A method and system for analyzing and determining the cleaning cycle of a photovoltaic power plant

Abstract

This application relates to a method for analyzing and determining the cleaning cycle of a photovoltaic (PV) power plant. The method includes: determining standard strings in the PV power plant based on preset rules; after cleaning the PV power plant, obtaining the deviation coefficient of each standard string based on the parameter ratio between each standard string and the standard string; determining the daily cleaning status of each standard string by comparing its daily and dynamic deviation coefficients; obtaining the daily relative loss coefficient of each standard string based on the string cleaning cost, and the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each standard string; and outputting the cleaning assessment status of the PV power plant by comparing the relative loss coefficient with a preset loss threshold, or by comparing the cleaning coefficient with a preset cleaning threshold. This application is more applicable to distributed PV power plants in areas with variable weather and complex power plant conditions, making the analyzed PV power plant cleaning cycle more reliable and stable.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation operation and maintenance optimization, and in particular to a method and system for analyzing and determining the cleaning cycle of a photovoltaic power station. Background Technology

[0002] Dust accumulation is a significant factor affecting the efficiency of photovoltaic (PV) power plants. Cleaning PV plants is the most direct and effective way to solve this problem; however, cleaning PV plants is costly and not suitable for frequent cleaning. This is especially true for distributed PV systems, whose sites are scattered and often located on the rooftops of factories or large buildings. Optimizing the cleaning cycle and striking a balance between improving PV efficiency and cleaning costs is a major challenge for the operation and maintenance of PV power plants.

[0003] The existing technology, "Optimal Cleaning Cycle for Dust Accumulation on Solar Photovoltaic Panels" (Proceedings of the CSEE, Vol. 38, No. 6, Mar. 20, 2018), uses a mathematical model and linear fitting to determine the optimal cleaning cycle. However, this method is based on a centralized large-scale photovoltaic power station in the Northwest region with sparse rainfall and stable sunshine. For the rainy Southeast region, especially for distributed photovoltaic power stations, the error will be unacceptably large.

[0004] For situations with frequent rain and unstable sunshine, the common method is to use the standard string comparison method. This conventional method compares the current of each string to determine compatibility. Therefore, it generally requires that the capacity, angle, and orientation of the strings match the standard string. This is suitable for large-scale centralized photovoltaic power plants. However, the site conditions of distributed photovoltaic power plants are more complex. Due to limitations in roof conditions (roof type, area, construction conditions, etc.), the selected photovoltaic modules may differ in capacity, angle, and orientation. For multi-type distributed photovoltaic power plants, the current conventional method can only define a standard string for each type of string (with the same capacity, angle, and orientation) for comparison. This inevitably increases the complexity of operation and maintenance significantly. For smaller-scale photovoltaic power plants with many different types, the increased operating costs will bring considerable operational pressure.

[0005] In addition, distributed photovoltaic systems may encounter situations where significant differences in dust accumulation occur in localized areas due to factors such as sewage discharge from surrounding factories and wind direction. Currently, there is no relevant consideration in the analysis and determination methods for cleaning cycles.

[0006] In summary, no effective solution has yet been proposed for the problems of high complexity and poor reliability in the current methods for analyzing and determining the cleaning cycle of photovoltaic power plants. Summary of the Invention

[0007] This application provides a method, system, computer equipment, and computer-readable storage medium for analyzing and determining the cleaning cycle of a photovoltaic power plant, in order to at least solve the problems of high complexity and poor reliability in related technologies for analyzing and determining the cleaning cycle of photovoltaic power plants.

[0008] In a first aspect, embodiments of this application provide a method for analyzing and determining the cleaning cycle of a photovoltaic power plant, the method comprising:

[0009] Standard strings are determined in the photovoltaic power station based on preset rules, wherein the standard strings are in a dust-free state;

[0010] After cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained according to the parameter ratio of each calibration string to the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained daily thereafter. The calibration string is other photovoltaic strings besides the standard string.

[0011] By comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, the daily cleaning coefficient of each calibration string is determined, wherein the cleaning coefficient reflects the degree of dust accumulation in the photovoltaic string.

[0012] Based on the string cleaning cost, as well as the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each calibration string, the daily relative loss coefficient of each calibration string is obtained.

[0013] The cleaning assessment status of the photovoltaic power station is output by comparing the relative loss coefficient with a preset loss threshold, or by comparing the cleaning coefficient with a preset cleaning threshold. The cleaning assessment status includes: ready to clean, recommended to clean, and immediate cleaning.

[0014] In some embodiments, for a first target string with the same capacity and orientation as the standard string, the deviation coefficient of each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including:

[0015] Obtain the output current or power generation efficiency of the first target string;

[0016] The deviation coefficient corresponding to the first target string is determined by comparing the output current of the first target string with that of the standard string, or by comparing the power generation efficiency of the first target string with that of the standard string.

[0017] In some embodiments, the deviation coefficient corresponding to the first target string is determined by the following formula:

[0018] C in =η in / ηBn Or C in =I in / I Bn

[0019] Among them, C in η is the deviation parameter of the calibration string numbered i on the nth day after the cleaning. in Let η be the average power generation efficiency of the calibration string numbered i on day n after cleaning. Bn I represents the average efficiency of the standard string on day n after cleaning; in Let I be the average current of the calibration string numbered i on day n after cleaning. Bn This represents the average current of the standard string on day n after cleaning.

[0020] In some embodiments, for a second target string whose capacity and orientation differ from the standard string, a deviation coefficient for each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including:

[0021] Obtain the daily power generation of the second target string;

[0022] The deviation coefficient of the second target string is determined by comparing the daily power generation of the second target string with that of the standard string.

[0023] In some embodiments, the deviation coefficient corresponding to the second target string is determined by the following formula:

[0024] C in =Q in / Q Bn

[0025] Among them, C in Q is the deviation parameter of the calibration string numbered i on the nth day after the cleaning. in The daily power generation of the calibration string numbered i on the nth day after cleaning, Q Bn It is the daily power generation of the calibration string on the nth day after the cleaning.

[0026] In some embodiments, determining the relative loss coefficients of each calibration string includes:

[0027] Based on the average electricity price, and the initial deviation coefficient, daily deviation coefficient, and daily power generation of each calibration string, the relative economic loss of each calibration string is calculated using the following formula:

[0028]

[0029] Among them, S in It is the relative economic loss of the calibration string numbered i on the nth day after cleaning, where D is the average electricity price, and C is the average economic loss of the calibration string numbered i on the nth day after cleaning.i1 C is the initial offset parameter of the calibration group string numbered i. ik Q is the initial offset parameter of the calibration string numbered i on the k-th day after cleaning. ik Let i be the daily power generation of the string numbered i on the k-th day after cleaning;

[0030] Based on the relative economic loss and the string cleaning cost, the relative loss coefficient corresponding to each calibration string is obtained by the following formula;

[0031] W in =S in / M

[0032] Among them, W in S is the relative loss coefficient of the calibration string numbered i on the nth day after cleaning. in It is the relative economic loss of the calibration string numbered i on the nth day after cleaning, and M is the cleaning cost.

[0033] In some embodiments, after outputting the cleaning assessment status of the photovoltaic power plant, the method further includes:

[0034] Acquire future weather data and establish a weather database, and obtain human resources information in real time;

[0035] If the cleaning assessment status of the calibration string is "immediate cleaning", then, in conjunction with the weather database and the human resources information, it is determined whether to send an immediate cleaning signal. The immediate cleaning signal is used to instruct the maintenance personnel to immediately clean the calibration string.

[0036] If the cleaning assessment status of the calibration string is recommended to be cleaned, the weather database and the human resources information are combined to determine whether to send a cleaning signal. The cleaning signal is used to instruct the operation and maintenance personnel to clean the calibration string within a preset time period.

[0037] When the cleaning assessment status of the calibration string is "ready to clean", the system combines the weather database and the human resources information to determine whether to send a "ready to clean" signal. The "ready to clean" signal is used to instruct the maintenance personnel to prepare the calibration string for cleaning.

[0038] In some embodiments, the cleaning evaluation status of each calibration string is output by comparing the relative loss coefficient with a preset loss threshold, or by comparing the cleaning coefficient with a preset cleaning threshold, including:

[0039] Obtain the preset cleaning threshold and the preset loss threshold, wherein the preset cleaning threshold includes a first warning threshold and a first cleaning threshold, and the preset loss threshold includes a second warning threshold and a second cleaning threshold;

[0040] Obtain the average cleanliness index and average relative loss coefficient of all calibrated strings in the photovoltaic power station;

[0041] If the average relative loss coefficient is greater than the second cleaning threshold, or the average cleanliness coefficient is less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is output as "immediate cleaning".

[0042] If the average relative loss coefficient is greater than the second warning threshold and less than the second cleaning threshold, and the average cleaning coefficient is greater than the first warning threshold and less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is output as recommended cleaning.

[0043] If the average relative loss coefficient is greater than the second warning threshold and less than the second cleaning threshold, or if the average cleaning coefficient is greater than the first warning threshold and less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is determined to be ready for cleaning.

[0044] In some embodiments, the method further includes:

[0045] Obtain the cleanliness index and relative loss coefficient of each calibrated string in the photovoltaic power station;

[0046] Based on the relationship between the cleanliness index of each calibration string and the first warning threshold and the first cleanliness threshold, each calibration string is distinguished and displayed on the photovoltaic power station topology map;

[0047] Based on the relationship between the relative loss coefficient of each calibration string and the second warning threshold and the second cleaning threshold, each calibration string is displayed separately on the photovoltaic power station topology map.

[0048] Secondly, embodiments of this application provide a system for determining the cleaning cycle of a photovoltaic power station, the system comprising: a parameter calculation module and a judgment and decision module, wherein,

[0049] The parameter calculation module is used to determine standard strings in the photovoltaic power station based on preset rules, wherein the standard strings are in a dust-free state.

[0050] Furthermore, after cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained based on the parameter ratio of each calibration string to the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained daily thereafter. The calibration strings are other photovoltaic strings besides the standard string.

[0051] Furthermore, by comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, the daily cleanliness coefficient of each calibration string is determined, wherein the cleanliness coefficient reflects the degree of dust accumulation in the photovoltaic string.

[0052] Furthermore, based on the string cleaning cost, as well as the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each calibration string, the daily relative loss coefficient of each calibration string is obtained.

[0053] The judgment and decision module is used to output the cleaning assessment status of the photovoltaic power station by comparing the relative loss coefficient with a preset loss threshold or comparing the cleaning coefficient with a preset cleaning threshold. The cleaning assessment status includes: preparatory cleaning, recommended cleaning, and immediate cleaning.

[0054] Compared to related technologies, the photovoltaic power station cleaning cycle analysis and determination method provided in this application addresses the unique characteristics of distributed photovoltaic power stations, such as their dispersed distribution, diverse rooftops, varied string orientations / angles / capacities within the same power station, and differences in dust accumulation in small areas, as well as the difficulties in model analysis caused by unstable sunlight. This invention proposes a novel method for analyzing and determining the optimal cleaning cycle of photovoltaic power stations. By combining quantitative calculations and qualitative analysis, the method uses the cleanliness coefficient and relative loss coefficient to quantitatively calculate the degree of dust accumulation and the economics of cleaning in photovoltaic power stations. Furthermore, by setting multiple thresholds, the urgency of power station cleaning is categorized, and a qualitative analysis is conducted based on factors such as weather forecasts and personnel conditions for the next few days. This comprehensive judgment is more applicable to distributed photovoltaic power stations in areas with variable weather and complex power station conditions, making the analyzed photovoltaic power station cleaning cycle more reliable and stable. Attached Figure Description

[0055] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0056] Figure 1 This is a schematic diagram illustrating the application environment of a photovoltaic power station cleaning cycle analysis and determination method according to an embodiment of this application;

[0057] Figure 2 This is a flowchart of a method for analyzing and determining the cleaning cycle of a photovoltaic power station according to an embodiment of this application;

[0058] Figure 3 This is a logical schematic diagram of a photovoltaic power station cleaning cycle analysis and determination method according to an embodiment of this application;

[0059] Figure 4A structural block diagram of a photovoltaic power station cleaning cycle determination system according to an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to 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. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0061] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0062] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0063] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0064] Figure 1 This is a schematic diagram illustrating the application environment of a photovoltaic power plant cleaning cycle analysis and determination method according to an embodiment of this application, such as... Figure 1 As shown, the main control unit 10 is located in the control room of the digital power station. It can receive and acquire the status information of each photovoltaic string 11, and based on this information, it makes a comprehensive analysis of the optimal cleaning time of the photovoltaic power station through a combination of quantitative calculation and qualitative analysis. Furthermore, it combines the weather conditions and manpower conditions of the area where the power station is located in the next few days for qualitative analysis, so as to be more suitable for photovoltaic power stations in areas with changeable weather and complex power station conditions, making the photovoltaic power station cleaning cycle obtained by the analysis more reliable and stable.

[0065] Figure 2 This is a flowchart of a method for analyzing and determining the cleaning cycle of a photovoltaic power station according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:

[0066] S201, Based on preset rules, standard strings are determined in the photovoltaic power station, wherein the standard strings are in a dust-free state;

[0067] In this embodiment, the standard string is used as a comparison benchmark with other strings. By comparing the parameters of the standard string with those of other strings, the deviation coefficient is determined, thereby achieving the calibration of other strings.

[0068] It is understandable that a standard photovoltaic string being in a dust-free state is an ideal state. In this embodiment, a standard photovoltaic string that is cleaned daily is considered to be in a dust-free state.

[0069] Furthermore, standard strings need to be determined according to certain preset rules. Specifically, the following rules are preferred:

[0070] 1) The capacity, angle, and orientation of the strings are quite representative, that is, they are consistent with most strings in a photovoltaic power station;

[0071] 2) The string current is relatively large, resulting in higher power generation efficiency;

[0072] 3) Stay away from tall buildings and avoid obstructions as much as possible;

[0073] 4) The location of the string is relatively easy to clean and test;

[0074] 5) Avoid pollution sources as much as possible;

[0075] 6) The components have a good appearance and no obvious hidden cracks.

[0076] S202 After cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained according to the parameter ratio of each calibration string to the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained every day thereafter. The calibration string is other photovoltaic strings besides the calibration string.

[0077] In this embodiment, after each cleaning of the photovoltaic power station, the deviation coefficient is determined by calculating the ratio of the measurement parameters of other components to the standard string; wherein, the deviation coefficient calculated on the day after cleaning is defined as the initial deviation coefficient, and the deviation coefficient of each day thereafter is defined as the dynamic deviation coefficient.

[0078] Furthermore, the parameters used for comparison can be string power generation efficiency, output current, or daily power generation. These can be flexibly selected based on the specific characteristics of the calibrated string. For example, for photovoltaic strings with the same or similar orientation and capacity as the standard string, efficiency and current can be used for comparison; for solar service strings that differ significantly from the standard string, daily power generation can be used as the comparison parameter.

[0079] In addition, to improve the accuracy of the comparison results, it is preferable to measure the efficiency or current of each calibration string during a period of good sunlight around noon.

[0080] S203. By comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, the daily cleaning coefficient of each calibration string is determined. The cleaning coefficient reflects the degree of dust accumulation in the photovoltaic string.

[0081] By comparing the deviation coefficient on day n with the initial deviation coefficient, the change in the deviation coefficient can be analyzed, thereby determining the cleanliness (degree of dust accumulation) of the string. In this embodiment, this ratio is defined as the cleanliness coefficient.

[0082] Furthermore, by comparing the cleaning coefficient with a preset cleaning threshold, it can be determined whether the cleanliness level of each photovoltaic string is normal, exceeds the warning range, or exceeds the extreme value range. In addition, the photovoltaic power station topology diagram in the control room distinguishes and displays the above three types of photovoltaic strings separately, thereby more intuitively reflecting the current dust accumulation level of each string.

[0083] S204. Based on the string cleaning cost, as well as the initial deviation coefficient, daily deviation coefficient and daily power generation of each calibration string, determine the relative loss coefficient of each calibration string.

[0084] First, the cumulative economic loss is calculated based on the average electricity price, as well as the initial deviation coefficient, daily deviation coefficient, and daily power generation of each calibration string.

[0085] Secondly, the relative loss coefficient is obtained by calculating the ratio of the cumulative economic loss to the string cleaning cost.

[0086] S205 outputs the cleaning assessment status of the photovoltaic power station by comparing the relative loss coefficient with the preset loss threshold or the cleaning coefficient with the preset cleaning threshold. The cleaning assessment status includes: ready to clean, recommended to clean, and immediate cleaning.

[0087] It is understandable that when the relative loss coefficient is greater than or equal to 1, it means that the current cumulative economic loss is greater than or equal to the cleaning cost.

[0088] Additionally, relative loss coefficient thresholds can be set based on experience in the field. Specifically, a warning threshold and a cleaning threshold are preferred. By comparing the relative loss coefficient of a photovoltaic module with these warning and cleaning thresholds, the overall cleaning urgency of the power plant, or the cleaning urgency of a specific area or string within the power plant, can be determined.

[0089] Through the above steps S201 to S205, compared with the existing methods for analyzing and determining the cleaning cycle of photovoltaic power plants, this application first sets up standard strings and performs quantitative analysis and calculation on the cleaning degree of each string. Furthermore, it considers factors such as the weather and personnel situation in the next few days to make a final qualitative assessment. By combining quantitative calculation and qualitative analysis, a comprehensive and accurate evaluation and analysis of the cleaning cycle of photovoltaic power plants is conducted. This approach is more suitable for distributed photovoltaic power plants in areas with variable weather and complex power plant conditions, making the analyzed cleaning cycle of photovoltaic power plants more reliable and stable.

[0090] Figure 3 This is a logical schematic diagram of a photovoltaic power station cleaning cycle analysis and determination method according to an embodiment of this application.

[0091] In some embodiments, considering that there are various types of photovoltaic strings in the power plant, and the specific parameters of each string may differ significantly from those of the standard string, this embodiment uses different methods to calculate the offset coefficient for different types of photovoltaic strings, including:

[0092] A first target string that has the same capacity and orientation as a standard string is defined as a first target string in this application.

[0093] The aforementioned offset coefficient is obtained by comparing the string efficiency or current of the first target string with that of the standard string. Specifically:

[0094] The offset coefficient of the calibration string that matches the orientation, angle, or capacity of the standard string can be obtained using formula 1 or 2 below:

[0095] Formula 1: C in =η in / η Bn

[0096] Formula 2: C in =I in / I Bn

[0097] Among them, C in It is the deviation parameter of the calibration string numbered i on the nth day after cleaning, and the ηth day... in Let η be the average power generation efficiency of the calibration string numbered i on day n after cleaning. Bn I represents the average efficiency of the standard string on day n after cleaning; in Let I be the average current of string numbered i on day n after cleaning. Bn The average current of the standard string on day n after cleaning;

[0098] It can be understood that when n=1, the obtained offset parameter is the initial offset parameter for the day after the power station cleaning; when N>1, the obtained offset parameter is the dynamic offset parameter for the day after the cleaning.

[0099] Considering the complex construction conditions of distributed photovoltaic (PV) systems, and the inconsistencies in capacity, orientation, and shading conditions between PV modules and standard strings, the aforementioned comparison methods may not be entirely applicable. In this embodiment, PV strings with capacities and orientations inconsistent with standard strings are defined as second target strings.

[0100] Furthermore, by comparing the daily power generation of the second target string with that of the standard string, the corresponding offset parameters are obtained. This allows for the comparison of all strings in a distributed photovoltaic power station using a single standard string, avoiding the simultaneous existence of multiple standard strings.

[0101] Specifically, the deviation coefficient corresponding to the second target string is determined using the following formula:

[0102]

[0103] Q in Let Q be the daily power generation of string i on day n after cleaning. Bn L represents the daily power generation of the standard string on day n after cleaning. in L represents the daily radiation dose radiated to string numbered i on day n after cleaning; Bn This represents the daily radiation dose to the standard string on the first day after cleaning.

[0104] The calculation of daily radiation requires distinguishing between direct radiation and scattering. Direct radiation is calculated by converting the solar angle onto the plane perpendicular to the photovoltaic module, integrating its real-time value over the entire day, and then adding the scattering value.

[0105] However, since photovoltaic power plants typically do not have direct radiation measurement equipment, and the areas where power plants are located may have a high proportion of scattering, this embodiment simplifies the above formula by comparing only the daily power generation to obtain the deviation coefficient. Specifically, the offset coefficient of the second target string is calculated using the following formula 3.

[0106] Formula 3: C in =Q in / Q Bn

[0107] Among them, C in Q is the deviation parameter of the calibration string numbered i on the nth day after cleaning. in The daily power generation of the calibration string numbered i on day n after cleaning, Q Bn It represents the daily power generation of the calibrated string on the nth day after cleaning.

[0108] Furthermore, considering that the calibration deviation coefficient based on daily power generation is less accurate than that calculated using current or efficiency, a correction factor can be applied by multiplying the coefficient based on actual operating conditions, followed by iterative optimization to improve the reliability of the analysis. Specifically, this correction parameter can be flexibly selected based on experience in the field, and this embodiment does not impose specific limitations on it.

[0109] Unlike the current conventional method of comparing standard strings of the same type, this application can compare multiple types of strings using one type of standard string. That is, it can differentiate the strings of photovoltaic power plants. For strings that are inconsistent with the standard strings in terms of capacity, angle, orientation, etc., different methods are used to calculate and calibrate them, thus avoiding the problem of setting too many standard string types.

[0110] In some embodiments, determining the relative loss coefficient of each string based on the above parameters specifically includes the following steps:

[0111] First, based on the average electricity price, and the initial deviation coefficient, daily deviation coefficient, and daily power generation for each calibration string, the relative economic loss is calculated using the following formula:

[0112]

[0113] Among them, S in It is the relative economic loss of the calibration string numbered i on the nth day after cleaning, where S is the average electricity price and C is the relative economic loss of the calibration string numbered i on the nth day after cleaning. i1 C is the initial offset parameter of the calibration group string numbered i. ik Q is the initial offset parameter of the calibration string numbered i on the k-th day after cleaning. ik Let i be the daily power generation of the string numbered i on the k-th day after cleaning;

[0114] Secondly, based on the relative economic loss and the string cleaning cost, the relative loss coefficient corresponding to each calibration string is obtained through the following formula;

[0115] W in =S in / M

[0116] Among them, W in S is the relative loss coefficient of the calibration string numbered i on the nth day after cleaning. in M is the relative economic loss of the calibrated string numbered i on the nth day after cleaning, and M is the cleaning cost.

[0117] Furthermore, the relative loss coefficient threshold W X The number can be set as needed, but generally two are set: one is the warning threshold W. X1 One is the cleaning threshold W X2 The specific values ​​are determined based on the actual situation or through iterative optimization.

[0118] For digital photovoltaic power plants, when W in >W X1 or W in >W X2By marking the strings on the photovoltaic power station topology map, it can be shown that the strings have approached or exceeded the critical point of economic viability for cleaning, thereby determining the necessity of cleaning.

[0119] Specifically, by comparing the cleaning coefficient R and the relative loss coefficient W with preset thresholds, the urgency of cleaning is quantified to determine the optimal cleaning time; the specific judgment method is as follows:

[0120] 1) If on day n after cleaning, the relative loss coefficient W of all strings in the power plant appears... n average

[0121] Greater than the cleaning threshold W X2 Or the cleanliness coefficient R of each string in the power plant n average Less than the cleaning threshold

[0122] R X2 The situation, namely or It is then believed that photovoltaic power plants have, in principle, met the standard of "immediate cleaning".

[0123] 2) If, on day n after cleaning, the average relative loss coefficient and the average cleanliness coefficient fall between the warning threshold and the cleaning threshold, i.e. and If both conditions are met, the photovoltaic power station is considered to have reached the "recommended cleaning" standard in principle.

[0124] 3) If on the nth day after cleaning, the following conditions are met... or One of the conditions is that the photovoltaic power station is in the "preparatory cleaning" stage in principle.

[0125] Finally, after the above quantitative calculations and analysis, technicians need to consider factors such as weather conditions and personnel availability for the next few days based on their experience, and choose a suitable time for cleaning. For cases meeting the "immediate cleaning" standard, relevant personnel should be immediately arranged to prepare for the power station cleaning, unless the weather forecast predicts continuous heavy rain for the next few days or encounters weather conditions that make operation difficult; cleaning should be carried out as soon as possible.

[0126] If the "recommended cleaning" standard is met, a cleaning order will be issued, and the cleaning will be put on the agenda. The implementation can be arranged according to the actual situation. For example, if the weather forecast predicts several consecutive sunny days or if personnel are available, the schedule can be brought forward. If there is continuous rain or manpower shortage, it can be postponed.

[0127] The "preparatory cleaning" stage only requires some pre-cleaning preparations. Since heavy rain can improve the cleanliness coefficient of photovoltaic modules to some extent, it may also reduce the urgency of cleaning. This should be a key factor to consider, and the reliability of qualitative analysis should be improved by establishing a corresponding relational database.

[0128] The qualitative analysis process, which incorporates weather information, can begin by utilizing a weather database. This involves acquiring future weather data and establishing a weather database, while simultaneously obtaining real-time human resources information. The qualitative analysis process then integrates this database with the human resources information to make informed decisions.

[0129] After the photovoltaic power station is cleaned, the above cycle is repeated, and each string is calibrated and then analyzed and calculated using standard strings.

[0130] In some embodiments, the cleanliness index and relative loss coefficient of each calibration string in the photovoltaic power station are obtained; and each calibration string is distinguished and displayed on the photovoltaic power station topology map according to the relationship between the cleanliness index of each calibration string and the first warning threshold and the first cleanliness threshold.

[0131] Based on the relationship between the relative loss coefficient of each calibration string and the second warning threshold and the second cleaning threshold, each calibration string is displayed separately on the photovoltaic power station topology map.

[0132] Optionally, different cleaning coefficient strings can be distinguished by configuring colors. In this embodiment, the first warning threshold and the first cleaning threshold can be set to 95% and 90%, respectively. For a digital photovoltaic power station, when R... X2 <R in <R X1 Or R in <R X2 The degree of dust accumulation on the strings is visually reflected by coloring them (e.g., yellow or red) on the photovoltaic power station topology map. Specifically, strings with less than 90% dust accumulation are colored red, 90%-95% are colored yellow, and those with more than 95% dust accumulation remain uncolored.

[0133] Optionally, different display of strings with varying loss coefficients can be achieved by adding borders. The number of relative loss coefficient thresholds can be set as needed, typically two: a warning threshold and a cleaning threshold. Specific values ​​are determined based on actual conditions or iteratively optimized; for example, 90% and 110% respectively. For digitized photovoltaic power plants, strings on the photovoltaic power plant topology map are identified by adding yellow or red borders. Specifically, strings with losses above 110% are marked with a red border, those between 90% and 110% with a yellow border, and those above 90% without a border.

[0134] By coloring and bordering the strings on the topology map of the photovoltaic power station, it is easy to see where the dust accumulation is serious in the topology map of the management platform. If the rendered strings are relatively concentrated in a region and it is easy to clean them at once, the rendered area can be separated from the photovoltaic power station as an independent cleaning area unit, which can speed up the cleaning frequency of the dust-prone areas and improve the overall cleaning efficiency of the power station.

[0135] As a preferred approach, based on the specific conditions of the photovoltaic site (differences in dust accumulation, cleaning costs, ease of management, etc.), the photovoltaic power station is divided into several zones, with particularly heavily dusty areas isolated for focused cleaning, thereby improving the overall cleaning and power generation efficiency of the photovoltaic power station. After cleaning each zone, the strings within that zone are initially calibrated using standard strings, forming a cycle for each zone.

[0136] As a preferred approach, we will summarize the correlation between weather forecasts and power generation, the impact of rainfall on the cleanliness coefficient, and establish a relevant historical database to provide a reference for technicians to conduct qualitative analysis and make cleaning decisions.

[0137] As a preferred option, the threshold settings for the relative loss coefficient and the cleanliness coefficient are not limited to the two threshold settings mentioned above. The number and value of the thresholds can be determined according to the actual situation, and the urgency of power plant cleaning can be further refined accordingly.

[0138] As a preferred option, the selection of the standard string can be adjusted according to actual conditions, such as cleaning several strings and taking the average value, or selecting a suitable string as the standard string after comparing them over a period of time.

[0139] Ideally, cleaning should be done when the light is weak, and if conditions permit, it can be done at night. In addition, the choice of cleaning date should take into account the accuracy of the initial deviation coefficient calibration, that is, the first day after cleaning should preferably be a sunny day.

[0140] This embodiment also provides a frequency control system for a thermal power plant, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0141] Figure 4 A photovoltaic power plant cleaning cycle determination system according to an embodiment of this application, such as... Figure 4 As shown, the system includes: a parameter calculation module 40 and a judgment and decision module 41, wherein,

[0142] The parameter calculation module 40 is used to determine standard strings in a photovoltaic power station based on preset rules, wherein the standard strings are in a dust-free state.

[0143] Furthermore, after cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained based on the parameter ratio of each calibration string to the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained daily thereafter. The calibration strings are all photovoltaic strings other than the standard strings.

[0144] Furthermore, by comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, the daily cleanliness coefficient of each calibration string is determined. The cleanliness coefficient reflects the degree of dust accumulation in the photovoltaic string.

[0145] Furthermore, based on the string cleaning cost, as well as the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each calibration string, the daily relative loss coefficient of each calibration string is obtained.

[0146] The judgment and decision module 41 is used to output the cleaning assessment status of the photovoltaic power station by comparing the relative loss coefficient with a preset loss threshold or the cleaning coefficient with a preset cleaning threshold. The cleaning assessment status includes: ready cleaning, recommended cleaning and immediate cleaning.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above embodiments merely illustrate 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 analyzing and determining the cleaning cycle of a photovoltaic power station, characterized in that, The method includes: Standard strings are determined in the photovoltaic power station based on preset rules, wherein the standard strings are in a dust-free state; After cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained according to the parameter ratio of each calibration string to the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained daily thereafter. The calibration string is other photovoltaic strings besides the standard string. For a first target string with the same capacity and orientation as the standard string, the deviation coefficient of each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including: Obtain the output current or power generation efficiency of the first target string; The deviation coefficient corresponding to the first target string is determined by comparing the output current of the first target string with that of the standard string, or by comparing the power generation efficiency of the first target string with that of the standard string. For a second target string whose capacity and orientation differ from the standard string, the deviation coefficient of each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including: Obtain the daily power generation of the second target string; The deviation coefficient of the second target string is determined by comparing the daily power generation of the second target string with that of the standard string. The daily cleaning coefficient of each calibration string is determined by comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, wherein the cleaning coefficient reflects the degree of dust accumulation in the photovoltaic string. Based on the string cleaning cost, as well as the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each calibration string, the daily relative loss coefficient of each calibration string is obtained. The cleaning assessment status of the photovoltaic power station is output by comparing the relative loss coefficient with a preset loss threshold, or by comparing the cleaning coefficient with a preset cleaning threshold. The cleaning assessment status includes: ready to clean, recommended to clean, and immediate cleaning.

2. The method according to claim 1, characterized in that, The deviation coefficient corresponding to the first target string is determined using the following formula: C in =the in / or Bn or C in =I in / I Bn Among them, C in η is the deviation parameter of the calibration string numbered i on the nth day after the cleaning. in Let η be the average power generation efficiency of the calibration string numbered i on day n after cleaning. Bn I represents the average efficiency of the standard string on day n after cleaning; in Let I be the average current of the calibration string numbered i on day n after cleaning. Bn This represents the average current of the standard string on day n after cleaning.

3. The method according to claim 2, characterized in that, The deviation coefficient corresponding to the second target string is determined using the following formula: C in =Q in / Q Bn Among them, C in Q is the deviation parameter of the calibration string numbered i on the nth day after the cleaning. in The daily power generation of the calibration string numbered i on the nth day after cleaning, Q Bn It is the daily power generation of the calibration string on the nth day after the cleaning.

4. The method according to claim 1, characterized in that, Determine the relative loss coefficients for each calibration string, including: Based on the average electricity price, and the initial deviation coefficient, daily deviation coefficient, and daily power generation of each calibration string, the relative economic loss of each calibration string is calculated using the following formula: Among them, S in It is the relative economic loss of the calibration string numbered i on the nth day after cleaning, where D is the average electricity price, and C is the average economic loss of the calibration string numbered i on the nth day after cleaning. i1 C is the initial offset parameter of the calibration group string numbered i. ik Q is the initial offset parameter of the calibration string numbered i on the k-th day after cleaning. ik Let i be the daily power generation of the string numbered i on the k-th day after cleaning; Based on the relative economic loss and the string cleaning cost, the relative loss coefficient corresponding to each calibration string is obtained by the following formula; W in =S in / M Among them, W in S is the relative loss coefficient of the calibration string numbered i on the nth day after cleaning. in It is the relative economic loss of the calibration string numbered i on the nth day after cleaning, and M is the cleaning cost.

5. The method according to claim 4, characterized in that, After outputting the cleaning assessment status of the photovoltaic power station, the method further includes: Acquire future weather data and establish a weather database, and obtain human resources information in real time; If the cleaning assessment status of the calibration string is "immediate cleaning", then, in conjunction with the weather database and the human resources information, it is determined whether to send an immediate cleaning signal. The immediate cleaning signal is used to instruct the maintenance personnel to immediately clean the calibration string. If the cleaning assessment status of the calibration string is recommended to be cleaned, the weather database and the human resources information are combined to determine whether to send a cleaning signal. The cleaning signal is used to instruct the operation and maintenance personnel to clean the calibration string within a preset time period. When the cleaning assessment status of the calibration string is "ready to clean", the system combines the weather database and the human resources information to determine whether to send a "ready to clean" signal. The "ready to clean" signal is used to instruct the maintenance personnel to prepare the calibration string for cleaning.

6. The method according to claim 1, characterized in that, By comparing the relative loss coefficient with a preset loss threshold, or comparing the cleanliness coefficient with a preset cleaning threshold, the cleaning evaluation status of each calibration string is output, including: Obtain the preset cleaning threshold and the preset loss threshold, wherein the preset cleaning threshold includes a first warning threshold and a first cleaning threshold, and the preset loss threshold includes a second warning threshold and a second cleaning threshold; Obtain the average cleanliness index and average relative loss coefficient of all calibrated strings in the photovoltaic power station; If the average relative loss coefficient is greater than the second cleaning threshold, or the average cleanliness coefficient is less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is output as "immediate cleaning". If the average relative loss coefficient is greater than the second warning threshold and less than the second cleaning threshold, and the average cleaning coefficient is greater than the first warning threshold and less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is output as recommended cleaning. If the average relative loss coefficient is greater than the second warning threshold and less than the second cleaning threshold, or if the average cleaning coefficient is greater than the first warning threshold and less than the first cleaning threshold, the cleaning assessment status of the photovoltaic power station is determined to be ready for cleaning.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the cleanliness index and relative loss coefficient of each calibrated string in the photovoltaic power station; Based on the relationship between the cleaning index of each calibration string and the first warning threshold and the first cleaning threshold, each calibration string is displayed separately on the photovoltaic power station topology map. Based on the relationship between the relative loss coefficient of each calibration string and the second warning threshold and the second cleaning threshold, each calibration string is displayed separately on the photovoltaic power station topology map.

8. A system for determining the cleaning cycle of a photovoltaic power station, characterized in that, The system includes: a parameter calculation module and a decision-making module, wherein, The parameter calculation module is used to determine standard strings in the photovoltaic power station based on preset rules, wherein the standard strings are in a dust-free state. Furthermore, after cleaning the photovoltaic power station, the deviation coefficient of each calibration string is obtained based on the parameter ratio between each calibration string and the standard string. For a first target string with the same capacity and orientation as the standard string, the deviation coefficient of each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including: Obtain the output current or power generation efficiency of the first target string; The deviation coefficient corresponding to the first target string is determined by comparing the output current of the first target string with that of the standard string, or by comparing the power generation efficiency of the first target string with that of the standard string. For a second target string whose capacity and orientation differ from the standard string, the deviation coefficient of each photovoltaic string is obtained based on the parameter ratio between each calibrated string and the standard string, including: Obtain the daily power generation of the second target string; The deviation coefficient of the second target string is determined by comparing the daily power generation of the second target string with that of the standard string. The deviation coefficient includes the initial deviation coefficient obtained the day after cleaning and the dynamic deviation coefficient obtained daily thereafter. The calibration string refers to other photovoltaic strings besides the calibration string. Furthermore, by comparing the daily deviation coefficient and dynamic deviation coefficient of each calibration string, the daily cleanliness coefficient of each calibration string is determined, wherein the cleanliness coefficient reflects the degree of dust accumulation in the photovoltaic string. Furthermore, based on the string cleaning cost, as well as the initial deviation coefficient, dynamic deviation coefficient, and daily power generation of each calibration string, the daily relative loss coefficient of each calibration string is obtained. The judgment and decision module is used to output the cleaning assessment status of the photovoltaic power station by comparing the relative loss coefficient with a preset loss threshold or comparing the cleaning coefficient with a preset cleaning threshold. The cleaning assessment status includes: preparatory cleaning, recommended cleaning, and immediate cleaning.

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