Decision-making method, evaluation method, device and cleaning system for cleaning photovoltaic modules

Abstract

An embodiment of the present invention discloses a decision-making method, an evaluation method, a device and a cleaning system for cleaning a photovoltaic module. The decision-making method includes: calculating a current evaluation day dust loss degree including a rainfall influence degree; wherein, the rainfall influence degree is determined according to historical rainfall data and an original day dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of a standard string, the daily power generation of a comparison string and historical weather data; calculating a predicted cleaning yield rate; wherein, the predicted cleaning yield rate is determined according to the current evaluation day dust loss degree, the historical weather data, weather forecast data, cleaning data and electricity selling price; comparing the predicted cleaning yield rate with a preset cleaning yield rate to determine whether to clean the photovoltaic module. The embodiment of the present invention improves the accuracy of calculating the day dust loss degree and the predicted cleaning income, thereby improving the accuracy of the cleaning decision-making.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of photovoltaic technology, and in particular, to a decision-making method, an evaluation method, a device, and a cleaning system for cleaning photovoltaic modules. Background Art

[0002] Solar energy is the cleanest, safest, and most reliable energy in the future. The photovoltaic industry is increasingly becoming an industry with explosive development after the IT and microelectronics industries internationally. Similarly, China's photovoltaic industry has developed rapidly and has formed a relatively complete photovoltaic manufacturing industry system. Like other industries, the photovoltaic industry needs to consider production costs and efficiency during the production process, for example, the cleaning cost of photovoltaic modules.

[0003] In the prior art, two methods are adopted for cleaning decision-making: the first method is based on the system efficiency decline rate; the second method is based on the power comparison of representative strings. Among them, the first method relies on high-precision irradiance data. However, the irradiance meters on-site in power stations often cannot meet the requirements of irradiance data accuracy. If high-precision irradiance meters are replaced, it will increase the equipment cost. Moreover, the system efficiency of a photovoltaic power station is related not only to dust but also to many other factors (such as shadow occlusion, inverter power limit, component power attenuation, etc.). The first method only judges based on the system efficiency decline rate, and the judgment method is relatively one-sided, and the judgment accuracy is low.

[0004] The second method improves the first method and requires the installation of string metering devices on-site. Compared with high-precision irradiance meters, the power metering accuracy of string metering devices is higher and the cost is lower. Therefore, compared with the first method, the second method has the advantage of lower cost. At the same time, the second method selects representative strings, which is beneficial to excluding the influence of other factors on the string power. Therefore, relatively speaking, the second method has higher theoretical accuracy than the first method. However, the second method still has problems such as inaccurate calculation of daily dust loss and estimated cleaning benefits, which affect the determination of the cleaning opportunity. Summary of the Invention

[0005] The embodiments of the present invention provide a decision-making method, an evaluation method, a device, and a cleaning system for cleaning photovoltaic modules to improve the accuracy of calculating daily dust loss and estimated cleaning benefits, thereby improving the accuracy of cleaning decision-making.

[0006] In a first aspect, the embodiments of the present invention provide a decision-making method for cleaning photovoltaic modules, including:

[0007] Calculate the dust loss degree of the current evaluation day including the rainfall influence degree; wherein, the rainfall influence degree is determined according to historical rainfall data and the original daily dust loss degree; the dust loss degree of the current evaluation day is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data;

[0008] Calculate the estimated cleaning yield rate; wherein, the estimated cleaning yield rate is determined according to the dust loss degree of the current evaluation day, the historical weather data, weather forecast data, cleaning data, and the electricity selling price;

[0009] Compare the estimated cleaning yield rate with the preset cleaning yield rate, and determine whether to clean the photovoltaic modules in combination with the weather forecast data.

[0010] Optionally, the calculation steps of the dust loss degree of the current evaluation day including the rainfall influence degree include:

[0011] Calculate the initial ratio; wherein, the initial ratio is determined according to the ratio of the daily power generation of the comparison string to the daily power generation of the standard string on the initial date;

[0012] Calculate the original daily dust loss degree; wherein, the original daily dust loss degree is determined according to the daily power generation of the standard string, the daily power generation of the comparison string, and the initial ratio;

[0013] Fit to obtain a fitted dust loss degree curve; wherein, the fitted dust loss degree curve performs data screening and fitting according to the original daily dust loss degree and the historical weather data;

[0014] Calculate the dust loss degree of the current evaluation day according to the fitted dust loss degree curve and the rainfall influence degree.

[0015] Optionally, the calculation steps of the fitted dust loss degree curve include:

[0016] Determine the calculation end date according to the historical weather data;

[0017] Construct a sequence of days between the initial date and the end date and the corresponding sequence of original daily dust loss degrees;

[0018] Select the longest increasing subsequence in the sequence of original daily dust loss degrees, and construct an optimized sequence of original daily dust loss degrees and the corresponding optimized sequence of days;

[0019] According to the accurate sequence of original daily dust loss degrees and the optimized sequence of days, fit the relationship curve between the dust loss degree and the number of days of the comparison string to obtain the fitted dust loss degree curve.

[0020] Optionally, the fitted dust loss degree curve is in For the fitting function, perform non - linear relationship fitting;

[0021] Among them, y represents the dust loss degree, x represents the number of days, and a, b, and c are coefficients.

[0022] Optionally, the calculation steps of the dust loss degree on the current evaluation date further include:

[0023] Determine the start date of the dust loss degree on the current evaluation date as the day that meets the preset conditions;

[0024] Deduce the daily dust loss degree of the whole station on the start date, which is defined as the starting ash accumulation loss; among them, the starting ash accumulation loss is determined according to the original daily dust loss degree on the start date, the equivalent utilization hours of the whole station, the daily power generation of the comparison string, the capacity of the comparison string, and the ratio of the equivalent utilization hours of the whole station after being cleaned to the equivalent utilization hours of the standard string compared to the equivalent utilization hours of the standard string.

[0025] Calculate the imaginary initial date; among them, the imaginary initial date is determined according to the fitted dust loss degree curve and the starting ash accumulation loss.

[0026] Calculate the dust loss degree on the current evaluation date; among them, the dust loss degree on the current evaluation date is calculated based on the imaginary initial date, the fitted dust loss degree curve, the rainfall influence degree, and the historical weather data.

[0027] Optionally, the calculation steps of the ratio of the equivalent utilization hours of the whole station after being cleaned to the equivalent utilization hours of the standard string include:

[0028] Calculate the equivalent utilization hours of the cleaned components on the second day of each day during the cleaning period; among them, the equivalent utilization hours of the cleaned components on the second day of each day during the cleaning period are determined according to the equivalent utilization hours of the whole station, the installed capacity of the whole station, the remaining uncleaned capacity, the equivalent utilization hours of the whole station on the day before cleaning, the equivalent utilization hours of the standard string on the current day, the equivalent utilization hours of the standard string on the day before cleaning, the fitted original daily dust loss degree on the second day of cleaning, the fitted original daily dust loss degree on the day before cleaning, and the cleaning capacity on the current day of cleaning.

[0029] Calculate the ratio of the equivalent utilization hours of the cleaned components on the second day of each day during the cleaning period to the equivalent utilization hours of the standard string on the second day of each day during the cleaning period, which is denoted as the cleaning ratio.

[0030] According to the cleaning ratio of the cleaned components on the second day of each day during the cleaning period, the cleaning capacity on the current day, and the installed capacity of the whole station, calculate the ratio of the equivalent utilization hours of the whole station after being cleaned to the equivalent utilization hours of the standard string.

[0031] Optionally, the calculation steps of the dust loss degree on the current evaluation date further include:

[0032] According to the historical weather data, find the dates corresponding to the rainy weather between the start date and the day before the decision day;

[0033] If there is no rainy weather, calculate the calculated dust loss degree of the previous day according to the current evaluation day dust loss degree and the fitted dust loss degree curve;

[0034] If there is rainy weather, starting from the first rainy weather, calculate the calculated dust loss degree of the day before the rainy weather according to the current evaluation day dust loss degree and the fitted dust loss degree curve; and, calculate the calculated dust loss degree of the day after the rainy weather according to the calculated dust loss degree of the day before the rainy weather and the rainfall impact degree; take the day after the rainy weather as the next start date, and calculate the corresponding initial dust accumulation loss.

[0035] Optionally, the historical rainfall data includes rainfall amount and rainfall duration; the calculation steps of the rainfall impact degree include:

[0036] Calculate the historical rainfall impact degree; wherein, the historical rainfall impact degree is determined according to the original daily dust loss degree of the day after rainfall and the daily dust loss degree of the day before rainfall;

[0037] Use a neural network algorithm to construct the relationship between the historical rainfall impact degree and the rainfall amount and the rainfall duration to obtain the rainfall impact degree relationship;

[0038] Substitute the rainfall amount and the rainfall duration to be calculated into the rainfall impact degree relationship to calculate the rainfall impact degree.

[0039] Optionally, the calculation steps of the estimated cleaning yield rate include:

[0040] Calculate the cleaning income of the income day; wherein, the cleaning income of the income day is determined respectively according to the similarities and differences between the weather during the weather forecast period and the historical weather;

[0041] Calculate the estimated cleaning yield rate according to the cleaning income and the cleaning cost.

[0042] Optionally, if the weather during the weather forecast period is the same as the historical weather, the calculation steps of the cleaning income include:

[0043] Calculate the equivalent utilization hours of the standard string per day during the weather forecast period according to the average value of the equivalent utilization hours of the standard string in the historical same weather group;

[0044] If the day before the revenue day is a non-rainy day, calculate the cleaning revenue of the revenue day according to the standard string equivalent utilization hours per day within the said weather forecast period, the dust loss degree of the assumed non-cleaned revenue day, the dust loss degree of the revenue day after cleaning, the daily cleaning capacity, and the electricity selling price.

[0045] If the day before the revenue day is a rainy day, calculate the cleaning revenue of the revenue day according to the dust loss degree of the two days before the assumed non-cleaned revenue day, the rainfall impact degree of the day before the revenue day, the dust loss degree of the two days before the revenue day after cleaning, the rainfall impact degree of the day before the revenue day, the standard string equivalent utilization hours per day within the said weather forecast period, the daily cleaning capacity, and the electricity selling price.

[0046] Accumulate the cleaning revenues of all the said revenue days to obtain the said cleaning revenue.

[0047] Optionally, if the weather within the said weather forecast period is different from the historical weather, or the revenue day is greater than the said weather forecast period, the calculation steps of the said cleaning revenue include:

[0048] Calculate the cleaning revenue of the revenue day according to the dust loss degree of the assumed non-cleaned revenue day, the dust loss degree of the revenue day after cleaning, the average daily radiation amount of the month of the revenue day, the average daily full-station system efficiency of the month before the revenue day, the daily cleaning capacity, and the electricity selling price.

[0049] Accumulate the cleaning revenues of all the said revenue days to obtain the said cleaning revenue.

[0050] Optionally, use a rain gauge to collect the said historical rainfall data.

[0051] In a second aspect, an embodiment of the present invention further provides a decision-making device for cleaning photovoltaic modules, including:

[0052] A current evaluation day dust loss degree calculation module, configured to calculate the current evaluation day dust loss degree including the rainfall impact degree; wherein, the rainfall impact degree is determined according to historical rainfall data and the original daily dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall impact degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data.

[0053] A predicted cleaning yield rate calculation module, configured to calculate the predicted cleaning yield rate; wherein, the predicted cleaning yield rate is determined according to the current evaluation day dust loss degree, the said historical weather data, weather forecast data, cleaning data, and electricity selling price.

[0054] A cleaning timing determination module, configured to compare the said predicted cleaning yield rate with a preset cleaning yield rate, and determine whether to clean the photovoltaic modules in combination with the said weather forecast data.

[0055] In a third aspect, an embodiment of the present invention further provides a decision evaluation method for cleaning a photovoltaic module, including:

[0056] Starting from the first day of cleaning, calculate the actual cleaning income and the actual cleaning cost; accumulate the actual cleaning income and the actual cleaning cost respectively to obtain the total actual cleaning income and the total actual cleaning cost;

[0057] Calculate the actual cleaning yield rate; wherein, the actual cleaning yield rate is used to evaluate the decision-making method for cleaning a photovoltaic module as described in any embodiment of the present invention.

[0058] Optionally, the calculation steps of the total actual cleaning income include:

[0059] Calculate the reference string ratio; wherein, the reference string ratio is determined according to the equivalent utilization hours of the whole station on the day before the most recent cleaning and the equivalent utilization hours of the comparison string on the day before the most recent cleaning;

[0060] Calculate the actual cleaning income per day; wherein, the actual cleaning income per day is determined according to the equivalent utilization hours of the whole station on that day, the power of the reference string on that day, the capacity of the reference string, the reference string ratio, the installed capacity of the whole station, and the electricity selling price;

[0061] Accumulate the actual cleaning income per day to obtain the total actual cleaning income.

[0062] In a fourth aspect, an embodiment of the present invention further provides a decision evaluation device for cleaning a photovoltaic module, including:

[0063] An income and cost calculation module, configured to start from the first day of cleaning, calculate the total actual cleaning income and the total actual cleaning cost; accumulate the actual cleaning income and the actual cleaning cost respectively to obtain the total actual cleaning income and the total actual cleaning cost;

[0064] An actual cleaning yield rate calculation module, configured to calculate the actual cleaning yield rate; wherein, the actual cleaning yield rate is used to evaluate the decision-making method for cleaning a photovoltaic module as described in any embodiment of the present invention.

[0065] In a fifth aspect, an embodiment of the present invention further provides a cleaning system for a photovoltaic module, including:

[0066] A standard string and its string metering device, the standard string is cleaned regularly;

[0067] A comparison string and its string metering device, the comparison string is consistent with the overall dust loss degree of the power station;

[0068] The reference string and its string metering device, where the reference string is consistent with the overall dust loss degree of the power station before the whole-station cleaning and remains uncleaned during the whole-station cleaning.

[0069] Optionally, the cleaning system of the photovoltaic module further includes: the comparison string and the reference string are alternately cleaned during the whole-station cleaning; wherein, the comparison string and the reference string are cleaned on the last day of the whole-station cleaning.

[0070] In the embodiment of the present invention, the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and the historical weather data, and the estimated cleaning yield rate is determined according to the current evaluation day dust loss degree, the historical weather data, the weather forecast data, the cleaning data, and the electricity selling price. Such a setting is beneficial to comprehensively consider the rainfall influence, the inconsistent dust accumulation of the photovoltaic modules at various places in the whole station, etc., and comprehensively consider the two situations within and outside the weather forecast period to calculate the estimated cleaning yield rate, thereby improving the accuracy of the calculation of the daily dust loss degree and the estimated cleaning income, and improving the accuracy of the cleaning decision. Brief Description of the Drawings

[0071] Figure 1 It is a schematic flowchart of a decision-making method for cleaning photovoltaic modules provided by an embodiment of the present invention;

[0072] Figure 2 It is a schematic flowchart of a calculation method for the current evaluation day dust loss degree provided by an embodiment of the present invention;

[0073] Figure 3 It is a schematic flowchart of a calculation method for fitting a dust loss degree curve provided by an embodiment of the present invention;

[0074] Figure 4 It is a schematic flowchart of a calculation method for the current evaluation day dust loss degree provided by an embodiment of the present invention;

[0075] Figure 5 It is a schematic flowchart of a calculation method for the rainfall influence degree provided by an embodiment of the present invention;

[0076] Figure 6 It is a schematic flowchart of a calculation method for the current evaluation dust loss degree provided by an embodiment of the present invention;

[0077] Figure 7 It is a schematic flowchart of a calculation method for the estimated cleaning yield rate provided by an embodiment of the present invention;

[0078] Figure 8 It is a schematic flowchart of a method for calculating the estimated cleaning income and making a cleaning decision provided by an embodiment of the present invention;

[0079] Figure 9 Structural schematic diagram of a decision-making device for cleaning photovoltaic modules provided by an embodiment of the present invention;

[0080] Figure 10 Flow schematic diagram of a decision-making evaluation method for cleaning photovoltaic modules provided by an embodiment of the present invention;

[0081] Figure 11 Flow schematic diagram of a calculation method for the actual total cleaning income provided by an embodiment of the present invention;

[0082] Figure 12 Flow schematic diagram of a calculation method for cleaning income and cleaning income rate provided by an embodiment of the present invention;

[0083] Figure 13 Structural schematic diagram of a decision-making evaluation device for cleaning photovoltaic modules provided by an embodiment of the present invention;

[0084] Figure 14 Structural schematic diagram of a cleaning system for photovoltaic modules provided by an embodiment of the present invention;

[0085] Figure 15 General idea schematic diagram of a decision-making and evaluation method for cleaning photovoltaic modules provided by an embodiment of the present invention. Detailed implementation manners

[0086] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings rather than all structures.

[0087] An embodiment of the present invention provides a decision-making method for cleaning photovoltaic modules. This method can be executed by a decision-making device for cleaning photovoltaic modules, and this device can be implemented by software and / or hardware. Figure 1 Flow schematic diagram of a decision-making method for cleaning photovoltaic modules provided by an embodiment of the present invention. Refer to Figure 1 , this decision-making method for cleaning photovoltaic modules includes the following steps:

[0088] S110. Calculate the current evaluation day dust loss degree including the rainfall influence degree; wherein, the rainfall influence degree is determined according to historical rainfall data and the original daily dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data.

[0089] Among them, the dust loss degree refers to the ratio of the lost power caused by dust in a component or power station to the power generation that should be generated under clean conditions. The power generation that should be generated refers to the sum of the actually grid-connected power and various types of lost power (including dispatching power curtailment losses, in-station fault losses, planned outage losses, and affected losses). The original daily dust loss degree is an intermediate variable for calculating the dust loss degree obtained by calculating the daily power generation of the standard string and the daily power generation of the comparison string. The current evaluation day dust loss degree refers to the calculated full-station daily dust loss degree of the day before the decision day (yesterday) calculated using a certain calculation strategy.

[0090] In the embodiment of the present invention, the calculation of the current evaluation day dust loss degree is determined by the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data. It can comprehensively consider the influence of rainfall and the inconsistent dust accumulation situation of photovoltaic modules everywhere in the whole station. It does not directly calculate using the daily power generation of the standard string and the comparison string. Therefore, the embodiment of the present invention is beneficial to improving the accuracy of the calculation.

[0091] S120. Calculate the estimated cleaning yield rate; among them, the estimated cleaning yield rate is determined according to the current evaluation day dust loss degree, historical weather data, weather forecast data, cleaning data, and electricity selling price.

[0092] Among them, the estimated cleaning income refers to the estimated cleaning income calculated by the system before cleaning. In the embodiment of the present invention, the calculation of the estimated cleaning yield rate can be calculated in two cases, within the weather forecast period and outside the weather forecast period, according to historical weather data and weather forecast data, rather than only considering the situation within the short-term weather forecast period. Therefore, the embodiment of the present invention is beneficial to improving the accuracy of the calculation.

[0093] S130. Compare the estimated cleaning yield rate with the preset cleaning yield rate, and determine whether to clean the photovoltaic modules in combination with the weather forecast data.

[0094] Among them, the preset cleaning yield rate is the cleaning yield rate that is expected to be achieved and is determined according to needs in actual applications.

[0095] It can be seen that in the embodiment of the present invention, the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data, and the estimated cleaning yield rate is determined according to the current evaluation day dust loss degree, historical weather data, weather forecast data, cleaning data, and electricity selling price. Such a setting is beneficial to comprehensively considering the influence of rainfall and the inconsistent dust accumulation situation of photovoltaic modules everywhere in the whole station, and comprehensively considering the two situations within the weather forecast period and outside the weather forecast period to calculate the estimated cleaning yield rate, thereby improving the accuracy of the calculation of the daily dust loss degree and the estimated cleaning income, and improving the accuracy of the cleaning decision.

[0096] In the above embodiment, there are multiple methods for calculating the dust loss degree on the current evaluation day and the estimated cleaning yield rate, which are described in detail below.

[0097] Figure 2 A flowchart of a method for calculating the dust loss degree of the current assessment day provided by an embodiment of the present invention. Figure 2 The calculation steps of the dust loss degree of the current assessment day including the rainfall influence degree include:

[0098] S210, calculating an initial ratio; wherein the initial ratio is determined according to the ratio of the daily power generation of the comparison string to the daily power generation of the standard string on the initial date.

[0099] S220, calculating the original daily dust loss degree; wherein the original daily dust loss degree is determined according to the daily power generation of the standard string, the daily power generation of the comparison string and the initial ratio.

[0100] Exemplarily, the original daily dust loss degree=1-daily power generation of the comparison string ÷ daily power generation of the standard string ÷ initial ratio.

[0101] S230, fitting to obtain a fitted dust loss degree curve; wherein the fitted dust loss degree curve is subjected to data screening and fitting based on the original daily dust loss degree and historical weather data.

[0102] Among them, the fitted dust loss curve is the relationship curve between the dust loss degree of the comparison group and the number of days. In the subsequent calculation steps, the calculation quantities such as the fitted original daily dust loss degree, the hypothetical date, yesterday's calculated daily dust loss degree of the entire station, the dust loss degree of the profit day if it is not cleaned, and the dust loss degree of the profit day after cleaning can all be obtained using the fitted dust loss curve.

[0103] The screening method may be, for example, to eliminate data with errors, including at least one of the following that needs to be eliminated: eliminating data in time periods when the standard string, comparison string, and reference string are neither fully operational nor fully out of service; eliminating data in time periods when the maximum value of the normalized power of the standard string, comparison string, and reference string is less than d. Wherein, d is a set value, which is determined according to the requirements of the data volume and calculation accuracy. When one or two of the standard string, comparison string, and reference string are out of service or the string power is too low, the accuracy of electricity metering will be reduced. Therefore, the data of the corresponding time period can be eliminated to improve the calculation accuracy. In the embodiment of the present invention, the data of the standard string, comparison string, and reference string in a certain time period are simultaneously eliminated. Since the comparison data is used in the subsequent calculation, the accuracy of the calculation will not be affected. It is also possible to eliminate all the data for one day, but the amount of data obtained is reduced. In practical applications, it can be set as needed to eliminate the data of one time period or one day.

[0104] S240. Calculate the dust loss degree on the current evaluation date according to the fitted dust loss degree curve and the rainfall influence degree.

[0105] It can be seen that through S210 - S240, the dust loss degree on the current evaluation date including the rainfall influence degree can be obtained. In the embodiment of the present invention, by screening the original daily dust loss degree and historical weather data, the calculation accuracy and the decision-making accuracy are further improved.

[0106] Figure 3 It is a schematic flowchart of a calculation method for fitting a dust loss degree curve provided by an embodiment of the present invention. Refer to Figure 3 , on the basis of the above embodiments, optionally, the calculation steps for fitting the dust loss degree curve include:

[0107] S310. Determine the calculation end date according to the historical weather data.

[0108] Among them, the initial date can be, for example, the next day after the last day of the last cleaning. The end date can be, for example, a rainfall weather or a snowfall weather. Optionally, the rainfall weather does not include light rain and shower. This is because light rain and shower have little influence on the calculation of the dust loss degree, and for areas with more light rain weather and shower weather, if the day of light rain weather or shower weather is used as the end date, the number of days between the initial date and the end date is small, which is not conducive to the fitting calculation of data.

[0109] S320. Construct a sequence of days between the initial date and the end date and the corresponding sequence of original daily dust loss degrees.

[0110] Exemplarily, the sequence of original daily dust loss degrees and the sequence of days can be traversed within the time period between the initial date and the end date (including the initial date and the end date), and the data with the equivalent utilization hours of the standard string less than the average daily radiation of the current month × k are excluded. Among them, the average daily radiation of the current month represents the average radiation level of the current month. By selecting the dates with the equivalent utilization hours of the standard string greater than the average daily radiation of the current month × k, the interference of the decrease in the electricity measurement accuracy in rainy weather can be excluded, and only sunny days and cloudy days with good weather are selected to participate in the calculation, further improving the calculation accuracy. k is a set value and can be set according to needs.

[0111] S330. Select the longest increasing subsequence in the sequence of original daily dust loss degrees, and construct an optimized sequence of original daily dust loss degrees and the corresponding optimized sequence of days.

[0112] Among them, the longest increasing subsequence refers to the subsequence with the longest length among all subsequences and the elements of this subsequence are increasing. The reason for such a setting is that the dust accumulation degree of the photovoltaic module increases day by day, and the corresponding daily dust loss degree increases day by day. Therefore, the decrease in the daily dust loss degree should be an abnormal situation. The embodiment of the present invention constructs a new original daily dust loss degree sequence and a new number of days sequence, which can eliminate obvious abnormal data and improve the success rate of curve fitting.

[0113] S340. According to the accurate original daily dust loss degree sequence and the optimized number of days sequence, fit the relationship curve between the dust loss degree and the number of days of the comparison string to obtain the fitted dust loss degree curve.

[0114] Exemplarily, the fitted dust loss degree curve takes as the fitting function for non-linear relationship fitting; where y represents the dust loss degree, x represents the number of days, and a, b, and c are coefficients. Through fitting, the specific values of a, b, and c can be obtained. In subsequent calculations, substituting the number of days into this functional relationship can obtain the corresponding dust loss degree.

[0115] Through S310 - S340, the fitting of the dust loss degree curve is achieved. The embodiment of the present invention uses a non-linear model to describe the dust loss degree. Compared with the prior art that uses a linear relationship to fit the component dust loss degree and the time since the last cleaning, the calculation of the embodiment of the present invention is more in line with the actual situation, which is beneficial to improving the accuracy of the dust loss degree calculation, thereby improving the accuracy of the cleaning decision.

[0116] Figure 4 It is a schematic flowchart of a calculation method for the current evaluation day dust loss degree provided by the embodiment of the present invention. Refer to Figure 4 Based on the above embodiments, optionally, the calculation steps of the current evaluation day dust loss degree further include:

[0117] S410. Determine the start date of the current evaluation day dust loss degree for the day that meets the preset conditions.

[0118] Among them, the start date is the date closest to the decision day before the decision day that meets the condition that the maximum value of the utilization hours of the standard string and the comparison string is greater than the average daily radiation of the current month × k and is a non-rainy day, and is not earlier than the dates determined by the following two situations: ① the mth day after the nearest snow day to the decision day; ② the next day of the nearest cleaning day to the decision day.

[0119] S420. Calculate the daily dust loss of the entire station on the starting date of the calculation, which is defined as the starting ash accumulation loss. Among them, the starting ash accumulation loss is determined based on the original daily dust loss on the starting date, the equivalent utilization hours of the entire station, the daily power generation of the comparison string, the capacity of the comparison string, and the ratio of the equivalent utilization hours of the entire station when cleaned to the equivalent utilization hours of the standard string.

[0120] Among them, the daily dust loss of the entire station on the starting date can also be called the starting ash accumulation loss. Exemplarily, the starting ash accumulation loss = 1 - (1 - the original daily dust loss on the starting date) × the equivalent utilization hours of the entire station on the starting date ÷ (the daily power generation of the comparison string on the starting date ÷ the installed capacity of the comparison string) ÷ the ratio of the equivalent utilization hours of the entire station when cleaned to the equivalent utilization hours of the standard string. The equivalent utilization hours of the entire station is the total power generation that the entire station should generate divided by the installed capacity of the entire station.

[0121] Among them, the calculation steps for the ratio of the equivalent utilization hours of the entire station when cleaned to the equivalent utilization hours of the standard string are as follows:

[0122] First, calculate the equivalent utilization hours of the components to be cleaned on the second day of each day during the cleaning period.

[0123] Among them, the first day of the cleaning period is the first day of the most recent cleaning. The last day of the cleaning period can be pushed forward from the first day of the cleaning period. If the next day is not cleaned, the cleaning ends on that day, and that day is determined as the last day of the cleaning period. The equivalent utilization hours of the components to be cleaned on the second day of each day during the cleaning period are determined based on the equivalent utilization hours of the entire station, the installed capacity of the entire station, the remaining uncleaned capacity, the equivalent utilization hours of the entire station on the day before cleaning, the equivalent utilization hours of the standard string on the current day, the equivalent utilization hours of the standard string on the day before cleaning, the fitted original daily dust loss on the second day of cleaning, the fitted original daily dust loss on the day before cleaning, and the cleaning capacity on the current day of cleaning. Among them, the cleaning of the entire station is carried out in stages. Generally, not all photovoltaic modules will be cleaned in one day. Therefore, it is necessary to calculate the ratio of the equivalent utilization hours of the entire station when cleaned to the equivalent utilization hours of the standard string. The ratio of the equivalent utilization hours of the entire station when cleaned to the equivalent utilization hours of the standard string can be regarded as a coefficient. This coefficient is calculated based on the data of the previous full-station cleaning and the total power generation that the entire station should generate on the current day. In the subsequent calculation process, the dust loss degree calculated using the daily power generation of the standard string and the comparison string is multiplied by a coefficient to obtain the dust loss degree of the entire station, and the calculation result is more accurate.

[0124] Exemplarily, the equivalent utilization hours of the components cleaned on the first day of the cleaning period on the second day = (the equivalent utilization hours of the whole station on the second day × the installed capacity of the whole station - the remaining uncleaned capacity on the first day × the equivalent utilization hours of the whole station on the day before cleaning × the ratio of the equivalent utilization hours of the standard string on that day to the equivalent utilization hours of the standard string on the day before cleaning × (1 - the fitted original daily dust loss degree on the second day of the cleaning period) ÷ (1 - the fitted original daily dust loss degree on the day before cleaning)) ÷ the cleaning capacity on the first day of cleaning. Among them, the calculation method of the equivalent utilization hours of the whole station is the generated electricity of the whole station divided by the installed capacity of the whole station; the calculation method of the equivalent utilization hours of the standard string is the generated electricity of the standard string divided by the installed capacity of the standard string; the fitted original daily dust loss degree can be obtained according to the fitted dust loss degree curve.

[0125] The equivalent utilization hours of the components cleaned on the second day of the cleaning period on the third day = (the equivalent utilization hours of the whole station on the third day × the capacity of the whole station - the remaining uncleaned capacity on the second day × the equivalent utilization hours of the whole station on the day before cleaning × the ratio of the equivalent utilization hours of the standard string on that day to the equivalent utilization hours of the standard string on the day before cleaning × (1 - the fitted original daily dust loss degree on the second day of cleaning) ÷ (1 - the fitted original daily dust loss degree on the day before cleaning) - the equivalent utilization hours of the components cleaned on the first day on the second day × the generated electricity of the standard string on the third day of the cleaning period ÷ the generated electricity of the standard string on the second day of the cleaning period × (1 - the fitted original daily dust loss degree on the third day of the cleaning period) ÷ (1 - the fitted original daily dust loss degree on the second day of the cleaning period)) ÷ the cleaning capacity of the components cleaned on the second day of the cleaning period.

[0126] And so on, calculate the equivalent utilization hours of the components cleaned each day in the cleaning period on the second day.

[0127] Then, calculate the ratio of the equivalent utilization hours of the components cleaned each day in the cleaning period on the second day to the equivalent utilization hours of the standard string of the components cleaned each day on the second day, denoted as the cleaning ratio.

[0128] Among them, the cleaning ratio for the first day of cleaning is the ratio of the equivalent utilization hours of the components cleaned on the first day of the cleaning period on the second day to the equivalent utilization hours of the standard string of the components cleaned on the first day of the cleaning period on the second day. The cleaning ratio for the second day of cleaning is the ratio of the equivalent utilization hours of the components cleaned on the second day of the cleaning period on the third day to the equivalent utilization hours of the standard string of the components cleaned on the second day of the cleaning period on the third day. And so on, calculate the cleaning ratio of the components cleaned each day in the cleaning period on the second day.

[0129] Finally, according to the cleaning ratio on the second day of cleaning the components every day during the cleaning period, the cleaning capacity on the current day, and the total installed capacity of the whole station, calculate the ratio of the equivalent utilization hours of the whole station being cleaned cleanly to the equivalent utilization hours of the standard string. Exemplarily, the ratio of the equivalent utilization hours of the whole station being cleaned cleanly to the equivalent utilization hours of the standard string = the cumulative value of (the cleaning ratio on the second day of cleaning the components every day during the cleaning period × the cleaning capacity on the current day ÷ the total installed capacity of the whole station).

[0130] S430. Calculate the imaginary initial date; wherein, the imaginary initial date is determined according to the fitted dust loss degree curve and the start of dust accumulation loss.

[0131] Among them, the imaginary date refers to the date when it is assumed that it will not rain after cleaning on this date, and the dust loss degree will reach the start of dust accumulation loss by the start date. According to the fitted dust loss degree curve, substituting the start of dust accumulation loss, the number of days from the start date to the imaginary date can be obtained, thereby determining the imaginary date.

[0132] S440. Calculate the dust loss degree on the current evaluation date; wherein, the dust loss degree on the current evaluation date is calculated based on the imaginary initial date, the fitted dust loss degree curve, the rainfall influence degree, and historical weather data.

[0133] Among them, the dust loss degree on the current evaluation date refers to the calculated daily dust loss degree of the whole station on the day before the decision day (yesterday). According to whether there is rainy weather, the calculation method is different, specifically as follows:

[0134] According to historical weather data, find the dates corresponding to the rainy weather between the start date and the day before the decision day (yesterday). If there is no rainy weather, calculate the calculated daily dust loss degree of the previous day according to the dust loss degree on the current evaluation date and the fitted dust loss degree curve.

[0135] If there is rainy weather, starting from the first rainy weather, calculate the calculated daily dust loss degree of the day before the rainy weather according to the dust loss degree on the current evaluation date and the fitted dust loss degree curve; and, calculate the calculated daily dust loss degree of the day after the rainy weather according to the calculated daily dust loss degree of the day before the rainy weather and the rainfall influence degree; take the day after the rainy weather as the next start date, and calculate the corresponding start of dust accumulation loss. Exemplarily, the calculated daily dust loss degree of the whole station on the day after the rainfall date = the calculated daily dust loss degree of the whole station on the day before the rainfall date × the rainfall influence degree on the rainfall date. Then take the day after the rainfall as the next start date, and the corresponding calculated daily dust loss degree of the whole station as the next start of dust accumulation loss. And so on, until after the calculation of the last rainy weather is completed, the start date is the day after the last rainy weather, and then calculate the calculated daily dust loss degree of yesterday according to the fitted dust loss degree curve, as the dust loss degree on the current evaluation date.

[0136] Through S410 - S440, the embodiments of the present invention achieve the calculation of the dust loss degree on the current evaluation date, and the dust loss degree on the current evaluation date refers to the calculated daily dust loss degree of the entire station on the day before the decision day (yesterday). During the calculation process, the embodiments of the present invention comprehensively consider situations such as the influence of rainfall and the inconsistent dust accumulation conditions of photovoltaic modules everywhere in the entire station, rather than directly calculating using the daily power generation of the standard string and the comparison string. Therefore, the embodiments of the present invention are beneficial to improving the accuracy of the calculation.

[0137] Figure 5 It is a schematic flowchart of a method for calculating the influence degree of rainfall provided by the embodiments of the present invention. Refer to Figure 5 , based on the above embodiments, optionally, the historical rainfall data includes rainfall amount and rainfall duration. The calculation steps of the rainfall influence degree include:

[0138] S510. Calculate the historical rainfall influence degree; wherein, the historical rainfall influence degree is determined according to the original daily dust loss degree on the day after rainfall and the daily dust loss degree on the day before rainfall.

[0139] Among them, the data time period used for calculating the rainfall influence degree and the data time period used for fitting the dust loss degree curve can be the same or different. Preferably, the data time period used for calculating the rainfall influence degree includes from the second day with data to the decision day. With such a setting, the amount of data is sufficient, which is beneficial to improving the accuracy of the calculation.

[0140] Exemplarily, starting from the second day with data and traversing two days (the day before yesterday) forward to the decision day, select a day with rainfall and no rainfall on the day before and after for the calculation of the rainfall influence degree. Preferably, there is no snowing weather in the m days before the rainfall day, where m is the number of days required for the snow on the component surface to completely melt after snowfall, which can be set according to actual needs to exclude the influence of snowfall on the calculation accuracy of the rainfall influence degree. Preferably, the equivalent utilization hours of the standard string on the day before and after the rainfall day are both greater than the average daily radiation amount of the current month × k to exclude the influence of rainy weather on the calculation accuracy of the rainfall influence degree. Preferably, the cleaning status on the day before, during, and after rainfall is the same and the day after rainfall is not the last day of cleaning to exclude the influence of component cleaning on the calculation accuracy of the rainfall influence degree. Optionally, the historical rainfall influence degree = the original daily dust loss degree on the day after the current day / the original daily dust loss degree on the day before the current day.

[0141] Optionally, a rain gauge is used to collect historical rainfall data. Among them, a rain gauge can be installed near the standard string to obtain the rainfall data of the rain gauge. The embodiments of the present invention obtain the relationship between rainfall amount and dust loss degree change based on historical measured rainfall data and the change of dust loss degree before and after rainfall, and the calculation result is more accurate.

[0142] S520: Use a neural network algorithm to construct a relationship between historical rainfall impact, rainfall amount, and rainfall duration to obtain a rainfall impact relationship.

[0143] S530: Substitute the rainfall amount and rainfall duration to be calculated into the rainfall impact relationship to calculate the rainfall impact.

[0144] The calculation of rainfall influence is realized through S510-S530. Compared with the prior art which does not consider the influence of rainfall or only roughly considers the influence of rainfall level (such as light rain, moderate rain, heavy rain, etc.), the embodiment of the present invention can collect accurate rainfall data by installing a rain gauge on site, which is conducive to finding the accurate relationship between rainfall and dust loss, making the calculation of dust loss and estimated cleaning benefit more accurate.

[0145] Figure 6 A schematic diagram of a flow chart of a current method for evaluating dust loss provided by an embodiment of the present invention. Figure 6 Based on the above embodiments, optionally, the method for calculating the current dust loss evaluation degree includes:

[0146] S610: Screen the daily power generation data of the standard string and the daily power generation data of the comparison string.

[0147] The screening method may be, for example, to eliminate data with errors, including at least one of the following that needs to be eliminated: eliminating data in time periods when the standard strings, comparison strings, and reference strings are neither fully operational nor fully out of service; eliminating data in time periods when the maximum value of the normalized power of the standard strings, comparison strings, and reference strings is less than d. Where d is a set value, which is determined according to the requirements of the data volume and calculation accuracy.

[0148] S620: Determine the initial date and initial ratio of the calculation.

[0149] The next day after the last day of the last cleaning can be used as the initial date. The initial ratio is the ratio of the power generation of the string on the initial date to the power generation of the standard string.

[0150] S630, calculating the original daily dust loss degree.

[0151] The original daily dust loss degree needs to be statistically calculated every day starting from the initial date. For example, the original daily dust loss degree = 1 - the daily power generation of the comparison string / the daily power generation of the standard string / the initial ratio.

[0152] S640: Determine the end date, and filter the daily dust loss degree sequence and the day number sequence between the initial date and the end date.

[0153] Among them, the end date can be pushed back from the initial date. When encountering rainy weather or snowy weather, the day before the current day is determined as the end date. Optionally, rainy weather does not include light rain and shower.

[0154] The original daily dust loss degree sequence and the number of days sequence can be traversed within the time period between the initial date and the end date (including the initial date and the end date), and the data with the equivalent utilization hours of the standard string less than the monthly average daily radiation amount × k are excluded. Among them, k is a set value and can be set as needed.

[0155] Select the data with the equivalent utilization hours of the standard string greater than the monthly average daily radiation amount × k, and construct the original daily dust loss degree sequence. Correspondingly, the selected dates and the number of days from these dates to the initial date are used to construct the number of days sequence. Among them, the initial date is used as the first day.

[0156] S650. Optimize the original daily dust loss degree sequence and the number of days sequence.

[0157] Among them, select the longest increasing subsequence of the original daily dust loss degree sequence from the original daily dust loss degree sequence and the number of days sequence as the new original daily dust loss degree sequence, and correspondingly construct a new number of days sequence.

[0158] S660. Perform the fitting of the dust loss degree curve.

[0159] Among them, the dust loss degree curve fitting is the relationship curve between the dust loss degree of the comparison string and the number of days. Optionally, this curve is fitted according to the new daily dust loss degree sequence and the new number of days sequence according to the functional relationship Perform the fitting. Among them, y represents the dust loss degree, x represents the number of days, and a, b, and c are coefficients. Through fitting, the specific values of a, b, and c can be obtained. In subsequent calculations, substituting the number of days into this functional relationship can obtain the corresponding dust loss degree.

[0160] S670. Calculate the rainfall influence degree.

[0161] Among them, the data time period used for calculating the rainfall impact degree and the data time period used for fitting the dust loss degree curve can be the same or different. Preferably, the data time period used for calculating the rainfall impact degree includes the second day after there is data until the decision-making day. With this setting, the amount of data is large enough, which is beneficial to improving the calculation accuracy. Specifically, starting from the second day after there is data and traversing two days back (the day before yesterday) until the decision-making day, select a day with rainfall and no rainfall on the day before and after for calculating the rainfall impact degree. Preferably, there is no snowing weather in the m days before the rainfall day, where m is the number of days required for the snow on the surface of the component to completely melt after snowfall, which can be set according to actual needs to exclude the influence of snowfall on the calculation accuracy of the rainfall impact degree. Preferably, the equivalent utilization hours of the standard string on the day before and after the rainfall day are both greater than the average daily radiation of the current month × k to exclude the influence of rainy weather on the calculation accuracy of the rainfall impact degree. Preferably, the cleaning status on the day before, during, and after the rainfall is the same and the day after is not the last day of cleaning to exclude the influence of component cleaning on the calculation accuracy of the rainfall impact degree.

[0162] Optionally, the historical rainfall impact degree = the original daily dust loss degree of the day after the current day / the original daily dust loss degree of the day before the current day. Obtain the rainfall amount and rainfall duration of all eligible dates, calculate the corresponding historical rainfall impact degree, and then use the neural network algorithm to find the relationship between the rainfall impact degree and the rainfall amount and rainfall duration. In subsequent calculations, substitute the rainfall amount and rainfall duration into this relationship to obtain the corresponding rainfall impact degree.

[0163] S680. Determine the cleaning period.

[0164] Among them, the first day of the cleaning period is the first day of the most recent cleaning. The last day of the cleaning period can be pushed back from the first day of the cleaning period. If the next day is not cleaned, the cleaning ends on that day, and that day is determined as the last day of the cleaning period.

[0165] S690. Calculate the ratio of the equivalent utilization hours when the whole station is cleaned cleanly to the equivalent utilization hours of the standard string.

[0166] Among them, the cleaning of the whole station is carried out in stages. Generally, all photovoltaic components will not be cleaned cleanly in one day. Therefore, it is necessary to calculate the ratio of the equivalent utilization hours when the whole station is cleaned cleanly to the equivalent utilization hours of the standard string. The ratio of the equivalent utilization hours when the whole station is cleaned cleanly to the equivalent utilization hours of the standard string can be regarded as a coefficient. This coefficient is calculated based on the data of the last whole-station cleaning and the expected power generation of the whole station on the current day. In subsequent calculation processes, multiply the dust loss degree calculated by the daily power generation of the standard string and the comparison string by a coefficient to obtain the dust loss degree of the whole station, and the calculation result is more accurate.

[0167] Exemplarily, the equivalent utilization hours of the components cleaned on the first day of the cleaning period on the second day = (the equivalent utilization hours of the whole station on the second day × the installed capacity of the whole station - the remaining uncleaned capacity on the first day × the equivalent utilization hours of the whole station on the day before cleaning × the ratio of the equivalent utilization hours of the standard string on that day to the equivalent utilization hours of the standard string on the day before cleaning × (1 - the fitting original daily dust loss degree on the second day of the cleaning period) ÷ (1 - the fitting original daily dust loss degree on the day before cleaning)) ÷ the cleaning capacity on the first day of cleaning. Among them, the calculation method of the equivalent utilization hours of the whole station is the power generation amount that the whole station should generate divided by the installed capacity of the whole station; the calculation method of the equivalent utilization hours of the standard string is the power generation amount that the standard string should generate divided by the installed capacity of the standard string; the fitting original daily dust loss degree can be obtained according to the fitting dust loss degree curve.

[0168] Calculate the cleaning ratio of the first day of cleaning. The cleaning ratio of the first day of cleaning is the ratio of the equivalent utilization hours of the components cleaned on the first day of the cleaning period on the second day to the equivalent utilization hours of the standard string of the components cleaned on the first day of the cleaning period on the second day.

[0169] The equivalent utilization hours of the components cleaned on the second day of the cleaning period on the third day = (the equivalent utilization hours of the whole station on the third day × the installed capacity of the whole station - the remaining uncleaned capacity on the second day × the equivalent utilization hours of the whole station on the day before cleaning × the ratio of the equivalent utilization hours of the standard string on that day to the equivalent utilization hours of the standard string on the day before cleaning × (1 - the fitting original daily dust loss degree on the second day of cleaning) ÷ (1 - the fitting original daily dust loss degree on the day before cleaning) - the equivalent utilization hours of the components cleaned on the first day on the second day × the power generation amount of the standard string on the third day of the cleaning period ÷ the power generation amount of the standard string on the second day of the cleaning period × (1 - the fitting original daily dust loss degree on the third day of the cleaning period) ÷ (1 - the fitting original daily dust loss degree on the second day of the cleaning period)) ÷ the cleaning capacity of the components cleaned on the second day of the cleaning period.

[0170] Calculate the cleaning ratio of the second day of cleaning. The cleaning ratio of the second day of cleaning is the ratio of the equivalent utilization hours of the components cleaned on the second day of the cleaning period on the third day to the equivalent utilization hours of the standard string of the components cleaned on the second day of the cleaning period on the third day.

[0171] And so on, calculate the equivalent utilization hours and cleaning ratio of the components cleaned every day in the cleaning period on the second day. The ratio of the equivalent utilization hours of the whole station when it is cleaned completely to the equivalent utilization hours of the standard string = the cumulative value of (the cleaning ratio of the components cleaned every day in the cleaning period on the second day × the cleaning capacity on that day ÷ the installed capacity of the whole station).

[0172] S6A0. Determine the start date for calculating the daily dust loss degree of the whole station.

[0173] Among them, the start date is the date closest to the decision-making day before the decision-making day that meets the condition that the maximum value of the utilization hours of the standard string and the comparison string is greater than the average daily radiation of the current month × k and the date of non-rainy weather, and is not earlier than the date determined by the following two situations: ① the mth day after the closest snow day to the decision-making day; ② the next day of the closest cleaning day to the decision-making day.

[0174] S6B0. Calculate the daily dust loss degree of the whole station on the start date.

[0175] Among them, the daily dust loss degree of the whole station on the start date can also be called the initial dust accumulation loss. Exemplarily, the initial dust accumulation loss = 1 - (1 - the original daily dust loss degree on the start date) × the equivalent utilization hours of the whole station on the start date ÷ (the daily power generation of the comparison string on the start date ÷ the installed capacity of the comparison string) ÷ the ratio of the equivalent utilization hours of the whole station after cleaning to the equivalent utilization hours of the standard string.

[0176] S6C0. Calculate the imaginary date.

[0177] Among them, the imaginary date refers to the date when it is assumed that it will not rain after cleaning on this date, and the dust loss degree will reach the initial dust accumulation loss by the start date. According to the fitted dust loss degree curve, substituting the initial dust accumulation loss, the number of days from the start date to the imaginary date can be obtained, so as to determine the imaginary date.

[0178] S6D0. Calculate the dust loss degree on the current evaluation day.

[0179] Among them, the dust loss degree on the current evaluation day refers to the calculated daily dust loss degree of the whole station on the day before the decision-making day (yesterday). According to whether there is rainy weather, the calculation method is different, as follows:

[0180] Screen the dates corresponding to the rainy weather between the start date (excluding) and yesterday (including). If there is no rainy weather, directly according to the fitted dust loss degree curve and the number of days from yesterday to the imaginary date, calculate the daily dust loss degree of the whole station on yesterday.

[0181] If there is rainy weather, starting from the first rainy date, first according to the fitted dust loss degree curve and the number of days from the rainy date to the imaginary date, calculate the calculated daily dust loss degree on the day before the rainy date. The calculated daily dust loss degree of the whole station on the day after the rainy date = the calculated daily dust loss degree of the whole station on the day before the rainy date × the rain impact degree on the rainy date. Then take the day after the rain as the next start date, and the corresponding calculated daily dust loss degree of the whole station as the next initial dust accumulation loss. And so on, until after the calculation of the last rainy weather is completed, the start date is the day after the last rainy weather, and then according to the fitted dust loss degree curve, calculate the calculated daily dust loss degree on yesterday, as the dust loss degree on the current evaluation day.

[0182] The calculation of the dust loss degree on the current evaluation date is realized through S610 - S6D0. Among them, in the embodiments of the present invention, data screening is performed on the original daily dust loss degree and historical weather data; a non - linear model is used to describe the dust loss degree; considering the influence of rainfall and the inconsistent dust accumulation of photovoltaic modules everywhere in the station, etc., it does not directly calculate using the daily power generation of the standard string and the comparison string; methods such as installing a rain gauge on - site to collect accurate rainfall data are used for calculation. The calculation of the embodiments of the present invention is more in line with the actual situation, which is conducive to improving the accuracy of the dust loss degree calculation, thereby improving the accuracy of the cleaning decision - making.

[0183] Figure 7 It is a flowchart of a calculation method for estimating the cleaning yield provided by the embodiments of the present invention. See Figure 7 , on the basis of the above - mentioned embodiments, optionally, the calculation steps for estimating the cleaning yield include:

[0184] S710. Calculate the cleaning income on the income day; among them, the cleaning income on the income day is determined respectively according to the similarities and differences between the weather during the weather forecast period and the historical weather.

[0185] Exemplarily, if the weather during the weather forecast period is the same as the historical weather, the calculation steps for the cleaning income include:

[0186] According to the average value of the equivalent utilization hours of the standard string for the same historical weather, calculate the equivalent utilization hours of the standard string for each day during the weather forecast period. Among them, the weather forecast period is p days, and the weather for the next p days can all be matched with the same weather within q days before the decision day. Then, the equivalent utilization hours of the standard string for each future day = the average value of the equivalent utilization hours of the standard string for the same weather within q days before the decision day.

[0187] If the day before the income day is a non - rainfall weather, then according to the equivalent utilization hours of the standard string for each day during the weather forecast period, the dust loss degree of the income day assuming no cleaning, the dust loss degree of the income day after cleaning, the daily cleaning capacity, and the electricity selling price, calculate the cleaning income on the income day. Exemplarily, it is defaulted that cleaning starts from the decision day, and the components cleaned every day start to generate income from the next day. The dates after the second day of the decision are all called income days. The cleaning income on the income day = (1 - (1 - the dust loss degree of the income day assuming no cleaning)÷(1 - the dust loss degree of the income day after cleaning))×the equivalent utilization hours of the standard string on the income day×the daily cleaning capacity×the electricity selling price. Among them, the dust loss degree of the income day assuming no cleaning can be calculated according to the fitted dust loss degree curve and the number of days from the income day to the imaginary initial date; the dust loss degree of the income day after cleaning can be calculated according to the fitted dust loss degree curve and the number of days from the income day to the decision day.

[0188] If the day before the revenue day is a rainy day, then based on the dust loss degree of the two days before the revenue day assuming no cleaning, the rainfall impact degree of the day before the revenue day, the dust loss degree of the two days before the revenue day after cleaning, the rainfall impact degree of the day before the revenue day, the standard string equivalent utilization hours per day during the weather forecast period, the daily cleaning capacity, and the electricity selling price, calculate the cleaning revenue for the revenue day. Exemplarily, if the day before the revenue day is a rainy day, the cleaning revenue for the revenue day = (1 - (1 - dust loss degree of the two days before the revenue day without cleaning × rainfall impact degree of the day before the revenue day) ÷ (1 - dust loss degree of the two days before the revenue day after cleaning × rainfall impact degree of the day before the revenue day)) × standard string equivalent utilization hours of the revenue day × daily cleaning capacity × electricity selling price. Among them, the rainfall impact degree is calculated based on the rainfall impact degree relationship formula, rainfall amount, and rainfall duration, and the rainfall impact degree is calculated from the rainfall duration and rainfall amount in the weather forecast. Recalculate the imaginary initial date according to the dust loss degree after rain, and the number of days from the imaginary initial date to the revenue day in the two cases of assuming no cleaning and after cleaning.

[0189] If the weather during the weather forecast period is different from the historical weather, or the revenue day is greater than the weather forecast period, the calculation steps for the cleaning revenue include: Based on the dust loss degree of the revenue day assuming no cleaning, the dust loss degree of the revenue day after cleaning, the average daily radiation amount of the month of the revenue day, the average daily overall station system efficiency of the month before the revenue day, the daily cleaning capacity, and the electricity selling price, calculate the cleaning revenue for the revenue day. Exemplarily, if there is no weather the same as the revenue day within q days before the decision day or the revenue day exceeds the weather forecast period, the cleaning revenue for the revenue day = (1 - (1 - dust loss degree of the revenue day without cleaning) ÷ (1 - dust loss degree of the revenue day after cleaning)) × average daily radiation amount of the month of the revenue day × average daily overall station system efficiency of the month before the revenue day × daily cleaning capacity × electricity selling price. Among them, the overall station system efficiency is the overall station equivalent utilization hours divided by the inclined plane radiation amount.

[0190] Accumulate the cleaning revenues of all revenue days to obtain the cleaning revenue.

[0191] S720. Calculate the estimated cleaning rate of return based on the cleaning revenue and the cleaning cost.

[0192] Exemplarily, the cleaning income = total cleaning revenue - cleaning unit price × total installed capacity of the station, and the cleaning rate of return = cleaning income ÷ (cleaning unit price × total installed capacity of the station).

[0193] The calculation of the estimated cleaning yield is achieved through S710 - S720. Compared with the prior art where the calculation of the estimated cleaning income only considers the estimated cleaning income within the short - term weather forecast period or, although considering the long - term cleaning income, does not consider the impact of short - term weather conditions on the cleaning income, in the embodiments of the present invention, when calculating the estimated cleaning income, it is calculated in two stages: the weather forecast period and the non - weather forecast period. During the weather forecast period, the average value of the equivalent utilization hours of the standard string for a certain type of weather in the recent period is used as the predicted value of the equivalent utilization hours of the standard string for the same type of weather in the future. During the non - weather forecast period, the average monthly daily tilted - plane radiation of the power station over the years is used as the predicted value of the daily tilted - plane radiation for the future days of that month. Therefore, the calculation method of the embodiments of the present invention is more comprehensive and is conducive to making more reliable decisions for cleaning activities.

[0194] Figure 8 It is a schematic flowchart of a method for calculating the estimated cleaning income and cleaning decision provided by the embodiments of the present invention. Refer to Figure 8 Based on the above - mentioned embodiments, optionally, assuming that the cleaning capacity per day during the cleaning period is equal, the method for calculating the estimated cleaning income and cleaning decision includes the following steps:

[0195] S810. When the weather during the weather forecast period is the same as the historical weather, estimate the equivalent utilization hours of the standard string for each future day.

[0196] Among them, if the weather forecast period is p days and the weather for the future p days can all be matched with the same weather within q days before the decision day, then the equivalent utilization hours of the standard string for each future day = the average value of the equivalent utilization hours of the standard string for the same weather within q days before the decision day.

[0197] S820. Calculate the cleaning income for the income days in the case of no rainfall.

[0198] Among them, it is defaulted that cleaning starts from the decision day, and the components cleaned every day start to generate income from the next day. The dates after the second day of the decision are all called income days. The cleaning income for the income days = (1 - (1 - the dust loss degree of the income day without cleaning) ÷ (1 - the dust loss degree of the income day after cleaning)) × the equivalent utilization hours of the standard string for the income day × the daily cleaning capacity × the electricity selling price. Among them, the dust loss degree of the income day without cleaning can be calculated based on the fitted dust loss degree curve and the number of days from the income day to the imaginary initial date; the dust loss degree of the income day after cleaning can be calculated based on the fitted dust loss degree curve and the number of days from the income day to the decision day.

[0199] S830. Calculate the cleaning income for the income days in the case of rainfall.

[0200] Among them, if the day before the revenue day is a rainy day, the cleaning income on the revenue day = (1 - (1 - the dust loss degree in the two days before the revenue day without cleaning × the rainfall impact degree on the day before the revenue day) ÷ (1 - the dust loss degree in the two days before the revenue day after cleaning × the rainfall impact degree on the day before the revenue day)) × the standard string equivalent utilization hours on the revenue day × the daily cleaning capacity × the electricity selling price. Among them, the rainfall impact degree is calculated according to the rainfall impact degree relationship formula, rainfall amount and rainfall duration, and the rainfall impact degree is calculated based on the rainfall duration and rainfall amount in the weather forecast. Recalculate the imaginary initial date according to the dust loss degree after rain, and the number of days from the revenue day to the imaginary initial date in the two cases of assuming no cleaning and after cleaning.

[0201] S840. When the weather during the weather forecast period is different from the historical weather, calculate the cleaning income on the revenue day.

[0202] Among them, if the same weather as the revenue day cannot be found within q days before the decision-making day or the revenue day exceeds the weather forecast period, the cleaning income on the revenue day = (1 - (1 - the dust loss degree on the revenue day without cleaning) ÷ (1 - the dust loss degree on the revenue day after cleaning)) × the average daily radiation amount in the month of the revenue day × the average daily overall station system efficiency in the month before the revenue day × the daily cleaning capacity × the electricity selling price. Among them, the overall station system efficiency is the overall station equivalent utilization hours divided by the inclined plane radiation amount.

[0203] S850. Calculate the cleaning revenue and cleaning rate of return.

[0204] Among them, the total cleaning income is obtained by accumulating the cleaning income of each revenue day. The components cleaned every day have at most n revenue days, and n is a set value that can be set as needed. Optionally, the cleaning revenue = the total cleaning income - the cleaning unit price × the overall station installed capacity, and the cleaning rate of return = the cleaning revenue ÷ (the cleaning unit price × the overall station installed capacity).

[0205] S860. Determine the cleaning decision.

[0206] Among them, the cleaning decision can be compared according to the cleaning rate of return and the preset rate of return. If the cleaning rate of return is greater than the preset rate of return and there is no snow weather in the weather forecast on and after the decision-making day, provide an indication to recommend cleaning, otherwise provide an indication not to recommend cleaning.

[0207] The calculation of the estimated cleaning yield rate and the determination of the cleaning decision are achieved through S810 - S860. Compared with the prior art where the calculation of the estimated cleaning income only considers the estimated cleaning income within the short - term period with weather forecasts or, although considering the long - term cleaning income, does not consider the impact of short - term weather conditions on the cleaning income, in the embodiments of the present invention, when calculating the estimated cleaning income, it is divided into two stages: the stage with weather forecasts and the stage without weather forecasts. During the stage with weather forecasts, the average equivalent utilization hours of the standard string for a certain type of weather in the recent period are used as the predicted value of the equivalent utilization hours of the standard string for the same type of weather in the future. During the stage without weather forecasts, the average monthly daily tilted - plane radiation of the power station over the years is used as the predicted value of the daily tilted - plane radiation for the future days of that month. Therefore, the calculation method of the embodiments of the present invention is more comprehensive and is conducive to making a more reliable decision on the cleaning activity.

[0208] The embodiments of the present invention also provide a decision - making device for cleaning photovoltaic modules. This device is used to execute the decision - making method for cleaning photovoltaic modules provided in any embodiment of the present invention and has corresponding beneficial effects. Figure 9 It is a schematic structural diagram of a decision - making device for cleaning photovoltaic modules provided by an embodiment of the present invention. Refer to Figure 9 , the decision - making device for cleaning photovoltaic modules includes:

[0209] The current evaluation day dust loss degree calculation module 910 is used to calculate the current evaluation day dust loss degree including the rainfall influence degree; wherein, the rainfall influence degree is determined according to historical rainfall data and the original daily dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string, and historical weather data;

[0210] The estimated cleaning yield rate calculation module 920 is used to calculate the estimated cleaning yield rate; wherein, the estimated cleaning yield rate is determined according to the current evaluation day dust loss degree, historical weather data, weather forecast data, cleaning data, and the electricity selling price;

[0211] The cleaning timing determination module 930 is used to compare the estimated cleaning yield rate with a preset cleaning yield rate and determine whether to clean the photovoltaic modules in combination with the weather forecast data.

[0212] Optionally, the current evaluation day dust loss degree calculation module is also used to calculate an initial ratio; wherein, the initial ratio is determined according to the ratio of the daily power generation of the comparison string to the daily power generation of the standard string on the initial date;

[0213] Calculate the original daily dust loss degree; wherein, the original daily dust loss degree is determined according to the daily power generation of the standard string, the daily power generation of the comparison string, and the initial ratio;

[0214] Obtain the fitted dust loss degree curve; wherein, the fitted dust loss degree curve is obtained by data screening and fitting based on the original daily dust loss degree and the historical weather data;

[0215] Calculate the current evaluation day dust loss degree according to the fitted dust loss degree curve and the rainfall influence degree.

[0216] Optionally, the current evaluation day dust loss degree calculation module is further configured to determine the calculation end date according to the historical weather data;

[0217] Construct a sequence of days between the initial date and the end date and the corresponding sequence of original daily dust loss degrees;

[0218] Select the longest increasing subsequence in the sequence of original daily dust loss degrees, and construct an optimized sequence of original daily dust loss degrees and the corresponding optimized sequence of days;

[0219] According to the accurate sequence of original daily dust loss degrees and the optimized sequence of days, fit the relationship curve between the dust loss degree and the number of days of the comparison string to obtain the fitted dust loss degree curve.

[0220] Optionally, the fitted dust loss degree curve uses as the fitting function for non-linear relationship fitting;

[0221] where y represents the dust loss degree, x represents the number of days, and a, b, and c are coefficients.

[0222] Optionally, the current evaluation day dust loss degree calculation module is further configured to determine the day that meets the preset conditions as the start date of the current evaluation day dust loss degree;

[0223] Deduce the whole station daily dust loss degree on the day of the start date, which is defined as the start ash accumulation loss; wherein, the start ash accumulation loss is determined according to the original daily dust loss degree on the day of the start date, the whole station equivalent utilization hours, the daily power generation of the comparison string, the capacity of the comparison string, and the ratio of the whole station clean equivalent utilization hours to the equivalent utilization hours of the standard string;

[0224] Calculate the imaginary initial date; wherein, the imaginary initial date is determined according to the fitted dust loss degree curve and the start ash accumulation loss;

[0225] Calculate the current evaluation day dust loss degree; wherein, the current evaluation day dust loss degree is calculated according to the imaginary initial date, the fitted dust loss degree curve, the rainfall influence degree, and the historical weather data.

[0226] Optionally, the dust loss degree calculation module on the current evaluation date is further configured to calculate the equivalent utilization hours of the components to be cleaned on the second day of each day during the cleaning period; wherein, the equivalent utilization hours of the components to be cleaned on the second day of each day during the cleaning period are determined according to the equivalent utilization hours of the whole station, the installed capacity of the whole station, the remaining capacity to be cleaned, the equivalent utilization hours of the whole station on the day before cleaning, the equivalent utilization hours of the standard string on the current day, the equivalent utilization hours of the standard string on the day before cleaning, the fitted original daily dust loss degree on the second day of cleaning, the fitted original daily dust loss degree on the day before cleaning, and the cleaning capacity on the day of cleaning;

[0227] Calculate the ratio of the equivalent utilization hours of the components to be cleaned on the second day of each day during the cleaning period to the equivalent utilization hours of the standard string of the components to be cleaned on the second day of each day, and denote it as the cleaning ratio;

[0228] According to the cleaning ratio of the components to be cleaned on the second day of each day during the cleaning period, the cleaning capacity on the current day, and the installed capacity of the whole station, calculate the ratio of the equivalent utilization hours of the whole station when cleaned to the equivalent utilization hours of the standard string.

[0229] Optionally, the dust loss degree calculation module on the current evaluation date is further configured to find the date corresponding to the rainfall weather between the start date and the day before the decision day according to the historical weather data;

[0230] If there is no rainfall weather, calculate the calculated daily dust loss degree of the previous day according to the dust loss degree on the current evaluation date and the fitted dust loss degree curve;

[0231] If there is rainfall weather, starting from the first rainfall weather, calculate the calculated daily dust loss degree of the day before the rainfall weather according to the dust loss degree on the current evaluation date and the fitted dust loss degree curve; and calculate the calculated daily dust loss degree of the day after the rainfall weather according to the calculated daily dust loss degree of the day before the rainfall weather and the rainfall impact degree; take the day after the rainfall weather as the next start date and calculate the corresponding starting dust accumulation loss.

[0232] Optionally, the historical rainfall data includes rainfall amount and rainfall duration; the dust loss degree calculation module on the current evaluation date is further configured to calculate the historical rainfall impact degree; wherein, the historical rainfall impact degree is determined according to the original daily dust loss degree on the day after rainfall and the daily dust loss degree on the day before rainfall;

[0233] Construct the relationship between the historical rainfall impact degree and the rainfall amount and the rainfall duration by using a neural network algorithm to obtain the rainfall impact degree relationship;

[0234] Substitute the rainfall amount and the rainfall duration to be calculated into the rainfall impact degree relationship to calculate the rainfall impact degree.

[0235] Optionally, the estimated cleaning yield calculation module is further configured to calculate the cleaning income on the income day; wherein, the cleaning income on the income day is determined according to the similarities and differences between the weather during the weather forecast period and the historical weather respectively;

[0236] Calculate the estimated cleaning yield according to the cleaning income and the cleaning cost.

[0237] Optionally, the estimated cleaning yield calculation module is further configured to if the weather during the weather forecast period is the same as the historical weather, the calculation steps of the cleaning income include:

[0238] Calculate the equivalent utilization hours of the standard string per day during the weather forecast period according to the average value of the equivalent utilization hours of the standard string in the historical same weather;

[0239] If the day before the income day is a non-rainy weather, calculate the cleaning income on the income day according to the equivalent utilization hours of the standard string per day during the weather forecast period, the dust loss degree of the assumed non-cleaned income day, the dust loss degree of the income day after cleaning, the daily cleaning capacity and the electricity selling price;

[0240] If the day before the income day is a rainy weather, calculate the cleaning income on the income day according to the dust loss degree of the assumed two days before the non-cleaned income day, the rainfall impact degree of the day before the income day, the dust loss degree of the two days before the income day after cleaning, the rainfall impact degree of the day before the income day, the equivalent utilization hours of the standard string per day during the weather forecast period, the daily cleaning capacity and the electricity selling price;

[0241] Accumulate the cleaning income of all the income days to obtain the cleaning income.

[0242] Optionally, the estimated cleaning yield calculation module is further configured to if the weather during the weather forecast period is different from the historical weather, or the income day is greater than the weather forecast period, the calculation steps of the cleaning income include:

[0243] Calculate the cleaning income on the income day according to the dust loss degree of the assumed non-cleaned income day, the dust loss degree of the income day after cleaning, the average daily radiation amount in the month of the income day, the average daily full-station system efficiency in the month before the income day, the daily cleaning capacity and the electricity selling price;

[0244] Accumulate the cleaning income of all the income days to obtain the cleaning income.

[0245] Optionally, a rain gauge is used to collect the historical rainfall data.

[0246] An embodiment of the present invention further provides a decision evaluation method for cleaning photovoltaic modules, which is executed by a decision evaluation device for cleaning photovoltaic modules, and the device can be implemented by software and / or hardware. Figure 10Schematic flow chart of a decision evaluation method for cleaning photovoltaic modules provided by an embodiment of the present invention. Refer to Figure 10 , the decision evaluation method for cleaning photovoltaic modules includes the following steps:

[0247] S1010. Starting from the first day of cleaning, calculate the actual cleaning income and actual cleaning cost.

[0248] Among them, the actual cleaning income refers to the actual cleaning income counted after cleaning. From the first day of cleaning to the day before the next cleaning, calculate the daily cleaning power increase according to the total power generation of the whole station and the equivalent utilization hours of the reference string, record the actual cleaning cost per day, and calculate the actual cleaning return rate.

[0249] S1020. Accumulate the actual cleaning income and actual cleaning cost respectively to obtain the total actual cleaning income and total actual cleaning cost.

[0250] S1030. Calculate the actual cleaning return rate; among them, the actual cleaning return rate is used to evaluate the decision method for cleaning photovoltaic modules provided by any embodiment of the present invention.

[0251] The embodiment of the present invention provides a method for calculating the actual cleaning return rate. This method can count the actual cleaning income after cleaning, so as to evaluate the cleaning decision.

[0252] Figure 11 Schematic flow chart of a calculation method for total actual cleaning income provided by an embodiment of the present invention. Refer to Figure 11 , on the basis of the above embodiments, optionally, the calculation steps of the total actual cleaning income include:

[0253] S1110. Calculate the reference string ratio; among them, the reference string ratio is determined according to the equivalent utilization hours of the whole station on the day before the most recent cleaning and the equivalent utilization hours of the comparison string on the day before the most recent cleaning.

[0254] Exemplarily, the reference string and the comparison string are alternately cleaned when cleaning the whole station. The reference string is the comparison string on the day before cleaning. Therefore, the comparison string on the day before the most recent cleaning is used to calculate the reference string ratio. The calculation method of the reference string ratio can be that the reference string ratio = the equivalent utilization hours of the whole station on the day before the most recent cleaning ÷ the equivalent utilization hours of the comparison string on the day before the most recent cleaning. The reference string ratio represents the difference between the equivalent utilization hours of the reference string and the equivalent utilization hours of the whole station, so as to correct the cleaning income calculated by using the reference string subsequently.

[0255] S1120. Calculate the actual daily cleaning income; wherein, the actual daily cleaning income is determined based on the full-station equivalent utilization hours of the day, the power consumption of the reference string of the day, the capacity of the reference string, the reference string ratio, the full-station installed capacity, and the electricity selling price.

[0256] Exemplarily, the actual daily cleaning income = (the full-station equivalent utilization hours of the day - the power consumption of the reference string of the day ÷ the capacity of the reference string × the reference string ratio) × the full-station installed capacity × the electricity selling price. The actual daily cleaning cost is determined according to the actual expenditure situation.

[0257] S1130. Accumulate the actual daily cleaning income to obtain the total actual cleaning income.

[0258] Through S1110 - S1130, the calculation of the total cleaning income is achieved.

[0259] Figure 12 This is a schematic flowchart of a method for calculating cleaning income and cleaning yield rate provided by an embodiment of the present invention. Refer to Figure 12 , on the basis of the above embodiments, optionally, the method for calculating cleaning income and cleaning yield rate includes:

[0260] S1210. Calculate the reference string ratio.

[0261] Among them, the calculation method of the reference string ratio can be that the reference string ratio = the full-station equivalent utilization hours of the day before the last cleaning ÷ the equivalent utilization hours of the comparison string of the day before the last cleaning. The reference string ratio represents the difference between the equivalent utilization hours of the reference string and the full-station equivalent utilization hours, so as to correct the cleaning income calculated using the reference string subsequently.

[0262] S1220. Calculate the actual daily cleaning income.

[0263] Among them, from the first day of the last cleaning to the day before the evaluation day, calculate the actual daily cleaning income. Exemplarily, the actual daily cleaning income = (the full-station equivalent utilization hours of the day - the power consumption of the reference string of the day ÷ the capacity of the reference string × the reference string ratio) × the full-station installed capacity × the electricity selling price. The actual daily cleaning cost is determined according to the actual expenditure situation.

[0264] S1230. Calculate the actual cleaning income and the actual cleaning yield rate.

[0265] Among them, the calculation method of the actual cleaning revenue can be: actual cleaning revenue = total actual cleaning income - total actual cleaning cost. Among them, the total actual cleaning income is equal to the accumulation of the actual cleaning income per day. The total actual cleaning cost is equal to the accumulation of the actual cleaning cost per day. The calculation of the actual cleaning yield can be: actual cleaning yield = actual cleaning revenue ÷ actual cleaning cost.

[0266] An embodiment of the present invention provides a method for calculating the actual cleaning yield, which can count the actual cleaning revenue after cleaning, so as to evaluate the cleaning decision.

[0267] An embodiment of the present invention also provides a decision evaluation device for cleaning photovoltaic modules, which is used to execute the decision evaluation method for cleaning photovoltaic modules provided in any embodiment of the present invention, and has corresponding beneficial effects. Figure 13 It is a schematic structural diagram of a decision evaluation device for cleaning photovoltaic modules provided by an embodiment of the present invention. Refer to Figure 13 , the decision evaluation device for cleaning photovoltaic modules includes:

[0268] The income and cost calculation module 1310 is used to calculate the total actual cleaning income and the total actual cleaning cost starting from the first day of cleaning; accumulate the actual cleaning income and the actual cleaning cost respectively to obtain the total actual cleaning income and the total actual cleaning cost.

[0269] The actual cleaning yield calculation module 1320 is used to calculate the actual cleaning yield; among them, the actual cleaning yield is used to evaluate the decision method for cleaning photovoltaic modules provided in any embodiment of the present invention.

[0270] Optionally, the income and cost calculation module 1310 calculates the reference string ratio; among them, the reference string ratio is determined according to the equivalent utilization hours of the whole station on the day before the most recent cleaning and the equivalent utilization hours of the comparison string on the day before the most recent cleaning;

[0271] Calculate the actual cleaning income per day; among them, the actual cleaning income per day is determined according to the equivalent utilization hours of the whole station on the current day, the power of the reference string on the current day, the capacity of the reference string, the reference string ratio, the installed capacity of the whole station, and the electricity selling price;

[0272] Accumulate the actual cleaning income per day to obtain the total actual cleaning income.

[0273] An embodiment of the present invention provides a cleaning system for photovoltaic modules, which can be used to implement the decision method and evaluation method for cleaning photovoltaic modules provided in any embodiment of the present invention, and has corresponding beneficial effects, which will not be elaborated here. Figure 14 It is a schematic structural diagram of a cleaning system for photovoltaic modules provided by an embodiment of the present invention. Refer to Figure 14, the cleaning system of the photovoltaic module includes: the standard string 1410 and its string metering device 1420, the comparison string 1430 and its string metering device 1440, and the reference string 1450 and its string metering device 1460.

[0274] Among them, the present invention does not limit the number of the standard string, the comparison string and the reference string. The number of the standard string is one or more, the number of the comparison string is one or more, and the number of the reference string is one or more. The standard string is cleaned regularly, and the regular cleaning is used to keep the standard string in a clean state all the time, which is used as a benchmark for evaluating the dirtiness degree of other photovoltaic modules in the photovoltaic power station. Optionally, the standard string is cleaned by a full-automatic cleaning robot every night. Compared with manual cleaning, the embodiment of the present invention can not only ensure the timeliness and qualification rate of cleaning, but also does not affect the power generation of the photovoltaic string during cleaning, which is further beneficial to improving the accuracy of the calculated dust loss degree.

[0275] The comparison string is consistent with the overall dust loss degree of the power station, and is used to calculate the dust loss degree of the power station by comparing with the standard string.

[0276] The reference string is consistent with the overall dust loss degree of the power station before the whole-station cleaning, and remains uncleaned during the whole-station cleaning.

[0277] On the basis of the above embodiments, optionally, the comparison string and the reference string are alternately cleaned during the whole-station cleaning; among them, the comparison string and the reference string are cleaned on the last day of the whole-station cleaning. Table 1 is a schematic illustration of the cleaning instructions for a standard string, a comparison string and a reference string provided by the embodiment of the present invention.

[0278] Table 1

[0279]

[0280] In the table, on the last day of the first whole-station cleaning, the second string is cleaned clean and used as the comparison string; the third string is not cleaned and used as the reference string. On the last day of the second whole-station cleaning, the third string is cleaned clean and used as the comparison string; the second string is not cleaned and used as the reference string. On the last day of the third whole-station cleaning, the second string is cleaned clean and used as the comparison string; the third string is not cleaned and used as the reference string. On the last day of the fourth whole-station cleaning, the third string is cleaned clean and used as the comparison string; the second string is not cleaned and used as the reference string. And so on, the second string and the third string are alternately used as the comparison string and the reference string to ensure that the reference string is consistent with the overall dust loss degree of the power station before cleaning.

[0281] Continue to refer to Figure 14, on the basis of the above embodiments, optionally, the cleaning system of the photovoltaic module further includes a decision-making device 1470, which is electrically connected to each metering device. The decision-making device includes a processor, a memory, an input device, and an output device; the number of processors in the decision-making device can be one or more, and the processor, memory, input device, and output device in the decision-making device can be connected through a bus or other means.

[0282] As a computer-readable storage medium, the memory can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the decision-making method for cleaning photovoltaic modules or the decision-making evaluation method for cleaning photovoltaic modules in the embodiments of the present invention. The processor executes various functional applications and data processing of the decision-making device by running the software programs, instructions, and modules stored in the memory, that is, implements the above-mentioned decision-making method for cleaning photovoltaic modules or the decision-making evaluation method for cleaning photovoltaic modules.

[0283] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the decision-making device. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory can further include a memory remotely set relative to the processor, and these remote memories can be connected to the decision-making device through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0284] The input device can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function control of the decision-making device. The output device can include display devices such as a display screen.

[0285] Figure 15 It is a schematic diagram of the overall idea of a decision-making and evaluation method for cleaning photovoltaic modules provided by an embodiment of the present invention. See Figure 15 , on the basis of the above embodiments, optionally, the overall idea of the decision-making and evaluation method for cleaning photovoltaic modules is as follows:

[0286] First, calculate the current evaluation day dust loss degree according to the daily power generation of the standard string and the daily power generation of the comparison string; then calculate the estimated cleaning yield rate in combination with future weather data, the historical average inclined plane radiation amount of the power station over the years, the estimated cleaning unit price, and the estimated cleaning days; according to the estimated cleaning yield rate, the preset yield rate, and the future weather forecast, give a cleaning decision. After the actual cleaning starts, calculate the actual cleaning yield rate according to the power generation of the comparison string, the power generation of the reference string, and the actual cleaning cost.

[0287] In summary, the embodiments of the present invention use a fully automatic cleaning robot to regularly clean the standard strings, reducing the workload of manual cleaning and ensuring the accuracy of the original data. The embodiments of the present invention use a non-linear model to describe the dust loss degree, and distinguish the dust loss degree of the comparison string from the dust loss degree of the whole station, making the calculation of the dust loss degree more in line with the actual situation. The embodiments of the present invention collect accurate rainfall data by installing a rain gauge on site, which is conducive to finding the accurate relationship between rainfall and dust loss degree, and making the calculation of dust loss degree and estimated cleaning benefit more accurate. The embodiments of the present invention calculate the estimated cleaning benefit in the weather forecast stage and the non-weather forecast stage, which is more comprehensive, and calculate the actual cleaning benefit, which is conducive to the economic evaluation of the cleaning activity.

[0288] Note that the above is only the preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here. Various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A decision-making method for cleaning photovoltaic modules, characterized in that, it includes: Calculating the dust loss degree on the current evaluation day including the rainfall influence degree; wherein, the rainfall influence degree is determined according to historical rainfall data and the original daily dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall influence degree, the daily power generation of the standard string, the daily power generation of the comparison string and historical weather data; Calculating the estimated cleaning yield rate; wherein, the estimated cleaning yield rate is determined according to the current evaluation day dust loss degree, the historical weather data, weather forecast data, cleaning data and electricity selling price; Comparing the estimated cleaning yield rate with a preset cleaning yield rate, and determining whether to clean the photovoltaic modules in combination with the weather forecast data; The calculation steps of the current evaluation day dust loss degree including the rainfall influence degree include: Calculating an initial ratio; wherein, the initial ratio is determined according to the ratio of the daily power generation of the comparison string to the daily power generation of the standard string on the initial date; Calculating the original daily dust loss degree; wherein, the original daily dust loss degree is determined according to the daily power generation of the standard string, the daily power generation of the comparison string and the initial ratio; Fitting to obtain a fitted dust loss degree curve; wherein, the fitted dust loss degree curve is subjected to data screening and fitting according to the original daily dust loss degree and the historical weather data; Calculating the current evaluation day dust loss degree according to the fitted dust loss degree curve and the rainfall influence degree; The calculation steps of the estimated cleaning yield rate include: Calculate the cleaning income for the income day; wherein, the cleaning income for the income day is determined according to the similarities and differences between the weather during the weather forecast period and the historical weather; the steps of calculating the cleaning income for the income day include: If the weather during the weather forecast period is the same as the historical weather, the calculation steps for the cleaning income include: Calculate the equivalent utilization hours of the standard string per day during the weather forecast period according to the average value of the equivalent utilization hours of the standard string in the historical same weather; If the day before the income day is a non-rainy day, calculate the cleaning income for the income day according to the equivalent utilization hours of the standard string per day during the weather forecast period, the dust loss degree of the assumed un-cleaned income day, the dust loss degree of the income day after cleaning, the daily cleaning capacity, and the electricity selling price; If the day before the income day is a rainy day, calculate the cleaning income for the income day according to the dust loss degree of the two days before the assumed un-cleaned income day, the rainfall impact degree of the day before the income day, the dust loss degree of the two days before the income day after cleaning, the rainfall impact degree of the day before the income day, the equivalent utilization hours of the standard string per day during the weather forecast period, the daily cleaning capacity, and the electricity selling price; Accumulate the cleaning income of all the income days to obtain the cleaning income; If the weather during the weather forecast period is different from the historical weather, or the income day is greater than the weather forecast period, the calculation steps for the cleaning income include: Calculate the cleaning income for the income day according to the dust loss degree of the assumed un-cleaned income day, the dust loss degree of the income day after cleaning, the average daily radiation amount of the month of the income day, the average daily station system efficiency of the month before the income day, the daily cleaning capacity, and the electricity selling price; Accumulate the cleaning income of all the income days to obtain the cleaning income; Calculate the estimated cleaning yield according to the cleaning income and the cleaning cost.

2. The decision-making method for cleaning photovoltaic modules according to claim 1, characterized in that, the calculation steps for fitting the dust loss degree curve include: Determine the calculation end date according to the historical weather data; Construct a sequence of days between the start date and the end date and the corresponding sequence of original daily dust loss degrees; Select the longest increasing subsequence in the sequence of original daily dust loss degrees to construct an optimized sequence of original daily dust loss degrees and the corresponding optimized sequence of days; According to the optimized sequence of original daily dust loss degrees and the optimized sequence of days, fit the relationship curve between the dust loss degree and the number of days of the comparison string to obtain the fitted dust loss degree curve.

3. The decision-making method for cleaning photovoltaic modules according to claim 2, characterized in that, The fitting dust loss degree curve uses as the fitting function to perform non-linear relationship fitting; wherein, y represents the dust loss degree, x represents the number of days, and a, b, and c are coefficients.

4. The decision-making method for cleaning photovoltaic modules according to claim 1, characterized in that, the calculation steps for the current evaluation day dust loss degree further include: Determine the start date of the current evaluation day dust loss degree as the day that meets the preset conditions; Estimate the daily dust loss of the entire station on the start date, which is defined as the start ash accumulation loss; wherein, the start ash accumulation loss is determined according to the original daily dust loss on the start date, the equivalent utilization hours of the entire station, the daily power generation of the comparison string, the capacity of the comparison string, and the ratio of the equivalent utilization hours of the entire station cleaned to the equivalent utilization hours of the standard string. Calculate the hypothetical initial date; wherein, the hypothetical initial date is determined according to the fitted dust loss curve and the start ash accumulation loss. Calculate the dust loss of the current evaluation date; wherein, the dust loss of the current evaluation date is calculated according to the hypothetical initial date, the fitted dust loss curve, the rainfall influence degree, and the historical weather data.

5. The decision-making method for cleaning photovoltaic modules according to claim 4, characterized in that, the calculation steps of the ratio of the equivalent utilization hours of the entire station cleaned to the equivalent utilization hours of the standard string include: Calculate the equivalent utilization hours of the cleaned modules on the day after the cleaning day during the cleaning period; wherein, the equivalent utilization hours of the cleaned modules on the day after the cleaning day during the cleaning period are determined according to the equivalent utilization hours of the entire station, the installed capacity of the entire station, the remaining uncleaned capacity, the equivalent utilization hours of the entire station on the day before the cleaning, the equivalent utilization hours of the standard string on the current day, the equivalent utilization hours of the standard string on the day before the cleaning, the fitted original daily dust loss on the second day of cleaning, the fitted original daily dust loss on the day before the cleaning, and the cleaning capacity on the cleaning day. Calculate the ratio of the equivalent utilization hours of the cleaned modules on the day after the cleaning day during the cleaning period to the equivalent utilization hours of the standard string on the day after the cleaning day, which is denoted as the cleaning ratio. According to the cleaning ratio of the cleaned modules on the day after the cleaning day during the cleaning period, the cleaning capacity on the current day, and the installed capacity of the entire station, calculate the ratio of the equivalent utilization hours of the entire station cleaned to the equivalent utilization hours of the standard string.

6. The decision-making method for cleaning photovoltaic modules according to claim 4, characterized in that, the calculation steps of the dust loss of the current evaluation date further include: According to the historical weather data, find the dates corresponding to the rainfall weather between the start date and the day before the decision day. If there is no rainfall weather, calculate the calculated dust loss of the previous day according to the dust loss of the current evaluation date and the fitted dust loss curve. If there is rainfall weather, starting from the first rainfall weather, calculate the calculated dust loss of the day before the rainfall weather according to the dust loss of the current evaluation date and the fitted dust loss curve; and, calculate the calculated dust loss of the day after the rainfall weather according to the calculated dust loss of the day before the rainfall weather and the rainfall influence degree; take the day after the rainfall weather as the next start date, and calculate the corresponding start ash accumulation loss.

7. The decision-making method for cleaning photovoltaic modules according to claim 1, characterized in that, the historical rainfall data includes rainfall amount and rainfall duration; the calculation steps of the rainfall influence degree include: Calculate the historical rainfall impact degree; wherein, the historical rainfall impact degree is determined according to the original daily dust loss degree on the day after rainfall and the daily dust loss degree on the day before rainfall; Use the neural network algorithm to construct the relationship between the historical rainfall impact degree, the rainfall amount, and the rainfall duration to obtain the rainfall impact degree relationship; Substitute the rainfall amount and the rainfall duration to be calculated into the rainfall impact degree relationship to calculate the rainfall impact degree.

8. The decision-making method for cleaning a photovoltaic module according to claim 1, wherein, A rain gauge is used to collect the historical rainfall data.

9. A decision-making device for cleaning a photovoltaic module, wherein, comprises: A current evaluation day dust loss degree calculation module, configured to calculate the current evaluation day dust loss degree including the rainfall impact degree; wherein, the rainfall impact degree is determined according to the historical rainfall data and the original daily dust loss degree; the current evaluation day dust loss degree is determined according to the rainfall impact degree, the daily power generation of the standard string, the daily power generation of the comparison string, and the historical weather data; the calculation steps of the current evaluation day dust loss degree including the rainfall impact degree include: calculating an initial ratio; wherein, the initial ratio is determined according to the ratio of the daily power generation of the comparison string to the daily power generation of the standard string on the initial date; calculating the original daily dust loss degree; wherein, the original daily dust loss degree is determined according to the daily power generation of the standard string, the daily power generation of the comparison string, and the initial ratio; fitting to obtain a fitted dust loss degree curve; wherein, the fitted dust loss degree curve performs data screening and fitting according to the original daily dust loss degree and the historical weather data; calculating the current evaluation day dust loss degree according to the fitted dust loss degree curve and the rainfall impact degree; Estimated cleaning yield calculation module, used to calculate the estimated cleaning yield; wherein, the estimated cleaning yield is determined according to the current evaluation date dust loss degree, the historical weather data, the weather forecast data, the cleaning data and the electricity selling price; the calculation steps of the estimated cleaning yield include: calculating the cleaning income on the income day; wherein, the cleaning income on the income day is determined respectively according to the similarities and differences between the weather during the weather forecast period and the historical weather; calculating the estimated cleaning yield according to the cleaning income and the cleaning cost; the steps of calculating the cleaning income on the income day include: if the weather during the weather forecast period is the same as the historical weather, the steps of calculating the cleaning income include: calculating the standard string equivalent utilization hours per day during the weather forecast period according to the average value of the standard string equivalent utilization hours of the historical same weather; if the day before the income day is a non-rainy day, calculating the cleaning income on the income day according to the standard string equivalent utilization hours per day during the weather forecast period, the dust loss degree of the assumed un-cleaned income day, the dust loss degree of the income day after cleaning, the daily cleaning capacity and the electricity selling price; if the day before the income day is a rainy day, calculating the cleaning income on the income day according to the dust loss degree of the two days before the assumed un-cleaned income day, the rainfall impact degree of the day before the income day, the dust loss degree of the two days before the income day after cleaning, the rainfall impact degree of the day before the income day, the standard string equivalent utilization hours per day during the weather forecast period, the daily cleaning capacity and the electricity selling price; accumulating the cleaning income of all the income days to obtain the cleaning income; if the weather during the weather forecast period is different from the historical weather, or the income day is greater than the weather forecast period, the steps of calculating the cleaning income include: calculating the cleaning income on the income day according to the dust loss degree of the assumed un-cleaned income day, the dust loss degree of the income day after cleaning, the average daily radiation amount of the month of the income day, the average daily total station system efficiency of the previous month of the income day, the daily cleaning capacity and the electricity selling price; accumulating the cleaning income of all the income days to obtain the cleaning income; Cleaning timing determination module, used to compare the estimated cleaning yield with the preset cleaning yield and determine whether to clean the photovoltaic modules in combination with the weather forecast data.

10. A decision-making evaluation method for cleaning photovoltaic modules, characterized in that, comprises: Starting from the first day of cleaning, calculating the actual cleaning income and the actual cleaning cost; Accumulating the actual cleaning income and the actual cleaning cost respectively to obtain the total actual cleaning income and the total actual cleaning cost; Calculating the actual cleaning yield; wherein, the actual cleaning yield is used to evaluate the decision-making method for cleaning photovoltaic modules according to any one of claims 1-8.

11. The decision-making evaluation method for cleaning photovoltaic modules according to claim 10, characterized in that, the steps of calculating the total actual cleaning income include: Calculating the reference string ratio; wherein, the reference string ratio is determined according to the equivalent utilization hours of the whole station on the day before the most recent cleaning and the equivalent utilization hours of the comparison string on the day before the most recent cleaning; Calculate the actual daily cleaning income; wherein, the actual daily cleaning income is determined according to the equivalent full - station utilization hours of the day, the power of the reference string of the day, the capacity of the reference string, the reference string ratio, the full - station installed capacity, and the electricity selling price. Accumulate the actual daily cleaning income to obtain the total actual cleaning income.

12. A decision - making evaluation device for cleaning photovoltaic modules, Characterized in that, It includes: An income - cost calculation module, which is used to calculate the total actual cleaning income and the total actual cleaning cost starting from the first day of cleaning; Accumulate the actual cleaning income and the actual cleaning cost respectively to obtain the total actual cleaning income and the total actual cleaning cost; An actual cleaning yield calculation module, which is used to calculate the actual cleaning yield; wherein, the actual cleaning yield is used to evaluate the decision - making method for cleaning photovoltaic modules according to any one of claims 1 - 8.

13. A cleaning system for photovoltaic modules, the cleaning system for photovoltaic modules is used to implement the decision - making method for cleaning photovoltaic modules according to any one of claims 1 - 8, Characterized in that, It includes: A standard string and its string metering device, the standard string is cleaned regularly; A comparison string and its string metering device, the comparison string is consistent with the overall dust loss degree of the power station; A reference string and its string metering device, the reference string is consistent with the overall dust loss degree of the power station before the full - station cleaning and remains uncleaned during the full - station cleaning.

14. The cleaning system for photovoltaic modules according to claim 13, Characterized in that, It further includes: The comparison string and the reference string are alternately cleaned during the full - station cleaning; wherein, the comparison string and the reference string are cleaned on the last day of the full - station cleaning.

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