A method for automatically cleaning and tracking a driving control of a bifacial photovoltaic module

Abstract

The present application relates to photovoltaic power station control technical field, provide a kind of automatic cleaning tracking drive control method of bifacial photovoltaic module, comprising: obtaining the weather forecast data of photovoltaic array site, determine the optimal azimuth according to weather forecast data and photovoltaic module cleaning frequency;Azimuth adjustment simulation is carried out to photovoltaic module according to the optimal azimuth, and the optimal bifacial azimuth is determined according to simulation result;Second adjustment simulation is carried out to photovoltaic module according to the optimal bifacial azimuth, and the azimuth adjustment strategy is determined according to secondary simulation result, and tracking control is carried out to photovoltaic module according to the azimuth adjustment strategy.In the guarantee system power generation while reducing the power consumption caused by component adjustment, improve the power generation efficiency of bifacial photovoltaic module.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant control technology, and in particular to an automatic cleaning tracking drive control method for bifacial photovoltaic modules. Background Technology

[0002] With the transformation of the global energy structure, solar photovoltaic power generation, as a clean and renewable energy form, has seen its installed capacity grow rapidly worldwide. To further improve the power generation efficiency of photovoltaic systems, bifacial photovoltaic module technology has emerged. Unlike traditional single-sided modules that only receive solar radiation on the front, bifacial photovoltaic modules can simultaneously utilize incident light from the front as well as light energy reaching the back through ground reflection and atmospheric scattering for photoelectric conversion.

[0003] However, traditional bifacial photovoltaic module tracking control strategies often take maximizing the irradiance on the front of the module as the sole optimization objective, lacking consideration for adjusting power consumption. This results in significant adjustment losses in the module support components, making it difficult to maximize the module's power generation efficiency. Summary of the Invention

[0004] This invention provides an automatic cleaning tracking drive control method for bifacial photovoltaic modules to solve the problem of low power generation efficiency of bifacial photovoltaic modules in the prior art.

[0005] On one hand, the present invention provides an automatic cleaning tracking drive control method for bifacial photovoltaic modules, comprising: Acquire weather forecast data for the photovoltaic array site, and determine the cleaning frequency and optimal azimuth angle of the photovoltaic modules based on the weather forecast data; simulate azimuth angle adjustment of the photovoltaic modules based on the optimal azimuth angle, and determine the optimal bifacial azimuth angle based on the simulation results; simulate secondary adjustment of the photovoltaic modules based on the optimal bifacial azimuth angle, determine the azimuth angle adjustment strategy based on the secondary simulation results, and perform tracking control of the photovoltaic modules based on the azimuth angle adjustment strategy.

[0006] Furthermore, determining the cleaning frequency of photovoltaic modules based on weather forecast data includes: determining the weather changes within a preset time period based on the weather forecast data, and determining the cleaning frequency of photovoltaic modules based on the weather changes.

[0007] Further, determining the optimal azimuth angle of the photovoltaic module based on weather forecast data includes: predicting solar trajectory data for future periods based on the weather forecast data, establishing a solar trajectory simulation model, inputting the solar trajectory data and photovoltaic array site data into the solar trajectory simulation model to obtain the solar trajectory simulation results of the photovoltaic array site; determining the solar azimuth angle change of the photovoltaic array site based on the solar trajectory simulation results, and determining the optimal module azimuth angle based on the solar azimuth angle change.

[0008] Furthermore, the step of predicting the solar trajectory data for future periods based on the weather forecast data includes: acquiring historical site data of the photovoltaic array site, the historical site data including historical weather data and historical solar trajectory data; establishing a training sample set based on the historical site data; establishing a long short-term memory network model based on the training sample set; inputting the weather forecast data into the long short-term memory network model; and outputting the solar trajectory data for future periods.

[0009] Furthermore, determining the optimal bifacial azimuth angle based on the simulation results includes: determining the back irradiance data of the photovoltaic module based on the simulation results; establishing a back irradiance data variation curve based on the changes in the back irradiance data; determining azimuth adjustment data based on the peak points in the back irradiance data variation curve to obtain the optimal bifacial azimuth angle.

[0010] Further, determining the back irradiance data of the photovoltaic module based on the simulation results includes: determining the back irradiance data of the photovoltaic module based on a back irradiance model, wherein the back irradiance model is... ; in, Data on back-side irradiation. For surface reflectance, The intensity of diffused radiation. The tilt angle of the photovoltaic module. The total irradiance on the horizontal plane, For perspective factors.

[0011] Further, determining the azimuth adjustment data based on the peak points in the back irradiance data variation curve includes: extracting the peak variation curve within a preset neighborhood of the peak point of the back irradiance data variation curve, dividing the peak variation curve into several sub-variation curves; obtaining the average back irradiance data of each sub-variation curve, and calculating the absolute value of the slope of adjacent average back irradiance data; obtaining the average value of all absolute slope values ​​in the peak variation curve to obtain the fluctuation parameter of the peak point; sorting the fluctuation parameters of each peak point in descending order, selecting the peak point with the lowest fluctuation parameter ranking based on the sorting result, and determining the azimuth adjustment data based on the selected peak point.

[0012] Furthermore, determining the azimuth adjustment strategy based on the secondary simulation results includes: dividing the azimuth adjustment process into several adjustment intervals based on the secondary simulation results, establishing an objective function, optimizing the adjustment intervals based on the objective function, and determining the azimuth adjustment strategy based on the optimization results.

[0013] Furthermore, establishing the objective function includes: establishing an objective function based on the interval power generation and interval azimuth adjustment energy consumption, wherein the objective function is, ; in, Let be the objective function. For the power generation capacity of the interval, Energy consumption for azimuth adjustment within the interval , These are the preset first weight and the preset second weight, respectively.

[0014] Further, the optimization of the adjustment interval based on the objective function and the determination of the azimuth adjustment strategy based on the optimization result include: Step 1, establishing particles based on the adjustment interval, randomly initializing the velocity and position of each particle in the search space, calculating the fitness function value of each particle, and the position of the particle with the largest fitness function value is the global optimal position; Step 2, correcting the particle flight direction using velocity update formulas and position update formulas; Step 3, evaluating the fitness function value of the particles, and updating the individual optimal position and the global optimal position of the particles; Step 4, determining whether the particle swarm algorithm meets the convergence condition. If it does, outputting the global optimal position and ending the algorithm, and determining the azimuth adjustment strategy based on the particle corresponding to the global optimal position; otherwise, repeating steps 1 to 3.

[0015] This invention provides an automatic cleaning and tracking drive control method for bifacial photovoltaic (PV) modules. The method involves acquiring weather forecast data for the PV array site, determining the cleaning frequency and optimal azimuth angle of the PV modules based on the forecast data, simulating azimuth angle adjustment of the PV modules according to the optimal azimuth angle, and determining the optimal bifacial azimuth angle based on the simulation results. A second adjustment simulation is then performed on the PV modules based on the optimal bifacial azimuth angle, and an azimuth angle adjustment strategy is determined based on the second simulation results. Finally, the PV modules are tracked and controlled according to the azimuth angle adjustment strategy. This method reduces power consumption caused by module adjustment while ensuring system power generation, thereby improving the power generation efficiency of bifacial PV modules. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an automatic cleaning tracking drive control method for bifacial photovoltaic modules provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] Figure 1 This is a flowchart illustrating an automatic cleaning tracking drive control method for bifacial photovoltaic modules provided in an embodiment of the present invention, including: S101, Obtain weather forecast data for the photovoltaic array site, and determine the cleaning frequency and optimal azimuth angle of the photovoltaic modules based on the weather forecast data; In some embodiments of this application, determining the cleaning frequency of photovoltaic modules based on weather forecast data includes: determining the weather changes within a preset time period based on the weather forecast data, and determining the cleaning frequency of photovoltaic modules based on the weather changes.

[0021] In this embodiment, when weather changes predict rainfall in the future, the cleaning frequency is reduced; if continuous sandstorms, dust storms, or smog are predicted, the cleaning frequency is increased, and cleaning tasks are arranged immediately after the weather ends.

[0022] In some embodiments of this application, determining the optimal azimuth angle of a photovoltaic module based on weather forecast data includes: predicting solar trajectory data for a future period based on the weather forecast data, establishing a solar trajectory simulation model, inputting the solar trajectory data and photovoltaic array site data into the solar trajectory simulation model to obtain the solar trajectory simulation result of the photovoltaic array site; determining the change in solar azimuth angle of the photovoltaic array site based on the solar trajectory simulation result, and determining the optimal module azimuth angle of the photovoltaic module based on the change in solar azimuth angle.

[0023] In this embodiment, a high-fidelity solar trajectory simulation model is constructed. The simulation model simulates the changes in the solar trajectory at the photovoltaic array site, and the changes in the solar azimuth angle at the photovoltaic array site are simulated based on the changes in the solar trajectory, so as to obtain the optimal azimuth angle of the photovoltaic module.

[0024] In some embodiments of this application, the step of predicting solar trajectory data for future periods based on the weather forecast data includes: acquiring historical site data of the photovoltaic array site, the historical site data including historical weather data and historical solar trajectory data; establishing a training sample set based on the historical site data; establishing a long short-term memory network model based on the training sample set; inputting the weather forecast data into the long short-term memory network model; and outputting solar trajectory data for future periods.

[0025] In this embodiment, a Long Short-Term Memory (LSTM) network model is established, comprising an input layer, two LSTM hidden layers, a fully connected layer, and an output layer. The loss function is mean squared error, and the optimizer is Adam. The LSTM network model is trained using historical weather data and historical solar trajectory data. The input is weather forecast data for the photovoltaic array site, and the output is solar trajectory data for future periods.

[0026] S102, Simulate the azimuth angle adjustment of the photovoltaic module according to the optimal azimuth angle, and determine the optimal bifacial azimuth angle based on the simulation results; In some embodiments of this application, determining the optimal bifacial azimuth angle based on simulation results includes: determining the back irradiance data of the photovoltaic module based on the simulation results; establishing a back irradiance data variation curve based on the changes in the back irradiance data; determining azimuth adjustment data based on the peak points in the back irradiance data variation curve to obtain the optimal bifacial azimuth angle.

[0027] In some embodiments of this application, determining the back irradiance data of the photovoltaic module based on simulation results includes: determining the back irradiance data of the photovoltaic module based on a back irradiance model, wherein the back irradiance model is... ; in, Data on back-side irradiation. For surface reflectance, The intensity of diffused radiation. The tilt angle of the photovoltaic module. The total irradiance on the horizontal plane, For perspective factors.

[0028] In this embodiment, the back irradiation data of the photovoltaic module is obtained through the back irradiation model during the simulation process. The maximum value of the back irradiation data of the most stable photovoltaic module is obtained through the back irradiation data change, and the optimal bifacial azimuth angle corresponding to the maximum value is obtained considering the back of the photovoltaic module.

[0029] In some embodiments of this application, determining the azimuth adjustment data based on the peak points in the back irradiance data change curve includes: extracting the peak change curve within a preset neighborhood of the peak point of the back irradiance data change curve, dividing the peak change curve into several sub-change curves; obtaining the average back irradiance data of each sub-change curve, and calculating the absolute value of the slope of adjacent average back irradiance data; obtaining the average value of all absolute slope values ​​in the peak change curve to obtain the fluctuation parameter of the peak point; sorting the fluctuation parameters of each peak point in descending order, selecting the peak point with the lowest fluctuation parameter ranking based on the sorting result, and determining the azimuth adjustment data based on the selected peak point.

[0030] In this embodiment, the azimuth angle corresponding to the peak point with the lowest fluctuation parameter is near the back irradiance data, which shows that the azimuth angle has the highest tolerance for tracking error. Therefore, the azimuth adjustment data is determined by the peak point selected by the fluctuation parameter.

[0031] S103, perform a secondary adjustment simulation on the photovoltaic module based on the optimal bifacial azimuth angle, determine the azimuth angle adjustment strategy based on the secondary simulation results, and perform tracking control on the photovoltaic module based on the azimuth angle adjustment strategy.

[0032] In some embodiments of this application, determining the azimuth adjustment strategy based on the secondary simulation results includes: dividing the azimuth adjustment process into several adjustment intervals based on the secondary simulation results, establishing an objective function, optimizing the adjustment intervals based on the objective function, and determining the azimuth adjustment strategy based on the optimization results.

[0033] In some embodiments of this application, establishing the objective function includes: establishing an objective function based on the interval power generation and interval azimuth adjustment energy consumption, wherein the objective function is, ; in, Let be the objective function. For the power generation capacity of the interval, Energy consumption for azimuth adjustment within the interval , These are the preset first weight and the preset second weight, respectively.

[0034] In some embodiments of this application, the step of optimizing the adjustment interval according to the objective function and determining the azimuth adjustment strategy based on the optimization result includes: Step 1, establishing particles according to the adjustment interval, randomly initializing the velocity and position of each particle in the search space, calculating the fitness function value of each particle, and the position of the particle with the largest fitness function value is the global optimal position; Step 2, correcting the particle flight direction using velocity update formulas and position update formulas; Step 3, evaluating the fitness function value of the particles, and updating the individual optimal position and the global optimal position of the particles; Step 4, determining whether the particle swarm algorithm meets the convergence condition. If it does, outputting the global optimal position and ending the process, and determining the azimuth adjustment strategy based on the particle corresponding to the global optimal position; otherwise, repeating steps 1 to 3.

[0035] In this embodiment, the particle swarm optimization algorithm is used to optimize each adjustment interval to obtain the optimal azimuth angle adjustment strategy, thereby reducing the power consumption caused by component adjustment while increasing the system's power generation, and improving the power generation efficiency of the bifacial photovoltaic module.

[0036] It should be noted that all relevant information that may be involved in the various embodiments of the present invention is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and is information that users actively provide or generate during the use of the product / service, as well as information obtained with user authorization.

[0037] The information processed by this invention may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, or other related information. This invention will treat the relevant information and its processing with the utmost diligence.

[0038] This invention places great emphasis on the security of relevant information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent unauthorized access, public disclosure, use, modification, damage or loss of relevant information.

[0039] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0040] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automatic cleaning tracking drive control method for bifacial photovoltaic modules, characterized in that, include: Obtain weather forecast data for the photovoltaic array site, and determine the cleaning frequency and optimal azimuth angle of the photovoltaic modules based on the weather forecast data; Based on the optimal azimuth angle, the azimuth angle of the photovoltaic module is simulated for adjustment, and the optimal bifacial azimuth angle is determined based on the simulation results. The photovoltaic module is subjected to a secondary adjustment simulation based on the optimal bifacial azimuth angle. The azimuth angle adjustment strategy is determined based on the secondary simulation results. The photovoltaic module is then tracked and controlled according to the azimuth angle adjustment strategy.

2. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 1, characterized in that, The frequency of photovoltaic module cleaning is determined based on weather forecast data, including: Based on the weather forecast data, determine the weather changes within a preset time period, and determine the cleaning frequency of the photovoltaic modules based on the weather changes.

3. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 1, characterized in that, Determining the optimal azimuth angle of photovoltaic modules based on weather forecast data includes: Based on the weather forecast data, predict the solar trajectory data for future periods, establish a solar trajectory simulation model, input the solar trajectory data and photovoltaic array site data into the solar trajectory simulation model, and obtain the solar trajectory simulation results for the photovoltaic array site. The solar azimuth angle variation of the photovoltaic array site is determined based on the solar trajectory simulation results, and the optimal module azimuth angle of the photovoltaic module is determined based on the solar azimuth angle variation.

4. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 3, characterized in that, The prediction of solar trajectory data for future periods based on the weather forecast data includes: Acquire historical site data for the photovoltaic array site, including historical weather data and historical solar trajectory data; A training sample set is established based on the historical site data, and a long short-term memory network model is established based on the training sample set. The weather forecast data is input into the Long Short-Term Memory network model, which outputs solar trajectory data for future periods.

5. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 3, characterized in that, The determination of the optimal dual-plane azimuth angle based on the simulation results includes: The back irradiance data of the photovoltaic module is determined based on the simulation results, and a back irradiance data change curve is established based on the changes in the back irradiance data. The azimuth adjustment data is determined based on the peak point in the back irradiation data change curve to obtain the optimal double-sided azimuth.

6. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 5, characterized in that, The determination of the back-side irradiance data of the photovoltaic module based on the simulation results includes: The back irradiance data of the photovoltaic module is determined based on the back irradiance model, wherein the back irradiance model is: ； in, Data on back-side irradiation. For surface reflectance, The intensity of diffused radiation. The tilt angle of the photovoltaic module. The total irradiance on the horizontal plane, For perspective factors.

7. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 5, characterized in that, The step of determining the azimuth adjustment data based on the peak point in the back irradiation data change curve includes: Extract the peak change curve within a preset neighborhood of the peak point of the back irradiation data change curve, and divide the peak change curve into several sub-change curves; Obtain the average back irradiance data of each sub-variation curve, and calculate the absolute value of the slope of adjacent average back irradiance data. The average of the absolute values ​​of all slopes in the peak change curve is obtained to get the fluctuation parameters of the peak point. The fluctuation parameters of each peak point are sorted in descending order. Based on the sorting results, the peak point with the lowest fluctuation parameter ranking is selected, and the azimuth adjustment data is determined based on the selected peak point.

8. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 1, characterized in that, The step of determining the azimuth adjustment strategy based on the secondary simulation results includes: Based on the results of the secondary simulation, the azimuth adjustment process is divided into several adjustment intervals. An objective function is established, and the adjustment intervals are optimized according to the objective function. Based on the optimization results, the azimuth adjustment strategy is determined.

9. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 8, characterized in that, The establishment of the objective function includes: An objective function is established based on the power generation and azimuth adjustment energy consumption within the adjustment range. The objective function is: ； in, Let be the objective function. For the power generation capacity of the interval, Energy consumption for azimuth adjustment within the interval , These are the preset first weight and the preset second weight, respectively.

10. The automatic cleaning tracking drive control method for bifacial photovoltaic modules according to claim 8, characterized in that, The process of optimizing the adjustment interval based on the objective function and determining the azimuth adjustment strategy based on the optimization result includes: Step 1: Establish particles according to the adjustment interval, randomly initialize the velocity and position of each particle in the search space, calculate the fitness function value of each particle, and the position of the particle with the largest fitness function value is the global optimal position. Step 2: Correct the particle's flight direction using velocity update formulas and position update formulas; Step 3: Evaluate the fitness function value of the particle and update the individual optimal position and global optimal position of the particle; Step 4: Determine whether the particle swarm optimization algorithm meets the convergence condition. If it does, output the global optimal position and end the process. Determine the azimuth adjustment strategy based on the particle corresponding to the global optimal position. Otherwise, repeat steps 1 to 3.

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