An intelligent photovoltaic dust removal method and system

Abstract

This invention provides an intelligent photovoltaic (PV) dust removal method and system, relating to the field of data processing technology. It involves analyzing dust accumulation on a PV panel array to obtain dust property analysis results; optimizing the initial PV panel tilt angle based on the dust property analysis results to obtain the optimal PV panel tilt angle; generating a dust accumulation power generation loss prediction curve based on the optimal PV panel tilt angle and the dust accumulation property analysis results; generating a dust accumulation cleaning cost curve based on historical cleaning data; and normalizing the dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve to obtain the PV dust removal cycle. This invention addresses the technical problem in existing technologies where the PV panel tilt angle setting focuses on irradiance acquisition, leading to high PV panel dust removal costs and hindering the development of the PV industry by reducing the economic efficiency of PV power generation. The invention achieves the technical effect of balancing PV panel tilt angle with surface dust accumulation and solar radiation, allowing the PV panel dust removal cycle to change in real time according to the PV panel status, and reducing dust removal costs.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent photovoltaic dust removal method and system. Background Technology

[0002] With the continuous improvement of photovoltaic power generation technology, the scale of photovoltaic power generation is growing rapidly, making it a leader among clean energy sources. Theoretically, the greater the amount of irradiance received by a photovoltaic panel per unit time, the greater the output power of the photovoltaic power generation device. However, in actual production operations, photovoltaic power generation devices receive solar radiation in open environments such as grasslands in the wild. At the same time, environmental dust gradually moves with the wind and air, adhering to or depositing on the photovoltaic panels, causing diffuse reflection and reducing the amount of solar radiation received by the photovoltaic panels.

[0003] In order for photovoltaic panels to generate electricity by receiving radiation, staff need to clean the photovoltaic panels regularly. The cleaning cycle of photovoltaic panels often depends on the economic losses and costs of generating electricity from accumulated dust.

[0004] Existing technologies present a technical problem: when installing photovoltaic panels, the tilt angle of the panel frame is prioritized for irradiance acquisition, resulting in high costs for cleaning dust accumulation on the photovoltaic panels, which reduces the economic efficiency of photovoltaic power generation and hinders the development of the photovoltaic industry. Summary of the Invention

[0005] This application provides an intelligent photovoltaic dust removal method and system to address the technical problem in the prior art where the tilt angle of the photovoltaic panel frame focuses on the acquisition of irradiance, resulting in high dust removal costs for photovoltaic panels and hindering the development of the photovoltaic industry by reducing the economic efficiency of photovoltaic power generation.

[0006] In view of the above problems, this application provides an intelligent photovoltaic dust removal method and system.

[0007] The first aspect of this application provides an intelligent photovoltaic (PV) dust removal method, the method comprising: obtaining an initial PV panel tilt angle of a PV array; performing dust accumulation analysis on the PV array to obtain dust property analysis results; optimizing the initial PV panel tilt angle based on the dust property analysis results to obtain an optimal PV panel tilt angle; obtaining a dust accumulation power generation loss prediction curve based on the optimal PV panel tilt angle and the dust accumulation property analysis results; collecting historical cleaning data of the PV array; performing data curve fitting analysis based on the historical cleaning data to output a dust accumulation cleaning cost curve; and normalizing the dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve to obtain a PV dust removal cycle.

[0008] A second aspect of this application provides a photovoltaic intelligent cleaning management system, the system comprising: a photovoltaic tilt angle generation module for obtaining an initial photovoltaic panel tilt angle of a photovoltaic panel array; a dust accumulation property analysis module for performing dust accumulation analysis on the photovoltaic panel array and obtaining dust accumulation property analysis results; a photovoltaic tilt angle optimization module for optimizing the initial photovoltaic panel tilt angle based on the dust accumulation property analysis results to obtain an optimal photovoltaic panel tilt angle; a power generation loss generation module for obtaining a dust accumulation power generation loss prediction curve based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results; a historical data acquisition module for collecting historical cleaning data of the photovoltaic panel array; a dust removal cost analysis module for performing data curve fitting analysis based on the historical cleaning data and outputting a dust accumulation cleaning cost curve; and a dust removal cycle acquisition module for normalizing the dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve to obtain a photovoltaic dust removal cycle.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The method provided in this application obtains the initial tilt angle of the photovoltaic panel array as a benchmark for the actual installation tilt angle design. Dust accumulation analysis is performed on the photovoltaic panel array to obtain dust property analysis results. Based on the dust property analysis results, the initial photovoltaic panel tilt angle is optimized to obtain the optimal photovoltaic panel tilt angle, achieving a tilt angle that considers the properties of the photovoltaic panel, the properties of dust accumulation, and irradiance requirements. Based on the optimal photovoltaic panel tilt angle and the dust property analysis results, a dust accumulation power generation loss prediction curve is obtained, providing a calculation reference for subsequent dust removal cycle determination. Historical cleaning data of the photovoltaic panel array is collected, and data curve fitting analysis is performed based on the historical cleaning data to output a dust accumulation cleaning cost curve. The dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve are normalized to achieve a balance between dust accumulation loss cost and cleaning consumption cost, thus obtaining the photovoltaic dust removal cycle. This achieves the technical effect of balancing the installation tilt angle of the photovoltaic panel with the requirements of dust accumulation and power generation radiation on the photovoltaic panel surface, with the photovoltaic panel dust removal cycle changing in real time according to the photovoltaic panel status, and low photovoltaic dust removal cost. Attached Figure Description

[0011] Figure 1 This application provides a schematic diagram of an intelligent photovoltaic dust removal method.

[0012] Figure 2 A flowchart illustrating the process of obtaining dust property analysis results in an intelligent photovoltaic dust removal method provided in this application;

[0013] Figure 3 A schematic diagram of the process for obtaining the optimal tilt angle of a photovoltaic panel in an intelligent photovoltaic dust removal method provided in this application;

[0014] Figure 4 This is a structural diagram of a photovoltaic intelligent cleaning management system provided in this application.

[0015] Figure labeling: 11 Photovoltaic tilt angle generation module, 12 Dust accumulation property analysis module, 13 Photovoltaic tilt angle optimization module, 14 Power generation loss generation module, 15 Historical data acquisition module, 16 Dust removal cost analysis module, 17 Dust removal cycle acquisition module. Detailed Implementation

[0016] This application provides an intelligent photovoltaic dust removal method and system to address the technical problem in the prior art where the tilt angle of the photovoltaic panel frame focuses on the acquisition of irradiance, resulting in high dust removal costs for photovoltaic panels and hindering the development of the photovoltaic industry by reducing the economic efficiency of photovoltaic power generation.

[0017] To address the aforementioned technical problems, the overall approach of the technical solution provided in this application is as follows:

[0018] Based on the inherent properties of photovoltaic panels, environmental dust accumulation characteristics, and power generation irradiance requirements, an optimal tilt angle for the photovoltaic panels is generated. Furthermore, at this optimal tilt angle, the power generation loss due to dust accumulation on the photovoltaic panel and the cleaning cost are calculated, resulting in the photovoltaic cleaning cycle with the lowest economic loss cost. This achieves a photovoltaic panel installation tilt angle that balances surface dust accumulation and power generation irradiance requirements. The photovoltaic panel cleaning cycle changes in real time according to the condition of the photovoltaic panel, resulting in low photovoltaic cleaning costs.

[0019] Example 1

[0020] like Figure 1 As shown, this application provides an intelligent photovoltaic dust removal method. The method is applied to a photovoltaic intelligent cleaning management system, which is communicatively connected to a photovoltaic panel tilt adjustment device and a branch current transformer. The method includes:

[0021] S100: Obtain the initial photovoltaic panel tilt angle of the photovoltaic panel array;

[0022] Furthermore, to obtain the initial photovoltaic panel tilt angle of the photovoltaic panel array, step S100 of the method provided in this application also includes:

[0023] S110: Obtain the geographical location information of the photovoltaic panel array;

[0024] S120: Determine historical irradiance data based on the geographical location information;

[0025] S130: Determine the initial tilt angle of the photovoltaic panel based on the geographical location information and the historical irradiance data.

[0026] Specifically, it should be understood that the tilt angle of a photovoltaic panel directly affects the amount of radiation it can receive, and thus the amount of photovoltaic power generated. The historical irradiance data refers to a set of annual irradiance data for a specific land surface, arranged chronologically.

[0027] In this embodiment, the latitude and longitude data of the photovoltaic array and the historical irradiance data are obtained based on the geographical location information of the photovoltaic array. The total annual radiation received by the photovoltaic array at different photovoltaic panel tilt angles is calculated and accumulated based on the historical irradiance data and the latitude and longitude data.

[0028] The annual total radiation received at different photovoltaic panel tilt angles is sorted, and the photovoltaic panel tilt angle corresponding to the maximum annual total radiation is obtained as the initial photovoltaic panel tilt angle. The photovoltaic panel array receives the maximum radiation at the initial photovoltaic panel tilt angle.

[0029] This embodiment obtains the geographical location information and historical irradiance data of the photovoltaic panel array, and calculates the tilt angle of the photovoltaic panel corresponding to the maximum annual irradiance by combining the tilt angle of the photovoltaic panel. This achieves the technical effect of providing accurate tilt angle values ​​for subsequent adjustment of the photovoltaic panel tilt angle in conjunction with dust cleaning.

[0030] S200: Perform dust accumulation analysis on the photovoltaic panel array to obtain the dust accumulation property analysis results;

[0031] Furthermore, such as Figure 2 As shown, dust accumulation analysis is performed on the photovoltaic panel array to obtain dust property analysis results. The method step S200 provided in this application further includes:

[0032] S210: Obtain the photovoltaic panel attribute information of the photovoltaic panel array;

[0033] S220: Obtain historical environmental data based on the geographical location information;

[0034] S230: Analyze the sources of dust accumulation based on the historical environmental data to obtain a set of dust type information composed of various dust accumulation information;

[0035] S240: Input the photovoltaic panel attribute information and the dust accumulation type information into the photovoltaic panel erosion damage analysis model to obtain the dust accumulation erosion damage results, wherein the dust accumulation erosion damage results are multiple sets of dust accumulation erosion score results;

[0036] S250: Input the photovoltaic panel attribute information and the dust accumulation type information into the photovoltaic panel adhesion analysis model to obtain the dust accumulation adhesion analysis results, wherein the dust accumulation adhesion analysis results are multiple sets of dust accumulation adhesion score results;

[0037] S260: Assign weights to the dust erosion damage results and the dust adhesion analysis results to generate the dust property analysis results.

[0038] Specifically, the photovoltaic panel attribute information refers to the material properties of the light-transmitting protective layer on the surface of the photovoltaic panel, such as glass or other organic or inorganic materials with high light transmittance. The historical environmental data includes, but is not limited to, environmental information related to surface dust accumulation, such as industrial plant information, soil pH information, desertification status, and soil moisture content at the geographical location.

[0039] The photovoltaic panel erosion damage analysis model is designed to determine whether dust accumulation interferes with the photovoltaic panel material and causes diffuse reflection, thus reducing the photovoltaic panel's radiation receiving capacity, based on the photovoltaic panel material properties and the chemical properties of the dust deposited on the photovoltaic panel. The photovoltaic panel erosion damage model has multiple dust accumulation erosion loss sub-models, with one sub-model corresponding to each type of dust accumulation.

[0040] The photovoltaic panel adhesion analysis model is designed to determine the specific tilt angle at which dust deposits on the photovoltaic panel will displace and slide off, or fail to settle, based on the photovoltaic panel material properties and the physical adhesion properties of the dust deposited on the panel. The photovoltaic panel erosion damage model has multiple dust adhesion analysis sub-models, with one sub-model corresponding to each type of dust. The dust adhesion score is generated based on the dust slippage tilt angle data on the photovoltaic panel.

[0041] In this embodiment, by obtaining the photovoltaic panel attribute information of the photovoltaic panel array, obtaining historical environmental data based on the geographical location information, and performing dust source analysis based on the historical environmental data, a set of dust type information composed of various dust information is obtained, such as acidic dust, alkaline dust, dry dust, sand particles, etc.

[0042] The photovoltaic panel attribute information and the dust type information are input into the photovoltaic panel erosion damage analysis model to obtain multiple sets of dust erosion score results, which constitute the dust erosion damage results. The photovoltaic panel attribute information and the dust type information are also input into the photovoltaic panel adhesion analysis model to obtain multiple sets of dust adhesion score results, which constitute the dust adhesion analysis results.

[0043] Multiple sets of dust erosion scores for various types of dust are summed to obtain a total dust erosion score. A weighted value is then assigned to the erosion ability of each dust accumulation based on this total score. Similarly, multiple sets of dust adhesion analysis results for various types of dust are summed to obtain a total dust adhesion score. A weighted value is then assigned to the adhesion ability of each dust accumulation based on this total score. Finally, the weighted values ​​for adhesion ability and erosion ability of each dust accumulation are summed and averaged to generate the dust property analysis result. This result reflects the erosion ability of the dust on the photovoltaic panel and its adhesion ability to the photovoltaic panel.

[0044] This embodiment does not impose any restrictions on the training and construction methods of the photovoltaic panel erosion damage analysis model and the photovoltaic panel adhesion analysis model. Optionally, in actual operation, the models can be constructed and trained based on historical photovoltaic panel erosion and dust sliding conditions.

[0045] This embodiment constructs a photovoltaic panel erosion damage analysis model and a photovoltaic panel adhesion analysis model to analyze the physical and chemical properties of dust accumulation on the photovoltaic panel surface and their interference with the photovoltaic panel's irradiation. This achieves the technical effect of accurately obtaining dust accumulation property analysis results that are relevant to the photovoltaic panel's attributes, providing reference information on dust accumulation properties for subsequent photovoltaic panel tilt angle optimization, and realizing a comprehensive optimization of photovoltaic panel tilt angle that takes into account both power generation irradiation requirements and dust deposition requirements.

[0046] S300: Optimize the initial photovoltaic panel tilt angle based on the dust accumulation property analysis results to obtain the optimal photovoltaic panel tilt angle;

[0047] Furthermore, such as Figure 3 As shown, based on the dust accumulation property analysis results, the initial photovoltaic panel tilt angle is optimized to obtain the optimal photovoltaic panel tilt angle. The method step S300 provided in this application further includes:

[0048] S310: Based on the aforementioned geographical location information, obtain the surface dust density;

[0049] S320: Construct a photovoltaic tilt angle optimization model;

[0050] S330: Input the surface dust density, the dust property analysis results, and the initial photovoltaic panel tilt angle into the photovoltaic tilt angle optimization model, and output the tilt angle adjustment coefficient;

[0051] S340: Obtain the optimal photovoltaic panel tilt angle based on the tilt angle adjustment coefficient and the initial photovoltaic panel tilt angle;

[0052] S350: The photovoltaic panel tilt angle adjustment device adjusts the tilt angle of the photovoltaic panel array according to the tilt angle adjustment coefficient.

[0053] Specifically, it should be understood that calculating surface dust based on short-term time sequences such as months or weeks is impractical. Therefore, this embodiment uses big data to obtain the natural dust accumulation at the geographical location of the photovoltaic array and incorporates rainwater runoff to calculate the surface dust density. The surface dust density represents the overall dust accumulation situation at the geographical location of the photovoltaic array.

[0054] The photovoltaic tilt angle optimization model is a model that coordinates the analysis results of ash accumulation properties, ash deposition amount, and power generation radiation to generate a tilt angle adjustment coefficient to guide the adjustment of the photovoltaic panel's reference tilt angle. The tilt angle adjustment coefficient is the angle value used to adjust the initial photovoltaic panel tilt angle, including positive and negative numbers. A positive angle value indicates a clockwise adjustment, while a negative angle value indicates a counter-clockwise adjustment.

[0055] This embodiment does not impose any limitations on the construction of the photovoltaic tilt angle optimization model. For example, the photovoltaic tilt angle optimization model can consist of a master control agent, a dust accumulation adjustment agent, an irradiation adjustment agent, and a tilt angle dust accumulation change agent. The master control agent coordinates the dust accumulation adjustment agent, the irradiation adjustment agent, and the tilt angle dust accumulation change agent; it outputs the tilt angle adjustment coefficient based on the coordinated action between multiple agents.

[0056] In this embodiment, the surface dust density, the dust property analysis results, and the initial photovoltaic panel tilt angle are input into the photovoltaic tilt angle optimization model, and the tilt angle adjustment coefficient is output. The optimal photovoltaic panel tilt angle is obtained by combining the tilt angle adjustment coefficient and the initial photovoltaic panel tilt angle. The photovoltaic panel tilt angle adjustment device adjusts the tilt angle of the photovoltaic panel array according to the tilt angle adjustment coefficient.

[0057] This embodiment improves the original initial photovoltaic panel tilt angle by combining the power generation irradiance requirements with the accumulation and erosion of dust on the photovoltaic panel surface. This achieves the technical effect of obtaining a photovoltaic panel tilt angle that increases the difficulty of dust accumulation without affecting the normal power generation irradiance required by the photovoltaic panel, and reducing the power generation loss caused by dust accumulation. Compared with the traditional method of determining the photovoltaic panel tilt angle, this method takes into account the impact of various environmental factors on photovoltaic panel power generation more comprehensively.

[0058] S400: Based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results, obtain the dust accumulation power generation loss prediction curve;

[0059] Furthermore, based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results, a dust accumulation power generation loss prediction curve is obtained. The method step S400 provided in this application further includes:

[0060] S410: Analyze the output power variation based on the dust accumulation and output power of the photovoltaic array to obtain the power time-series variation curve;

[0061] S420: The highest output power is obtained as the clean output power of the photovoltaic panel based on the power time-series variation curve;

[0062] S430: Perform de-differentiation processing on the power time-series change curve to generate a power change correction curve;

[0063] S440: Generate an ideal power output curve based on the dust-free output power of the photovoltaic panel;

[0064] S450: Obtains comprehensive electricity price information based on big data calculations;

[0065] S460: Based on the power change correction curve and the ideal power output curve, and combined with the comprehensive electricity price information, generate the dust accumulation power generation loss prediction curve.

[0066] Specifically, it should be understood that regardless of the tilt angle of the photovoltaic panel, it is impossible to avoid the deposition of environmental dust on the surface of the photovoltaic panel. Therefore, this embodiment calculates the power generation loss due to dust accumulation based on the theoretical power generation of the photovoltaic panel and the actual power generation of the photovoltaic panel.

[0067] In this embodiment, the output power change is analyzed based on the dust accumulation and output power of the photovoltaic panel array to obtain the photovoltaic panel power change data due to dust accumulation on the photovoltaic panel surface over time. The power time series change curve is generated based on the photovoltaic panel power change data and the corresponding recording time sequence, and the power time series change curve is a scatter plot.

[0068] The highest output power is obtained from the power time-series change curve as the clean output power of the photovoltaic panel. The clean output power of the photovoltaic panel is the power generation of the photovoltaic panel under ideal conditions. An ideal power output curve is generated based on the clean output power of the photovoltaic panel.

[0069] The power time-series change curve is processed to remove abnormal scattered points that deviate from the curve, and the power change correction curve is generated. The power change correction curve can more accurately reflect the degree of influence of dust accumulation on the power generation of photovoltaic panels.

[0070] Based on big data calculations to obtain comprehensive electricity price information, the power change correction curve and the ideal power output curve are subtracted, and the processing result is calculated with the comprehensive electricity price information according to the time series to generate the dust accumulation power generation loss prediction curve. The dust accumulation power generation loss curve is the power generation economic loss curve caused by dust accumulation under the time series.

[0071] This embodiment achieves the technical effect of accurately knowing the economic loss of power generation of the photovoltaic array installed in a specific geographical location under the influence of dust accumulation without dust cleaning by obtaining the market electricity price and combining it with the power generation loss caused by dust accumulation.

[0072] S500: Collect historical cleaning data of the photovoltaic panel array;

[0073] S600: Perform data curve fitting analysis based on the historical cleaning data, and output the dust cleaning cost curve;

[0074] Specifically, it should be understood that as dust remains on the photovoltaic panel for a longer period of time, the difficulty of cleaning the photovoltaic panel increases. The historical cleaning data represents the cleaning cost per unit area when the photovoltaic panel array was cleaned in the past. As the amount of dust accumulation and the time that the dust has been deposited and adhered to the photovoltaic panel vary, the cleaning cost shows an upward trend. Based on the historical data, a data curve fitting analysis is performed to obtain the dust cleaning cost curve.

[0075] S700: Normalize the dust cleaning cost curve and the dust power generation loss prediction curve to obtain the photovoltaic dust removal cycle.

[0076] Furthermore, the dust cleaning cost curve and the dust power generation loss prediction curve are normalized to obtain the photovoltaic dust removal cycle. Step S700 of the method provided in this application also includes:

[0077] S710: Normalize the dust cleaning cost curve and the dust power generation loss prediction curve to obtain the image intersection of the dust cleaning cost curve and the dust power generation loss prediction curve.

[0078] S720: The photovoltaic dust removal cycle is obtained by tracing back the intersection points of the images;

[0079] S730: Obtain the expected precipitation information during the photovoltaic dust removal cycle;

[0080] S740: Input the predicted precipitation information, the dust accumulation property analysis results, and the optimal photovoltaic panel tilt angle into the cleaning effect prediction model. The cleaning effect prediction model is used to determine whether to clean the photovoltaic panel array according to the photovoltaic dust removal cycle.

[0081] S750: When the output of the cleaning effect prediction model is 1, the photovoltaic panel array is cleaned according to the photovoltaic dust removal cycle;

[0082] S760: When the output of the cleaning effect prediction model is 0, the photovoltaic panel cleaning optimization cycle is generated based on the predicted precipitation information, and the photovoltaic panel array is cleaned.

[0083] Specifically, in this embodiment, the dust cleaning cost curve and the dust power generation loss prediction curve are normalized and included in the same curve image to obtain the image intersection of the dust cleaning cost curve and the dust power generation loss prediction curve. The power decline time sequence is obtained by tracing back the image intersection, and the photovoltaic dust removal cycle is generated based on the power decline time sequence and the initial power recording time sequence.

[0084] The cleaning effect prediction model can analyze and output the cleaning effect of photovoltaic panels based on the tilt angle of the photovoltaic panels, the properties of dust accumulation, and precipitation information. This embodiment does not impose any restrictions on the construction of the cleaning effect prediction model; in actual implementation, the model can be constructed and trained as needed.

[0085] It should be understood that rainwater can also clean photovoltaic panels during environmental precipitation. Therefore, to further reduce dust accumulation, this embodiment obtains the expected precipitation information within the photovoltaic dust removal cycle. The expected precipitation information includes the expected precipitation time and intensity. The expected precipitation information, the dust accumulation property analysis results, and the optimal photovoltaic panel tilt angle are input into a cleaning effect prediction model to determine whether to clean the photovoltaic panel array according to the photovoltaic dust removal cycle. When the output of the cleaning effect prediction model is 1, it indicates that the cleaning effect does not meet the photovoltaic panel cleaning requirements, and the photovoltaic panel array is cleaned according to the photovoltaic dust removal cycle. When the output of the cleaning effect prediction model is 0, the surface cleaning effect meets the photovoltaic panel cleaning requirements, and an optimized photovoltaic panel dust removal cycle is generated based on the expected precipitation information, and the photovoltaic panel array is cleaned accordingly. The "1" and "0" have no practical meaning; they simply represent whether the data results are acceptable or not.

[0086] This embodiment achieves the technical effect of further reducing cleaning costs and improving the economic benefits of photovoltaic power generation by constructing a cleaning effect prediction model and adjusting the original cleaning cycle in combination with natural environmental precipitation.

[0087] The method provided in this embodiment obtains the initial tilt angle of the photovoltaic panel array as a benchmark for the actual installation tilt angle design. Dust accumulation analysis is performed on the photovoltaic panel array to obtain dust property analysis results. Based on the dust property analysis results, the initial photovoltaic panel tilt angle is optimized to obtain the optimal photovoltaic panel tilt angle, achieving a tilt angle that considers the properties of the photovoltaic panel, the properties of dust accumulation, and irradiance requirements. Based on the optimal photovoltaic panel tilt angle and the dust property analysis results, a dust accumulation power generation loss prediction curve is obtained, providing a calculation reference for subsequent dust removal cycle determination. Historical cleaning data of the photovoltaic panel array is collected, and data curve fitting analysis is performed based on the historical cleaning data to output a dust accumulation cleaning cost curve. The dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve are normalized to achieve a balance between dust accumulation loss cost and cleaning consumption cost, thus obtaining the photovoltaic dust removal cycle. This method achieves the technical effect of balancing the installation tilt angle of the photovoltaic panel with the requirements of dust accumulation and power generation radiation on the photovoltaic panel surface, with the photovoltaic panel dust removal cycle changing in real time according to the photovoltaic panel status, and low photovoltaic dust removal cost.

[0088] Furthermore, the method steps provided in this application also include:

[0089] S810: Obtain historical monitoring data of the photovoltaic panel array;

[0090] S820: Obtain the surface temperature data of the photovoltaic branch of the photovoltaic panel array based on the branch current transformer;

[0091] S830: Compare the surface temperature data of the photovoltaic branch with the historical monitoring data, and output the abnormal detection data;

[0092] S840: Based on the abnormal detection data, determine whether the photovoltaic panel array has hot spot phenomenon;

[0093] S850: When it is determined that there is a hot spot phenomenon in the photovoltaic array, the hot spot photovoltaic panel of the photovoltaic panel array is located according to the abnormal detection data.

[0094] Specifically, it should be understood that the hot spot effect, compared to the interference of dust accumulation on the power generation irradiance of photovoltaic panels, can directly damage the photovoltaic panels. Therefore, while periodically cleaning the dust accumulation on the photovoltaic panel array, this embodiment also monitors the hot spot phenomenon in real time and provides timely early warning to avoid irreversible damage to the photovoltaic panels caused by the hot spot effect.

[0095] In this embodiment, the branch current transformer is assembled based on the photovoltaic panel array. The branch current transformer is a sensing device that monitors and records the current, voltage, power, and surface temperature data of the photovoltaic modules generated by each photovoltaic branch of the photovoltaic panel array in real time.

[0096] The historical monitoring data of each photovoltaic branch obtained by the branch current transformers are traversed to generate an anomaly comparison benchmark data set. Based on the branch current transformers, the surface temperature data of each photovoltaic branch of the photovoltaic panel array are obtained.

[0097] Real-time temperature data is obtained based on the surface temperature data of the photovoltaic branch. Combined with environmental weather information, the historical monitoring data is traversed to obtain historical monitoring data of the photovoltaic branch that is consistent with the environmental weather information. This data is then compared to output anomaly detection data.

[0098] Based on the anomaly detection data, the photovoltaic branch corresponding to the anomaly detection data is analyzed, and the presence of hot spots on some photovoltaic panels in the photovoltaic array is determined using an image acquisition device. When both the anomaly detection data and the image acquisition device indicate the presence of hot spots on the corresponding photovoltaic panel, the staff performs rapid and precise cleaning of the photovoltaic panel with the hot spots.

[0099] This embodiment introduces branch sensors combined with image acquisition devices to quickly analyze and process occasional hot spot phenomena on photovoltaic panels, achieving the technical effect of cleaning occasional contamination of photovoltaic panels and eliminating the cleaning hazards caused by relying solely on periodic cleaning.

[0100] Example 2

[0101] Based on the same inventive concept as the intelligent photovoltaic dust removal method in the foregoing embodiments, such as Figure 4 As shown, this application provides a photovoltaic intelligent cleaning management system, wherein the system includes:

[0102] The photovoltaic tilt angle generation module 11 is used to obtain the initial photovoltaic panel tilt angle of the photovoltaic panel array;

[0103] Dust accumulation property analysis module 12 is used to perform dust accumulation analysis on the photovoltaic panel array and obtain dust accumulation property analysis results;

[0104] Photovoltaic tilt angle optimization module 13 is used to optimize the initial photovoltaic panel tilt angle based on the dust accumulation property analysis results to obtain the optimal photovoltaic panel tilt angle;

[0105] The power generation loss generation module 14 is used to obtain the dust accumulation power generation loss prediction curve based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results;

[0106] Historical data acquisition module 15 is used to collect historical cleaning data of the photovoltaic panel array;

[0107] The dust removal cost analysis module 16 is used to perform data curve fitting analysis based on the historical cleaning data and output the dust cleaning cost curve.

[0108] The dust removal cycle acquisition module 17 is used to normalize the dust cleaning cost curve and the dust power generation loss prediction curve to obtain the photovoltaic dust removal cycle.

[0109] Furthermore, the photovoltaic tilt angle generation module 11 also includes:

[0110] A location information retrieval unit is used to obtain the geographical location information of the photovoltaic panel array;

[0111] An irradiation data acquisition unit is used to determine historical irradiation data based on the geographical location information;

[0112] The photovoltaic tilt angle calculation unit is used to determine the initial photovoltaic panel tilt angle based on the geographical location information and the historical irradiance data.

[0113] Furthermore, the dust accumulation property analysis module 12 also includes:

[0114] A panel attribute acquisition unit is used to acquire photovoltaic panel attribute information of the photovoltaic panel array;

[0115] An environmental data retrieval unit is used to obtain historical environmental data based on the geographical location information;

[0116] The dust type acquisition unit is used to analyze the dust source based on the historical environmental data and obtain a set of dust type information composed of multiple dust information.

[0117] The dust erosion analysis unit is used to input the photovoltaic panel attribute information and the dust type information into the photovoltaic panel erosion damage analysis model to obtain the dust erosion damage results, which are multiple sets of dust erosion score results;

[0118] The dust adhesion analysis unit is used to input the photovoltaic panel attribute information and the dust type information into the photovoltaic panel adhesion analysis model to obtain the dust adhesion analysis results, which are multiple sets of dust adhesion score results;

[0119] The dust accumulation property analysis unit is used to assign weights to the dust accumulation erosion damage results and the dust accumulation adhesion analysis results, and generate the dust accumulation property analysis results.

[0120] Furthermore, the photovoltaic tilt angle optimization module 13 also includes:

[0121] A surface dust retrieval unit is used to obtain the surface dust density based on the geographical location information;

[0122] An optimized model building unit is used to construct a photovoltaic tilt angle optimization model.

[0123] The adjustment coefficient calculation unit is used to input the surface dust density, the dust property analysis results and the initial photovoltaic panel tilt angle into the photovoltaic tilt angle optimization model, and output the tilt angle adjustment coefficient.

[0124] The optimal tilt angle calculation unit is used to obtain the optimal photovoltaic panel tilt angle based on the tilt angle adjustment coefficient and the initial photovoltaic panel tilt angle.

[0125] The tilt angle adjustment execution unit is used by the photovoltaic panel tilt angle adjustment device to adjust the tilt angle of the photovoltaic panel array according to the tilt angle adjustment coefficient.

[0126] Furthermore, the power generation loss generation module 14 also includes:

[0127] The interference curve generation unit is used to analyze the output power change based on the dust accumulation and output power of the photovoltaic array to obtain the power time-series change curve;

[0128] An ideal power acquisition unit is used to obtain the highest output power as the dust-free output power of the photovoltaic panel based on the power time-series variation curve.

[0129] The curve correction execution unit is used to perform de-differentiation processing on the power timing change curve to generate a power change correction curve.

[0130] An ideal power mapping unit is used to generate an ideal power output curve based on the dust-free output power of the photovoltaic panel.

[0131] The electricity price information retrieval unit is used to obtain comprehensive electricity price information based on big data calculations.

[0132] The loss curve generation unit is used to generate the dust accumulation power generation loss prediction curve based on the power change correction curve and the ideal power output curve, combined with the comprehensive electricity price information.

[0133] Furthermore, the dust removal cycle acquisition module 17 also includes:

[0134] The curve intersection point generation unit is used to normalize the dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve to obtain the image intersection point of the dust accumulation cleaning cost curve and the dust accumulation power generation loss prediction curve.

[0135] A dust removal cycle generation unit is used to obtain the photovoltaic dust removal cycle by tracing the intersection points of the images.

[0136] The prediction information acquisition unit is used to obtain the expected precipitation information during the photovoltaic dust removal cycle;

[0137] The dust removal cycle determination unit is used to input the expected precipitation information, the dust accumulation property analysis results, and the optimal photovoltaic panel tilt angle into the cleaning effect prediction model. The cleaning effect prediction model is used to determine whether to clean the photovoltaic panel array according to the photovoltaic dust removal cycle.

[0138] The judgment result execution unit is used to perform cleaning of the photovoltaic panel array according to the photovoltaic dust removal cycle when the output result of the cleaning effect prediction model is 1.

[0139] The dust removal cycle change unit is used to generate an optimized dust removal cycle for the photovoltaic panel array based on the expected precipitation information when the output result of the cleaning effect prediction model is 0.

[0140] Furthermore, the method steps provided in this application also include:

[0141] A monitoring data acquisition unit is used to acquire historical monitoring data of the photovoltaic panel array;

[0142] A photovoltaic temperature acquisition unit is used to acquire the surface temperature data of the photovoltaic branch of the photovoltaic panel array based on the branch current transformer.

[0143] An anomaly data comparison unit is used to compare the surface temperature data of the photovoltaic branch with the historical monitoring data and output anomaly detection data;

[0144] A hot spot phenomenon determination unit is used to determine whether the photovoltaic panel array has a hot spot phenomenon based on the abnormal detection data.

[0145] A photovoltaic hot spot location unit is used to locate the hot spot photovoltaic panel of the photovoltaic array based on the abnormal detection data after it is determined that there is a hot spot phenomenon in the photovoltaic array.

[0146] In summary, any of the methods or steps described above can be stored as computer instructions or programs in various types of computer memory, and the computer instructions or programs can be recognized by various types of computer processors to implement any of the above methods or steps.

[0147] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principle of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. An intelligent photovoltaic dust removal method, characterized in that, The method is applied to a photovoltaic intelligent cleaning management system, which is communicatively connected to a photovoltaic panel tilt adjustment device and a branch current transformer. The method includes: Obtain the initial photovoltaic panel tilt angle of the photovoltaic panel array; Dust accumulation analysis was performed on the photovoltaic panel array to obtain the dust accumulation property analysis results; Based on the dust accumulation property analysis results, the initial photovoltaic panel tilt angle is optimized to obtain the optimal photovoltaic panel tilt angle. Based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results, a dust accumulation power generation loss prediction curve is obtained; Collect historical cleaning data of the photovoltaic panel array; Based on the historical cleaning data, a data curve fitting analysis is performed to output the dust cleaning cost curve. The dust cleaning cost curve and the dust power generation loss prediction curve are normalized to obtain the photovoltaic dust removal cycle. Obtaining the initial photovoltaic panel tilt angle of the photovoltaic panel array includes: Obtain the geographical location information of the photovoltaic panel array; Historical irradiation data are determined based on the aforementioned geographical location information; The initial tilt angle of the photovoltaic panels is determined based on the geographical location information and the historical irradiance data. Based on the dust accumulation property analysis results, the initial photovoltaic panel tilt angle is optimized to obtain the optimal photovoltaic panel tilt angle, including: Based on the aforementioned geographical location information, the surface dust density is obtained; Construct a photovoltaic tilt angle optimization model; The surface dust density, the dust property analysis results, and the initial photovoltaic panel tilt angle are input into the photovoltaic tilt angle optimization model, and the tilt angle adjustment coefficient is output. The optimal photovoltaic panel tilt angle is obtained based on the tilt angle adjustment coefficient and the initial photovoltaic panel tilt angle; The photovoltaic panel tilt adjustment device adjusts the tilt angle of the photovoltaic panel array according to the tilt adjustment coefficient; The dust cleaning cost curve and the dust power generation loss prediction curve are normalized to obtain the photovoltaic dust removal cycle, including: The dust cleaning cost curve and the dust power generation loss prediction curve are normalized to obtain the image intersection of the dust cleaning cost curve and the dust power generation loss prediction curve. The photovoltaic dust removal cycle is obtained by tracing the intersection points of the images. Obtain the predicted precipitation information during the photovoltaic dust removal cycle; The predicted precipitation information, the dust accumulation property analysis results, and the optimal photovoltaic panel tilt angle are input into the cleaning effect prediction model. The cleaning effect prediction model is used to determine whether the photovoltaic panel array should be cleaned according to the photovoltaic dust removal cycle. When the output of the cleaning effect prediction model is 1, the photovoltaic panel array is cleaned according to the photovoltaic dust removal cycle. When the output of the cleaning effect prediction model is 0, the photovoltaic panel cleaning optimization cycle is generated based on the predicted precipitation information, and the photovoltaic panel array is cleaned.

2. The method as described in claim 1, characterized in that, Dust accumulation analysis was performed on the photovoltaic panel array to obtain the dust accumulation property analysis results, including: Obtain the photovoltaic panel attribute information of the photovoltaic panel array; Historical environmental data is obtained based on the aforementioned geographical location information; Based on the historical environmental data, dust source analysis is performed to obtain a set of dust type information composed of various dust information. The photovoltaic panel attribute information and the dust accumulation type information are input into the photovoltaic panel erosion damage analysis model to obtain the dust accumulation erosion damage results, which are multiple sets of dust accumulation erosion score results; The photovoltaic panel attribute information and the dust accumulation type information are input into the photovoltaic panel adhesion analysis model to obtain the dust accumulation adhesion analysis results, which are multiple sets of dust accumulation adhesion score results. The dust erosion damage results and the dust adhesion analysis results are weighted and assigned to generate the dust property analysis results.

3. The method as described in claim 1, characterized in that, Based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results, a dust accumulation power generation loss prediction curve is obtained, including: Based on the dust accumulation and output power of the photovoltaic array, an analysis of output power variation is performed to obtain a power time-series variation curve; The highest output power is obtained as the dust-free output power of the photovoltaic panel based on the power time-series variation curve. The power time-series change curve is subjected to de-differentiation processing to generate a power change correction curve. Generate an ideal power output curve based on the dust-free output power of the photovoltaic panel; Comprehensive electricity price information is obtained based on big data calculations; Based on the power change correction curve and the ideal power output curve, and combined with the comprehensive electricity price information, the dust accumulation power generation loss prediction curve is generated.

4. The method as described in claim 1, characterized in that, include: Obtain historical monitoring data of the photovoltaic panel array; Based on the branch current transformer, obtain the photovoltaic branch surface temperature data of the photovoltaic panel array; The surface temperature data of the photovoltaic branch is compared with the historical monitoring data to output anomaly detection data; Based on the abnormal detection data, it is determined whether the photovoltaic panel array has hot spot phenomenon; Once it is determined that there is a hot spot phenomenon in the photovoltaic array, the hot spot photovoltaic panel of the photovoltaic panel array is located based on the abnormal detection data.

5. A photovoltaic intelligent cleaning management system, characterized in that, The system is used to implement an intelligent photovoltaic dust removal method according to any one of claims 1 to 4, the system comprising: A photovoltaic tilt angle generation module is used to obtain the initial photovoltaic panel tilt angle of the photovoltaic panel array; The dust accumulation property analysis module is used to perform dust accumulation analysis on the photovoltaic panel array and obtain the dust accumulation property analysis results; The photovoltaic tilt angle optimization module is used to optimize the initial photovoltaic panel tilt angle based on the dust accumulation property analysis results to obtain the optimal photovoltaic panel tilt angle. The power generation loss generation module is used to obtain the dust accumulation power generation loss prediction curve based on the optimal photovoltaic panel tilt angle and the dust accumulation property analysis results; The historical data acquisition module is used to collect historical cleaning data of the photovoltaic panel array; The dust removal cost analysis module is used to perform data curve fitting analysis based on the historical cleaning data and output the dust cleaning cost curve. The dust removal cycle acquisition module is used to normalize the dust cleaning cost curve and the dust power generation loss prediction curve to obtain the photovoltaic dust removal cycle.

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