A method for predicting cleaning cycle of large photovoltaic power station based on soot deposition condition monitoring

Abstract

The application discloses a kind of based on dust accumulation condition monitoring large photovoltaic power station cleaning cycle prediction method, belong to photovoltaic power generation field. Including: establish the economic loss model caused by dust accumulation in operation time photovoltaic power station, and determine the total economic loss E tol That dust accumulation causes in operation time photovoltaic power station by future meteorological data, determine after cleaning by cycle the economic loss ΔE that dust accumulation causes in operation time photovoltaic power station;Consider dust accumulation upper limit, determine the economic loss H that dust accumulation causes in operation time photovoltaic power station without cleaning;Determine the economic benefit S that cleaning by cycle brings in operation time photovoltaic power station, and the best cleaning cycle T corresponding to economic benefit S maximum is solved, actual cleaning cycle T real The application considers the mathematical model of the condition that photovoltaic panel has been completed cleaning continues to dust accumulation in the whole large photovoltaic power station cleaning process, more in line with actual situation, to more accurately predict the best cleaning cycle, bring maximum economic benefit for photovoltaic power station.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation, and more particularly to the field of intelligent operation and maintenance technology for photovoltaic power plants, and especially to a method for predicting the cleaning cycle of large-scale photovoltaic power plants based on ash accumulation monitoring. Background Technology

[0002] As the core component of photovoltaic power generation, the cleanliness of the photovoltaic panel surface has a significant impact on the power generation efficiency. Dust accumulation on the surface of the photovoltaic panel will reduce the solar irradiance received by the panel, thereby reducing its power generation efficiency. If the dust is not cleaned for a long time, the output power of the photovoltaic power station will be severely reduced, resulting in a large amount of energy waste.

[0003] Dust accumulation in photovoltaic (PV) power plants exhibits regional and seasonal characteristics. However, current cleaning frequencies for PV power plants are mostly fixed or adjusted based on experience, without real-time quantitative analysis of the impact of dust on the module surfaces. Therefore, it's impossible to optimally balance the increased power generation after cleaning with cleaning costs. This is particularly problematic for large-scale PV power plants; excessively high cleaning frequencies significantly increase manual cleaning costs, while insufficient cleaning frequencies result in substantial power generation losses, reducing the plant's economic returns. Therefore, real-time monitoring of dust accumulation on PV panels, accurate analysis and prediction of power generation losses caused by dust accumulation based on monitoring data, and the development of reasonable cleaning strategies are crucial for the economical operation of large-scale PV power plants.

[0004] However, large-scale photovoltaic power plants have a large number of photovoltaic panels, and cleaning the entire plant takes a considerable amount of time. During the cleaning process, the panels that are cleaned first will begin to accumulate dust again. If the prediction of the cleaning cycle does not take into account the continued dust accumulation on the cleaned panels, it will lead to inaccurate estimates of the overall dust accumulation situation of the power plant, failing to accurately reflect the economic losses caused by dust accumulation, and consequently, failing to accurately predict the optimal cleaning cycle. However, existing cleaning cycle prediction technologies do not yet consider the continued dust accumulation on the cleaned panels during the entire cleaning process of large-scale photovoltaic power plants. Summary of the Invention

[0005] Therefore, it is necessary to address the aforementioned technical problems: existing cleaning cycle prediction technologies do not consider the situation where cleaned photovoltaic panels continue to accumulate dust during the entire cleaning process of large-scale photovoltaic power plants. This leads to inaccurate estimations of the overall dust accumulation situation of the power plant, failing to accurately reflect the economic losses caused by dust accumulation, and consequently, failing to accurately predict the optimal cleaning cycle. This invention provides a method for predicting the cleaning cycle of large-scale photovoltaic power plants based on dust accumulation condition monitoring.

[0006] This invention provides a method for predicting the cleaning cycle of a large-scale photovoltaic power plant based on ash accumulation monitoring, comprising:

[0007] A model is established to illustrate the economic losses of photovoltaic power plants caused by dust accumulation during operation. Based on the model, the total economic loss E caused by dust accumulation over N cleaning cycles is determined. tol ;

[0008] Based on future meteorological data, determine the economic loss ΔE of the photovoltaic power station caused by ash accumulation during the operating period after periodic cleaning;

[0009] Considering the upper limit of ash accumulation, determine the economic loss H of the photovoltaic power station caused by ash accumulation during the operating time without cleaning;

[0010] Determine the economic benefit S = H - ΔE brought by the photovoltaic power station during the operating period after periodic cleaning, and find the optimal cleaning period T corresponding to the maximum economic benefit S, and the actual cleaning period.

[0011]

[0012] The economic loss model for photovoltaic power plants caused by dust accumulation during the operating period is as follows: During the first cleaning cycle, the number of photovoltaic panels (f1, f2, ..., fn) cleaned from day 1 to day n is... n Economic losses E1, E2, ..., E caused by dust accumulation n The total economic loss caused by dust accumulation is denoted as E; within a period of (N-1), f1, f2, ..., f n The economic losses caused by dust accumulation are the same, and the total economic loss caused by dust accumulation is denoted as E'; the total economic loss caused by dust accumulation over N periods is E. tol =E+E';

[0013] The expression for the economic loss ΔE is: ΔE=(E+E')+N·C=E tol +N·C; C is the cost required to complete one cleaning of the power plant, and the number of cleaning cycles. Assume the optimal cleaning cycle is T, D is the operating time, and b is the predicted number of rainy or snowy days within the operating time.

[0014] The economic loss H is expressed as follows: k represents the daily power generation revenue loss of a photovoltaic power station under ash accumulation conditions. Let μ be the upper limit of the power generation loss rate. Then, the number of days it takes to reach the ash accumulation limit is: r is the average rate of decline in power generation efficiency of photovoltaic power plants caused by dust accumulation.

[0015] Furthermore, the determination of the average attenuation rate r includes:

[0016] Obtain the instantaneous maximum output power P under standard test conditions for both dust accumulation and cleaning. s Radiance G at the actual collection time t ;

[0017] Based on the instantaneous maximum output power P under standard conditions s Irradiance G at the actual collection time t Given the total area S of the solar panels, the actual power generation efficiency under standard conditions is determined as follows: And the photovoltaic panel power generation efficiency sequence η1={η under the two working conditions of dust accumulation and cleanliness was calculated respectively. 11 η 12 η 13 …,η 1n} and η2={η 21 η 22 η 23 …,η 2n};

[0018] Based on the power generation efficiency series of photovoltaic panels under two operating conditions—dust accumulation and cleaning—the power generation efficiency loss rate caused by dust accumulation in photovoltaic panels was calculated. And fit the power generation efficiency loss rate sequence η loss ={η loss1 η loss2 η loss3 …,η lossn} Obtain the average power generation efficiency of photovoltaic power plants caused by dust accumulation

[0019] , attenuation rate r.

[0020] Furthermore, the instantaneous maximum output power P under the standard conditions s The determination includes:

[0021] Standard test conditions are set: temperature T s =25℃, radiance G s =1000W / m 2 Atmospheric spectrum AM1.5;

[0022] Calculation of instantaneous maximum output power under standard conditions:

[0023]

[0024] Among them, P m It is the instantaneous maximum output power at the actual acquisition moment, T t It is the temperature at the actual time of data collection, T s The temperature corresponds to standard conditions, R is the temperature coefficient of maximum power of the photovoltaic panel, and G... t It is the irradiance at the actual time of data collection, G sThis is the irradiance corresponding to standard conditions.

[0025] Furthermore, the determination of the gain / loss k includes:

[0026] The moment when all photovoltaic panels in the power station are clean is recorded as the start of dust accumulation. After t days of dust accumulation, the power generation efficiency loss rate of the photovoltaic power station is η. t = r·t × 100%;

[0027] The daily power generation revenue loss of a power plant under ash accumulation conditions is k = P·r·m·c, with the unit being yuan / day. In the formula, P is the power plant capacity, m is the average daily utilization hours of the power plant, and c is the grid-connected electricity price.

[0028] Furthermore, the determination of the economic loss E during the first cleaning cycle includes:

[0029] In the first period, the economic loss of f1 caused by ash accumulation:

[0030]

[0031] During the first period, f n Economic losses caused by dust accumulation:

[0032]

[0033] During the first period, when 1 < m < n, f m Economic losses caused by dust accumulation:

[0034]

[0035] The total economic loss caused by dust accumulation during the first cycle is:

[0036]

[0037] Furthermore, during the (N-1)th cleaning cycle, the economic loss E' is:

[0038]

[0039] Furthermore, the method for predicting the cleaning cycle of a large-scale photovoltaic power plant based on ash accumulation monitoring provided in this embodiment of the invention also includes:

[0040] Based on the actual cleaning cycle T real Calculate the date for the next cleaning;

[0041] Based on the average degradation rate r and the date of the next cleaning, the photovoltaic power plant's power generation efficiency loss rate η on the date of the next cleaning is calculated. next ;

[0042] The daily power generation efficiency loss rate η of the photovoltaic power station before the next cleaning date is obtained based on real-time monitoring data. t ;

[0043] If the power generation efficiency loss rate η t Below the power generation efficiency loss rate η next If so, no cleaning command will be issued;

[0044] If the power generation efficiency loss rate η t Higher than or equal to the power generation efficiency loss rate η nex Based on meteorological forecast data, if there is no heavy rainfall in the next 1 day, a cleaning instruction will be given; otherwise, no cleaning will be performed.

[0045] Furthermore, the value of the future day is: 3 days in summer and 7 days in winter.

[0046] The method for predicting the cleaning cycle of a large photovoltaic power plant based on ash accumulation monitoring provided in this invention has the following advantages compared with the prior art:

[0047] Based on historical and real-time data, this invention considers the possibility of continued dust accumulation on cleaned photovoltaic panels during the entire photovoltaic power plant cleaning process. A mathematical model is established to address the economic losses caused by dust accumulation. Based on this model, and with the goal of maximizing the power plant's economic benefits, the optimal cleaning cycle is calculated and real-time cleaning instructions are provided by combining real-time monitoring and meteorological data. In other words, this invention's mathematical model, which considers the possibility of continued dust accumulation on cleaned photovoltaic panels during the entire large-scale photovoltaic power plant cleaning process, is more consistent with reality and can more accurately predict the optimal cleaning cycle, thus maximizing the economic benefits for the photovoltaic power plant. This method is based on a certain operating time, considering historical and measured data, and taking into account future meteorological data to provide cleaning recommendations. Attached Figure Description

[0048] Figure 1 This is a flowchart of a method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring, provided in one embodiment.

[0049] Figure 2 A schematic diagram of a mathematical model for the economic losses of a power plant caused by dust accumulation, provided in one embodiment, considering the situation where dust continues to accumulate on photovoltaic panels after cleaning has been completed.

[0050] Figure 3 This is a graph showing the relationship between the power generation efficiency degradation rate of a photovoltaic panel and the time of dust accumulation in one embodiment.

[0051] Figure 4 This is a graph showing the relationship between the economic benefits of a photovoltaic power plant and the optimal cleaning cycle for two methods provided in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] This invention provides a method for predicting the cleaning cycle of a large-scale photovoltaic power plant based on ash accumulation monitoring. The method includes:

[0054] First, historical monitoring data for both dust accumulation and cleaning conditions were collected and standardized. Then, the average degradation rate of photovoltaic power plant efficiency caused solely by dust accumulation was obtained by comparing the processed data. Considering the possibility of continued dust accumulation on cleaned photovoltaic panels during the entire cleaning process, a mathematical model was established to calculate the economic losses caused by dust accumulation within a certain operating period. Based on the model, the losses from cleaning the photovoltaic power plant at certain intervals within a certain operating period were calculated, as well as the losses from not cleaning the photovoltaic power plant within a certain operating period, considering the upper limit of dust accumulation. Next, comparing the results with the case of not cleaning, the benefits of cleaning the photovoltaic power plant at certain intervals within a certain operating period were calculated. The optimal cleaning cycle corresponding to the maximum economic benefit was determined, and the date of the next cleaning was predicted. Finally, to mitigate the impact of extreme weather events such as sandstorms, real-time cleaning instructions were given based on daily monitoring data and meteorological data of the photovoltaic power plant before the next cleaning date.

[0055] Combination Figure 1 The specific details of the above technical solutions are as follows:

[0056] Step 1: Obtain the collected historical monitoring data and perform standardization processing.

[0057] By collecting data on the instantaneous maximum output power, temperature, and total irradiance of photovoltaic panels under both dust accumulation and cleaning conditions, the instantaneous maximum output power of the photovoltaic module under standard test conditions was calculated. Standard test conditions include: temperature T... s =25℃; Emissivity G s =1000W / m 2 From the atmospheric spectrum AM1.5, the converted instantaneous maximum output power can be obtained as follows: In the formula, P s It is the instantaneous maximum output power converted to standard conditions, P m It is the instantaneous maximum output power at the actual acquisition moment, T t It is the temperature at the actual time of data collection, T s The temperature corresponds to standard conditions, R is the temperature coefficient of maximum power of the photovoltaic panel, and G... tIt is the irradiance at the actual time of data collection, G s This is the irradiance corresponding to standard conditions.

[0058] Step 2: Based on the processed data, obtain the average degradation rate of photovoltaic power plant power generation efficiency caused solely by dust accumulation.

[0059] The actual power generation efficiency of photovoltaic panels under standard test conditions is S is the total area of ​​the solar panels. Based on historical data, the power generation efficiency η1 of the photovoltaic panels under clean operating conditions is obtained as follows: η1 = {η1, η2, ..., η3, ..., η4, ..., η5, ..., η6, ..., η7, ..., η8, ..., η9, ..., η1 ...2, ..., η3, ... 11 η 12 η 13 …,η 1n The power generation efficiency η2 of the photovoltaic panel under dust accumulation conditions is η2 = {η 21 η 22 η 23 …,η 2n}

[0060] The power generation efficiency loss rate caused by dust accumulation on photovoltaic panels Based on the power generation efficiency loss rate sequence η loss ={η loss1 η loss2 η loss3 …,η lossn The data fitting yielded a linear relationship between the photovoltaic panel power generation efficiency degradation rate and the dust accumulation time t. The slope of the fitted line is then obtained, which is the average degradation rate r of the photovoltaic power station power generation efficiency caused by dust accumulation.

[0061] Step 3: Considering the scenario where cleaned photovoltaic panels continue to accumulate dust during the entire photovoltaic power station cleaning process, a mathematical model is established to calculate the economic losses caused by dust accumulation within a certain operating period. The model is as follows: Figure 2 As shown.

[0062] First, let's assume the photovoltaic power station has a total of Y photovoltaic panels, and it takes n days to complete one cleaning cycle. Assume the number of photovoltaic panels cleaned each day is the same, and the number of photovoltaic panels cleaned each day is... (blocks), the cleaning time for each photovoltaic panel is negligible, and the number of photovoltaic panels cleaned from day 1 to day n is denoted as a sequence F, F = {f1, f2, ..., fn}. n}, where f1 = f2 = ... = f n .

[0063] A predictive model is built based on local historical meteorological data to forecast future rain and snow conditions, assuming zero dust accumulation and no cleaning during rain or snow. Assuming the optimal cleaning cycle is T over a given operating period, the number of cycles is... In the formula, D is the operating time, and b is the number of days with rain or snow within a certain operating time as predicted.

[0064] From the model, we can see that within the first period, f1, f2, ..., f n Economic losses E1, E2, ..., E caused by dust accumulation n The total economic loss caused by dust accumulation is denoted as E; within a period of (N-1), f1, f2, ..., f n The economic losses caused by dust accumulation are the same, and the total economic loss caused by dust accumulation is denoted as E'. The total economic loss caused by dust accumulation over N periods is E. tol =E+E'.

[0065] Step 4: Calculate the economic losses of the photovoltaic power station caused by dust accumulation under the condition that cleaning is carried out at a certain cycle within a certain operating time.

[0066] The moment when all photovoltaic panels in the power station are clean is recorded as the start of dust accumulation. After t days of dust accumulation, the power generation efficiency loss rate of the photovoltaic power station is η. t =r·t×100%, we can get the daily power generation revenue loss of the power station under the ash accumulation condition k=P·r·m·c(yuan / day), where P is the power station capacity, m is the average daily utilization hours of the power station, and c is the grid-connected electricity price.

[0067] During the first period, f1, f2, ..., f n The economic losses caused by dust accumulation are different; f1 is the economic loss caused by dust accumulation. f n Economic losses caused by dust accumulation f m (1 < m < n) Economic losses caused by ash accumulation Therefore, the total economic loss caused by dust accumulation in the first cycle is:

[0068]

[0069] Within (N-1) periods, f1, f2, ..., f n The economic losses caused by dust accumulation are the same for all photovoltaic panels.

[0070] From the above, we can derive the economic loss caused by ash accumulation in a photovoltaic power station after cleaning at certain intervals within a certain operating period: ΔE=(E+E')+N·C=E tol +N·C, where C is the cost required for the power plant to complete one cleaning cycle.

[0071] Step 5: Considering the upper limit of dust accumulation, calculate the economic loss of the photovoltaic power station caused by dust accumulation if cleaning is not carried out within a certain operating period.

[0072] When the ash accumulation reaches its upper limit, further ash accumulation will no longer affect the power generation loss rate. Let the upper limit of the power generation loss rate be μ, then the number of days to reach the upper limit is... The economic losses caused by dust accumulation over a certain period of operation without cleaning can be calculated.

[0073] Step 6: Compare the results of not cleaning, calculate the benefits of cleaning the photovoltaic power station at a certain cycle within a certain operating period, determine the optimal cleaning cycle corresponding to the maximum economic benefit, and predict the date of the next cleaning of the photovoltaic power station.

[0074] Based on the above steps, the revenue S = H - ΔE brought to the photovoltaic power station by cleaning at a certain cycle within a certain operating period is obtained. Therefore, the optimal cleaning cycle T corresponding to the maximum economic benefit is calculated. Since the influence of rain and snow was removed in the aforementioned model, the actual cleaning cycle... And according to T real Calculate the date for the next cleaning of the photovoltaic power station.

[0075] Step 7: The step of combining real-time monitoring data and meteorological data to provide real-time cleaning instructions includes:

[0076] First, based on the obtained average power generation efficiency degradation rate and the date of the next cleaning, calculate the photovoltaic power generation efficiency loss rate η on the next cleaning date. next ;

[0077] Next, based on real-time monitoring data, the daily power generation efficiency loss rate η of the photovoltaic power station before the next cleaning date is obtained. t If η t Photovoltaic power plant power generation efficiency loss rate η below the next cleaning date next If so, no cleaning command will be issued;

[0078] Next, if the photovoltaic power plant's power generation efficiency loss rate η is higher than or equal to the date of the next cleaning, next Based on meteorological forecast data, if there is no heavy rainfall in the next 1 day, a cleaning instruction will be given; otherwise, no cleaning will be given. The 1 day is 3 days in summer and 7 days in winter.

[0079] Case Study:

[0080] The following specific embodiment illustrates how the method of the present invention achieves the goal of obtaining the optimal cleaning cycle for photovoltaic power plants.

[0081] The relevant parameters in this embodiment are as follows: the photovoltaic power station capacity is 50MW, the average daily utilization hours are 4.114 hours, the grid-connected electricity price is 0.267 yuan / kWh, the cost of cleaning once is 200,000 yuan, and the cleaning time is 30 days. The required predicted operating time is 180 days, with 20 rainy / snowy days in the next 180 days, and the upper limit of ash accumulation, i.e., the power generation efficiency loss rate, is 25%.

[0082] After obtaining historical monitoring data and performing standardization processing, the photovoltaic panel power generation efficiency loss rate sequence is shown in Table 1:

[0083] Table 1 Historical Data on Power Plant Power Generation Efficiency Loss Rate

[0084]

[0085] Based on historical data on power plant efficiency loss rate, a linear relationship between photovoltaic panel efficiency degradation rate and dust accumulation time t was obtained through fitting, such as... Figure 3 As shown, the average degradation rate of photovoltaic power generation efficiency is r = 0.1152%.

[0086] The cleaning cycle is predicted separately for both scenarios: not considering the continued dust accumulation on the cleaned photovoltaic panels and considering the latter. The relationship between the power plant's economic benefits and the cleaning cycle is as follows: Figure 4 As shown in Table 2, the actual cleaning cycle and maximum economic benefits are finally obtained:

[0087] Table 2 Historical Data on Power Plant Power Generation Loss Rate

[0088]

[0089] As can be seen from the figure, the cleaning cycles obtained by both algorithms can bring considerable economic benefits to the power plant. However, predicting the optimal cleaning cycle while considering the continued accumulation of dust on the cleaned photovoltaic panels results in higher economic benefits for the power plant than the case where no such consideration is taken into account.

[0090] In summary, dust levels have a significant impact on the power generation of photovoltaic (PV) power plants, and cleaning PV power plants according to a reasonable cleaning cycle can bring considerable economic benefits. This invention proposes a method for predicting the cleaning cycle of large-scale PV power plants based on dust accumulation monitoring. Building upon existing technologies, this method considers the possibility of continued dust accumulation on cleaned PV panels during the entire cleaning process. It establishes a mathematical model to assess the economic losses caused by dust accumulation within a certain operating period and, based on real-time data, provides appropriate cleaning recommendations, thereby maximizing the benefits of the PV power plant.

[0091] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for predicting the cleaning cycle of a large-scale photovoltaic power station based on ash accumulation monitoring, characterized in that, include: Establish a model for the economic losses of photovoltaic power plants caused by dust accumulation during operation, and determine the losses based on the model. Total economic losses caused by dust accumulation within a cleaning cycle ; Based on future meteorological data, determine the economic losses caused by dust accumulation in photovoltaic power plants during the operational period after periodic cleaning. ; Considering the upper limit of dust accumulation, determine the economic losses of a photovoltaic power station due to dust accumulation during its operating time without cleaning. ; Determine the economic benefits of the photovoltaic power station during its operating period after periodic cleaning. And calculate the economic benefits. The optimal cleaning cycle corresponding to the maximum And the actual cleaning cycle ; The economic loss model for photovoltaic power plants caused by dust accumulation during the operating period is as follows: During the first cleaning cycle, from day 1 to day 2... Solar panels cleaned by the sky Economic losses caused by dust accumulation The total economic loss caused by dust accumulation is different; it is recorded as follows: ;exist During the period, The economic losses caused by dust accumulation are the same, and the total economic losses caused by dust accumulation are denoted as... ; The total economic loss caused by ash accumulation within each cycle is ; Among them, the economic losses The expression is: ; C This refers to the cost required to complete one cleaning cycle of the power plant, and the number of cleaning cycles. Assuming the optimal cleaning cycle is , It refers to operating hours. It is the predicted number of days with rain or snow during the operating period; Among them, the economic losses The expression is as follows: ; To account for the daily power generation revenue loss of a photovoltaic power station under dust accumulation conditions, the upper limit of the power generation loss rate is set as follows: The number of days to reach the maximum dust accumulation limit is: , Average degradation rate of photovoltaic power plant power generation efficiency caused by dust accumulation; During the first cleaning cycle, the economic losses The determination includes: In the first cycle, Economic losses caused by dust accumulation: In the first cycle, Economic losses caused by dust accumulation: During the first cycle, when hour, Economic losses caused by dust accumulation: The total economic loss caused by dust accumulation during the first cycle is: 。 2. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 1, characterized in that, The average attenuation rate The determination includes: Obtain the instantaneous maximum output power under standard test conditions for both dust accumulation and cleaning. Irradiance at the actual collection time ; Based on the instantaneous maximum output power under standard conditions Irradiance at the actual collection time and the total area of ​​the solar panels The actual power generation efficiency under standard conditions is determined to be: The photovoltaic panel power generation efficiency sequences for the two working conditions of dust accumulation and cleaning were calculated respectively. and ; Based on the power generation efficiency series of photovoltaic panels under two operating conditions—dust accumulation and cleaning—the power generation efficiency loss rate caused by dust accumulation in photovoltaic panels was calculated. And fit the power generation efficiency loss rate sequence The average degradation rate of photovoltaic power generation efficiency caused by dust accumulation was obtained. .

3. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 2, characterized in that, The instantaneous maximum output power under standard conditions The determination includes: Standard test conditions are set: temperature radiance Atmospheric spectrum AM1.5; Calculation of instantaneous maximum output power under standard conditions: in, It is the instantaneous maximum output power at the actual acquisition moment. It is the temperature at the actual moment of data collection. This is the temperature corresponding to standard conditions. It is the temperature coefficient of maximum power of the photovoltaic panel. It is the irradiance at the actual moment of data collection. This is the irradiance corresponding to standard conditions.

4. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 1, characterized in that, The loss of revenue The determination includes: The moment when all photovoltaic panels in the power station are in a clean state is recorded as the start of dust accumulation. The power generation efficiency loss rate of Tianhou photovoltaic power station is ; Daily power generation revenue loss of power plant under ash accumulation conditions The unit is yuan / day, and the formula is as follows: It refers to the power station capacity. This refers to the average daily utilization hours of the power station. It is the grid-connected electricity price.

5. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 1, characterized in that, In the The economic losses during each cleaning cycle for: 。 6. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 1, characterized in that, Also includes: Based on the actual cleaning cycle Calculate the date for the next cleaning; Based on average attenuation rate Calculate the photovoltaic power plant's power generation efficiency loss rate on the next cleaning date, based on the date of the next cleaning. ; The daily power generation efficiency loss rate of the photovoltaic power station before the next cleaning date is obtained based on real-time monitoring data. ; If the power generation efficiency loss rate Below the power generation efficiency loss rate If so, no cleaning command will be issued; If the power generation efficiency loss rate Higher than or equal to the power generation efficiency loss rate Based on meteorological forecast data, if in the future If there is no heavy rainfall within the day, a cleaning instruction will be issued; otherwise, cleaning will not be carried out.

7. The method for predicting the cleaning cycle of a large photovoltaic power station based on ash accumulation monitoring as described in claim 6, characterized in that, The future Value for the day: Summer Take 3 days, winter Take 7 days.

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