

[0005] In some embodiments of this application, a photovoltaic module cleaning optimization system based on pollution monitoring is provided, characterized in that it includes: The monitoring unit is used to set up multiple monitoring sub-areas, set up multiple device points in the monitoring sub-areas, and set up sensor modules on the device points. The sensor modules are used to collect the operating data of each device point. The central control unit is used to set the dust accumulation model and prediction period, and to set the dust accumulation amount based on environmental data and predicted weather data within the prediction period. The central control unit includes: The first processing module is used to acquire soil data and set soil scenarios for each monitoring sub-area. The second processing module is used to acquire predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and the soil conditions. The third processing module is used to generate a soil change coefficient based on the predicted rainfall data, and to generate a second dust accumulation based on the soil change coefficient and the first dust accumulation. The cleaning unit is used to generate a cleaning time based on the second dust accumulation amount and to execute a cleaning strategy based on the cleaning time. The cleaning unit further includes: The fourth processing module is used to generate a first cleaning time based on the second dust accumulation amount, and to adjust the photovoltaic module according to the first cleaning time and preset conditions to generate a cleaning time.

[0006] In some embodiments of this application, the second processing module is further configured to: Obtain the predicted wind data for the predicted period, which includes: wind direction data, wind level data, and duration data; Based on historical operational data, the dust accumulation rate corresponding to the wind level data is generated; Based on wind direction and duration data, determine the duration of wind in each soil type scenario; Based on soil data, the initial dust accumulation on each photovoltaic module is generated.

[0007] In some embodiments of this application, the third processing module is further configured to: Obtain the predicted rainfall data for the forecast period; The predicted rainfall data is divided into primary accuracy data and secondary accuracy data; Construct a rainfall curve based on the first-level precision data; The rainfall curve is divided into rainfall periods, and there are N rainfall periods in total; An influence coefficient is set for the rainfall in each time period. ; Determine the soil variation coefficient based on the influence coefficient. ; Calculate the amount of second dust accumulation when the rain stops. ; in, Let be the soil quality variation coefficient for the i-th time period; The initial dust accumulation at the point when rainfall begins.

[0008] In some embodiments of this application, the step of dividing the rainfall curve into rainfall periods includes: Obtain the slope data of the rainfall curve; The intervals in the rainfall curve that meet the invalidation criteria are set as invalid time periods; the intervals that do not meet the invalidation criteria are set as valid time periods. The effective time period is further divided into effective rainfall time periods according to the division conditions to generate a time period sequence D, D=( , ,… … ),in, This is the first period of effective rainfall. For the i-th effective rainfall period, The total number of periods with effective rainfall; The dividing condition is that the change in rainfall during the rainfall period reaches a change threshold. The invalid condition is: if the slope of the time period is always negative or the rainfall data in the time period is not the first occurrence.

[0009] In some embodiments of this application, the setting of the influence coefficient includes: For the aforementioned valid time period: Set influence coefficient ; = * ; in, The first coefficient; is the average rainfall in the i-th time period.

[0010] In some embodiments of this application, determining the soil variation coefficient based on the influence coefficient includes: Pre-define the soil quality sequence T, and set T = ( , , ),in, It is a type of soil. It is classified as Class II soil. It is classified as Class III soil; When T is When, select * As ; When T is When, select * As ; When T is When, select * As ; in, The first influence correction factor; This is the second influence correction factor; This is the third influence correction factor.

[0011] In some embodiments of this application, the cleaning unit is further configured to: The first cleaning time is generated based on the second dust accumulation amount; Determine the monitoring sub-area that meets the preset conditions; Based on the first cleaning time, all photovoltaic modules in the monitoring sub-area that meet the preset conditions are adjusted to generate a cleaning time.

[0012] In some embodiments of this application, generating the first cleaning time based on the second dust accumulation includes: Let the data subset sequence S be defined. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. when When the cleaning conditions are met, it is determined that the monitored sub-area needs to be cleaned, and the time is set as the first cleaning time. ; The cleaning conditions are as follows: * > ; in, This represents the sum of dust accumulation in the i-th data subset; The second coefficient; This represents the dust level threshold.

[0013] In some embodiments of this application, the adjustment of all photovoltaic modules in the monitoring sub-area that meets the preset conditions according to the first cleaning time is further used for: Define a sequence Z of monitoring sub-regions that satisfy the first preset condition. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. Obtain the cleaning time corresponding to each monitoring sub-area in the sequence; The average cleaning time is set as the cleaning time of the monitored sub-region in the series. ; when earlier than Adjust at the time point The tilt angle of the photovoltaic module.

[0014] In some embodiments of this application, a photovoltaic module cleaning optimization method based on pollution monitoring is provided, including: Multiple monitoring sub-areas are set up, and multiple device points are set up in the monitoring sub-areas. Sensor modules are set up on the device points, and the sensor modules are used to collect the operating data of each device point. Set up a dust accumulation model and forecast period, and set the dust accumulation amount based on environmental data and forecast weather data within the forecast period; Acquire soil data and set soil scenarios for each monitoring sub-area; Obtain predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and soil conditions; A soil quality change coefficient is generated based on the predicted rainfall data, and a second dust accumulation is generated based on the soil quality change coefficient and the first dust accumulation. A cleaning time is generated based on the second dust accumulation amount, and a cleaning strategy is executed based on the cleaning time, including: generating a first cleaning time based on the second dust accumulation amount, adjusting the photovoltaic modules based on the first cleaning time and preset conditions, and generating a cleaning time.

[0015] Compared with existing technologies, the photovoltaic module cleaning optimization system based on pollution monitoring in this application has the following advantages: A smart operation and maintenance mechanism integrating prediction, decision-making, and optimization has been established, bringing significant technical and economic benefits to photovoltaic power plants; it has achieved accurate prediction and proactive operation and maintenance of dust accumulation. The system no longer passively responds to pollution but actively integrates predicted weather and environmental data, dynamically calculating future dust accumulation amounts by establishing a scientific dust accumulation model. This enables operation and maintenance personnel to plan operations in advance, upgrading the approach from "remedial action" to "prevention," greatly enhancing the planning and initiative of operation and maintenance.

[0016] The design takes into account the changes in dust morphology and shading capacity on photovoltaic modules caused by different rainfall amounts under different dust compositions, and considers that clay components become more difficult to clean after rain, thus causing a further decrease in power generation. This breaks away from the traditional logic of only considering the cleaning effect of rainwater on photovoltaic panels.

[0017] The plan also takes into account the ability to manually adjust the rate of dust accumulation, thereby cleaning up photovoltaic modules in more remote areas in one go, thus saving manpower and resources. Attached Figure Description

[0018] Figure 1 This application embodiment describes a photovoltaic module cleaning optimization system based on pollution monitoring. Detailed Implementation

[0019] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0020] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] like Figure 1 As shown in the figure, a photovoltaic module cleaning optimization system based on pollution monitoring according to an embodiment of this application includes: The monitoring unit is used to set up multiple monitoring sub-areas, set up multiple device points in the monitoring sub-areas, and set up sensor modules on the device points. The sensor modules are used to collect the operating data of each device point. The central control unit is used to set the dust accumulation model and prediction period, and to set the dust accumulation amount based on environmental data and predicted weather data within the prediction period. The central control unit includes: The first processing module is used to acquire soil data and set soil scenarios for each monitoring sub-area. The second processing module is used to acquire predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and the soil conditions. The third processing module is used to generate a soil change coefficient based on the predicted rainfall data, and to generate a second dust accumulation based on the soil change coefficient and the first dust accumulation. The cleaning unit is used to generate a cleaning time based on the second dust accumulation amount and to execute a cleaning strategy based on the cleaning time. The cleaning unit also includes: The fourth processing module is used to generate a first cleaning time based on the second dust accumulation amount, and to adjust the photovoltaic module according to the first cleaning time and preset conditions to generate a cleaning time.

[0024] Specifically, the sensor module can collect various operational data, including: image data, power data, and dust accumulation data on photovoltaic modules; operational data includes historical operational data and real-time operational data, as well as various weather data.

[0025] Specifically, each complete photovoltaic subarray is designated as a monitoring sub-region.

[0026] Specifically, the preferred forecast period is 20 days.

[0027] Specifically, the dust accumulation model is a model that describes the accumulation of dust on the surface of photovoltaic modules.

[0028] Specifically, the environmental data includes: soil composition data of the environment surrounding the monitored sub-region, and distribution data of various soil types.

[0029] Specifically, forecasted weather data includes forecasted rainfall data and forecasted wind data. Rainfall data includes the amount and duration of rainfall. Wind data includes wind direction, wind speed (or gravitational force), and the duration of the wind.

[0030] Specifically, when setting up soil scenarios, the geological survey first determines how far dust can be blown to the monitoring sub-area. The location where the dust can reach the photovoltaic modules from the farthest direction is set as the soil boundary of the monitoring sub-area. The soil composition at each location within the soil boundary is obtained, and the soil scenario is determined based on the clay content in the soil at different locations. If the clay content is between 0% and 5%, it is set as a sandy soil scenario; if the clay content is between 5% and 30%, it is set as a loamy soil scenario; if the clay content is between 30% and 60%, it is set as a clay soil scenario. The higher the clay content, the easier it is for the dust to turn into more difficult-to-clean soil when it falls on the photovoltaic modules and comes into contact with water.

[0031] Specifically, the cleaning strategy is to determine the cleaning date, and then, starting from the starting point, clean each monitoring sub-area in sequence from far to near on that date.

[0032] In some embodiments of this application, the second processing module is further configured to: Obtain the predicted wind data for the forecast period. The predicted wind data includes: wind direction data, wind level data, and duration data. Based on historical operational data, generate the dust accumulation rate corresponding to the wind level data; Based on wind direction and duration data, determine the duration of wind in each soil type scenario; Based on soil data, the initial dust accumulation on each photovoltaic module is generated.

[0033] Specifically, when acquiring forecast wind data, a data acquisition period is set. This acquisition period is minutes for the next 0 to 3 days, i.e., acquiring data minute by minute; hours for the next 3 to 15 days; and days for the next 15 to 20 days.

[0034] Specifically, by using historical operational data to determine the wind levels in the predicted wind data, and then selecting time points in the historical operational data that have the same wind level values, the dust accumulation rate at these time points is set as the dust accumulation rate at each time point in the predicted period.

[0035] Specifically, the dust accumulation rate is selected based on a fixed data acquisition period. That is, when calculating the first dust accumulation amount, the dust accumulation rate of each time point is set to the average accumulation rate of the corresponding data acquisition period. After obtaining the dust accumulation rate of each complete and continuous time point, it is integrated and then added to the real-time dust accumulation amount at this time to obtain the first dust accumulation amount. The real-time dust accumulation amount is obtained through a sensor.

[0036] When obtaining the first dust accumulation amount, the proportion of each type of dust on the photovoltaic module at each non-rainy time during the prediction period is calculated based on the soil type and duration of the seam in the soil scenario and the soil type of that soil scenario (i.e., sand, loam, clay).

[0037] Specifically, wind direction data refers to the parameters of dust migration direction collected by meteorological sensors, which can be implemented using electronic compasses or wind vanes, and is used to determine the relationship between the direction of wind action and the position of photovoltaic modules.

[0038] Specifically, wind level data refers to the wind intensity level parameters obtained through meteorological stations, which can be implemented using the Beaufort scale to quantify the ability of wind to drive dust migration.

[0039] Specifically, duration data refers to the continuous time parameters of wind force action under specific wind direction and wind level conditions, which can be achieved using time series recording devices.

[0040] In some embodiments of this application, the third processing module is further configured to: Obtain the predicted rainfall data for the forecast period; Predicted rainfall data is divided into primary accuracy data and secondary accuracy data; Construct a rainfall curve based on first-level precision data; The rainfall curve is divided into rainfall periods, with a total of N rainfall periods; An influence coefficient is set for the rainfall in each time period. ; Determine the soil variation coefficient based on the influence coefficient. ; Calculate the amount of second dust accumulation when the rain stops. ; in, Let be the soil quality variation coefficient for the i-th time period; The initial dust accumulation at the point when rainfall begins.

[0041] Specifically, Level 1 precision data is the predicted rainfall data for the next 0 to 7 days, with higher accuracy; Level 2 precision data is the predicted rainfall data for the next 8 to 20 days, with lower accuracy.

[0042] The accuracy of secondary precision data is relatively low; when calculating the second dust accumulation amount, only data within the next 7 days is considered. The first dust accumulation amount for other time periods is equal to the second dust accumulation amount.

[0043] Specifically, rainfall data within 7 days is acquired hourly, and the data is first interpolated to obtain a complete and smooth rainfall curve.

[0044] Specifically, each rain event is set as a complete rainfall curve from start to finish, while different heavy rain events are set as independent rainfall curves. For example, if there is a rain event in the next 1 to 3 days and on the 5th day, then two rainfall events are set to correspond to two rainfall curves to calculate the second dust accumulation in segments.

[0045] Specifically, the amount of dust accumulated at the point when the rain stops is the amount accumulated at the end of the heavy rain, and the amount accumulated at the point when the rain starts is the amount accumulated at the beginning of the heavy rain. The change in the amount of dust accumulated before and after is due to the change in dust caused by the rain wetting the dust.

[0046] In some embodiments of this application, the rainfall curve is divided into rainfall periods, including: Obtain the slope data of the rainfall curve; The intervals in the rainfall curve that meet the invalidation criteria are set as invalid time periods; the intervals that do not meet the invalidation criteria are set as valid time periods. The effective time period is further divided into effective rainfall time periods according to the division conditions to generate a time period sequence D, D=( , ,… … ),in, This is the first period of effective rainfall. For the i-th effective rainfall period, The total number of periods with effective rainfall; The criteria for classification are: the change in rainfall during a given period reaches a certain threshold. The invalid conditions are: if the slope of the time period is always negative or the rainfall data in the time period is not the first occurrence.

[0047] Specifically, effective and ineffective periods refer to the division of time periods based on slope data to determine whether rainfall has an actual scouring effect. Ineffective periods are divided into two types: one is a period where rainfall is continuously decreasing, and the other is a period where the rainfall amount has already occurred before, meaning the rainfall is less than the rainfall at a previous moment, i.e., it has decreased in a zigzag manner from a previous higher rainfall amount to this current amount. In both cases, dust is more likely to be washed away when rainfall is higher than when rainfall is lower. Therefore, if the subsequent rainfall is less than the previous rainfall, any changes in the dust would have already occurred when the rainfall was higher, and the later, lower rainfall will not affect the dust, thus classifying it as an ineffective period.

[0048] Specifically, rainfall periods include effective rainfall periods and ineffective rainfall periods.

[0049] Specifically, the preferred change threshold is 3 mm.

[0050] In some embodiments of this application, an influence coefficient is set, including: For valid time periods: Set influence coefficient ; = * ; in, The first coefficient; is the average rainfall in the i-th time period.

[0051] Specifically, for invalid time periods: Set to 0; Specifically, Preferably, it is 80‰ per millimeter per hour; This is the average of the rainfall at the beginning and end of the time period, expressed in millimeters per hour.

[0052] Specifically, dividing the time period according to the amount of rainfall ensures that for some rainfall amounts that remain constant, they can be set as a whole time period to reduce the amount of calculation.

[0053] In some embodiments of this application, the soil variation coefficient is determined based on the influence coefficient, including: Pre-define the soil quality sequence T, and set T = ( , , ),in, It is a type of soil. It is classified as Class II soil. It is classified as Class III soil; When T is When, select * As ; When T is When, select * As ; When T is When, select * As ; in, The first influence correction factor; This is the second influence correction factor; This is the third influence correction factor.

[0054] Specifically, the first type of soil is sandy soil; the second type is loamy soil; and the third type is clay soil.

[0055] Specifically, In some embodiments of this application, the cleaning unit is also used for: The first cleaning time is generated based on the second dust accumulation amount; Determine the monitoring sub-area that meets the preset conditions; Based on the first cleaning time, all photovoltaic modules in the monitoring sub-area that meet the preset conditions are adjusted to generate a cleaning time.

[0056] Specifically, the first cleaning time is a specific point in time, such as August 26th.

[0057] Specifically, the preset conditions are: the distance between two monitoring sub-areas is less than one-tenth of the minimum distance between them to the starting point of the cleaning personnel; the cleaning time between the two is no more than 5 days; and the distance between the two to the starting point of the cleaning personnel is greater than 25 kilometers. If all three conditions are met, the angle of the photovoltaic modules in the two monitoring sub-areas can be adjusted until their cleaning time is on the same day. If multiple monitoring sub-areas meet the preset conditions at the same time, they can be adjusted until the cleaning time of all monitoring sub-areas is on the same day.

[0058] Specifically, adjustments are made to the photovoltaic modules, specifically adjusting their tilt angle.

[0059] In some embodiments of this application, the first cleaning time is generated based on the second dust accumulation amount, including: Let the data subset sequence S be defined. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. when When the cleaning conditions are met, the monitored sub-area is determined to need cleaning, and this time is set as the first cleaning time. ; Cleaning conditions are: * > ; in, This represents the sum of dust accumulation in the i-th data subset; The second coefficient; This represents the dust level threshold.

[0060] Specifically, The preferred value is 0.8. Specifically, The average amount of dust on all photovoltaic modules in the monitored sub-region is calculated based on historical operating data at times when the power generation of all monitored sub-regions was 70% of the rated power.

[0061] In some embodiments of this application, adjusting all photovoltaic modules in the monitoring sub-region that meet preset conditions based on the first cleaning time is also used for: Define a sequence Z of monitoring sub-regions that satisfy the first preset condition. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. Obtain the cleaning time corresponding to each monitoring sub-region in the sequence; The average cleaning time is set as the cleaning time of the monitored sub-region in the series. ; when earlier than Adjust at the time point The tilt angle of the photovoltaic module.

[0062] Specifically, multiple sets of sequence Z can exist simultaneously. For example, if a, b, and c satisfy the first preset condition, then they can be set as a sequence Z; at the same time, if e and f mutually satisfy the first preset condition, then they can each be set as a sequence Z.

[0063] Specifically, when earlier than At a certain point in time, that is If the cleaning time is delayed, then it is necessary to... The increased tilt angle of the photovoltaic modules slows down the rate of dust accumulation. The time required to reach the cleaning conditions. This tilt angle is the tilt angle formed by the photovoltaic module and the ground. The more upright the photovoltaic module is, the larger the tilt angle formed. Before changing the tilt angle, the tilt angle of the photovoltaic module is the optimal tilt angle with the highest power generation efficiency.

[0064] During the process of increasing the tilt angle (the minimum tilt angle change is 5°, meaning it can only change from 10° to 5° or 15°), experiments are first conducted to determine the power generation ratio between different tilt angles and the optimal tilt angle. Under the same conditions of the same cavity, temperature, and dust accumulation, the photovoltaic modules are adjusted to all adjustable tilt angles of the optimal tilt angle, and the dust accumulation rate data is recorded. This data is then divided by the dust accumulation rate at the optimal tilt angle to obtain the speed loss ratio. For example, if the ratio is 10 at the optimal tilt angle and 5 after adjustment, the speed loss ratio is 0.5. The modified dust accumulation rate is used to calculate the modified first dust accumulation amount, and thus the modified first cleaning time is obtained. This process is repeated to obtain the first cleaning time for each adjustable tilt angle. The tilt angle closest to the cleaning time is selected and set as the angle to which the photovoltaic modules in the monitoring sub-area need to be adjusted.

[0065] The adjustable tilt angle is as follows: if the optimal tilt angle is 13°, then the adjustable tilt angle is (13+5n)°, where n is (-2, -1, ... 15).

[0066] In some embodiments of this application, a photovoltaic module cleaning optimization method based on pollution monitoring is disclosed, including: Multiple monitoring sub-areas are set up, and multiple device points are set up in each monitoring sub-area. Sensor modules are set up on each device point to collect the operating data of each device point. Set up a dust accumulation model and forecast period, and set the dust accumulation amount based on environmental data and forecast weather data within the forecast period; Acquire soil data and set soil scenarios for each monitoring sub-area; Obtain predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and soil conditions; A soil quality change coefficient is generated based on the predicted rainfall data, and a second dust accumulation is generated based on the soil quality change coefficient and the first dust accumulation. A cleaning time is generated based on the second dust accumulation amount, and a cleaning strategy is executed based on the cleaning time, including: generating a first cleaning time based on the second dust accumulation amount, adjusting the photovoltaic modules based on the first cleaning time and preset conditions, and generating a cleaning time.

[0067] The above are merely preferred embodiments of this application. It should be noted that, for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A photovoltaic module cleaning optimization system based on pollution monitoring, characterized in that, include: The monitoring unit is used to set up multiple monitoring sub-areas, set up multiple device points in the monitoring sub-areas, and set up sensor modules on the device points. The sensor modules are used to collect the operating data of each device point. The central control unit is used to set the dust accumulation model and prediction period, and to set the dust accumulation amount based on environmental data and predicted weather data within the prediction period. The central control unit includes: The first processing module is used to acquire soil data and set soil scenarios for each monitoring sub-area. The second processing module is used to acquire predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and the soil conditions. The third processing module is used to generate a soil change coefficient based on the predicted rainfall data, and to generate a second dust accumulation based on the soil change coefficient and the first dust accumulation. The cleaning unit is used to generate a cleaning time based on the second dust accumulation amount and to execute a cleaning strategy based on the cleaning time. The cleaning unit further includes: The fourth processing module is used to generate a first cleaning time based on the second dust accumulation amount, and to adjust the photovoltaic module according to the first cleaning time and preset conditions to generate a cleaning time.

2. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 1, characterized in that, The second processing module is also used for: Obtain the predicted wind data for the predicted period, which includes: wind direction data, wind level data, and duration data; Based on historical operational data, the dust accumulation rate corresponding to the wind level data is generated; Based on wind direction and duration data, determine the duration of wind in each soil type scenario; Based on soil data, the initial dust accumulation on each photovoltaic module is generated.

3. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 1, characterized in that, The third processing module is also used for: Obtain the predicted rainfall data for the forecast period; The predicted rainfall data is divided into primary accuracy data and secondary accuracy data; Construct a rainfall curve based on the first-level precision data; The rainfall curve is divided into rainfall periods, and there are N rainfall periods in total; An influence coefficient k is set for the rainfall in each time period. 影 ; Determine the soil quality variation coefficient k1 based on the influence coefficient; Calculate the amount of second dust accumulation H when the rain stops. 2后 ; in, Let be the soil quality variation coefficient for the i-th time period; The initial dust accumulation at the point when rainfall begins.

4. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 3, characterized in that, The division of rainfall curves into rainfall periods includes: Obtain the slope data of the rainfall curve; The intervals in the rainfall curve that meet the invalidation criteria are set as invalid time periods; the intervals that do not meet the invalidation criteria are set as valid time periods. The effective time period is further divided into effective rainfall time periods according to the division conditions to generate a time period sequence D, D=( , ,… … ),in, This is the first period of effective rainfall. For the i-th effective rainfall period, The total number of periods with effective rainfall; The dividing condition is that the change in rainfall during the rainfall period reaches a change threshold. The invalidation condition is: if the slope of the time period is always negative or the rainfall data in the time period is not the first occurrence.

5. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 4, characterized in that, The set influence coefficient includes: For the effective time period, an influence coefficient is set. ; = * ； in, The first coefficient; is the average rainfall in the i-th time period.

6. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 5, characterized in that, The determination of the soil variation coefficient based on the influence coefficient includes: Pre-define the soil quality sequence T, and set T = ( , , ),in, It is a type of soil. It is classified as Class II soil. It is classified as Class III soil; When T is When, select * As ; When T is When, select * As ; When T is When, select * As ; in, The first influence correction factor; This is the second influence correction factor; This is the third influence correction factor.

7. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 6, characterized in that, The cleaning unit is also used for: The first cleaning time is generated based on the second dust accumulation amount; Determine the monitoring sub-area that meets the preset conditions; Based on the first cleaning time, all photovoltaic modules in the monitoring sub-area that meet the preset conditions are adjusted to generate a cleaning time.

8. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 7, characterized in that, The step of generating the first cleaning time based on the second dust accumulation includes: Let the data subset sequence S be defined. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. when When the cleaning conditions are met, it is determined that the monitored sub-area needs to be cleaned, and the time is set as the first cleaning time. ; The cleaning conditions are as follows: * ＞ ； in, This represents the sum of dust accumulation in the i-th data subset; The second coefficient; This represents the dust level threshold.

9. The photovoltaic module cleaning optimization system based on pollution monitoring as described in claim 8, characterized in that, The step of adjusting all photovoltaic modules in the monitoring sub-area that meet the preset conditions according to the first cleaning time is also used for: Define a sequence Z of monitoring sub-regions that satisfy the first preset condition. =( , ,… … ), n represents the total number of monitored sub-regions; This is a dataset containing the second dust accumulation amount for all photovoltaic modules in the i-th monitoring sub-region. Obtain the cleaning time corresponding to each monitoring sub-area in the sequence; The average cleaning time is set as the cleaning time of the monitored sub-region in the series. ; when earlier than Adjust at the time point The tilt angle of the photovoltaic module.

10. A method for optimizing the cleaning of photovoltaic modules based on pollution monitoring, applied to the photovoltaic module cleaning optimization system based on pollution monitoring as described in any one of claims 1-9, characterized in that, include: Multiple monitoring sub-areas are set up, and multiple device points are set up in the monitoring sub-areas. Sensor modules are set up on the device points, and the sensor modules are used to collect the operating data of each device point. Set up a dust accumulation model and forecast period, and set the dust accumulation amount based on environmental data and forecast weather data within the forecast period; Acquire soil data and set soil scenarios for each monitoring sub-area; Obtain predicted wind data and calculate the first dust accumulation amount based on the predicted wind data and soil conditions; A soil quality change coefficient is generated based on the predicted rainfall data, and a second dust accumulation is generated based on the soil quality change coefficient and the first dust accumulation. A cleaning time is generated based on the second dust accumulation amount, and a cleaning strategy is executed based on the cleaning time, including: generating a first cleaning time based on the second dust accumulation amount, adjusting the photovoltaic modules based on the first cleaning time and preset conditions, and generating a cleaning time.

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