Photovoltaic panel adjustment method and device, storage medium and electronic equipment

Abstract

The invention relates to the field of sand photovoltaic technologies, in particular to a photovoltaic panel adjusting method, a photovoltaic panel adjusting device, a storage medium and electronic equipment. The photovoltaic panel adjustment method comprises the following steps: acquiring environmental data, and outputting sand and dust prediction parameters corresponding to the environmental data based on a sand and dust prediction model; solving a multi-target optimization model based on the sand and dust prediction parameters, and calculating to obtain a target inclination angle and a target height of the photovoltaic panel array; the multi-objective optimization model comprises a first objective function taking the maximum photovoltaic conversion efficiency of the photovoltaic panel as a target, a second objective function taking the minimum wear value of the photovoltaic panel as a target, and a third objective function taking the minimum operation and maintenance cost as a target; and generating photovoltaic panel adjustment information according to the target inclination angle and the target height. According to the photovoltaic panel adjusting method provided by the invention, the photovoltaic panel adjusting information can be automatically generated in the sand and dust weather, so that high photoelectric conversion efficiency is ensured, and meanwhile, the plate surface abrasion and the operation and maintenance cost are low.

Description

Technical Field

[0001] This disclosure relates to the field of photovoltaic technology in sandy areas, specifically to photovoltaic panel adjustment methods, photovoltaic panel adjustment devices, storage media, and electronic equipment. Background Technology

[0002] Desert photovoltaic projects are clean energy projects built in desert areas, achieving coordinated development of ecology and energy through the "power generation on the panels, sand control beneath" model. Photovoltaic panels can reduce surface temperature and moisture evaporation, and combined with windbreak and sand-fixing measures, they can inhibit desertification.

[0003] However, sandstorms have many impacts on photovoltaic projects. For example, sand deposition can significantly reduce the light transmittance of photovoltaic panels, leading to a decrease in power generation efficiency. Sand particles carried by strong winds can also scratch the coating, thus affecting the lifespan of the photovoltaic panels. Therefore, it is crucial to study how to adjust the tilt angle and height of photovoltaic panels during sandstorms.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a photovoltaic panel adjustment method, a photovoltaic panel adjustment device, a storage medium, and an electronic device, which aim to automatically generate photovoltaic panel adjustment information under sandstorm weather, so as to ensure high photoelectric conversion efficiency while minimizing panel wear and maintenance costs.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a photovoltaic panel adjustment method is provided, comprising:

[0008] Collect environmental data and output dust prediction parameters corresponding to the environmental data based on a pre-built dust prediction model;

[0009] Based on the dust prediction parameters, a pre-constructed multi-objective optimization model is solved to calculate the target tilt angle and target height of the photovoltaic array. The multi-objective optimization model includes a first objective function that aims to maximize the photovoltaic conversion efficiency, a second objective function that aims to minimize the wear value of the photovoltaic panel, and a third objective function that aims to minimize the operation and maintenance cost.

[0010] Photovoltaic panel adjustment information is generated based on the target tilt angle and the target height.

[0011] Optionally, the method further includes constructing the first objective function, wherein constructing the first objective function includes:

[0012] The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the deposition influence correction parameters are calculated based on the dust deposition rate, duration, and deposition influence coefficient; and

[0013] The shadow shading area of ​​adjacent photovoltaic panels is calculated based on the height of the photovoltaic panels, and the shadow loss correction parameter is calculated based on the shadow shading area and the shadow loss coefficient.

[0014] The photovoltaic panel's photoelectric conversion efficiency is calculated based on the photoelectric conversion efficiency under clean conditions, the deposition effect correction parameter, and the shadow loss correction parameter.

[0015] The first objective function is constructed by maximizing the photoelectric conversion efficiency of the photovoltaic panel.

[0016] Optionally, the method further includes constructing the second objective function, wherein constructing the second objective function includes:

[0017] Impact wear is calculated based on wind direction, wind speed, dust concentration, photovoltaic panel tilt angle, and impact wear coefficient; and

[0018] The dust deposition rate is calculated based on the tilt angle of the photovoltaic panel, and the deposition wear amount is calculated based on the dust deposition rate, surface friction coefficient, and deposition wear coefficient.

[0019] The wear value of the photovoltaic panel is obtained based on the impact wear amount and the deposition wear amount;

[0020] The second objective function is constructed by minimizing the wear value of the photovoltaic panel.

[0021] Optionally, the method further includes constructing the third objective function, wherein constructing the third objective function includes:

[0022] The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the dust removal cost is calculated based on the dust deposition rate and duration; and

[0023] Calculate the cost of high-level cleaning based on the height of the photovoltaic panel;

[0024] Calculate the operation and maintenance costs based on the dust cleaning cost and the high-level cleaning cost;

[0025] The third objective function is constructed to minimize the aforementioned operation and maintenance costs.

[0026] Optionally, the method further includes constraints for constructing the multi-objective optimization model, the constraints for constructing the multi-objective optimization model including:

[0027] The tilt angle of the photovoltaic panels is within the preset tilt angle range;

[0028] The height of the photovoltaic panels is within the preset height range;

[0029] The area obscured by shadows shall not exceed the shadow tolerance threshold.

[0030] The wear value of the photovoltaic panel does not exceed the wear tolerance threshold.

[0031] Optionally, the dust prediction parameters include wind direction, wind speed, dust concentration, and duration; the method further includes pre-constructing the dust prediction model, wherein pre-constructing the dust prediction model includes:

[0032] A historical dust storm dataset was collected and divided into training samples and test samples; the historical dust storm dataset includes historical meteorological data, historical surface data, historical remote sensing data, and historical dust storm parameters;

[0033] Construct a neural network model and train the neural network model based on the training samples;

[0034] The neural network model is tested using the test samples, and the neural network model is released when the test passes.

[0035] The neural network model is tested, and the neural network model that passes the test is used as the dust prediction model.

[0036] Optionally, after generating photovoltaic panel adjustment information based on the target tilt angle and the target height, the method further includes:

[0037] Collect the current real-world geographic information of the photovoltaic power station and generate a virtual 3D scene based on the real-world geographic information;

[0038] Based on the dust prediction parameters, a dynamic simulation of dust weather is performed on the virtual 3D scene, and the simulation results are displayed.

[0039] The photovoltaic panels are adjusted according to the photovoltaic panel adjustment information to generate a simulation report of the photovoltaic power station under the sandstorm weather, and the parameters of the multi-objective optimization model are adjusted based on the simulation report.

[0040] According to a second aspect of this disclosure, a photovoltaic panel adjustment device is provided, comprising:

[0041] The prediction module is used to collect environmental data and output dust prediction parameters corresponding to the environmental data based on a pre-built dust prediction model; the dust prediction parameters include wind direction, wind speed, dust concentration and duration.

[0042] The calculation module is used to solve a pre-built multi-objective optimization model based on the dust prediction parameters to calculate the target tilt angle and target height of the photovoltaic array. The multi-objective optimization model includes a first objective function that aims to maximize the photovoltaic conversion efficiency of the photovoltaic panel, a second objective function that aims to minimize the wear value of the photovoltaic panel, and a third objective function that aims to minimize the operation and maintenance cost.

[0043] The adjustment module is used to generate photovoltaic panel adjustment information based on the target tilt angle and the target height.

[0044] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the photovoltaic panel adjustment method as described in the above embodiments.

[0045] According to a fourth aspect of the present disclosure, an electronic device is provided, characterized in that it includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the photovoltaic panel adjustment method as described in the above embodiments.

[0046] The exemplary embodiments disclosed herein may have some or all of the following beneficial effects:

[0047] In some embodiments of this disclosure, the technical solutions provided can, on the one hand, predict sandstorm environments and automatically generate photovoltaic panel adjustment information that includes both tilt angle and height based on the prediction results, providing an effective solution for photovoltaic projects to cope with sandstorm weather; on the other hand, when adjusting photovoltaic panels, three objectives are comprehensively considered, which can ensure that the adjusted photovoltaic panels have high photoelectric conversion efficiency while minimizing panel wear and maintenance costs.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0050] Figure 1 The schematic diagram illustrates a process flow of a photovoltaic panel adjustment method according to an exemplary embodiment of the present disclosure;

[0051] Figure 2The illustration schematically shows a flowchart of a method for constructing a dust prediction model in an exemplary embodiment of the present disclosure;

[0052] Figure 3 The illustration shows a flowchart of a method for constructing a multi-objective optimization model in an exemplary embodiment of the present disclosure;

[0053] Figure 4 This schematic diagram illustrates the composition of a photovoltaic panel adjustment device according to an exemplary embodiment of the present disclosure;

[0054] Figure 5 The schematic diagram illustrates the structure of a computer system of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0055] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0056] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., may be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0057] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0058] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0059] The implementation details of the technical solutions of the embodiments of this disclosure are described in detail below.

[0060] Figure 1 This schematically illustrates a flowchart of a photovoltaic panel adjustment method according to an exemplary embodiment of the present disclosure. Figure 1 As shown, the photovoltaic panel adjustment method includes steps S101 to S103:

[0061] Step S101: Collect environmental data and output the dust prediction parameters corresponding to the environmental data based on the pre-built dust prediction model;

[0062] Step S102: Based on the dust prediction parameters, solve the pre-constructed multi-objective optimization model to calculate the target tilt angle and target height of the photovoltaic array; the multi-objective optimization model includes a first objective function with the goal of maximizing the photovoltaic conversion efficiency, a second objective function with the goal of minimizing the wear value of the photovoltaic panel, and a third objective function with the goal of minimizing the operation and maintenance cost;

[0063] Step S103: Generate photovoltaic panel adjustment information based on the target tilt angle and the target height.

[0064] In some embodiments of this disclosure, the technical solutions provided can, on the one hand, predict sandstorm environments and automatically generate photovoltaic panel adjustment information that includes both tilt angle and height based on the prediction results, providing an effective solution for photovoltaic projects to cope with sandstorm weather; on the other hand, when adjusting photovoltaic panels, three objectives are comprehensively considered, which can ensure that the adjusted photovoltaic panels have high photoelectric conversion efficiency while minimizing panel wear and maintenance costs.

[0065] The following will describe in more detail each step of the photovoltaic panel adjustment method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0066] In step S101, environmental data is collected, and dust prediction parameters corresponding to the environmental data are output based on a pre-built dust prediction model.

[0067] Specifically, the first step is to predict dust storms. The collected environmental data can include one or more of the following: meteorological data, surface data, and remote sensing data. Meteorological data includes current wind speed, wind direction, temperature, humidity, air pressure, and visibility; surface data includes vegetation cover, soil moisture, and surface roughness; and remote sensing data can be satellite-derived dust optical thickness and PM10 / PM2.5 concentrations.

[0068] When performing dust storm prediction, environmental data is input into a pre-trained dust storm prediction model to obtain the dust storm prediction parameters output by the model.

[0069] Therefore, it is also necessary to build a dust prediction model in advance. Figure 2 This schematic diagram illustrates a flowchart of a method for constructing a dust storm prediction model according to an exemplary embodiment of this disclosure, such as... Figure 2 As shown, the construction of the dust storm prediction model includes:

[0070] Step S201: Collect historical dust dataset and divide the historical dust dataset into training samples and test samples; the historical dust dataset includes historical meteorological data, historical surface data, historical remote sensing data and historical dust parameters;

[0071] Step S202: Construct a neural network model and train the neural network model based on the training samples;

[0072] Step S203: Test the neural network model using the test sample, and release the neural network model when the test passes.

[0073] Step S204: Test the neural network model and use the neural network model that passes the test as the dust prediction model.

[0074] Specifically, during the training of the neural network model, historical dust datasets are collected and divided into training samples and test samples. The training samples are used to train the model, and the test samples are used to test it, ultimately resulting in a trained dust prediction model.

[0075] The input to the dust prediction model is environmental data such as meteorological data, surface data, and remote sensing data. The output is dust parameters, which mainly include wind direction α (the angle between the dust wind direction and the horizontal plane), wind speed v, dust concentration c, and duration T.

[0076] It should be noted that adjustments to the photovoltaic panels can be made only when the dust storm reaches a certain scale. For example, corresponding thresholds can be set for various dust storm parameters. Adjustments to the photovoltaic panels can only be made when one or more parameters exceed the threshold. This can avoid frequent adjustments to the photovoltaic panels and save computing resources.

[0077] In step S102, the pre-constructed multi-objective optimization model is solved based on the dust prediction parameters to calculate the target tilt angle and target height of the photovoltaic array. The multi-objective optimization model includes a first objective function with the goal of maximizing the photovoltaic conversion efficiency, a second objective function with the goal of minimizing the wear value of the photovoltaic panel, and a third objective function with the goal of minimizing the operation and maintenance cost.

[0078] Specifically, by pre-constructing a multi-objective optimization model, the optimal tilt angle and optimal height of the photovoltaic array can be calculated under the dust prediction parameters.

[0079] Figure 3 This illustration schematically depicts a flowchart of a method for constructing a multi-objective optimization model according to an exemplary embodiment of this disclosure. Figure 3 As shown in one embodiment of this disclosure, the specific process of pre-constructing a multi-objective optimization model is as follows:

[0080] Step S301: Construct a first objective function with the goal of maximizing the photovoltaic panel's photoelectric conversion efficiency; construct a second objective function with the goal of minimizing the photovoltaic panel's wear value; and construct a third objective function with the goal of minimizing operation and maintenance costs; and

[0081] Step S302: Construct the constraints of the multi-objective optimization model.

[0082] First, wind speed and dust concentration vary with altitude, so we first calculate the wind speed and dust concentration at a certain altitude.

[0083] If the height of the photovoltaic panel is h, then the wind speed at point h is v. h for:

[0084]

[0085] In the formula, h0 is the reference height, v0 is the wind speed at h0, which can be determined by the dust prediction parameters, and is generally taken as the wind speed at the ground. h1 It is an index that measures wind speed as a function of altitude, and is related to surface roughness and atmospheric stability. It is usually taken as 0.1 to 0.3.

[0086] The dust concentration at point h is:

[0087]

[0088] In the formula, c0 is the dust concentration at the ground, which can be determined by dust prediction parameters, and k h2 This is the dust concentration attenuation coefficient.

[0089] In step S301, regarding the first objective function, in one embodiment of this disclosure, constructing the first objective function with the goal of maximizing the photoelectric conversion efficiency of the photovoltaic panel includes:

[0090] The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the deposition influence correction parameters are calculated based on the dust deposition rate, duration, and deposition influence coefficient; and

[0091] The shadow shading area of ​​adjacent photovoltaic panels is calculated based on the height of the photovoltaic panels, and the shadow loss correction parameter is calculated based on the shadow shading area and the shadow loss coefficient.

[0092] The photovoltaic panel's photoelectric conversion efficiency is calculated based on the photoelectric conversion efficiency under clean conditions, the deposition effect correction parameter, and the shadow loss correction parameter.

[0093] The first objective function is constructed by maximizing the photoelectric conversion efficiency of the photovoltaic panel.

[0094] In dusty environments, the tilt angle of photovoltaic panels can affect the degree of dust accumulation, which in turn affects the photoelectric conversion efficiency of the photovoltaic panels. Therefore, a deposition effect correction parameter needs to be introduced when calculating the photoelectric conversion efficiency.

[0095] First, calculate the dust deposition rate F. d ,as follows:

[0096]

[0097] In the formula, k d c is the deposition coefficient, which is related to the surface roughness of the photovoltaic panel. h Let v be the dust concentration at height h of the photovoltaic panel. h Let h be the wind speed at the height of the photovoltaic panel, and θ be the tilt angle of the photovoltaic panel.

[0098] Then the deposition effect correction parameter ρ1 is obtained as follows:

[0099] ρ1=k1·F d ·T (4)

[0100] In the formula, k1 is the deposition influence coefficient, which can be taken as an empirical value, and T is the duration.

[0101] When the height of a photovoltaic panel changes, the radiation received by the panel may change, which in turn affects the photoelectric conversion efficiency. Therefore, a shading loss correction parameter needs to be introduced when calculating the photoelectric conversion efficiency.

[0102] First, calculate the shading area ΔS(h) of adjacent photovoltaic panels, as follows:

[0103] ΔS(h)=k s ·h·tanφ (5)

[0104] In the formula, k s Here are the shading parameters: h is the height of the photovoltaic panel, and φ is the solar altitude angle.

[0105] Then the shadow loss correction parameter ρ2 is calculated as follows:

[0106]

[0107] In the formula, S0 is the surface area of ​​a single photovoltaic panel.

[0108] Finally, the photovoltaic conversion efficiency η(θ,h) of the photovoltaic panel is calculated as follows:

[0109] η(θ,h)=η0·(1-ρ1)·(1-ρ2) (7)

[0110] In the formula, η0 represents the photoelectric conversion efficiency under clean conditions. The first objective function, maxη(θ,h), is then obtained.

[0111] Regarding the second objective function, in one embodiment of this disclosure, the second objective function is constructed with the goal of minimizing the wear value of the photovoltaic panel, including:

[0112] Impact wear is calculated based on wind direction, wind speed, dust concentration, photovoltaic panel tilt angle, and impact wear coefficient; and

[0113] The dust deposition rate is calculated based on the tilt angle of the photovoltaic panel, and the deposition wear amount is calculated based on the dust deposition rate, surface friction coefficient, and deposition wear coefficient.

[0114] The wear value of the photovoltaic panel is obtained based on the impact wear amount and the deposition wear amount;

[0115] The second objective function is constructed by minimizing the wear value of the photovoltaic panel.

[0116] Specifically, in a dusty environment, dust particles can impact photovoltaic panels, causing wear and tear that affects their lifespan. Therefore, when calculating the wear value of photovoltaic panels, the impact wear must be taken into account, as calculated below:

[0117]

[0118] In the formula, c h Let v be the dust concentration at height h of the photovoltaic panel. h Let θ be the wind speed at the height h of the photovoltaic panel, θ be the tilt angle of the photovoltaic panel, and α be the wind direction. sin(θ+α) reflects the angle between the impact direction of the dust particles and the surface of the photovoltaic panel. The larger the tilt angle of the photovoltaic panel, the stronger the impact force and the higher the impact wear. μ·cosθ reflects the residence time of the dust particles on the photovoltaic panel surface. A higher coefficient of friction or a smaller tilt angle of the photovoltaic panel will prolong the residence time and exacerbate wear.

[0119] In addition to the impact and wear caused by sand and dust, the accumulation of sand and dust also causes depositional wear, as calculated below:

[0120] W2 = F d ·μ (9)

[0121] In the formula, F d denoted as the dust deposition rate, μ as the surface friction coefficient of the photovoltaic panel, and θ as the tilt angle of the photovoltaic panel.

[0122] Finally, the combined wear value W(θ,h) of the photovoltaic panel is obtained as follows:

[0123] W(θ,h)=k2·W1+k3·W2 (10)

[0124] In the formula, k2 is the impact wear coefficient, and k3 is the deposition wear coefficient. The final objective function is minW(θ,h).

[0125] Regarding the third objective function, in one embodiment of this disclosure, the third objective function is constructed with the goal of minimizing operational costs, including:

[0126] The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the dust removal cost is calculated based on the dust deposition rate and duration; and

[0127] Calculate the cost of high-level cleaning based on the height of the photovoltaic panel;

[0128] Calculate the operation and maintenance costs based on the dust cleaning cost and the high-level cleaning cost;

[0129] The third objective function is constructed to minimize the aforementioned operation and maintenance costs.

[0130] Specifically, when sandstorms occur, the sand and dust accumulate on the photovoltaic panels, requiring cleaning and incurring related cleaning costs.

[0131] C(θ,h)=k4·F d ·T+k5·h (11)

[0132] In the formula, F d Where is the dust deposition rate, T is the duration, h is the height of the photovoltaic panel, and k4 and k5 are the dust cleaning cost coefficient and height cleaning cost coefficient, respectively. The coefficients can be determined based on empirical values ​​or by fitting the simulation model with the height of the photovoltaic panel and then determining them based on the height of the photovoltaic panel.

[0133] Among them, k4·F d •T represents the cost of cleaning up sand and dust, and F represents the amount of sand and dust deposited. d The value of T determines the cleaning frequency. The greater the amount of sand and dust deposits, the higher the cleaning frequency, and the higher the cost of cleaning sand and dust. k5·h is the cost of cleaning at a certain height. When the height h of the photovoltaic panel increases, it may be accompanied by certain difficulties in cleaning, requiring additional costs such as lifting equipment and high-altitude operations.

[0134] Finally, the third objective function minC(θ,h) is obtained.

[0135] In step S302, the constraints of the multi-objective optimization model are constructed.

[0136] In one embodiment of this disclosure, the constraints for constructing the multi-objective optimization model include: the photovoltaic panel tilt angle is within a preset tilt angle range; the photovoltaic panel height is within a preset height range; the shading area does not exceed the shading tolerance threshold; and the photovoltaic panel wear value does not exceed the wear tolerance threshold.

[0137] Specifically, the constraints for construction are as follows:

[0138]

[0139] In the formula, the preset tilt angle range is [θ min θ max The preset height range is [h]. min ,h max ], S tol W is the shadow tolerance threshold. tol This represents the wear tolerance threshold.

[0140] There has been considerable research on solving multi-objective optimization models. For example, a weighted summation method can be used to linearly combine multiple objectives into a single objective through weights, transforming it into a single-objective optimization problem. The photovoltaic panel photoelectric conversion efficiency, photovoltaic panel wear value, and operation and maintenance cost mentioned above are normalized to construct the overall objective function as follows:

[0141]

[0142] In the formula, λ1, λ2, and λ3 are the weights of each objective function, and η is the weight of each objective function. max W max C max These represent the theoretical maximum values ​​of the photovoltaic panel's photoelectric conversion efficiency, photovoltaic panel wear value, and operation and maintenance cost within the given parameter range, respectively.

[0143] Of course, Pareto sorting can also be used to directly compare the dominance relationships of solutions, or multi-objective evolutionary algorithms, such as the NSGA-II non-dominated sorting genetic algorithm, which maintains the diversity and convergence of solutions through non-dominated sorting and crowding calculation, or the MOEA / D decomposition-based multi-objective evolutionary algorithm, which decomposes the multi-objective problem into multiple single-objective sub-problems and updates the solution using neighborhood information, etc. These will not be elaborated upon further here.

[0144] By solving the multi-objective optimization model constructed above, the optimal decision variables, namely the target tilt angle θ' of the photovoltaic panel and the target height h', can be calculated.

[0145] In step S103, photovoltaic panel adjustment information is generated based on the target tilt angle and the target height.

[0146] Specifically, the tilt angle and height of the photovoltaic panel can be obtained. By comparing the current tilt angle and height of the photovoltaic panel with the calculated target tilt angle and target height, photovoltaic panel adjustment information can be generated. Adjustments can then be made according to the photovoltaic panel adjustment information to cope with sandstorm attacks.

[0147] Based on the above method, after predicting the dust storm situation based on environmental data, a multi-objective optimization model is solved to obtain the photovoltaic panel adjustment information under the dust storm prediction parameters. By jointly optimizing the tilt angle and height of the photovoltaic panels, it is possible to ensure high photoelectric conversion efficiency while minimizing panel wear and maintenance costs. This approach not only addresses the special challenges of sandy environments but also provides a technological paradigm for the large-scale promotion of global desert photovoltaic projects, promoting the deep integration of new energy and ecological governance.

[0148] In one embodiment of this disclosure, when severe sandstorm weather such as a sandstorm is predicted, VR can be used to replicate AR scenes to simulate emergency response plans for extreme scenarios.

[0149] Specifically, after generating photovoltaic panel adjustment information based on the target tilt angle and the target height, the method further includes: collecting current real-world geographic information of the photovoltaic power station to generate a virtual three-dimensional scene based on the real-world geographic information; performing dynamic simulation of sandstorm weather on the virtual three-dimensional scene based on the sandstorm prediction parameters, and displaying the simulation results; adjusting the photovoltaic panels according to the photovoltaic panel adjustment information to generate a simulation report of the photovoltaic power station under the dynamic simulation of sandstorm weather, and adjusting the parameters of the multi-objective optimization model based on the simulation report.

[0150] Specifically, the first step is to construct a 3D scene and synchronize it with the status of the photovoltaic panels. This can be done by using drone oblique photography or laser scanning technology to collect real-world geographic information about the photovoltaic power station, including the tilt angle and height parameters of the photovoltaic panels before adjustment. Then, a high-precision 3D model is reconstructed using point cloud generation technology to ensure that the spatial position and angle of the photovoltaic panels after adjustment are consistent with the real scene.

[0151] Secondly, sandstorm weather simulation and impact analysis are conducted. A sandstorm particle system is loaded into the VR engine to dynamically simulate parameters such as sandstorm intensity, visibility, and wind speed according to sandstorm prediction parameters. The sandstorm coverage process can also be visualized, and the dust thickness on the photovoltaic panel surface and the power generation efficiency decay curve can be displayed in real time.

[0152] Next, the AR-VR fusion simulation process is carried out. A virtual sandstorm model is overlaid onto real-time footage of a photovoltaic power station using an AR technology framework, enabling split-screen display within the VR headset. The display can use the real scene on the left screen and the virtual disaster on the right.

[0153] Finally, the simulation and performance evaluation are carried out. The tilt angle and height are modified in real time according to the photovoltaic panel adjustment information. The effectiveness of the photovoltaic panel adjustment strategy is fed back in real time by calculating the photoelectric conversion efficiency, dust accumulation, etc. The simulation report is generated and key event nodes are marked so that the parameters in the photovoltaic panel adjustment method can be dynamically corrected by using the simulation report.

[0154] Figure 4 This schematic diagram illustrates the composition of a photovoltaic panel adjustment device according to an exemplary embodiment of the present disclosure, such as... Figure 4 As shown, the photovoltaic panel adjustment device 400 may include a prediction module 401, a calculation module 402, and an adjustment module 403. Wherein:

[0155] The prediction module 401 is used to collect environmental data and output dust prediction parameters corresponding to the environmental data based on a pre-built dust prediction model; the dust prediction parameters include wind direction, wind speed, dust concentration and duration.

[0156] The calculation module 402 is used to solve the pre-constructed multi-objective optimization model based on the dust prediction parameters to calculate the target tilt angle and target height of the photovoltaic array; the multi-objective optimization model includes a first objective function with the goal of maximizing the photovoltaic conversion efficiency, a second objective function with the goal of minimizing the wear value of the photovoltaic panel, and a third objective function with the goal of minimizing the operation and maintenance cost;

[0157] The adjustment module 403 is used to generate photovoltaic panel adjustment information based on the target tilt angle and the target height.

[0158] According to an exemplary embodiment of this disclosure, the calculation module 402 further includes a multi-objective model construction unit, which is used to calculate the dust deposition rate based on the photovoltaic panel tilt angle, and calculate the deposition influence correction parameter based on the dust deposition rate, duration, and deposition influence coefficient; calculate the shading area of ​​adjacent photovoltaic panels based on the photovoltaic panel height, and calculate the shading loss correction parameter based on the shading area and shading loss coefficient; calculate the photovoltaic panel photoelectric conversion efficiency based on the photoelectric conversion efficiency under clean conditions, the deposition influence correction parameter, and the shading loss correction parameter; and construct the first objective function with the photovoltaic panel photoelectric conversion efficiency maximized.

[0159] According to an exemplary embodiment of this disclosure, the multi-objective model construction unit is further configured to calculate impact wear based on wind direction, wind speed, dust concentration, photovoltaic panel tilt angle, and impact wear coefficient; calculate dust deposition rate based on photovoltaic panel tilt angle, and calculate deposition wear based on the dust deposition rate, surface friction coefficient, and deposition wear coefficient; obtain photovoltaic panel wear value based on the impact wear and the deposition wear; and construct the second objective function with the photovoltaic panel wear value being minimized.

[0160] According to an exemplary embodiment of this disclosure, the multi-objective model construction unit is further configured to calculate the dust deposition rate based on the photovoltaic panel tilt angle, and calculate the dust cleaning cost based on the dust deposition rate and duration; and calculate the height cleaning cost based on the photovoltaic panel height; calculate the operation and maintenance cost based on the dust cleaning cost and the height cleaning cost; and construct the third objective function with the minimum operation and maintenance cost.

[0161] According to an exemplary embodiment of this disclosure, the multi-objective model construction unit is further configured to construct constraints for the multi-objective optimization model, including: the photovoltaic panel tilt angle is within a preset tilt angle range; the photovoltaic panel height is within a preset height range; the shading area does not exceed the shading tolerance threshold; and the photovoltaic panel wear value does not exceed the wear tolerance threshold.

[0162] According to an exemplary embodiment of this disclosure, the dust prediction parameters include wind direction, wind speed, dust concentration, and duration; the prediction module 401 further includes a prediction model construction unit, which is used to collect historical dust datasets and divide the historical dust datasets into training samples and test samples; the historical dust datasets include historical meteorological data, historical surface data, historical remote sensing data, and historical dust parameters; construct a neural network model and train the neural network model based on the training samples; test the neural network model using the test samples, and when the test is passed, use the neural network model; test the neural network model, and use the neural network model that passes the test as the dust prediction model.

[0163] According to an exemplary embodiment of this disclosure, the photovoltaic panel adjustment device further includes a simulation unit. The simulation unit is configured to: collect current real-world geographic information of the photovoltaic power station after generating photovoltaic panel adjustment information based on the target tilt angle and the target height; generate a virtual three-dimensional scene based on the real-world geographic information; dynamically simulate sandstorm weather in the virtual three-dimensional scene based on the sandstorm prediction parameters; display the simulation results; adjust the photovoltaic panels according to the photovoltaic panel adjustment information to generate a simulation report of the photovoltaic power station under sandstorm weather; and adjust the parameters of the multi-objective optimization model based on the simulation report.

[0164] The specific details of each module in the photovoltaic panel adjustment device 400 have been described in detail in the corresponding photovoltaic panel adjustment method, so they will not be repeated here.

[0165] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0166] In exemplary embodiments of this disclosure, a storage medium capable of implementing the above-described methods is also provided. It may be a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a mobile phone. However, the program product of this disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0167] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided. Figure 5 The schematic diagram illustrates the structure of a computer system of an electronic device according to an exemplary embodiment of the present disclosure.

[0168] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0169] like Figure 5 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage section 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0170] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0171] In particular, according to embodiments of this disclosure, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this disclosure.

[0172] It should be noted that the computer-readable medium shown in the embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0173] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0174] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0175] In another aspect, this disclosure also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0176] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0177] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0178] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0179] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for adjusting a photovoltaic panel, characterized in that, include: Collect environmental data and output dust prediction parameters corresponding to the environmental data based on a pre-built dust prediction model; Based on the dust prediction parameters, a pre-built multi-objective optimization model is solved to calculate the target tilt angle and target height of the photovoltaic panel array; The multi-objective optimization model includes a first objective function that aims to maximize the photovoltaic conversion efficiency of the photovoltaic panel, a second objective function that aims to minimize the wear value of the photovoltaic panel, and a third objective function that aims to minimize the operation and maintenance cost. Photovoltaic panel adjustment information is generated based on the target tilt angle and the target height.

2. The photovoltaic panel adjustment method according to claim 1, characterized in that, The method further includes constructing the first objective function, wherein constructing the first objective function includes: The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the deposition influence correction parameters are calculated based on the dust deposition rate, duration, and deposition influence coefficient; and The shadow shading area of ​​adjacent photovoltaic panels is calculated based on the height of the photovoltaic panels, and the shadow loss correction parameter is calculated based on the shadow shading area and the shadow loss coefficient. The photovoltaic panel's photoelectric conversion efficiency is calculated based on the photoelectric conversion efficiency under clean conditions, the deposition effect correction parameter, and the shadow loss correction parameter. The first objective function is constructed by maximizing the photoelectric conversion efficiency of the photovoltaic panel.

3. The photovoltaic panel adjustment method according to claim 1, characterized in that, The method further includes constructing the second objective function, wherein constructing the second objective function includes: Impact wear is calculated based on wind direction, wind speed, dust concentration, photovoltaic panel tilt angle, and impact wear coefficient; and The dust deposition rate is calculated based on the tilt angle of the photovoltaic panel, and the deposition wear amount is calculated based on the dust deposition rate, surface friction coefficient, and deposition wear coefficient. The wear value of the photovoltaic panel is obtained based on the impact wear amount and the deposition wear amount; The second objective function is constructed by minimizing the wear value of the photovoltaic panel.

4. The photovoltaic panel adjustment method according to claim 1, characterized in that, The method further includes constructing the third objective function, wherein constructing the third objective function includes: The dust deposition rate is calculated based on the photovoltaic panel tilt angle, and the dust removal cost is calculated based on the dust deposition rate and duration; and Calculate the cost of high-level cleaning based on the height of the photovoltaic panel; Calculate the operation and maintenance costs based on the dust cleaning cost and the high-level cleaning cost; The third objective function is constructed to minimize the aforementioned operation and maintenance costs.

5. The photovoltaic panel adjustment method according to claim 1, characterized in that, The method further includes constraints for constructing the multi-objective optimization model, the constraints for constructing the multi-objective optimization model including: The tilt angle of the photovoltaic panels is within the preset tilt angle range; The height of the photovoltaic panels is within the preset height range; The area obscured by shadows shall not exceed the shadow tolerance threshold. The wear value of the photovoltaic panel does not exceed the wear tolerance threshold.

6. The photovoltaic panel adjustment method according to claim 1, characterized in that, The dust storm prediction parameters include wind direction, wind speed, dust concentration, and duration. The method further includes pre-constructing the dust storm prediction model, wherein pre-constructing the dust storm prediction model includes: A historical dust storm dataset was collected and divided into training samples and test samples; the historical dust storm dataset includes historical meteorological data, historical surface data, historical remote sensing data, and historical dust storm parameters; Construct a neural network model and train the neural network model based on the training samples; The neural network model is tested using the test samples, and the neural network model is released when the test passes. The neural network model is tested, and the neural network model that passes the test is used as the dust prediction model.

7. The photovoltaic panel adjustment method according to claim 1, characterized in that, After generating photovoltaic panel adjustment information based on the target tilt angle and the target height, the method further includes: Collect the current real-world geographic information of the photovoltaic power station and generate a virtual 3D scene based on the real-world geographic information; Based on the dust prediction parameters, a dynamic simulation of dust weather is performed on the virtual 3D scene, and the simulation results are displayed. The photovoltaic panels are adjusted according to the photovoltaic panel adjustment information to generate a simulation report of the photovoltaic power station under the sandstorm weather, and the parameters of the multi-objective optimization model are adjusted based on the simulation report.

8. A photovoltaic panel adjustment device, characterized in that, include: The prediction module is used to collect environmental data and output dust prediction parameters corresponding to the environmental data based on a pre-built dust prediction model; the dust prediction parameters include wind direction, wind speed, dust concentration and duration. The calculation module is used to solve a pre-built multi-objective optimization model based on the dust prediction parameters to calculate the target tilt angle and target height of the photovoltaic array; The multi-objective optimization model includes a first objective function that aims to maximize the photovoltaic conversion efficiency of the photovoltaic panel, a second objective function that aims to minimize the wear value of the photovoltaic panel, and a third objective function that aims to minimize the operation and maintenance cost. The adjustment module is used to generate photovoltaic panel adjustment information based on the target tilt angle and the target height.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic panel adjustment method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the photovoltaic panel adjustment method as described in any one of claims 1 to 7.

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