Decision-making method for cleaning accumulated dust of photovoltaic module

Abstract

The invention discloses a photovoltaic module accumulated dust cleaning decision-making method, which is characterized by comprising the following steps of: S101, comparing the cleaned operation data of a photovoltaic module of a photovoltaic power station with the output power of the photovoltaic power station in a theoretical state, and determining the attenuation condition of the photovoltaic power station after grid connection; s102, based on the theoretical output power of the photovoltaic power station in a clean state after attenuation, calculating the power generation loss of the photovoltaic power station caused by a dust deposition state; s103, introducing the reduction effect of rainfall on the ash deposition density and the number of peak hours to predict the influence of future ash deposition on the generating capacity of the photovoltaic power station; and S104, based on the accumulated power generation loss of the photovoltaic power station caused by current and future dust deposition, comparing with the photovoltaic module cleaning cost of the photovoltaic power station, and determining the optimal cleaning day.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation, and more specifically to a photovoltaic power station component dust cleaning auxiliary decision system. Background Art

[0002] The proportion of annual photovoltaic power generation in the total national energy generation has increased significantly, indicating that the development of photovoltaics has a bright future. However, since photovoltaic power stations are built outdoors and exposed to the natural environment for a long time, dust often accumulates on the surface of photovoltaic modules, reducing the intensity of light they receive, which in turn leads to a decrease in output power and affects power generation efficiency. In addition, dust accumulation may also cause local temperature increases, causing mismatches, thereby reducing power generation and even threatening the safety of photovoltaic power stations. According to relevant research data, long-term dust accumulation can reduce the peak output power of photovoltaic modules by about 17% to 40%. In order to reduce the loss of photovoltaic power generation caused by dust accumulation and increase the revenue of photovoltaic power stations, photovoltaic modules need to be cleaned.

[0003] At present, photovoltaic module cleaning usually adopts a fixed cleaning cycle or relies on on-site inspections by operation and maintenance personnel. However, this manual observation method is easily affected by subjective factors. Fixed cleaning cycles and manual observation cannot ensure the maximization of the economic benefits of photovoltaic power plants, and there is no quantitative analysis of the relationship between the power generation loss caused by dust accumulation and the cleaning costs. Therefore, a photovoltaic power station module dust cleaning auxiliary decision-making system is needed to improve the operating benefits of photovoltaic power plants.

[0004] The closest existing technical solution is: first, determine the power generation loss caused by dust on a single day within a predetermined time period and the corresponding single-day weather parameters; then, establish a power generation loss prediction model caused by dust accumulation based on the power generation loss caused by dust on a single day within the predetermined time period and the corresponding single-day weather parameters; finally, obtain future weather parameters, and predict the power generation loss caused by dust accumulation on a certain day or several days in the future based on the prediction model.

[0005] Some studies have considered the cleaning judgment method of photovoltaic components, but they only compare the current profit loss of photovoltaic power stations with the cleaning costs, without predicting the subsequent situation, and cannot play a role in predicting and planning the cleaning date. Secondly, most studies do not take the aging of photovoltaic power stations into account, which is also an important factor affecting the power generation of photovoltaic power stations. By considering the aging of photovoltaic power stations, the subsequent power generation loss of photovoltaic power stations can be more accurately predicted, and the cleaning date can be more accurately planned.

[0006] Some existing studies have used artificial intelligence methods. However, using artificial intelligence to implement this solution has the following disadvantages:

[0007] 1. High data requirements: AI algorithms require a large amount of data for training. If the data quality is poor or the amount of data is too small, the accuracy of the model will be affected;

[0008] 2. Poor model interpretability: The prediction process of artificial intelligence algorithms is usually a black box, that is, it is impossible to explain how the model reaches its prediction results, which makes it difficult to understand the model's prediction results and difficult to discover potential problems with the model;

[0009] 3. Poor model robustness: AI algorithms are very sensitive to changes in input data. If the input data changes, the model’s prediction results may change significantly;

[0010] 4. Requires a lot of computing resources: Artificial intelligence algorithms require a lot of calculations, so a lot of computing resources are needed to support model training and prediction.

[0011] In some studies, the prediction of cleaning date is not reflected. Please refer to the patented method and system for calculating the cleaning date of photovoltaic modules.

[0012] Therefore, there is an urgent need in the art for a photovoltaic module dust cleaning decision-making method to overcome the above-mentioned defects, thereby rationally planning the photovoltaic power station component cleaning cycle and improving the efficiency of the photovoltaic power station. Summary of the invention

[0013] The purpose of the present invention is to provide a photovoltaic module dust cleaning decision-making method, the method of the present invention can reasonably plan the photovoltaic power station module cleaning cycle, and improve the efficiency of the photovoltaic power station.

[0014] The present invention provides a photovoltaic module dust cleaning decision-making method, the method comprising:

[0015] Step S101: Based on the operation data of the photovoltaic components of the photovoltaic power station after cleaning, the attenuation of the photovoltaic power station after grid connection is determined by comparing it with the output power of the photovoltaic power station under theoretical conditions;

[0016] Step S102: Calculate the power generation loss of the photovoltaic power station caused by dust accumulation based on the theoretical output power of the photovoltaic power station in a clean state after attenuation;

[0017] Step S103: introducing the effect of rainfall on reducing dust density and the peak hours of radiation intensity to predict the impact of dust accumulation on the power generation of the photovoltaic power station in the future; and

[0018] Step S104: Determine the best cleaning day based on the current and future cumulative power generation loss of the photovoltaic power station caused by dust accumulation and the cleaning cost of the photovoltaic components of the photovoltaic power station.

[0019] In another preferred embodiment, the step S101 includes:

[0020] Calculate the theoretical output power P0 of the photovoltaic power station when it is not attenuated. The calculation formula is:

[0021] P0=0.001EL PV [1-0.005(T C -T STC )];

[0022] Where: P0 is the total output power of the photovoltaic power station, unit: kW; E is the solar radiation intensity received by the photovoltaic module, unit: W / m 2 ; L PV is the installed capacity of the photovoltaic power station, unit: kWp; T C is the operating temperature of the photovoltaic module, unit: °C; T STC It is the standard test condition temperature, unit: ℃.

[0023] In another preferred embodiment, the T C The calculation formula is:

[0024] T c =T+kE;

[0025] Where: T is the current ambient temperature, unit: °C; k is the temperature coefficient of the photovoltaic module; E is the solar radiation intensity received by the photovoltaic module, unit: W / m 2 .

[0026] In another preferred embodiment, the theoretical output power P of the photovoltaic power station in the clean state after attenuation is:

[0027] P=0.001ηEL PV [1-0.005(T C -T STC )];

[0028] Wherein: P is the theoretical output power of the photovoltaic power station in a clean state after attenuation, unit: kW; η is the overall attenuation rate of the photovoltaic power station after the photovoltaic power station is connected to the grid and put into use.

[0029] In another preferred embodiment, the step S102 includes:

[0030] The daily power generation of the photovoltaic power station component cleaning centralized control platform is collected and used as the actual power generation; at the same time, meteorological data at different times of the day are collected, including ambient temperature and radiation intensity, to calculate the theoretical output power of the photovoltaic power station at each time after attenuation:

[0031] The theoretical output power of the photovoltaic power station in the clean state after theoretical attenuation is integrated to obtain the theoretical power generation W of the photovoltaic power station in a single day. theory for:

[0032]

[0033] Where: W theory is the theoretical power generation of the photovoltaic power station in a single day, unit: kWh; t1 is the time when the grid-connected power generation starts on the same day, unit: h; t2 is the time when the grid-connected power generation ends on the same day, unit: h.

[0034] In another preferred embodiment, the cumulative power generation loss of the photovoltaic power station caused by dust accumulation is calculated as:

[0035]

[0036] Where: W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; is the theoretical power generation of the photovoltaic power station on the i-th day, unit: kWh; is the actual power generation of the PV power station under dust accumulation state on the i-th day, unit: kWh; N is the number of days monitored in a monitoring cycle.

[0037] In another preferred embodiment, the step S103 includes:

[0038] Step S201, calculating the dust accumulation density of the photovoltaic module on the day according to the designed dust accumulation density;

[0039] By collecting the dust on the photovoltaic modules, the curve of dust density and relative transmittance is fitted by experimental calculation and expressed by the following formula:

[0040] γ=ab*ln(R+c);

[0041] Where: γ is the relative transmittance; a, b and c are the fitting curve parameters; R is the dust density, unit: g / m 2 .

[0042] The attenuation of radiation intensity caused by dust accumulation is measured by the dust accumulation density measuring device, and then the relative transmittance γ is calculated; the glass plate of the device is made of the same material as the photovoltaic module panel, and the installation angle of the glass plate is also the same as the photovoltaic module. A light intensity measuring instrument is installed at the upper and lower positions of the glass plate of the device, respectively, to measure the radiation intensity above the glass plate and the radiation intensity below the glass plate:

[0043]

[0044] Where: E is the radiation intensity measured by the light intensity measuring instrument above the glass plate, unit: W / m 2 , E 下 The radiation intensity measured by the light intensity meter under the glass plate, unit: W / m 2 ;

[0045] Substitute the above formula to calculate the dust accumulation density as the dust accumulation density on the surface of the photovoltaic module on that day;

[0046] Step S202, based on fitting the curve of dust accumulation density changing with date when there is no rainfall in S201, predict the dust accumulation density of the photovoltaic module in the next 5 days by using the Asymptoticl model:

[0047] R D =x-yz D ;

[0048] Among them: x, y and z are the coefficients to be determined, which are determined by the data distribution of dust density; D is the time, i.e. the 1st to 5th day; R D To predict the dust accumulation density on the corresponding day.

[0049] At the same time, the cleaning effect of rainfall on photovoltaic modules is introduced, and the BoxLucas exponential function is used to establish the cleaning efficiency η clean The rainfall washoff model is shown in the following formula:

[0050] η clean =A-Ae -bt ;

[0051] Where: A is the washable ratio (%), t is the rainfall time (min); b is the attenuation coefficient (min -1 ). A and b are determined by different rainfall intensities;

[0052] Considering the dust accumulation density R during rainfall d The calculation formula is:

[0053]

[0054] Step S203, analyzing historical data, and using a combination of season, weather, and air quality to predict the peak hours of radiation intensity for the next five days;

[0055] The calculation formula for the peak hours H of radiation intensity is as follows:

[0056]

[0057] Where: E(t) is the relationship between radiation intensity and time, t1 and t2 are illumination times;

[0058] Analyze the historical data of the meteorological station at the location of the photovoltaic power station for 5 years, and divide the data into four seasons according to season: spring, summer, autumn and winter; four types according to weather: sunny, cloudy, overcast and rainy; and five types according to air quality: excellent, good, lightly polluted, moderately polluted and heavily polluted. A total of 80 situations are clustered using the K-means algorithm;

[0059] Step S204, based on S201-S203, predict the power generation of the photovoltaic power station for the next five days and calculate the daily power generation loss.

[0060] Specifically, calculate the daily power generation loss in the next 5 days:

[0061] W prediction =H*L PV *(1-η y );

[0062] Where: L PV is the installed capacity of the photovoltaic power station, unit: kWp; η y is the relative power generation efficiency of the photovoltaic power station;

[0063] Calculate the relative power generation efficiency η of a photovoltaic power station y , the relative power generation efficiency of the photovoltaic power station is linearly related to the dust accumulation density, which can be expressed by the following formula:

[0064] η y =1-dR d ;

[0065] Where: d is the fitting parameter; R d To consider the dust density during rainfall.

[0066] In another preferred embodiment, the step S104 includes:

[0067] Step S301: Calculate the sum of the current and predicted losses of the photovoltaic power station power generation, and convert the losses into an amount Q according to the grid-connected electricity price. L In the form of:

[0068]

[0069] Where: Q L is the amount of loss, unit: yuan; W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; To predict the power generation loss of the photovoltaic power station on the jth day, unit: kWh, C PV is the photovoltaic grid-connected electricity price, unit: yuan / kWh;

[0070] Step S302: Calculate the cleaning cost Q required for cleaning the photovoltaic power station components once C , unit: Yuan:

[0071] Q C =L PV *S*C CL ;

[0072] Where: L PVis the installed capacity of the photovoltaic power station, unit: kWp; S is the area of ​​photovoltaic modules per kilowatt; C CL is the cleaning cost of photovoltaic modules per unit area, unit: Yuan;

[0073] Step S303: When the cumulative power generation loss of the photovoltaic power station is equal to or greater than the cleaning cost of cleaning the photovoltaic power station once, and there is no heavy rain in the following three days, it is the best day to clean the photovoltaic power station components.

[0074] In another preferred embodiment, the step S303 includes:

[0075] Compare the Q calculated in step S301 L and Q calculated in step S302 C , when Q L ≥Q C When the minimum j value that satisfies this condition is determined, and there is no heavy rain in the following three days, the photovoltaic power station components are cleaned on the jth day, otherwise the auxiliary decision-making system continues to run to predict the best cleaning day.

[0076] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0078] Figure 1 It is a schematic diagram of a process flow of a photovoltaic power station component dust cleaning auxiliary decision system in an example of the present invention;

[0079] Figure 2 It is a flow chart of power generation loss prediction of a photovoltaic power station component dust cleaning auxiliary decision system in an example of the present invention;

[0080] Figure 3 is a schematic diagram of a dust accumulation density measuring device in one embodiment of the present invention;

[0081] Figure 4 It is a flow chart of the best cleaning day prediction of a photovoltaic power station component dust cleaning auxiliary decision system in an example of the present invention;

[0082] Figure 5It is an overall flow chart of a photovoltaic power station component dust cleaning auxiliary decision system in an example of the present invention.

[0083] The following are marked in the attached figure:

[0084] 1- Light intensity measuring instrument;

[0085] 2- Glass plate;

[0086] 3- Bracket. DETAILED DESCRIPTION

[0087] After extensive and in-depth research and a large number of screenings, the inventor has developed a photovoltaic module dust cleaning decision method for the first time. When calculating the actual power generation loss of the photovoltaic power station, a specific analysis is conducted on the photovoltaic power station, and the attenuation rate of the photovoltaic power station is calculated, which improves the accuracy of calculating the theoretical power generation of the photovoltaic power station. After each photovoltaic power station component cleaning, the attenuation rate is recalculated and updated; when predicting the power generation loss of the photovoltaic power station, the prediction of dust density and peak hours is considered at the same time. A dust density measurement device is designed for the prediction of dust density, and the influence of rainfall on the cleaning efficiency of photovoltaic modules is introduced; when predicting the peak hours, the K-means algorithm is used to perform historical data analysis and clustering; when deciding the best cleaning day, it is considered whether there will be heavy rain in the next 3 days. If there is heavy rain, the photovoltaic modules will be fully cleaned on that day, and the surface of the photovoltaic modules will be restored to a clean state, saving the cleaning cost of the photovoltaic power station modules once, and the present invention is completed on this basis.

[0088] The main advantages of the present invention include:

[0089] Some studies have considered the cleaning judgment method of photovoltaic modules, but they only compare the current photovoltaic power station's profit loss and cleaning costs, without predicting the subsequent situation, and cannot play a role in predicting and planning the cleaning date. Secondly, most studies do not take the aging of photovoltaic modules into account, which is also an important factor affecting the power generation of photovoltaic power stations. By considering the aging of photovoltaic power stations, the subsequent power generation loss of photovoltaic power stations can be predicted more accurately, and the cleaning date can be planned more accurately.

[0090] In the present application, the attenuation rate of the photovoltaic power station is firstly considered, which can improve the accuracy of calculating the theoretical power generation of the photovoltaic power station, more accurately calculate the actual power generation loss of the photovoltaic power station, and recalculate and update the attenuation rate after each cleaning of the photovoltaic power station components; secondly, when predicting the power generation loss of the photovoltaic power station, the prediction of dust density and peak hours is taken into account at the same time. Among them, a dust density measurement device is designed for the prediction of dust density, and the influence of rainfall on the cleaning efficiency of photovoltaic components is introduced. When predicting the peak hours, the K-means algorithm is used for historical data analysis and clustering; finally, when deciding the last best cleaning day, it is considered whether there will be heavy rain in the next 3 days. If there is heavy rain, the photovoltaic components will be fully cleaned on that day, and the surface of the photovoltaic components will be restored to a clean state, saving the cleaning cost of the photovoltaic power station components once.

[0091] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, the accompanying drawings are schematic diagrams, so the device and apparatus of the present invention are not limited by the size or ratio of the schematic diagrams.

[0092] It should be noted that in the claims and description of this patent, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0093] Example

[0094] Since photovoltaic power stations are built outdoors and exposed to the natural environment for a long time, dust often accumulates on the surface of photovoltaic modules, reducing the intensity of light they receive, which in turn causes a decrease in output power and affects power generation efficiency.

[0095] In order to rationally plan the cleaning cycle of photovoltaic power station components and improve the efficiency of photovoltaic power stations, this embodiment provides a decision method for cleaning dust accumulated on photovoltaic power station components.

[0096] The flowchart of the photovoltaic module dust cleaning decision method of this embodiment is as follows: Figure 1 As shown:

[0097] Step S101: Based on the operation data of the photovoltaic power station after the last cleaning of the photovoltaic components, the attenuation of the photovoltaic power station after grid connection is determined by comparing it with the output power of the photovoltaic power station under theoretical conditions.

[0098] It should be noted that the operating data after the most recent cleaning of the PV modules refers to the operating data of the grid-connected power generation on the day when the PV modules were in a clean state.

[0099] Specifically, the output power P of the photovoltaic power station when it is connected to the grid and operating normally in a clean state is obtained. clean At the same time, the meteorological data of the environment where the photovoltaic power station is located at this time, including the ambient temperature and radiation intensity, is obtained. According to the obtained meteorological data, the theoretical output power P0 of the photovoltaic power station when it is not attenuated is calculated accordingly. The calculation formula is:

[0100] P0=0.001EL PV [1-0.005(T C -T STC )] (1)

[0101] Where: P0 is the total output power of the photovoltaic power station, unit: kW; E is the solar radiation intensity received by the photovoltaic module, unit: W / m 2 ; L PV is the installed capacity of the photovoltaic power station, unit: kWp; T C is the operating temperature of the photovoltaic module; T STC is the standard test condition temperature, unit: °C; where T C The calculation formula is:

[0102] T c =T+kE (2)

[0103] Where: T is the current ambient temperature, unit: °C; k is the temperature coefficient of the photovoltaic module.

[0104] Furthermore, according to the output power P of the photovoltaic module in the clean state clean The attenuation rate of the photovoltaic power station as a whole is calculated by the output power P when the photovoltaic power station is connected to the grid and put into use:

[0105]

[0106] Here, the theoretical output power of the photovoltaic power station in the clean state after attenuation is:

[0107] P=0.001ηEL PV [1-0.005(T C -T STC )] (4)

[0108] In this way, the attenuation of the photovoltaic power station after grid connection is determined.

[0109] Step S102: Based on the theoretical output power of the photovoltaic power station in a clean state after attenuation, the power generation loss caused by the dust accumulation state of the photovoltaic power station is calculated.

[0110] Specifically, when photovoltaic modules are exposed outdoors, dust will accumulate on their surfaces, referred to as dust accumulation. During this period, as time goes by, in the absence of rainfall, the dust accumulation density increases linearly, greatly affecting the power output of photovoltaic modules. Therefore, after cleaning the photovoltaic modules of a photovoltaic power station, it is necessary to monitor and calculate the power generation loss caused by dust accumulation.

[0111] Specifically, the daily power generation of the photovoltaic power station component cleaning control platform is collected and used as the actual power generation. At the same time, meteorological data at different times of the day are collected, including ambient temperature and radiation intensity, and are substituted into Formula 4 to calculate the theoretical output power of the photovoltaic power station at each time after attenuation.

[0112] Furthermore, in order to quantify the power generation loss of the photovoltaic power station caused by dust accumulation, the calculated output power of the photovoltaic power station after theoretical attenuation is integrated. The theoretical power generation of the photovoltaic power station in a single day is:

[0113]

[0114] Among them: t1 is the time when grid-connected power generation starts on the same day; t2 is the time when grid-connected power generation ends on the same day.

[0115] The cumulative power generation loss of the photovoltaic power station due to dust accumulation is calculated as:

[0116]

[0117] Where: W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; is the theoretical power generation of the photovoltaic power station on the i-th day, unit: kWh; is the actual power generation of the PV power station under dust accumulation state on the i-th day, unit: kWh; N is the number of days monitored in this monitoring cycle.

[0118] Specifically, The daily power generation of the photovoltaic power station collected by the centralized control platform.

[0119] Step S103: introducing the reduction effect of rainfall on dust density and the peak hours of radiation intensity to predict the impact of dust accumulation on the power generation of the photovoltaic power station in the future.

[0120] Specifically, to predict the power generation loss caused by dust accumulation on the surface of photovoltaic modules in the future to photovoltaic power stations, it is necessary to predict the dust density on the surface of photovoltaic modules and meteorological data for the next few days respectively. The peak hours are used instead of the peak hours for meteorological data prediction, which can reduce the difficulty of prediction while ensuring the accuracy of the prediction.

[0121] In order to more clearly illustrate the implementation process of predicting and calculating the power generation loss of a photovoltaic power station in the following days in this application, the following is an exemplary description of a calculation method for predicting the power generation loss of a photovoltaic power station on a daily basis proposed in an embodiment of this application. Figure 2 FIG. 1 is a flow chart of power generation loss prediction of a photovoltaic power station component dust cleaning auxiliary decision system in an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0122] Step S201, calculating the dust accumulation density of the photovoltaic module on the day according to the designed dust accumulation density calculation device.

[0123] Specifically, firstly, the dust accumulation on the photovoltaic module is collected, and the curve of dust accumulation density and relative transmittance is fitted by experimental calculation, as shown in the following formula:

[0124] γ=ab*ln(R+c) (7)

[0125] Where: γ is the relative transmittance; a, b and c are the fitting curve parameters; R is the dust density, unit: g / m 2 .

[0126] Furthermore, the present application designs a dust accumulation density measuring device, such as Figure 3 As shown, the glass plate 2 of the device is made of the same material as the photovoltaic module panel. The glass plate 2 is installed on the bracket 3. The installation angle of the glass plate 2 is also the same as the photovoltaic module. A light intensity measuring instrument 1 is installed at the upper and lower positions of the glass plate 2 of the device to measure the attenuation of radiation intensity caused by dust accumulation, transmit the data to the computer, and then calculate the relative light transmittance, as shown in the following formula:

[0127]

[0128] Where: E is the radiation intensity measured by the light intensity measuring instrument above the glass plate, unit: W / m 2 , E 下 The radiation intensity measured by the light intensity meter under the glass plate, unit: W / m 2 In order to ensure the accuracy of the relative transmittance, the data of the two light intensity measuring instruments on the upper and lower glass plates were obtained from 11:00 to 13:00 every day, with a sampling interval of 15 minutes. After eliminating the abnormal values, the average value was calculated, and the relative transmittance was calculated by Formula 8.

[0129] Furthermore, the dust accumulation density is calculated by substituting the relative transmittance into Formula 7, which is used as the dust accumulation density on the surface of the photovoltaic module on that day.

[0130] Step S202: Based on S201, a curve of dust accumulation density changing with date when there is no rainfall is fitted, and the dust accumulation density of the photovoltaic module in the next five days is predicted based on the Asymptoticl model:

[0131] R D =x-yz D (9)

[0132] Among them: x, y and z are the coefficients to be determined, which are determined by the data distribution of dust density; D is the time, i.e. the 1st to 5th day; R D To predict the dust accumulation density on the corresponding day.

[0133] At the same time, the cleaning effect of rainfall on photovoltaic modules is introduced, and the BoxLucas exponential function is used to establish the cleaning efficiency η clean The rainfall washoff model is as shown in the following formula:

[0134] η clean =A-Ae -bt (10)

[0135] Where: A is the washable ratio (%), t is the rainfall time (min); b is the attenuation coefficient (min -1 ). A and b are determined by different rainfall intensities;

[0136] Specifically, the dust accumulation density R during rainfall is introduced d Calculation, the calculation formula is:

[0137]

[0138] If there is rainfall in the predicted subsequent 5 days, the cleaning effect is taken into consideration. The dust density calculation formula for the first few days without rainfall is the corresponding formula for no rainfall in Formula 10. The dust density on the day of rainfall is calculated using the rainfall formula in Formula 10. The dust density calculation formula for the subsequent days after rainfall is still the corresponding formula for no rainfall. It should be noted that the dust density after rainfall needs to be substituted into Formula 9 to calculate the equivalent D′, and then substituted into Formula 11 to calculate the dust density for the subsequent days. The time at this time should be D′+1.

[0139] Step S203, analyzing historical data, and using a combination of season, weather and air quality to predict the peak hours of radiation amplitude for the next five days.

[0140] Specifically, the size of the peak hours is positively correlated with the degree of clearness of the weather and the degree of air pollution, and it also has strong seasonality. By predicting the weather conditions in different seasons, the peak hours can be indirectly predicted. The calculation formula for the peak hours H of the radiation amplitude is as follows:

[0141]

[0142] Where: E(t) is the relationship between radiation intensity and time, t1 and t2 are the illumination time.

[0143] Specifically, the seasons are divided into spring, summer, autumn and winter; the weather is divided into sunny, cloudy, overcast and rainy; the air quality is divided into excellent, good, lightly polluted, moderately polluted and heavily polluted, totaling 80 situations.

[0144] Furthermore, the historical data of the meteorological station at the location of the photovoltaic power station for 5 years are analyzed and divided into the above 80 situations. The K-means algorithm is used for clustering. At the same time, the future operation data collection can be added to the historical data to improve the accuracy of the model.

[0145] Step S204, based on S201-S203, predict the power generation of the photovoltaic power station for the next five days and calculate the daily power generation loss.

[0146] Specifically, the daily power generation loss in the following 5 days is calculated as shown in the following formula:

[0147] W prediction =H*L PV *(1-η y ) (13)

[0148] Where: L PV is the installed capacity of the photovoltaic power station, unit: kWp; η y is the relative power generation efficiency of the photovoltaic power station.

[0149] Furthermore, the relative power generation efficiency η of the photovoltaic power station is calculated y , the relative power generation efficiency of the photovoltaic power station is linearly related to the dust accumulation density, as shown in the following formula:

[0150] η y =1-dR d (14)

[0151] Where: d is the fitting parameter; R d To consider the dust density during rainfall.

[0152] Step S104: Determine the best cleaning day based on the current and future cumulative power generation loss of the photovoltaic power station caused by dust accumulation and the cleaning cost of the photovoltaic components of the photovoltaic power station.

[0153] In order to more clearly quantify the loss of power generation in the photovoltaic power station, the loss of power generation is converted into a monetary amount according to the on-grid electricity price.

[0154] Further, in order to more clearly illustrate the implementation process of the best cleaning day prediction of the present application, the following is an exemplary description of a calculation method for predicting the best cleaning day of a photovoltaic power station proposed in an embodiment of the present application. Figure 4 FIG. 1 is a flow chart of the best cleaning day prediction of a photovoltaic power station component dust cleaning auxiliary decision system in an embodiment of the present invention. Figure 4 As shown, the method comprises the following steps:

[0155] Step S301: Calculate the sum of the current and predicted power generation losses of the photovoltaic power station.

[0156] Specifically, the sum of the losses of the photovoltaic power station's power generation is calculated, and the losses are converted into an amount Q according to the grid-connected electricity price. L The form is as shown in the following formula:

[0157]

[0158] Where: W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; To predict the power generation loss of the photovoltaic power station on the jth day, unit: kWh, C PV It is the photovoltaic grid-connected electricity price, unit: Yuan / kWh.

[0159] Step S302: Calculate the cleaning cost Q required for cleaning the photovoltaic power station components once C , and its calculation formula is as follows:

[0160] Q C =L PV *S*C CL (16)

[0161] Where: L PV is the installed capacity of the photovoltaic power station, unit: kWp; S is the area of ​​photovoltaic modules per kilowatt; C CL It is the cleaning cost of photovoltaic modules per unit area, unit: Yuan.

[0162] Step S303: When the cumulative power generation loss of the photovoltaic power station is equal to or greater than the cleaning cost of cleaning the photovoltaic power station once, and there is no heavy rain in the following three days, it is the best day to clean the photovoltaic power station components.

[0163] Specifically, compare the Q calculated in S301 L and Q calculated in S302 C , when QL ≥Q C When the minimum j value that satisfies this condition is determined, and there is no heavy rain in the following three days, the photovoltaic power station components are cleaned on the jth day, otherwise the auxiliary decision-making system continues to run to predict the best cleaning day.

[0164] It should be noted that heavy rain in this application refers to rainfall with an intensity greater than 8 mm. Under this condition, the dust accumulated on the surface of the photovoltaic modules can be fully washed away, that is, the photovoltaic modules are restored to a clean state. Therefore, heavy rain scenarios must be considered when assisting in deciding the best cleaning day.

[0165] Figure 5 FIG. 1 is an overall flow chart of a photovoltaic power station component dust cleaning auxiliary decision system according to an embodiment of the present invention. Figure 5 As shown in the figure, when calculating the power generation loss, it is divided into two parts, namely the actual cumulative power generation loss part of the photovoltaic power station and the predicted cumulative power generation loss part of the photovoltaic power station. When assisting in the decision-making of the cleaning date, the power generation loss caused by dust accumulation and the cleaning cost of the photovoltaic power station are taken into account. After the preliminary calculation of the cleaning date, the final cleaning date is decided according to the rainfall in the next 3 days according to the weather forecast, in order to maximize the overall benefits of the photovoltaic power station. Finally, the actual cleaning date of the photovoltaic power station is recorded, and the attenuation rate of the photovoltaic power station is calculated and updated to improve the accuracy of the theoretical power generation model.

[0166] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A photovoltaic module dust cleaning decision-making method, characterized in that: The method comprises: Step S101: Based on the operation data of the photovoltaic components of the photovoltaic power station after cleaning, the attenuation of the photovoltaic power station after grid connection is determined by comparing it with the output power of the photovoltaic power station under theoretical conditions; Step S102: Calculating the power generation loss of the photovoltaic power station caused by dust accumulation based on the theoretical output power of the photovoltaic power station in a clean state after attenuation; Step S103: introducing the effect of rainfall on reducing dust density and the peak hours of radiation intensity to predict the impact of dust accumulation on the power generation of the photovoltaic power station in the future; and Step S104: Determine the best cleaning day based on the current and future cumulative power generation loss of the photovoltaic power station caused by dust accumulation and the cleaning cost of the photovoltaic components of the photovoltaic power station.

2. The method according to claim 1, characterized in that The step S101 includes: Calculate the theoretical output power P0 of the photovoltaic power station when it is not attenuated. The calculation formula is: P0=0.001EL PV [1-0.005(T C -T STC )]; Where: P0 is the total output power of the photovoltaic power station, unit: kW; E is the solar radiation intensity received by the photovoltaic module, unit: W / m 2 ; L PV is the installed capacity of the photovoltaic power station, unit: kWp; T C is the operating temperature of the photovoltaic module, unit: °C; T STC It is the standard test condition temperature, unit: ℃.

3. The method according to claim 2, characterized in that The T C The calculation formula is: T c =T+kE; Where: T is the current ambient temperature, unit: °C; k is the temperature coefficient of the photovoltaic module; E is the solar radiation intensity received by the photovoltaic module, unit: W / m 2 .

4. The method according to claim 3, characterized in that The theoretical output power P of the photovoltaic power station in the clean state after attenuation is: P=0.001ηEL PV [1-0.005(T C -T STC )]; Wherein: P is the theoretical output power of the photovoltaic power station in a clean state after attenuation, unit: kW; η is the overall attenuation rate of the photovoltaic power station after the photovoltaic power station is connected to the grid and put into use.

5. The method according to claim 4, characterized in that The step S102 includes: The daily power generation of the photovoltaic power station component cleaning centralized control platform is collected and used as the actual power generation; at the same time, meteorological data at different times of the day are collected, including ambient temperature and radiation intensity, to calculate the theoretical output power of the photovoltaic power station at each time after attenuation: The theoretical output power of the photovoltaic power station in the clean state after theoretical attenuation is integrated to obtain the theoretical power generation W of the photovoltaic power station in a single day. theory for: Where: W theory is the theoretical power generation of the photovoltaic power station in a single day, unit: kWh; t1 is the time when the grid-connected power generation starts on the same day, unit: h; t2 is the time when the grid-connected power generation ends on the same day, unit: h.

6. The method according to claim 5, characterized in that The cumulative power generation loss of the photovoltaic power station due to dust accumulation is calculated as: Where: W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; is the theoretical power generation of the photovoltaic power station on the i-th day, unit: kWh; is the actual power generation of the PV power station under dust accumulation state on the i-th day, unit: kWh; N is the number of days monitored in a monitoring cycle.

7. The method according to claim 1, characterized in that The step S103 includes: Step S201, calculating the dust accumulation density of the photovoltaic module on the day according to the designed dust accumulation density; By collecting the dust on the photovoltaic modules, the curve of dust density and relative transmittance is fitted by experimental calculation and expressed by the following formula: γ=ab*ln(R+c); Where: γ is the relative transmittance; a, b and c are the fitting curve parameters; R is the dust density, unit: g / m 2 ; The attenuation of radiation intensity caused by dust accumulation is measured by the dust accumulation density measuring device, and then the relative transmittance γ is calculated; the glass plate of the device is made of the same material as the photovoltaic module panel, and the installation angle of the glass plate is also the same as the photovoltaic module. A light intensity measuring instrument is installed at the upper and lower positions of the glass plate of the device, respectively, to measure the radiation intensity above the glass plate and the radiation intensity below the glass plate: Where: E is the radiation intensity measured by the light intensity measuring instrument above the glass plate, unit: W / m 2 , E 下 The radiation intensity measured by the light intensity meter under the glass plate, unit: W / m 2 ; Substitute the above formula to calculate the dust accumulation density as the dust accumulation density on the surface of the photovoltaic module on that day; Step S202, based on fitting the curve of dust accumulation density changing with date when there is no rainfall in S201, predict the dust accumulation density of the photovoltaic module in the next 5 days by using the Asymptoticl model: R D =x-yz D ; Among them: x, y and z are the coefficients to be determined, which are determined by the data distribution of dust density; D is the time, i.e. the 1st to 5th day; R D To predict the dust accumulation density of the corresponding day; At the same time, the cleaning effect of rainfall on photovoltaic modules is introduced, and the BoxLucas exponential function is used to establish the cleaning efficiency η clean The rainfall washoff model is shown in the following formula: η clean =A-Ae -bt ; Where: A is the washable ratio; t is the rainfall time, unit: min; b is the attenuation coefficient, unit: min -1 , where A and b are determined by different rainfall intensities; Considering the dust accumulation density R during rainfall d The calculation formula is: Step S203, analyzing historical data, and using a combination of season, weather, and air quality to predict the peak hours of radiation intensity for the next five days; The calculation formula for the peak hours H of radiation intensity is as follows: Where: E(t) is the relationship between radiation intensity and time, t1 and t2 are illumination times; Analyze the historical data of the meteorological station at the location of the photovoltaic power station for 5 years, and divide the data into four seasons according to season: spring, summer, autumn and winter; four types according to weather: sunny, cloudy, overcast and rainy; and five types according to air quality: excellent, good, lightly polluted, moderately polluted and heavily polluted. A total of 80 situations are clustered using the K-means algorithm; Step S204, predicting the power generation of the photovoltaic power station for the next five days based on S201-S203, and calculating the daily power generation loss; Calculate the daily power generation loss for the next 5 days: W prediction =H*L PV *(1-th y ); Where: L PV is the installed capacity of the photovoltaic power station, unit: kWp; η y is the relative power generation efficiency of the photovoltaic power station; Calculate the relative power generation efficiency η of a photovoltaic power station y , the relative power generation efficiency of the photovoltaic power station is linearly related to the dust accumulation density, which can be expressed by the following formula: or y =1-dR d ; Where: d is the fitting parameter; R d To consider the dust density during rainfall.

8. The method according to claim 1, characterized in that The step S104 includes: Step S301: Calculate the sum of the current and predicted losses of the photovoltaic power station power generation, and convert the losses into an amount Q according to the grid-connected electricity price. L In the form of: Where: Q L is the amount of loss, unit: yuan; W loss It is the cumulative power generation loss caused by dust accumulation of photovoltaic modules in the photovoltaic power station, unit: kWh; To predict the power generation loss of the photovoltaic power station on the jth day, unit: kWh, C PV is the photovoltaic grid-connected electricity price, unit: yuan / kWh; Step S302: Calculate the cleaning cost Q required for cleaning the photovoltaic power station components once C , unit: Yuan: Q C =L PV *S*C CL ; Where: L PV is the installed capacity of the photovoltaic power station, unit: kWp; S is the area of ​​photovoltaic modules per kilowatt; C CL is the cleaning cost of photovoltaic modules per unit area, unit: Yuan; Step S303: When the cumulative power generation loss of the photovoltaic power station is equal to or greater than the cleaning cost of cleaning the photovoltaic power station once, and there is no heavy rain in the following three days, it is the best day to clean the photovoltaic power station components.

9. The method according to claim 8, characterized in that The step S303 includes: Compare the Q calculated in step S301 L and Q calculated in step S302 C , when Q L ≥Q C When the minimum j value that satisfies this condition is determined, and there is no heavy rain in the following three days, the photovoltaic power station components are cleaned on the jth day, otherwise the auxiliary decision-making system continues to run to predict the best cleaning day.

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