A method and device for early warning of dust deposition in a photovoltaic power station, an electronic device and a storage medium

Abstract

This invention relates to the field of photovoltaic power plant operation and maintenance technology, specifically to a method, device, electronic equipment, and storage medium for early warning of dust accumulation in photovoltaic power plants. In this invention, the slope change of irradiance data is used to accurately screen completely sunny periods, eliminating the interference of cloud cover on power data and providing a reliable basis for subsequent analysis. Then, inverters are screened based on the correlation between inverter output power and irradiance during sunny periods, ensuring that the selected inverters accurately reflect the performance of the photovoltaic power plant. Finally, an early warning signal is generated based on the relationship between the theoretical and actual power of the screened inverters during sunny periods. This allows for precise early warning of situations affecting power generation efficiency, such as dust accumulation, in photovoltaic power plants, achieving efficient operation and maintenance, and improving power generation reliability and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant operation and maintenance technology, specifically to a method, device, electronic equipment, and storage medium for early warning of dust accumulation in photovoltaic power plants. Background Technology

[0002] Photovoltaic (PV) modules are located outdoors, and dust particles from the environment easily accumulate on their surfaces, forming a dusty surface. This directly affects the radiation level of the PV surface and the cell temperature, reducing PV power generation efficiency. For example, dust can reduce PV panel performance by 25-35% within a month, and a single sandstorm can reduce PV output by 20%. Besides direct power generation losses, dust accumulation on PV module surfaces further affects the accuracy of PV power prediction, which in turn reduces the efficiency and reliability of PV-enabled energy management systems. Furthermore, dust accumulation can lead to hot spots on the module surface, or even fires, reducing the reliability of the PV system and potentially causing complete module damage. Therefore, accurate detection of PV dust accumulation is crucial for the safe and stable operation and maintenance of PV power plants. However, existing dust accumulation early warning methods suffer from inaccurate prediction results. Summary of the Invention

[0003] This invention provides a method, device, electronic device, and storage medium for early warning of dust accumulation in photovoltaic power plants, in order to solve the problem of inaccurate early warning results in existing dust accumulation early warning methods.

[0004] In a first aspect, the present invention provides a method for early warning of dust accumulation in photovoltaic power plants. The method includes: screening completely sunny periods based on the slope changes of irradiance data of photovoltaic power plants at different times; screening inverters based on the correlation between the output power of inverters in the photovoltaic power plants and irradiance during the completely sunny periods; and generating an early warning signal based on the relationship between the theoretical power and actual power of the screened inverters during the completely sunny periods.

[0005] In this invention, the slope change of irradiance data is used to accurately screen for completely clear days, eliminating the interference of cloud cover on power data and providing a reliable basis for subsequent analysis. Inverters are then screened based on the correlation between inverter output power and irradiance during clear days, ensuring that the selected inverters accurately reflect the performance of the photovoltaic power station. Finally, an early warning signal is generated based on the relationship between the theoretical and actual power of the screened inverters during clear days. This signal can accurately warn of situations affecting power generation efficiency, such as dust accumulation, in photovoltaic power stations, enabling efficient operation and maintenance of the photovoltaic power station and improving power generation reliability and economic benefits.

[0006] In one optional implementation, the process of filtering completely sunny periods based on the slope changes of irradiance data from a photovoltaic power station at different times includes: acquiring irradiance data from the photovoltaic power station during a preset time period each day, and filtering irradiance data that consistently meets preset conditions; determining the noon time at the location of the photovoltaic power station using a simplified solar hour angle model, and dividing the time period into morning and afternoon based on the noon time; removing periods with an irradiance curve slope less than zero during the morning period and periods with an irradiance curve slope greater than zero during the afternoon period from the filtered irradiance data to obtain the irradiance data for completely sunny periods each day.

[0007] In this invention, by acquiring irradiance data for a preset time period and filtering data that consistently meets the conditions, and by combining a simplified solar hour angle model to divide the time periods into morning and afternoon, and then eliminating time periods with a slope less than zero in the morning and a slope greater than zero in the afternoon, irradiance data for completely sunny days can be accurately selected. This effectively eliminates the interference of abnormal fluctuations in irradiance during non-sunny days (such as cloud cover), providing cleaner and more reliable basic data for subsequent photovoltaic power plant-related analyses based on irradiance (such as dust accumulation loss early warning and power prediction), thus improving the accuracy and credibility of the analysis results.

[0008] In one optional implementation, inverters are screened based on the correlation between the output power and irradiance of inverters in the photovoltaic power station during a completely sunny day. This includes: obtaining the output power and irradiance data of all inverters in the photovoltaic power station during a completely sunny day; calculating the correlation coefficient between the output power and the irradiance data using a preset correlation algorithm; and screening inverters among all inverters whose correlation coefficient is greater than a preset threshold.

[0009] In this invention, by acquiring inverter output power and irradiance data during completely sunny days, a preset correlation algorithm is used to calculate the correlation coefficient and screen out inverters with correlation coefficients greater than a preset threshold. This allows for the accurate screening of inverters that are less affected by non-dust accumulation and whose power changes stably with irradiance. This provides a more representative and reliable sample of equipment for subsequent work based on inverter data, such as early warning of dust accumulation loss in photovoltaic power plants and evaluation of power generation efficiency, effectively improving the accuracy and credibility of the relevant analysis results.

[0010] In one optional implementation, an early warning signal is generated based on the relationship between the theoretical power and the actual power of the selected inverter during the completely sunny period, including: determining the theoretical power using a temperature compensation model based on the irradiance data of the selected inverter during the completely sunny period; obtaining the output power of the selected inverter during the completely sunny period as the actual power; determining the dust accumulation loss rate based on 1 and the ratio of the actual power to the theoretical power, and generating an early warning signal based on the dust accumulation loss rate.

[0011] In this invention, a temperature compensation model is used to determine the theoretical power by combining irradiance data from a completely sunny day. Simultaneously, the actual power of the selected inverter is obtained. The ash loss rate is calculated by determining the ratio of the actual power to the theoretical power, and an early warning signal is generated. This allows for precise quantification of power generation loss in photovoltaic power plants due to ash accumulation, timely warnings, and facilitates early cleaning measures by maintenance personnel. This ensures the power generation efficiency of the photovoltaic power plant, reduces energy waste and economic losses caused by long-term ash accumulation, and improves the overall reliability and economy of the power plant operation.

[0012] In one optional implementation, generating an early warning signal based on the ash accumulation loss rate includes: generating a level one early warning signal when the ash accumulation loss rate is within a first preset range for a consecutive preset number of days; generating a level two early warning signal when the ash accumulation loss rate is within a second preset range for a consecutive preset number of days; and generating a level three early warning signal when the ash accumulation loss rate is within a third preset range for a consecutive preset number of days, wherein the third preset range is greater than the second preset range, and the second preset range is greater than the first preset range.

[0013] In this invention, different levels of early warning signals are generated based on the different preset ranges in which the ash loss rate falls within a consecutive preset number of days. This tiered early warning mechanism can accurately and promptly reflect the severity of ash accumulation in photovoltaic power plants, allowing maintenance personnel to take corresponding cleaning and other maintenance measures according to different early warning levels. This effectively prevents a significant drop in power generation efficiency due to excessive ash accumulation, ensuring the stable and efficient operation of photovoltaic power plants and reducing energy waste and economic losses.

[0014] In one alternative implementation, the noon time is determined using the following formula:

[0015]

[0016] In the formula, the time zone center precision represents the longitude of the time zone center where the photovoltaic power station is located, the power station longitude represents the longitude of the photovoltaic power station, and h represents the time unit. This indicates the time difference correction value.

[0017] This invention combines the longitude of the time zone center, the longitude of the power station, and the time difference correction value to determine noon time, enabling precise calculation of the noon time at the location of the photovoltaic power station. This provides an accurate time reference for subsequent division of morning and afternoon periods and screening of irradiance data for completely sunny periods, ensuring that the time period division conforms to the actual variation pattern of solar radiation. This improves the accuracy of irradiance data screening based on time periods, laying a more reliable time dimension foundation for subsequent analysis work such as early warning of ash accumulation losses in photovoltaic power stations, and ensuring the accuracy of related analysis results.

[0018] In one optional implementation, the temperature compensation model is expressed by the following formula:

[0019]

[0020] In the formula, R represents theoretical power, and R represents irradiance data. Indicates system efficiency. This represents the temperature coefficient, where T represents the real-time temperature. Indicates standard temperature. This indicates the installed capacity of the selected inverters.

[0021] This invention, by introducing parameters such as irradiance, system efficiency, temperature coefficient, the difference between real-time and standard temperatures, and installed capacity, enables accurate calculation of the theoretical power of a photovoltaic inverter during completely sunny days. This effectively compensates for the impact of temperature variations on photovoltaic power output, making the theoretical power closer to the actual expected power generation capacity. This provides a more accurate theoretical basis for subsequent calculations of ash accumulation loss rates and the generation of early warning signals based on the comparison of actual and theoretical power, thus improving the accuracy of ash accumulation loss assessment for photovoltaic power plants.

[0022] Secondly, the present invention provides a photovoltaic power plant dust accumulation early warning device, the device comprising: an irradiance screening module for screening completely sunny periods based on the slope changes of irradiance data of the photovoltaic power plant at different time periods; an inverter screening module for screening inverters based on the correlation between the output power of the inverters in the photovoltaic power plant and the irradiance during the completely sunny periods; and an early warning module for generating an early warning signal based on the relationship between the theoretical power and the actual power of the screened inverters during the completely sunny periods.

[0023] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power plant dust accumulation early warning method described in the first aspect or any corresponding embodiment.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the photovoltaic power plant ash accumulation early warning method of the first aspect or any corresponding embodiment described above.

[0025] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the photovoltaic power plant ash accumulation early warning method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the first process of the photovoltaic power plant dust accumulation early warning method according to an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the second process of the photovoltaic power plant dust accumulation early warning method according to an embodiment of the present invention;

[0029] Figure 3 This is a structural block diagram of a photovoltaic power plant dust accumulation early warning device according to an embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] As described in the background section, existing dust accumulation early warning methods suffer from inaccurate warning results. Specifically, these methods directly rely on irradiance data exceeding a certain threshold for dust accumulation assessment. However, they fail to account for the influence of cloudy weather and only extract data for periods exceeding a specific threshold, resulting in insufficient data volume and unstable, inaccurate calculations. Dust accumulation loss calculations are easily affected by cloudy weather, thus lacking a refined mechanism for filtering effective data.

[0032] In view of this, this embodiment uses the slope change of irradiance data to accurately screen completely clear days, eliminating the interference of cloud cover on power data and providing a reliable basis for subsequent analysis; then, it screens inverters based on the correlation between inverter output power and irradiance during clear days, ensuring that the selected inverters can accurately reflect the performance of the photovoltaic power station; finally, it generates early warning signals based on the relationship between the theoretical power and actual power of the screened inverters during clear days, which can accurately warn of situations affecting power generation efficiency, such as dust accumulation in the photovoltaic power station, thereby achieving efficient operation and maintenance of the photovoltaic power station and improving power generation reliability and economic benefits.

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0035] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0036] As an optional application scenario of this invention, the photovoltaic power station ash accumulation early warning method can be applied to SCADA (Supervisory Control And Data Acquisition) systems.

[0037] According to an embodiment of the present invention, a method for early warning of dust accumulation in a photovoltaic power plant is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] This embodiment provides a method for early warning of dust accumulation in photovoltaic power plants. Figure 1 This is a flowchart of a photovoltaic power plant dust accumulation early warning method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0039] Step S101: Based on the slope changes of the irradiance data of the photovoltaic power station at different times, completely sunny periods are selected. Specifically, by selecting completely sunny periods, more representative periods can be identified. When selecting completely sunny periods, cloudy periods need to be eliminated. During sunny periods, based on the Earth's rotation and revolution, the solar altitude angle shows a trend of first increasing and then decreasing throughout the day. If there are no clouds or haze, the irradiance should also show a trend of first increasing and then decreasing. Therefore, based on the slope of the irradiance change curve over time in the irradiance data, periods that do not meet the preset change conditions can be eliminated, and the remaining periods can be considered completely sunny periods.

[0040] Step S102 involves screening inverters based on the correlation between their output power and irradiance during the completely sunny period in the photovoltaic power station. Specifically, after screening out the completely sunny period, the output power and irradiance data of all inverters in the photovoltaic power station during the corresponding period are extracted, and the inverters are screened based on the extracted data. Specifically, a correlation analysis is performed on the output power and irradiance data of each inverter to identify inverters with a high correlation between the two, thus making the subsequent dust accumulation judgment based on output power more accurate.

[0041] Step S103: A warning signal is generated based on the relationship between the theoretical power and actual power of the selected inverter during the completely sunny period. Specifically, the theoretical power represents the power that the selected inverter should output during the completely sunny period. Since the inverter's output power is mainly related to irradiance, this embodiment determines the theoretical power using the irradiance data of the inverter during the completely sunny period. The actual power represents the actual power output of the selected inverter during the completely sunny period. Comparing the actual power and the theoretical power allows it to be determined whether the inverter has reached its expected output power. If there is a large difference between the actual and theoretical power, it indicates that there is a lot of dust accumulation on the corresponding photovoltaic module, resulting in a large power loss, thus requiring a warning signal to be issued.

[0042] The photovoltaic power plant dust accumulation early warning method provided in this embodiment utilizes the slope change of irradiance data to accurately screen completely sunny periods, eliminating the interference of cloud cover on power data and providing a reliable basis for subsequent analysis. Then, it screens inverters based on the correlation between inverter output power and irradiance during sunny periods, ensuring that the selected inverters accurately reflect the performance of the photovoltaic power plant. Finally, it generates early warning signals based on the relationship between the theoretical and actual power of the screened inverters during sunny periods. This allows for precise early warning of situations affecting power generation efficiency, such as dust accumulation in photovoltaic power plants, enabling efficient operation and maintenance of photovoltaic power plants and improving power generation reliability and economic benefits.

[0043] This embodiment provides a method for early warning of dust accumulation in photovoltaic power plants, which includes the following steps:

[0044] Step S201: Based on the location of the photovoltaic power station, use the irradiance data of the photovoltaic power station to screen for completely sunny periods.

[0045] Specifically, step S201 includes:

[0046] Step S2011: Obtain the irradiance data of the photovoltaic power station during a preset time period each day, and filter the irradiance data that continuously meet the preset conditions.

[0047] This embodiment can be applied to a SCADA system, which is used for real-time data acquisition and monitoring during the operation of a photovoltaic power station. The system includes a database to store the collected data for subsequent analysis and processing. Specifically, in this embodiment, the dust accumulation in the photovoltaic power station can be assessed daily. At each assessment, relevant data can be retrieved from the SCADA system's server.

[0048] Specifically, in this embodiment, irradiance data for a preset daily time period is retrieved from the database each time an alert is issued. This preset time period can be irradiance data from 8:00 to 16:00 local time, or other sunny daytime periods. After retrieving the irradiance data, a continuity check is performed. This continuity check is to select periods with relatively stable irradiance for subsequent processing to achieve accurate analysis. In this embodiment, during the continuity check, periods with a sustained irradiance greater than or equal to 400 W / m² are selected from the irradiance data for the preset daily time period. 2 Data periods with a continuous duration of 50 minutes or more are considered to meet the continuity verification requirement. Other intermittent irradiation data that do not meet this continuity verification requirement are removed.

[0049] Step S2012: Based on the location of the photovoltaic power station, a simplified solar hour angle model is used to determine the noon time of the location of the photovoltaic power station, and the morning and afternoon periods are divided based on the noon time.

[0050] Specifically, in order to accurately filter irradiance based on changes in solar altitude, it is necessary to precisely determine the time when the solar altitude angle reaches its highest point of the day, i.e., noon. Since solar irradiance varies at different locations, the noon time also differs for photovoltaic power stations in different locations. Using the same time to filter irradiance data for all regions may lead to inaccurate warning results. Therefore, this embodiment considers the location of the photovoltaic power station to accurately determine the noon time at its location. Furthermore, for the same location, the noon time may also differ at different times of the year. This embodiment uses a simplified solar perspective model to determine the noon time at the location of the photovoltaic power station, specifically expressed by the following formula:

[0051]

[0052] In the formula, the longitude of the time zone center represents the longitude of the center of the time zone where the photovoltaic power station is located. For example, the longitude of the center of the Beijing time zone (UTC+8) is 120°E. The longitude of the power station represents the longitude of the photovoltaic power station. h represents the unit of time, which represents the noon time under ideal conditions (the local time when the sun is directly overhead at the prime meridian). It is used to clarify that the unit of "12" is hours, and to ensure that the units of each item in the formula are consistent. This represents the time difference correction value. The time difference correction value can be determined by looking up the date in a table or by using relevant formulas. For example, it can be determined using the following formula:

[0053]

[0054] In the formula, N represents the accumulated days of the year.

[0055] Specifically, after determining the noon time, the aforementioned preset time period is further divided into a morning period and an afternoon period. For example, if the preset time period is from 8:00 AM to 4:00 PM local time, then the morning period is from 8:00 AM to noon local time, and the afternoon period is from noon to 4:00 PM local time. It should be noted that this model needs to be used to determine the noon time for the same photovoltaic power station on different dates.

[0056] Step S2013: Remove the periods in the morning when the slope of the irradiance curve is less than zero and the periods in the afternoon when the slope of the irradiance curve is greater than zero from the filtered irradiance data to obtain the irradiance data for the completely sunny days of each day.

[0057] Specifically, since noon is when the solar altitude angle is highest, the solar altitude angle gradually increases during the morning, so the irradiance data should gradually increase as well. This means the slope of the irradiance-time curve should be greater than 0. Therefore, periods with a slope less than 0 in the morning irradiance curve can be removed, thus avoiding the decrease in irradiance caused by morning cloud cover. Similarly, for the afternoon, the irradiance data should gradually decrease, meaning the slope of the irradiance-time curve should be less than 0. Therefore, periods with a slope greater than 0 in the afternoon irradiance curve can be removed, thus avoiding the phenomenon of irradiance recovery after the clouds pass, which occurs when clouds move over the photovoltaic power station and cause irradiance blockage. It should be noted that the daily irradiance data for the photovoltaic power station needs to be filtered according to its corresponding noon time.

[0058] Step S202: Screen inverters based on the correlation between the output power and irradiance of the inverters in the photovoltaic power station during the completely sunny period.

[0059] Specifically, step S202 includes:

[0060] Step S2021: Obtain the output power and irradiance data of all inverters in the photovoltaic power station during the completely sunny period. Specifically, after filtering the completely sunny period based on the irradiance data according to the above steps, the output power during the completely sunny period can be filtered out from the SCADA system. Each photovoltaic power station typically has multiple photovoltaic modules, each composed of photovoltaic cells, used to receive sunlight and convert light energy into electrical energy. Each photovoltaic module is connected to an inverter, which converts the electrical energy converted by the photovoltaic module into electrical energy before outputting it. The output power obtained in this embodiment is the output power of each inverter.

[0061] Step S2022: The correlation coefficient between the output power and the irradiance data is calculated using a preset correlation algorithm. The correlation calculation can be performed using a correlation algorithm from the field of correlation techniques. Specifically, this embodiment uses the Pearson correlation coefficient for correlation calculation, as shown in the following formula:

[0062]

[0063] In the formula, r represents the correlation coefficient. Let represent the output power of any selected inverter at the i-th time during a completely sunny period. This represents the irradiance data at the corresponding time. This represents the average output power of the corresponding inverter over n time points (e.g., a completely sunny day includes n time points). This represents the average irradiance of a photovoltaic power station at n time points.

[0064] Step S2023: Filter all inverters whose correlation coefficient is greater than a preset threshold. Specifically, the preset threshold can be determined according to actual needs. In this embodiment, the preset threshold is selected as 0.9. That is, after calculating the correlation coefficient of each inverter, inverters with a correlation coefficient greater than 0.9 are filtered out as a set of representative inverters with high correlation.

[0065] Step S203: Generate an early warning signal based on the relationship between the theoretical power and actual power of the selected inverter during the completely clear day period.

[0066] Specifically, step S203 includes:

[0067] Step S2031: Based on the irradiance data of the selected inverters during the completely sunny day period, a temperature compensation model is used to determine the theoretical power. In this embodiment, the effect of temperature is considered when calculating the theoretical power; that is, a temperature coefficient is added to correct the power. Specifically, the theoretical power is determined using the temperature compensation model as follows:

[0068]

[0069] In the formula, R represents theoretical power, and R represents irradiance data. Indicates system efficiency. This represents the temperature coefficient, where T represents the real-time temperature. Indicates standard temperature. This indicates the installed capacity of the selected inverters. Specifically, the system efficiency (PR, Performance Ratio) can be obtained from the annualized degradation. For example, for mainstream crystalline silicon modules (monocrystalline / polycrystalline), the first-year degradation is approximately 1-3%, followed by an average annual degradation of 0.4%-0.8% (e.g., 0.5% can be used as a baseline). N-type modules (such as TOPCon, HJT) can have an average annual degradation as low as 0.25%-0.4%. Therefore, the service life of the corresponding type of photovoltaic module can be used to calculate... The irradiance data in this model represents the mean irradiance data during a completely clear day. This represents the average installed capacity of all the inverters selected.

[0070] Step S2032: Obtain the output power of the selected inverters during a completely sunny day as the actual power. Specifically, this actual power can be the average output power of the selected inverters during a completely sunny day. If output power data is not available, voltage and current data can be obtained and multiplied together to obtain the power data.

[0071] Step S2033: Determine the ash accumulation loss rate based on the ratio of 1 to the actual power and the theoretical power, and generate a warning signal based on the ash accumulation loss rate. Specifically, the ash accumulation loss rate can be understood as the power loss caused by ash accumulation. This ash accumulation loss rate is determined using the following formula:

[0072]

[0073] In an optional implementation, step S2033 above includes the following steps:

[0074] Step a1: When the ash loss rate reaches the first preset range for a consecutive preset number of days, a first-level early warning signal is generated.

[0075] Step a2: When the ash loss rate reaches the second preset range for a consecutive preset number of days, a secondary warning signal is generated.

[0076] Step a3: When the ash loss rate is within a third preset range for a consecutive preset number of days, a level three warning signal is generated. The third preset range is greater than the second preset range, and the second preset range is greater than the first preset range.

[0077] Specifically, based on the above steps, the daily dust accumulation loss rate of a photovoltaic power station can be calculated by selecting completely sunny periods each day and the inverter's output power during the corresponding periods. Since there may be occasional fluctuations on a single day, this embodiment considers the dust accumulation loss rate over several consecutive days when issuing a warning. These consecutive days can be determined based on actual conditions. For example, the dust accumulation loss rate can be judged based on three consecutive days. The three preset ranges can be determined based on actual conditions, for example, by considering the cost of dust removal. In this embodiment, the first preset range is set to 5% to 10%, the second preset range is set to 10% to 15%, and the third preset range is greater than 15%.

[0078] For example, when 5% or less for 3 consecutive days L 积灰 A Level 1 warning (observation level) is triggered when the percentage is <10%; a warning is triggered when the percentage is ≤10% for three consecutive days. L 积灰 A Level II warning (reserve level) is triggered when the concentration is <15%; a warning is triggered when the concentration is <15% for three consecutive days. L 积灰 A Level 3 alert (emergency level) is triggered. The generated alert signal can be sent to the operation and maintenance terminal through the SCADA system.

[0079] As one or more specific application embodiments of the present invention, such as Figure 2 As shown, the dust accumulation early warning method for this photovoltaic power station is implemented using the following process:

[0080] S1. Dynamic Clear Sky Data Filtering Mechanism: Combining irradiance threshold and time-based slope constraints, it accurately identifies historical data segments of completely clear days, eliminating the interference of cloud cover on power data.

[0081] Specifically, step S1 includes:

[0082] S11: Dynamic time period division based on solar hour angle.

[0083] Determine the geographical location parameters of the photovoltaic power station.

[0084] Obtain the latitude and longitude coordinates of the power station and its time zone (e.g., UTC+8).

[0085] S12: Calculate the local daily solar noon time based on a simplified solar hour angle model.

[0086] S13: Define dynamic time period division rules.

[0087] Morning session: 8:00 AM local time - Solarnoon local time.

[0088] Afternoon session: Local time Solarnoon - 16:00 local time.

[0089] S14: Data time period truncation.

[0090] Extract the irradiance and inverter output power time-series data from the photovoltaic power plant's historical database for the period from 8:00 to 16:00 local time each day.

[0091] S15: Irradiance continuity verification.

[0092] Screening irradiance ≥400 W / m 2 Furthermore, for data segments with a continuous duration of ≥50 minutes, intermittent lighting data are excluded.

[0093] S16: Cloud interference eliminated.

[0094] Morning period: Remove data segments with an irradiance curve slope of less than 0 (to avoid irradiance reduction caused by morning cloud cover);

[0095] Afternoon period: Remove data segments with an irradiance curve slope greater than 0 (to avoid irradiance rebound caused by cloud cover in the afternoon).

[0096] S2. Inverter Dynamic Optimization Algorithm: Based on the correlation coefficient between inverter output power and irradiance, highly correlated devices are selected to ensure the representativeness of the dust accumulation rate calculation.

[0097] Specifically, step S2 includes:

[0098] S21: Correlation coefficient calculation.

[0099] Calculate the Pearson correlation coefficient between the output power and irradiance of all inverters in the entire site. r .

[0100] S22: Screening of highly correlated inverters.

[0101] Selecting the correlation coefficient r Inverters with a dust accumulation rate greater than 0.9 are used as representatives for dust accumulation rate calculation, generating a "representative inverter set".

[0102] S3. Temperature-compensated theoretical power model: Introducing a temperature coefficient correction theoretical power calculation improves the reliability of the benchmark array and enhances the accuracy of dust accumulation loss rate calculation.

[0103] Specifically, step S3 includes:

[0104] S31: Theoretical power calculation. Specifically, a temperature-compensated model is used to calculate the benchmark theoretical power.

[0105] S32: Calculation of ash accumulation loss rate. Calculate the ash accumulation loss rate for a representative inverter set.

[0106] S4. Multi-dimensional early warning triggering mechanism: Combines real-time ash accumulation loss rate threshold and trend prediction to generate graded early warning signals.

[0107] Specifically, step S4 includes:

[0108] S41: Warning signal generated.

[0109] The following three levels of warning signals are defined by the following criteria:

[0110] Judgment condition 1: When 5% or less for 3 consecutive days L 积灰 A Level 1 warning (observation level) is triggered when the level is <10%.

[0111] Judgment condition 2: When 10% ≤ L 积灰 A level 2 warning (preparatory level) is triggered when the level is less than 15%.

[0112] Judgment condition 3: When 15% < for 3 consecutive days L 积灰 The system will trigger a Level 3 alert (emergency level).

[0113] The warning signal is pushed to the operation and maintenance terminal through the SCADA system.

[0114] In this embodiment, the irradiance duration threshold (≥400 W / m) is used. 2 This invention innovatively eliminates cloud interference by using time-segmented slope constraints on the irradiance curve (eliminating negative slope segments in the morning and positive slope segments in the afternoon) to dynamically filter historical data from completely clear days without cloud cover. Secondly, it calculates the correlation coefficient between the output power of all inverters and irradiance, selecting representative inverters with a correlation coefficient ≥ 0.9. Finally, it calculates the benchmark theoretical power using a temperature-compensated theoretical power model, dynamically estimating the dust accumulation loss rate based on the ratio of the actual power to the theoretical power of representative inverters, and triggering tiered early warnings based on thresholds and trends. This invention also incorporates a temperature compensation mechanism to reduce environmental factor errors, achieving a dust accumulation loss rate calculation accuracy improvement of over 20%, significantly reducing the frequency of ineffective cleaning and maintenance costs.

[0115] This embodiment also provides a photovoltaic power plant dust accumulation early warning device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0116] This embodiment provides a dust accumulation early warning device for photovoltaic power plants, such as... Figure 3 As shown, it includes:

[0117] Irradiance screening module 31 is used to screen completely sunny periods based on the slope changes of irradiance data of photovoltaic power plants at different times;

[0118] Inverter screening module 32 is used to screen inverters based on the correlation between the output power and irradiance of the inverters in the photovoltaic power station during the completely sunny period.

[0119] The early warning module 33 is used to generate an early warning signal based on the relationship between the theoretical power and the actual power of the selected inverter during the completely clear day period.

[0120] The photovoltaic power plant dust accumulation early warning device provided in this embodiment of the invention can execute the photovoltaic power plant dust accumulation early warning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0121] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0122] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 11, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 12 or a program loaded from memory 18 into random access memory (RAM) 13. The RAM 13 also stores various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0123] Typically, the following devices can be connected to I / O interface 15: input devices 16 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 17 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 18 including, for example, magnetic tapes, hard disks, etc.; and communication devices 19. Communication device 19 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0124] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 19, or installed from a memory 18, or installed from a ROM 12. When the computer program is executed by the processor 11, it performs the functions defined in the photovoltaic power plant dust accumulation early warning method of the embodiments of the present invention.

[0125] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0126] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic power plant dust accumulation early warning method shown in the above embodiments is implemented.

[0127] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0128] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for early warning of dust accumulation in photovoltaic power plants, characterized in that, The method includes: Screening completely sunny periods based on the slope changes of irradiance data from photovoltaic power plants at different times; Inverters were selected based on the correlation between the output power and irradiance of the inverters in the photovoltaic power station during the completely sunny period. A warning signal is generated based on the relationship between the theoretical power and the actual power of the screened inverter during the completely clear day period. Based on the slope changes of irradiance data from photovoltaic power plants at different times, completely sunny periods were selected, including: Obtain irradiance data of photovoltaic power plants during preset time periods each day, and filter the irradiance data that continuously meet preset conditions. Based on the location of the photovoltaic power station, a simplified solar hour angle model is used to determine the noon time at the location of the photovoltaic power station, and the morning and afternoon periods are divided based on the noon time; The periods with a slope of less than zero in the morning and the periods with a slope of greater than zero in the afternoon were removed from the filtered irradiance data to obtain the irradiance data for completely sunny days each day. Based on the relationship between the theoretical and actual power of the selected inverter during the completely clear day period, an early warning signal is generated, including: The theoretical power is determined using a temperature compensation model based on the irradiance data of the selected inverters during the completely clear day period. The output power of the selected inverters during a completely sunny day is taken as the actual power. The ash accumulation loss rate is determined based on the ratio of the actual power to the theoretical power, and an early warning signal is generated based on the ash accumulation loss rate.

2. The method according to claim 1, characterized in that, Inverters are screened based on the correlation between the output power and irradiance of the inverters in the photovoltaic power plant during a completely sunny day, including: Acquire the output power and irradiance data of all inverters in the photovoltaic power plant during the completely sunny day; The correlation coefficient between the output power and the irradiance data is calculated using a preset correlation algorithm; Filter all inverters whose correlation coefficient is greater than a preset threshold.

3. The method according to claim 1, characterized in that, Generate an early warning signal based on the ash accumulation loss rate, including: When the ash loss rate reaches the first preset range for a continuous preset number of days, a level one early warning signal is generated; When the ash loss rate reaches the second preset range after a continuous preset number of days, a level two early warning signal is generated; When the ash loss rate is within a third preset range for a consecutive preset number of days, a level three warning signal is generated. The third preset range is greater than the second preset range, and the second preset range is greater than the first preset range.

4. The method according to claim 1, characterized in that, The noon time is determined using the following formula: In the formula, the longitude of the time zone center represents the longitude of the center of the time zone where the photovoltaic power station is located, the longitude of the power station represents the longitude of the photovoltaic power station, and h represents the time unit. This indicates the time difference correction value.

5. The method according to claim 1, characterized in that, The temperature compensation model is expressed by the following formula: In the formula, R represents the theoretical power, and R represents the irradiance data. Indicates system efficiency. This represents the temperature coefficient, where T represents the real-time temperature. Indicates standard temperature. This indicates the installed capacity of the selected inverters.

6. A dust accumulation early warning device for photovoltaic power plants, characterized in that, The device includes: The irradiance filtering module is used to filter completely sunny periods based on the slope changes of irradiance data from photovoltaic power plants at different times. An inverter screening module is used to screen inverters based on the correlation between the output power and irradiance of the inverters in the photovoltaic power station during a completely sunny day. The early warning module is used to generate an early warning signal based on the relationship between the theoretical power and the actual power of the selected inverter during the completely clear day period; Based on the slope changes of irradiance data from photovoltaic power plants at different times, completely sunny periods were selected, including: Obtain irradiance data of photovoltaic power plants during preset time periods each day, and filter the irradiance data that continuously meet preset conditions. Based on the location of the photovoltaic power station, a simplified solar hour angle model is used to determine the noon time at the location of the photovoltaic power station, and the morning and afternoon periods are divided based on the noon time; The periods with a slope of less than zero in the morning and the periods with a slope of greater than zero in the afternoon were removed from the filtered irradiance data to obtain the irradiance data for completely sunny days each day. Based on the relationship between the theoretical and actual power of the selected inverter during the completely clear day period, an early warning signal is generated, including: The theoretical power is determined using a temperature compensation model based on the irradiance data of the selected inverters during the completely clear day period. The output power of the selected inverters during a completely sunny day is taken as the actual power. The ash accumulation loss rate is determined based on the ratio of the actual power to the theoretical power, and an early warning signal is generated based on the ash accumulation loss rate.

7. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power plant ash accumulation early warning method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic power plant ash accumulation early warning method according to any one of claims 1 to 5.

Images