Method and related device for calculating soiling losses and estimating cleaning times of photovoltaic modules

Abstract

A method and related device for calculating dust loss of a photovoltaic module and estimating cleaning time, comprising: selecting a sample set; dividing the sample set into three groups; fitting a linear relationship of output power of a daily cleaned module and a module cleaned this time to obtain a linear fitting coefficient; calculating a dust shading rate and dust loss power of the module cleaned this time; calculating cumulative power improvement of the module cleaned this time after cleaning; and judging cleaning time according to the dust shading rate, the dust loss power, the cumulative power improvement of the module cleaned this time after cleaning, a cleaning failure period and a determinable cleanable day of the module cleaned this time. The method can calculate dust loss of the module and next cleaning time by means of comprehensive analysis of data correlation, and the dust loss power before cleaning and the improvement benefit after cleaning can be quantified. The method can guide effective cleaning of a photovoltaic station and avoid blind cleaning decision.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic module cleaning technology, and specifically relates to a method and related apparatus for calculating the dust accumulation loss of photovoltaic modules and estimating cleaning time. Background Technology

[0002] Dust accumulation on photovoltaic (PV) modules reduces the amount of radiation received by the module surface by decreasing light transmittance, resulting in significant power generation losses in PV power plants. Furthermore, it creates localized shadows on the module surface, causing hot spot effects, accelerating module degradation, and increasing the risk of fire. Therefore, cleaning PV modules is necessary. However, excessively frequent cleaning will increase total costs and reduce the overall operational efficiency of the power plant.

[0003] Currently, there are two common methods for cleaning photovoltaic power plant modules. One is manual cleaning, in which the cleaning time for most photovoltaic power plant modules is determined arbitrarily, usually a fixed number of times a year, such as twice or four times a year, without knowing how many times to achieve the optimal benefit for the power plant. The other method is to install cleaning robots, which can clean more frequently according to the settings, thereby increasing power generation. However, the one-time investment cost of purchasing and installing cleaning robots is too high. Summary of the Invention

[0004] The purpose of this invention is to provide a method and related apparatus for calculating dust accumulation loss of photovoltaic modules and estimating cleaning time, so as to solve the problems of high investment costs in existing technologies and uncertainty about how many cleaning cycles are needed to achieve optimal power plant revenue.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Methods for calculating dust accumulation loss and estimating cleaning time in photovoltaic modules include:

[0007] String inverters with strong correlation between their instantaneous power time series were selected as the sample set.

[0008] The sample set was divided into three groups: daily cleaning components, components not cleaned this time, and components cleaned this time.

[0009] The linear relationship between the output power of the daily cleaning component and the current cleaning component was fitted to obtain the linear fitting coefficients.

[0010] Based on the daily power generation of the cleaning components and the linear fitting coefficient, the dust coverage rate and power loss due to dust accumulation of the cleaning components are calculated.

[0011] Based on the daily power generation of the uncleaned components and the linear fitting coefficient, the cumulative increase in power generation after cleaning the components was calculated.

[0012] Determine the cleaning failure cycle of the photovoltaic power station and determine the days when cleaning is possible;

[0013] The cleaning time is determined based on the dust coverage rate of the components being cleaned, the power loss due to dust accumulation, the cumulative power increase after cleaning, the cleaning failure cycle, and the date when cleaning can be performed.

[0014] Furthermore, using the correlation matrix method, string inverters with strong correlation between their instantaneous power time series were selected as the sample set:

[0015] There are N inverters with historical power time series P1, P2, P3, ..., PN; each inverter has m measured power values.

[0016] Calculate the correlation matrix R between inverter time series data:

[0017]

[0018] Where r ij (i = 1, 2, ... N; j = 1, 2, ... N) is the correlation coefficient between the power Pi of the i-th inverter and the power Pj of the j-th inverter;

[0019]

[0020] When calculating the correlation coefficient, the inverter power sample should meet the following conditions:

[0021] (1) The two inverters must operate at the same time and cannot be out of sync. The time period is from 11:00 a.m. to 1:00 p.m. and the radiation intensity must be greater than 700.

[0022] (2) Inverter power > 0;

[0023] (3) Cannot be an out-of-limit value, a dead value, or a null value;

[0024] (4) The inverter is in normal power generation state; samples under fault shutdown, fault operation, power rationing and other states are not included in the calculation;

[0025] Inverters with a correlation coefficient greater than 0.9 were used as the sample set of power generation units.

[0026] Furthermore, the sample set is divided into three groups:

[0027] Group A power generation units are daily cleaning components: several units, the connected components are cleaned once a day, used to calculate the daily power generation of the inverter during cleaning;

[0028] Group B power generation units are the components that will not be cleaned in this instance, and are used for comparison of benefits after cleaning.

[0029] Group C power generation units are the components to be cleaned this time: they account for the largest proportion of the sample set and are the components to be cleaned this time.

[0030] Furthermore, the linear fit coefficients:

[0031] Fit a linear relationship between the output power of A and B.

[0032] Linear fitting was performed using historical data on the daily power generation of inverters in group A and group C, and the linear relationship was obtained as PC = a1*PA + b1.

[0033] Group C's daily power generation = Group A's daily power generation * a1 + b1

[0034] Let the historical daily power generation data of inverter group A and inverter group C be PA=(PA1, PA2, ... PAm) and PC=(PC1, PC2, ... PCm), respectively. Let PA=a1*PC+b1

[0035] The regression coefficients are estimated using the least squares method:

[0036] Take the sum of squares of the deviations (PAt - PCt) Minimum is the optimal criterion;

[0037] The final fitted regression coefficients a1 and b1 are:

[0038]

[0039]

[0040] Furthermore, the theoretical daily power generation of Group C under clean conditions = daily power generation of Group A * a1 + b1

[0041] Dust coverage rate = (Theoretical daily power generation of Group C in clean state / Actual daily power generation of Group C - 1) * 100%

[0042] Daily power loss of Group C = Theoretical daily power generation of Group C in clean state - Actual daily power generation of Group C = Actual daily power generation of Group C * Dust occlusion rate; where a1 and b1 are linear fitting coefficients;

[0043] Theoretical power generation of Group C Japan without cleaning = Daily power generation of Group B * a² + b²

[0044] The cumulative increase in power generation after cleaning = ∑ Daily actual power generation of Group C - Theoretical power generation of Group C if it were not cleaned; where a2 and b2 are linear fitting coefficients.

[0045] Further, the cleaning failure cycle is calculated:

[0046] Define the cleaning failure period T2: that is, under the assumption of no precipitation, if the cleaning effect reaches a critical point after N days, and there is no further increase in power consumption due to cleaning, then the cleaning failure period is considered to be N days, T2 = N.

[0047] During periods without heavy rainfall, conduct cleaning tests and analyze the cumulative increase in electricity generation after cleaning in Group C. When the increase stops or slows down, determine the approximate cleaning failure period T2 of the photovoltaic power station.

[0048] Component cleaning daily calculation:

[0049] Definition of Cleanable Day T1: Starting from the day after this cleaning, if the percentage increase in profit from the cumulative cleaning power after Group C cleaning is greater than the cost of a single cleaning, then that day is defined as Cleanable Day T1. In other words, it is considered that the revenue obtained from the increased power after cleaning meets the owner's expected requirements.

[0050] Profit from increased cleaning power generation Q(t) = Cumulative increase in cleaning power generation (t) * Electricity price

[0051] Cleaning cost = Installed capacity (MW) * Cleaning cost per kW (RMB / kW).

[0052] Furthermore, the principles for determining cleaning time are as follows:

[0053] (1) Effective rainfall before the owner's expected profit requirements are met

[0054] This situation is judged using the dust coverage rate index. If the dust coverage rate is greater than the threshold, the date is considered the next cleaning date.

[0055] (2) When the cumulative increase in electricity profit after cleaning is Q(t)*proportional coefficient d>=the cost of a single cleaning, the day is T1, which is a cleanable day;

[0056] (3) When T1>T2, that is, if the current cleaning has not yet met the owner's expected profit requirements, the cleaning effect has failed. The shading rate index is used to determine the next cleaning time.

[0057] (4) If the expected profit requirements of the owners are met, and the rainfall in the next seven days is greater than the set threshold, the time will be postponed, and the subsequent cleaning time will be judged by the shading rate index.

[0058] (5) The cleaning time should be scheduled after the cleaning date and before the cleaning expiration date.

[0059] Furthermore, the photovoltaic module dust accumulation loss calculation and cleaning time estimation system includes:

[0060] The sample selection module is used to select string inverters with strong correlation between their instantaneous power time series as a sample set.

[0061] The grouping module is used to divide the sample set into three groups: daily cleaning components, non-cleaning components, and current cleaning components;

[0062] The linear fitting coefficient acquisition module is used to fit the linear relationship between the output power of the daily cleaning component and the non-cleaning component to obtain the linear fitting coefficient.

[0063] The calculation module is used to calculate the dust cover rate and power loss due to dust accumulation of the cleaning components based on the daily power generation and linear fitting coefficient of the cleaning components; and to calculate the cumulative power increase after cleaning the cleaning components based on the daily power generation and linear fitting coefficient of the uncleaned components.

[0064] The judgment module is used to determine the cleaning failure cycle of the photovoltaic power station and the date on which it can be cleaned. Based on the dust shading rate of the components to be cleaned, the power loss due to dust accumulation, the cumulative power increase after the cleaning of the components, the cleaning failure cycle, and the date on which it can be cleaned, the cleaning time is determined.

[0065] Furthermore, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for calculating dust accumulation loss of photovoltaic modules and estimating cleaning time.

[0066] Furthermore, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a method for calculating dust accumulation loss in photovoltaic modules and estimating cleaning time.

[0067] Compared with the prior art, the present invention has the following technical effects:

[0068] This invention provides a method for calculating dust accumulation loss and estimating cleaning time in photovoltaic (PV) modules based on correlation matrix analysis. This provides a reference for PV power plant module cleaning. Using the correlation matrix method, string inverters with high operational similarity are selected as a sample set. This sample set is divided into three groups: A (daily cleaning), B (no cleaning), and C (normal cleaning), representing cleaning times respectively. A regression model is established using historical data from these three sample sets, and the electricity loss due to dust accumulation in PV modules is estimated based on the regression model. Cleaning tests are conducted on the power generation units connected to the three groups of inverters. The test data is used to estimate the PV power plant cleaning failure time, and combined with numerical weather forecast data, the next cleaning time is determined. This method utilizes comprehensive data correlation analysis to calculate module dust accumulation loss and the next cleaning time, quantifying the electricity loss before cleaning and the improved efficiency after cleaning. This guides effective cleaning of PV power plants and avoids blind cleaning decisions.

[0069] By using data analysis, the energy loss due to dust accumulation in components and the time to the next cleaning can be estimated. The energy loss due to dust accumulation before cleaning and the improved efficiency after cleaning can be quantified. This invention combines numerical weather prediction precipitation results to avoid situations where precipitation occurs shortly after cleaning. Attached Figure Description

[0070] Figure 1 Calculation process for dust accumulation loss and module cleaning time in photovoltaic modules. Detailed Implementation

[0071] The present invention will be further described below with reference to the accompanying drawings:

[0072] Please see Figure 1 This invention is applicable to photovoltaic power plants that install string inverters. The string inverter and the photovoltaic modules connected to it constitute a power generation unit. Figure 1 The process for calculating dust accumulation loss and module cleaning time for photovoltaic modules is implemented after each cleaning cycle.

[0073] 1) Sample set selection

[0074] The correlation matrix method was used to select string inverters with strong correlation between their instantaneous power time series as the sample set.

[0075] There are N inverters with historical power time series P1, P2, P3, ..., PN. Each inverter has m measured power values.

[0076] Calculate the correlation matrix R between inverter time series data:

[0077]

[0078] Where rij (i = 1, 2, ..., N; j = 1, 2, ..., N) is the correlation coefficient between the power Pi of the i-th inverter and the power Pj of the j-th inverter.

[0079]

[0080] Note:

[0081] When calculating the correlation coefficient, the inverter power sample should meet the following conditions:

[0082] (1) The two inverters must operate at the same time and cannot be out of sync. The time period is from 11:00 a.m. to 1:00 p.m. and the radiation intensity must be greater than 700.

[0083] (2) Inverter power > 0.

[0084] (3) It cannot be an abnormal value, such as an out-of-limit value, a dead value, or a null value.

[0085] (4) The inverter is in normal power generation mode. Samples under fault shutdown, fault operation, power rationing, etc. are not included in the calculation.

[0086] Inverters with a correlation coefficient greater than 0.9 were used as the sample set of power generation units.

[0087] 2) Sample set grouping

[0088] The sample set was divided into three groups.

[0089] Group A power generation units (cleaning components): Several units, the connected components are cleaned once a day, used to calculate the daily power generation of the inverter during cleaning.

[0090] Group B power generation units (modules not cleaned this time): Power generation units other than Group A are divided into Group B and Group C. Group B consists of several power generation units that are reserved for each time and will not be cleaned this time, for comparison of benefits after cleaning.

[0091] Group C power generation units: These constitute the largest proportion of the sample set, and they will be cleaned in this study.

[0092] Note: Groups B and C will be regrouped before the next cleaning. The original Group B power generation units will be cleaned in the next cleaning to ensure that Group B always has reference value, while reducing the impact of component degradation on power generation and calculation results.

[0093] 3) Calculation of component dust cover rate and power loss due to dust accumulation

[0094] Theoretical daily power generation of Group C under clean conditions = Daily power generation of Group A * a1 + b1

[0095] Dust coverage rate = (Theoretical daily power generation of Group C in clean state / Actual daily power generation of Group C - 1) * 100%

[0096] Daily power loss of Group C = Theoretical daily power generation of Group C in clean state - Actual daily power generation of Group C = Actual daily power generation of Group C * Dust obscuration rate

[0097] Where a1 and b1 are linear fitting coefficients.

[0098] 4) Calculation of cumulative power increase after component cleaning

[0099] Theoretical power generation of Group C Japan without cleaning = Daily power generation of Group B * a² + b²

[0100] Cumulative increase in power generation after cleaning = ∑ Daily actual power generation of Group C - Theoretical power generation of Group C if cleaning were not performed

[0101] Where a2 and b2 are linear fitting coefficients.

[0102] Linear fitting algorithm:

[0103] (a) Fitting the linear relationship between the output power of A and B

[0104] Linear fitting was performed using historical data of the total daily power generation of inverters in group A and group C, and the linear relationship PC = a1*PA + b1 was obtained.

[0105] Group C's daily power generation = Group A's daily power generation * a1 + b1

[0106] Let the historical data of the total daily power generation of inverters in group A and group C be PA = (PA1, PA2, ..., PAm) and PC = (PC1, PC2, ..., PCm), respectively. Let PA = a1*PC + b1

[0107] The regression coefficients are estimated using the least squares method:

[0108] Take the sum of squares of the deviations (PAt - PCt) Minimum value is the optimal criterion.

[0109] The final fitted regression coefficients a1 and b1 are:

[0110]

[0111]

[0112] 5) Cleaning failure cycle calculation

[0113] Define the cleaning failure period T2 as follows: under the assumption of no precipitation, if the cleaning effect reaches a critical point after N days, and there is no further increase in power consumption due to cleaning, then the cleaning failure period is considered to be N days (T2 = N).

[0114] During periods without heavy rainfall, the above grouping method was used to conduct cleaning tests. The cumulative increase in electricity generation after cleaning in group C was analyzed, and the rate of increase was determined when it stopped increasing or when the increase slowed down. The approximate cleaning failure period T2 of the photovoltaic power station was then determined.

[0115] 6) Component cleaning daily calculation

[0116] Definition of Cleanable Day T1: Starting from the day after this cleaning, if the percentage increase in profit from the cumulative cleaning power generation after Group C cleaning exceeds the cost of a single cleaning, that day is designated as Cleanable Day T1. This means that the revenue gained from the increased power generation after cleaning is considered to meet the owner's expectations. After this day, cleaning can be performed as needed.

[0117] Profit from increased cleaning capacity Q(t) = Cumulative increase in cleaning capacity (t) * Electricity price Cleaning cost = Installed capacity (MW) * Cleaning cost per kW (RMB / kW)

[0118] Principles for determining cleaning time:

[0119] (1) Effective rainfall before the owner's expected profit requirements are met

[0120] This situation is judged using the shading rate index. The date on which the shading rate reaches the threshold (i.e., dust shading rate > threshold) is considered the next cleaning date.

[0121] (2) When the cumulative increase in electricity generation profit after cleaning Q(t) * proportional coefficient d >= the cost of a single cleaning, the day is considered a cleanable day T1.

[0122] (3) When T1 > T2, meaning the cleaning has not yet met the owner's expected profit requirements, the cleaning effect has failed. The shading rate index is used to determine the next cleaning time. The next cleaning should be completed before the previous cleaning fails.

[0123] (4) If the expected profit requirements of the owners are met, and the rainfall in the next seven days is greater than the set threshold, the time will be postponed, and the subsequent cleaning time will be judged by the shading rate index.

[0124] (5) It is recommended to schedule the cleaning time after the date when it can be cleaned and before the date when the cleaning expires.

[0125] In another embodiment of the present invention, a photovoltaic module dust accumulation loss calculation and cleaning time estimation system is provided, which can be used to implement the above-mentioned photovoltaic module dust accumulation loss calculation and cleaning time estimation method. Specifically, the system includes:

[0126] The sample selection module is used to select string inverters with strong correlation between their instantaneous power time series as a sample set.

[0127] The grouping module is used to divide the sample set into three groups: daily cleaning components, non-cleaning components, and current cleaning components;

[0128] The linear fitting coefficient acquisition module is used to fit the linear relationship between the output power of the daily cleaning component and the non-cleaning component to obtain the linear fitting coefficient.

[0129] The calculation module is used to calculate the dust cover rate and power loss due to dust accumulation of the cleaning components based on the daily power generation and linear fitting coefficient of the cleaning components; and to calculate the cumulative power increase after cleaning the cleaning components based on the daily power generation and linear fitting coefficient of the uncleaned components.

[0130] The judgment module is used to determine the cleaning failure cycle of the photovoltaic power station and the date on which it can be cleaned. Based on the dust shading rate of the components to be cleaned, the power loss due to dust accumulation, the cumulative power increase after the cleaning of the components, the cleaning failure cycle, and the date on which it can be cleaned, the cleaning time is determined.

[0131] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0132] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of photovoltaic module dust accumulation loss calculation and cleaning time estimation methods.

[0133] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the photovoltaic module dust accumulation loss calculation and cleaning time estimation methods in the above embodiments.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating dust accumulation loss and estimating cleaning time in photovoltaic modules, characterized in that, Includes the following steps: String inverters with strong correlation between their instantaneous power time series were selected as the sample set. The sample set is divided into three groups: daily cleaning modules, modules not cleaned this time, and modules cleaned this time. The modules not cleaned this time and the modules cleaned this time are regrouped before the next cleaning. The power generation units of the modules not cleaned this time are cleaned next time. The linear relationship between the output power of the daily cleaning component and the current cleaning component was fitted to obtain the linear fitting coefficients. Based on the daily power generation of the cleaning components and the linear fitting coefficient, the dust coverage rate and power loss due to dust accumulation are calculated over time after the cleaning of the components. Based on the daily power generation and linear fitting coefficient of the uncleaned components, the cumulative increase in power generation of the cleaned components over time is calculated. Determine the cleaning failure cycle of the photovoltaic power station and determine the days when cleaning is possible; The cleaning time is determined based on the dust coverage rate of the components being cleaned, the power loss due to dust accumulation, the cumulative power increase after cleaning, the cleaning failure cycle, and the number of days that can be cleaned. The sample set was divided into three groups: Group A power generation units are daily cleaning components: several units, the connected components are cleaned once a day, used to calculate the daily power generation of the inverter during cleaning; Group B power generation units are the components that will not be cleaned in this instance, and are used for comparison of benefits after cleaning. Group C power generation units are the components being cleaned this time: they account for the largest proportion of the sample set and are the components being cleaned this time. Groups B and C will be regrouped before the next cleaning, and the original Group B power generation units will be cleaned in the next cleaning. Principles for determining cleaning time: (1) Effective rainfall before the owner's expected profit requirements are met; The dust coverage rate is used to determine the next cleaning date. If the dust coverage rate is greater than the threshold, the date is considered the next cleaning date. (2) When the cumulative increase in electricity profit after cleaning is Q(t)*proportion coefficient d>=the cost of a single cleaning, the day is the cleaning day T1; (3) When T1>T2, the cleaning failure period is T2. That is, if the cleaning has not yet met the owner's expected profit requirements, the cleaning effect has failed. The shading rate index is used to judge the next cleaning time. (4) If the expected profit requirement of the owner is met, and the rainfall in the next seven days is greater than the set threshold, the time will be postponed, and the subsequent cleaning time will be judged by the shading rate index. (5) The cleaning time should be arranged after the cleaning date and before the cleaning expiration date.

2. The method for calculating dust accumulation loss and estimating cleaning time of photovoltaic modules according to claim 1, characterized in that, Using the correlation matrix method, string inverters with strong correlation between their instantaneous power time series were selected as the sample set: There are N inverters with historical power time series P1, P2, P3, ..., PN; each inverter has m measured power values. Calculate the correlation matrix R between inverter time series data: Where, r ij (i=1,2,…N;j=1,2,…N) is the correlation coefficient between the power Pi of the i-th inverter and the power Pj of the j-th inverter; When calculating the correlation coefficient, the inverter power samples must meet the following conditions: (1) The two inverters must operate at the same time and cannot be out of sync. The time period is from 11:00 a.m. to 1:00 p.m. and the radiation intensity must be greater than 700. (2) Inverter power > 0; (3) Cannot be an out-of-limit value, a dead value, or a null value; (4) The inverter is in normal power generation state; samples under fault shutdown, fault operation, and power rationing states are not included in the calculation. Inverters with a correlation coefficient greater than 0.9 were used as the sample set of power generation units.

3. The method for calculating dust accumulation loss and estimating cleaning time of photovoltaic modules according to claim 1, characterized in that, Linear fitting coefficients: Fit a linear relationship between the output power of A and B; use historical data of the daily power generation of inverter A and inverter C to perform linear fitting, and obtain the linear relationship PC=a1*PA+b1; daily power generation of C = daily power generation of A*a1+b1. Historical data for the daily power generation of inverters in group A and group C are PA=(PA1, PA2, ... PAm) and PC=(PC1, PC2, ... PCm), respectively. Let PC=a1*PA+b1 The regression coefficients are estimated using the least squares method: Take the sum of squares of the deviations (PAt - PCt) Minimum is the optimal criterion; The final fitted regression coefficients a1 and b1 are: 。 4. The method for calculating dust accumulation loss and estimating cleaning time of photovoltaic modules according to claim 1, characterized in that, Theoretical daily power generation of Group C under clean conditions = Daily power generation of Group A * a1 + b1; Dust coverage rate = (Theoretical daily power generation of Group C in clean state / (Actual daily power generation of Group C - 1)) * 100%; Daily power loss of Group C = Theoretical daily power generation of Group C in clean state - Actual daily power generation of Group C = Actual daily power generation of Group C * Dust occlusion rate; where a1 and b1 are linear fitting coefficients; If the daily power generation of Group C is not cleaned, the theoretical power generation of Group C is equal to that of Group B, which is a² + b². The cumulative increase in power generation after cleaning = ∑ (actual daily power generation of Group C - theoretical power generation of Group C if cleaning were not performed); a2 and b2 are linear fitting coefficients.

5. The method for calculating dust accumulation loss and estimating cleaning time of photovoltaic modules according to claim 1, characterized in that, The cleaning failure cycle is calculated as follows: Define the cleaning failure period T2: that is, under the assumption of no precipitation, if the cleaning effect reaches a critical point after N days, and there is no further increase in power consumption due to cleaning, then the cleaning failure period is considered to be N days, and T2=N. During periods without heavy rainfall, conduct cleaning tests and analyze the cumulative increase in electricity generation after cleaning in Group C. When the increase stops or slows down, determine the approximate cleaning failure period T2 of the photovoltaic power station. Component cleaning daily calculation: Definition of Cleanable Day T1: Starting from the day after this cleaning, if the percentage increase in profit from the cumulative cleaning power after Group C cleaning is greater than the cost of a single cleaning, then that day is defined as Cleanable Day T1. In other words, it is considered that the revenue obtained from the increased power after cleaning meets the owner's expected requirements. Profit from increased cleaning power Q(t) = Cumulative increase in cleaning power * Electricity price; Cleaning cost = Installed capacity (MW) * Cleaning cost per unit (RMB / kW).

6. A system for calculating dust accumulation loss and estimating cleaning time in photovoltaic modules, characterized in that, include: The sample selection module is used to select string inverters with strong correlation between their instantaneous power time series as a sample set. The grouping module is used to divide the sample set into three groups: daily cleaning components, non-cleaning components, and current cleaning components; The linear fitting coefficient acquisition module is used to fit the linear relationship between the output power of the daily cleaning component and the non-cleaning component to obtain the linear fitting coefficient. The calculation module is used to calculate the dust cover rate and power loss due to dust accumulation of the cleaning components based on the daily power generation and linear fitting coefficient of the cleaning components; and to calculate the cumulative power increase after cleaning the cleaning components based on the daily power generation and linear fitting coefficient of the components that are not cleaned. The judgment module is used to determine the cleaning failure cycle of the photovoltaic power station and the date when it can be cleaned. Based on the dust shading rate of the components being cleaned, the power loss due to dust accumulation, the cumulative power increase after cleaning, the cleaning failure cycle, and the date when it can be cleaned, the cleaning time is determined. The sample set was divided into three groups: Group A power generation units are daily cleaning components: several units, the connected components are cleaned once a day, used to calculate the daily power generation of the inverter during cleaning; Group B power generation units are the components that will not be cleaned in this instance, and are used for comparison of benefits after cleaning. Group C power generation units are the components being cleaned this time: they account for the largest proportion of the sample set and are the components being cleaned this time. Groups B and C will be regrouped before the next cleaning, and the original Group B power generation units will be cleaned in the next cleaning. Principles for determining cleaning time: (1) Effective rainfall before the owner's expected profit requirements are met; The dust coverage rate is used to determine the next cleaning date. If the dust coverage rate is greater than the threshold, the date is considered the next cleaning date. (2) When the cumulative increase in electricity profit after cleaning is Q(t)*proportion coefficient d>=the cost of a single cleaning, the day is the cleaning day T1; (3) When T1>T2, that is, if the cleaning has not yet met the owner's expected profit requirements, the cleaning effect has failed. The shading rate index is used to determine the next cleaning time. (4) If the expected profit requirement of the owner is met, and the rainfall in the next seven days is greater than the set threshold, the time will be postponed, and the subsequent cleaning time will be judged by the shading rate index. (5) The cleaning time should be arranged after the cleaning date and before the cleaning expiration date.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic module dust accumulation loss calculation and cleaning time estimation method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic module dust accumulation loss calculation and cleaning time estimation method as described in any one of claims 1 to 5.

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