Adaptive photovoltaic module inclination angle adjusting method and system based on environmental conditions

Abstract

The invention relates to the technical field of intelligent control and optimization of a photovoltaic power generation system, and discloses a self-adaptive photovoltaic module inclination angle adjusting method and system based on environmental conditions, and the method comprises the steps: obtaining short-term weather forecast data through a meteorological data interface, analyzing the weather forecast data, and predicting the environmental change in several hours in the future; monitoring pollution data of the surface of the photovoltaic module in real time through a sensor, and judging whether the pollution degree affects the power generation efficiency or not; according to the prediction result and the pollution degree, the inclination angle of the photovoltaic module is dynamically adjusted, the power generation efficiency under the upcoming illumination condition is optimized, the inclination angle of the photovoltaic module is dynamically adjusted in combination with short-term weather forecast data obtained by the meteorological data interface, and the power generation efficiency of the photovoltaic system under the complex environment is remarkably improved. The system can quickly respond to environmental changes, and efficiency loss caused by adjustment lag is avoided. And multi-factor collaborative optimization is realized, and the maximum power generation efficiency of the photovoltaic system under various conditions is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control and optimization of photovoltaic power generation systems, and in particular to a method and system for adaptively adjusting the inclination angle of photovoltaic components based on environmental conditions. Background Art

[0002] With the continuous growth of global energy demand and the increasing prominence of environmental problems, photovoltaic power generation technology, as a clean and renewable form of energy, has received widespread attention and rapid development. In recent years, the efficiency and performance of photovoltaic modules have been continuously improved, and related control technologies have become increasingly mature to cope with power generation needs under different environmental conditions. In traditional photovoltaic power generation systems, the inclination of the module is often fixed, or only adjusted by a simple single-axis or dual-axis tracking system to maximize sunlight absorption. However, this fixed or single adjustment method cannot fully cope with complex and changing environmental conditions, such as changes in cloud cover, wind speed, precipitation, and accumulation of pollutants, resulting in the power generation efficiency not being able to reach the optimal level in actual operation. With the advancement of meteorological forecasting technology and sensor technology, dynamic and intelligent adjustment of the inclination of photovoltaic modules by combining real-time meteorological data and pollution monitoring data has gradually become an important research direction for improving the power generation efficiency of photovoltaic systems.

[0003] Although there have been some studies and applications that attempt to combine meteorological data to regulate photovoltaic systems, existing technologies still have obvious deficiencies in many aspects. First, existing systems usually rely on historical data and short-term weather forecasts for adjustment, lacking the ability to respond quickly to real-time environmental changes, resulting in the lag of adjustment under rapidly changing weather conditions, affecting the overall efficiency of the system. Secondly, in terms of pollution monitoring, existing technologies mostly focus on the simple shielding effect of pollutants on the surface of photovoltaic modules, while ignoring the differential effects of different types of pollutants (such as dust, bird droppings, etc.) on photovoltaic power generation efficiency. This results in the inability of existing systems to accurately determine whether cleaning is required when dealing with complex polluted environments, which further affects power generation efficiency. In addition, most existing tilt adjustment systems are based on the optimization of a single parameter, lack the ability to comprehensively consider multi-dimensional environmental parameters such as cloud cover, wind speed, and sunshine intensity, and cannot give full play to the effect of multi-factor interaction on improving power generation efficiency. In short, the shortcomings of existing technologies in dealing with dynamic environmental conditions and multi-dimensional pollution influences limit the optimization potential of photovoltaic power generation systems. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that under complex and changeable environmental conditions, the traditional tilt adjustment method of the photovoltaic power generation system cannot respond to environmental changes and pollutant accumulation in real time and accurately, resulting in a decrease in power generation efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for adjusting the tilt angle of a photovoltaic module based on environmental conditions, comprising:

[0007] Obtain short-term weather forecast data through the meteorological data interface, analyze weather forecast data, and predict environmental changes in the next few hours;

[0008] Use sensors to monitor pollution data on the surface of photovoltaic modules in real time to determine whether the degree of pollution affects power generation efficiency;

[0009] Based on the prediction results and combined with the degree of pollution, the inclination of the photovoltaic panels is dynamically adjusted to optimize the power generation efficiency under the upcoming lighting conditions.

[0010] As a preferred solution of the method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions described in the present invention, the weather forecast data includes cloud cover, precipitation, wind speed, wind direction, temperature, humidity and sunshine intensity;

[0011] The pollution data include dust concentration, pollutant type and composition, particle size distribution, surface coverage, light reflectivity changes and humidity;

[0012] The sensors include a dust sensor, a pollutant sensor, a particle size distribution sensor, a surface coverage sensor, a light reflectivity sensor, a humidity sensor and an ambient light intensity sensor.

[0013] As a preferred solution of the method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions described in the present invention, wherein: the analyzing weather forecast data includes preprocessing the weather forecast data, identifying key meteorological parameters that affect the tilt angle adjustment of photovoltaic modules, and integrating the key meteorological parameters and the historical output power data of the photovoltaic power generation system into a multidimensional data set;

[0014] Use correlation analysis to evaluate the linear relationship between each key meteorological parameter and PV power generation efficiency, and determine the degree of influence of these key meteorological parameters on the output of the PV power generation system;

[0015] Predicting environmental changes in the next few hours involves combining the ARIMA model with the LSTM model, using ARIMA to process the linear part of the time series and using LSTM to process the nonlinear relationship in the data to predict rainfall within one hour. The formula is expressed as:

[0016]

[0017] Among them, P rain represents the probability of rainfall in the next hour, σ represents the Sigmoid activation function, ωi represents the model parameter weight, φ j Y t-j represents the autoregressive term of the ARIMA model, θ k ∈ t-k represents the moving average term of the ARIMA model, α l h t-l Represents the hidden state weight of the LSTM model;

[0018] Considering the nonlinear effects of multiple factors such as cloud cover, sunshine intensity, wind speed and light intensity, an efficiency model is constructed, and the formula is expressed as follows:

[0019]

[0020] Among them, η PV represents the power generation efficiency of the photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 and λ2 are environmental parameters representing the influence coefficient of the photovoltaic system, cloud(t) represents the cloud cover percentage at time t, cos(θ(t)) represents the influence of the light angle on the photovoltaic module, θ(t) represents the inclination angle of the module, wind(t) represents the wind speed at time t, S max (t) represents the maximum sunshine intensity;

[0021] P rain The value range of is 0 to 1, and the closer the value is to 1, the more likely it is to rain;

[0022] η PV The value range is from 0 to 1. The closer the value is to 1, the closer the power generation efficiency of the photovoltaic system under the current environmental conditions is to the ideal state.

[0023] As a preferred scheme of the adaptive photovoltaic module tilt adjustment method based on environmental conditions described in the present invention, the judgment of whether the pollution degree affects the power generation efficiency includes evaluating the pollution degree in four dimensions: pollution coverage, pollution thickness, pollution type and environmental factors, and constructing a quantitative model to quantify its potential impact on the power generation efficiency.

[0024] The pollution coverage rate includes analyzing the coverage area of ​​the pollutants on the surface of the component;

[0025] The pollution thickness includes that the thickness of the pollution directly affects the transmittance of light;

[0026] The pollution types include: different types of pollutants have different light blocking effects on the surface of photovoltaic modules, and different evaluation standards are set;

[0027] The environmental factors include, combining environmental conditions, incorporating dynamic factors into the evaluation system;

[0028] The formula of the quantitative model is expressed as:

[0029]

[0030] Among them, η eff represents the power generation efficiency of the quantified photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 represents the influence coefficient of the pollution type, Type(t) represents the pollutant type at time t, α i Represents the weight coefficient of pollution coverage, Cov(t i ) represents the time t i The pollution coverage rate at the time, Thick(t i ) represents the time t i The pollution thickness at time t, ∈ represents a small positive number to prevent the denominator from being zero, tanh represents the hyperbolic tangent function, σ represents the Sigmoid function, Env(t) represents the environmental factor at time t, Env max Indicates the maximum value of environmental factors, S max (t) represents the maximum sunshine intensity at time t;

[0031] η eff The value range is from 0 to 1. eff =1, it means that pollution has no effect on power generation efficiency;

[0032] When η eff When =0, it means that pollution has seriously affected the power generation efficiency.

[0033] As a preferred solution of the method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions described in the present invention, the method of dynamically adjusting the tilt angle of photovoltaic modules based on the prediction results and the pollution degree to optimize the power generation efficiency under the upcoming lighting conditions includes:

[0034] When the predicted rainfall probability P rain When ≥0.7, it is adjusted to the contaminated self-cleaning mode;

[0035] The system adjusts the inclination of the PV panels to an angle that is conducive to rainwater cleaning, maximizing the self-cleaning effect, and no manual cleaning operations are performed during this period;

[0036] When P rain ≤0.7, and η eff When ≤0.85, it is adjusted to the polluted manual cleaning mode to ensure the operating efficiency of the photovoltaic system;

[0037] The perspective of rainwater cleaning needs to consider the type of pollution, rainfall intensity and wind speed;

[0038] Different types of contaminants have different adhesion to the surface of PV modules and different self-cleaning difficulties. For lighter contaminants, the tilt angle adjustment does not need to be too large, while for heavier or highly adhesive contaminants, the tilt angle adjustment needs to be larger;

[0039] In the case of heavy rainfall, a larger inclination angle can more effectively utilize the flushing effect of rainwater and help pollutants fall off the surface of photovoltaic modules.

[0040] In the case of light rainfall, the tilt angle should be adjusted to a smaller value to keep the rainwater on the surface of the module longer and increase the cleaning effect;

[0041] When the wind speed is high, a larger inclination angle can make it easier for the wind to carry away pollutants. When the wind speed is low, the inclination angle should be adjusted to a smaller value to avoid insufficient wind to disperse pollutants.

[0042] The angle formula of rainwater cleaning is expressed as:

[0043]

[0044] Among them, θ base Indicates the current photovoltaic module power generation optimization inclination, Rain intensity Indicates the real-time rainfall intensity, Rain max Indicates the maximum rainfall intensity, Wind speed Indicates real-time wind speed, Wind max Indicates the maximum wind speed, Type factor It represents the influencing factor of pollution type, and k1, k2, and k3 represent adjustment coefficients.

[0045] As a preferred solution of the method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions described in the present invention, the method of dynamically adjusting the tilt angle of photovoltaic modules to optimize the power generation efficiency under the upcoming lighting conditions includes:

[0046] When the power generation efficiency η PV When it is >0.9, it is in high efficiency state, the system keeps the existing inclination angle unchanged and maintains efficient power generation;

[0047] When the power generation efficiency is 0.7<η PV When ≤0.9, it is a medium efficiency state, and the system will make slight tilt adjustments;

[0048] The minor adjustments include, based on the real-time cloud cover, the system fine-tuning the tilt angle to maximize the scattered light;

[0049] When the power generation efficiency η PV When ≤0.7, it is a low efficiency state and the system performs substantial adjustments to optimize scattered light absorption.

[0050] As a preferred solution of the method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions described in the present invention, the method of dynamically adjusting the tilt angle of photovoltaic modules to optimize the power generation efficiency under the upcoming lighting conditions also includes the system recording the power generation efficiency change after each adjustment and comparing it with the previous efficiency value;

[0051] When the efficiency is significantly improved after adjustment, the system records the adjusted parameters for future reference;

[0052] When there is no significant improvement in efficiency after adjustment, the system adjusts parameter settings through a feedback mechanism and tries new adjustment strategies;

[0053] By accumulating historical data, the system will gradually optimize the parameters and adjustment strategies in the model to ensure that the system's power generation efficiency always remains at the best state under various complex environments.

[0054] An adaptive photovoltaic module tilt adjustment system based on environmental conditions, wherein:

[0055] Environmental prediction module, which obtains short-term weather forecast data through the meteorological data interface, analyzes the weather forecast data, and predicts environmental changes in the next few hours;

[0056] The pollution monitoring module uses sensors to monitor the pollution data on the surface of photovoltaic modules in real time to determine whether the pollution level affects the power generation efficiency;

[0057] The optimization module dynamically adjusts the inclination of the photovoltaic modules according to the prediction results and the pollution level to optimize the power generation efficiency under the upcoming lighting conditions.

[0058] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0059] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0060] Beneficial effects of the present invention: The adaptive photovoltaic module tilt adjustment method based on environmental conditions provided by the present invention dynamically adjusts the tilt of the photovoltaic module by real-time monitoring of environmental conditions and the pollution status of the surface of the photovoltaic module, combined with the short-term weather forecast data obtained by the meteorological data interface, thereby significantly improving the power generation efficiency of the photovoltaic system in complex environments. The system can respond quickly to environmental changes and avoid efficiency losses caused by adjustment lags. By comprehensively considering multiple key environmental parameters and pollutant types, the present invention achieves multi-factor collaborative optimization to ensure that the photovoltaic system maximizes power generation efficiency under various conditions. In addition, the present invention can accurately judge the impact of pollution on power generation efficiency, take cleaning measures when necessary, reduce maintenance costs, realize intelligent and automated control of the photovoltaic system, and improve the reliability and sustainability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 An overall flow chart of an environmental condition-based adaptive photovoltaic module tilt adjustment method provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a method for adjusting the tilt angle of a photovoltaic module based on environmental conditions, comprising:

[0065] S1: Obtain short-term weather forecast data through the meteorological data interface, analyze the weather forecast data, and predict environmental changes in the next few hours.

[0066] Weather forecast data includes cloud cover, precipitation, wind speed, wind direction, temperature, humidity and sunshine intensity.

[0067] Pollution data includes dust concentration, pollutant type and composition, particle size distribution, surface coverage, light reflectance variation and humidity.

[0068] The sensors include dust sensors, pollutant sensors, particle size distribution sensors, surface coverage sensors, light reflectivity sensors, humidity sensors and ambient light intensity sensors.

[0069] Analyzing the weather forecast data includes preprocessing the weather forecast data, identifying key meteorological parameters that affect the tilt adjustment of photovoltaic modules, and integrating the key meteorological parameters and historical output power data of the photovoltaic power generation system into a multidimensional data set.

[0070] Correlation analysis was used to evaluate the linear relationship between each key meteorological parameter and PV power generation efficiency, and to determine the extent to which these key meteorological parameters affected the output of the PV power generation system.

[0071] Predicting environmental changes in the next few hours involves combining the ARIMA model with the LSTM model, using ARIMA to process the linear part of the time series and using LSTM to process the nonlinear relationship in the data to predict rainfall within one hour. The formula is expressed as:

[0072]

[0073] Among them, P rain represents the probability of rainfall in the next hour, σ represents the Sigmoid activation function, ω i represents the model parameter weight, φ j Y t-j represents the autoregressive term of the ARIMA model, θ k ∈ t-k represents the moving average term of the ARIMA model, α l h t-l Represents the hidden state weight of the LSTM model.

[0074] Considering the nonlinear effects of multiple factors such as cloud cover, sunshine intensity, wind speed and light intensity, an efficiency model is constructed, and the formula is expressed as follows:

[0075]

[0076] Among them, η PV represents the power generation efficiency of the photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 and λ2 are environmental parameters representing the influence coefficient of the photovoltaic system, cloud(t) represents the cloud cover percentage at time t, cos(θ(t)) represents the influence of the light angle on the photovoltaic module, θ(t) represents the inclination angle of the module, wind(t) represents the wind speed at time t, S max (t) represents the maximum sunshine intensity.

[0077] P rainThe value range is from 0 to 1, and the closer the value is to 1, the more likely it is to rain.

[0078] η PV The value range is from 0 to 1. The closer the value is to 1, the closer the power generation efficiency of the photovoltaic system under the current environmental conditions is to the ideal state.

[0079] By obtaining short-term weather forecast data through the meteorological data interface and analyzing it, we can timely understand environmental changes in the next few hours. The design of this module enables the photovoltaic system to make response strategies in advance, such as pre-adjusting the tilt angle of the components to cope with upcoming changes in cloud cover, increased wind speed or rainfall. This ability to predict in advance greatly improves the system's response speed to environmental changes and reduces the decline in power generation efficiency caused by sudden changes in the environment.

[0080] By introducing the analysis of short-term weather forecast data, the photovoltaic system is given a "predictive" adjustment capability, no longer relying solely on the feedback of real-time data. The design of this module enables the system to change from passive response to active adjustment, optimizes the overall performance of the photovoltaic system in complex and changing environments, and ensures a more stable and efficient power generation process.

[0081] S2: Use sensors to monitor the pollution data on the surface of photovoltaic modules in real time to determine whether the degree of pollution affects the power generation efficiency.

[0082] Judging whether the degree of pollution affects power generation efficiency includes evaluating the degree of pollution in four dimensions: pollution coverage, pollution thickness, pollution type and environmental factors, and constructing a quantitative model to quantify its potential impact on power generation efficiency.

[0083] Contamination coverage involves analyzing the coverage area of ​​contaminants on the component surface.

[0084] Pollution thickness includes, the thickness of the pollutant directly affects the transmittance of light.

[0085] The pollution types include: different types of pollutants have different light blocking effects on the surface of photovoltaic modules, and different evaluation standards are set.

[0086] Environmental factors include combining environmental conditions and incorporating dynamic factors into the evaluation system.

[0087] The formula of the quantitative model is expressed as:

[0088]

[0089] Among them, η eff represents the power generation efficiency of the quantified photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 represents the influence coefficient of the pollution type, Type(t) represents the pollutant type at time t, α iRepresents the weight coefficient of pollution coverage, Cov(t i ) represents the time t i The pollution coverage rate at the time, Thick(t i ) represents the time t i The pollution thickness at time t, ∈ represents a small positive number to prevent the denominator from being zero, tanh represents the hyperbolic tangent function, σ represents the Sigmoid function, Env(t) represents the environmental factor at time t, Env max Indicates the maximum value of environmental factors, S max (t) represents the maximum sunshine intensity at time t.

[0090] η eff The value range is from 0 to 1. eff =1, it means that pollution has no effect on power generation efficiency.

[0091] When η eff When =0, it means that pollution has seriously affected the power generation efficiency.

[0092] The sensor monitors the pollution on the surface of the photovoltaic module in real time, and can identify the type of pollution, coverage and thickness, and quantify its impact on power generation efficiency. The design of this module enables the system to accurately determine whether cleaning is needed, thereby avoiding unnecessary manual cleaning operations, reducing maintenance costs, and ensuring that the photovoltaic system remains in an efficient working state.

[0093] Pollution is one of the key factors affecting the efficiency of photovoltaic power generation. Through precise monitoring and evaluation, the system can scientifically decide when to perform cleaning operations. The design of this module ensures the adaptive ability of the photovoltaic system in a polluted environment, improves the long-term reliability and power generation efficiency of the system, and reduces the waste of resources caused by excessive or insufficient cleaning.

[0094] S3: Based on the prediction results and combined with the pollution level, the inclination angle of the photovoltaic modules is dynamically adjusted to optimize the power generation efficiency under the upcoming lighting conditions.

[0095] Based on the prediction results and combined with the pollution level, the tilt angle of the photovoltaic panels is dynamically adjusted to optimize the power generation efficiency under the upcoming lighting conditions, including:

[0096] When the predicted rainfall probability P rain When ≥0.7, it is adjusted to the contaminated self-cleaning mode.

[0097] The system adjusts the inclination of the PV panels to an angle that is conducive to rainwater cleaning, maximizing the self-cleaning effect, and no manual cleaning operations are performed during this period.

[0098] When P rain ≤0.7, and η effWhen ≤0.85, it is adjusted to the polluted manual cleaning mode to ensure the operating efficiency of the photovoltaic system.

[0099] The perspective of rainwater cleaning needs to consider the type of pollution, rainfall intensity and wind speed.

[0100] Different types of contaminants have different adhesion to the surface of PV modules and different levels of self-cleaning difficulty. For lighter contaminants, the tilt angle adjustment does not need to be too large, while for heavier or highly adhesive contaminants, the tilt angle adjustment needs to be larger.

[0101] In the case of heavy rainfall, a larger inclination angle can more effectively utilize the flushing effect of rainwater and help pollutants fall off the surface of photovoltaic modules.

[0102] In the case of light rainfall, the tilt angle adjustment should be smaller to keep the rainwater on the component surface longer and increase the cleaning effect.

[0103] When the wind speed is high, a larger inclination angle can make it easier for the wind to carry away pollutants. When the wind speed is low, the inclination angle should be adjusted to a smaller value to avoid insufficient wind to disperse pollutants.

[0104] The angle formula of rainwater cleaning is expressed as:

[0105]

[0106] Among them, θ base Indicates the current photovoltaic module power generation optimization inclination, Rain intensity Indicates the real-time rainfall intensity, Rain max Indicates the maximum rainfall intensity, Wind speed Indicates real-time wind speed, Wind max Indicates the maximum wind speed, Type factor It represents the influencing factor of pollution type, and k1, k2, and k3 represent adjustment coefficients.

[0107] Dynamically adjust the tilt angle of the PV panels to optimize the power generation efficiency under the upcoming light conditions including:

[0108] When the power generation efficiency η PV When >0.9, it is in high efficiency state, the system keeps the existing inclination angle unchanged and maintains efficient power generation.

[0109] When the power generation efficiency is 0.7<η PV When ≤0.9, it is in medium efficiency state and the system will make slight tilt adjustment.

[0110] Minor adjustments include fine-tuning the tilt angle to maximize diffuse light based on real-time cloud cover.

[0111] When the power generation efficiency η PVWhen ≤0.7, it is a low efficiency state and the system performs substantial adjustments to optimize scattered light absorption.

[0112] The system records the change in power generation efficiency after each adjustment and compares it with the previous efficiency value.

[0113] When efficiency is significantly improved after adjustment, the system records the adjusted parameters for future reference.

[0114] When there is no significant improvement in efficiency after adjustment, the system adjusts parameter settings through a feedback mechanism and tries new adjustment strategies.

[0115] By accumulating historical data, the system will gradually optimize the parameters and adjustment strategies in the model to ensure that the system's power generation efficiency always remains at the best state under various complex environments.

[0116] According to the predicted environmental change results and real-time monitoring of pollution data, the tilt angle of the photovoltaic module is dynamically adjusted to optimize the power generation efficiency under the upcoming light conditions. The design of this module enables the photovoltaic system to automatically select the optimal tilt angle under different environmental conditions, ensuring that the photovoltaic module is always in the best working state under various environmental conditions.

[0117] Dynamically adjusting the inclination of photovoltaic modules is an important means to improve power generation efficiency. By comprehensively considering environmental predictions and pollution conditions, precise inclination adjustment is implemented to solve the limitations of traditional fixed inclination or simple tracking systems. The design of this module greatly improves the system's ability to adapt to environmental changes, allowing photovoltaic systems to maintain efficient operation in a wider range of application scenarios, and promoting the further development and popularization of photovoltaic technology.

[0118] On the other hand, this embodiment also provides an adaptive photovoltaic module tilt adjustment system based on environmental conditions, which includes:

[0119] The environmental prediction module obtains short-term weather forecast data through the meteorological data interface, analyzes the weather forecast data, and predicts environmental changes in the next few hours.

[0120] The pollution monitoring module uses sensors to monitor the pollution data on the surface of photovoltaic modules in real time to determine whether the degree of pollution affects the power generation efficiency.

[0121] The optimization module dynamically adjusts the inclination of the photovoltaic modules according to the prediction results and the pollution level to optimize the power generation efficiency under the upcoming lighting conditions.

[0122] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0124] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0125] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0126] Example 2, the experiment was conducted in a coastal area with obvious temperature difference between day and night, frequent cloud changes and high air humidity. The experimental equipment includes multiple photovoltaic modules of the same specifications, all equipped with real-time environmental monitoring sensors and pollution monitoring sensors, and connected to a central control system that integrates a meteorological data interface and a short-term weather forecast data analysis module. The goal of the experiment is to optimize the power generation efficiency of the photovoltaic system under complex environmental conditions through meteorological forecasting, pollution monitoring and dynamic tilt adjustment.

[0127] First, two groups of photovoltaic modules were installed, marked as Group A (control group) and Group B (experimental group). Group A used the traditional fixed tilt setting without any real-time adjustment; Group B used the intelligent control system of the present invention to perform dynamic tilt adjustment based on the meteorological data and pollution data obtained in real time. The tilt of the photovoltaic modules of Group B was automatically adjusted every 15 minutes through the real-time data collected by the sensor and the dynamic calculation of the central control system to ensure that the photovoltaic modules were always in the best power generation state under different environmental conditions.

[0128] During the experiment, the photovoltaic modules of Group B first obtained weather forecast data for the next 6 hours through the meteorological data interface, including cloud cover, wind speed, humidity and rainfall probability. After analyzing these data, the system combined with the real-time monitoring of pollution conditions, such as dust coverage and pollutant thickness, to determine whether the pollution has an impact on power generation efficiency, and adjust the inclination of the modules accordingly. For example, when the forecast cloud cover increases, the system pre-adjusts the inclination to a position that is more conducive to capturing scattered light; if rainfall is predicted, if the pollution level is high, the system will increase the inclination to enhance the self-cleaning effect.

[0129] Throughout the experiment, the dynamic adjustment strategy of Group B was combined with multiple real-time monitoring feedback. After each adjustment, the system recorded the actual power generation efficiency and compared it with the expected effect. If the actual efficiency did not meet expectations, the system would automatically adjust the algorithm parameters and recalculate the optimal tilt angle for the next adjustment. This feedback mechanism has gradually optimized the power generation efficiency of the photovoltaic modules, and the system can maintain high adaptability and response speed in complex and changing environments. The experimental data is shown in Table 1.

[0130] Table 1 Experimental data table

[0131]

[0132]

[0133] By comparing the experimental data of Group A and Group B, it can be clearly seen that the adaptive photovoltaic module tilt adjustment method of the present invention has significant advantages in power generation efficiency and self-cleaning effect. First, in the comparison of power generation efficiency, the efficiency of Group B is higher than that of Group A under various environmental conditions. For example, when the average daily cloud cover reaches 50%, the power generation efficiency of Group A drops to 0.68, while Group B remains at 0.81. This shows that the present invention can adjust the tilt angle of the photovoltaic module in real time, so that the system can still maintain a high photoelectric conversion efficiency when the cloud cover increases.

[0134] In addition, when the wind speed is high, Group B uses wind power to enhance the natural removal of dust by appropriately adjusting the inclination angle, further improving the power generation efficiency. When the average daily wind speed reaches 8m / s, the self-cleaning effect of Group B reaches a high level, and the power generation efficiency remains at 0.81, while the power generation efficiency of Group A drops to 0.68 due to dust accumulation. This result verifies the innovation of the present invention in pollution monitoring and dynamic inclination adjustment, which can significantly reduce the impact of dust on power generation efficiency and extend the efficient operation time of the photovoltaic system.

[0135] More importantly, the experimental results of Group B also show that the adaptive adjustment system of the present invention has strong environmental adaptability. Whether in low sunlight intensity or high cloud cover, the system can quickly respond and adjust the component inclination angle to maximize the use of scattered light. This multi-dimensional comprehensive optimization capability is unattainable by traditional fixed inclination systems, which fully reflects the innovation and technological advancement of the present invention.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions, characterized in that: include: Obtain short-term weather forecast data through the meteorological data interface, analyze weather forecast data, and predict environmental changes in the next few hours; Use sensors to monitor pollution data on the surface of photovoltaic modules in real time to determine whether the degree of pollution affects power generation efficiency; Based on the prediction results and combined with the degree of pollution, the inclination of the photovoltaic panels is dynamically adjusted to optimize the power generation efficiency under the upcoming lighting conditions.

2. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 1, characterized in that: The weather forecast data includes cloud cover, precipitation, wind speed, wind direction, temperature, humidity and sunshine intensity; The pollution data include dust concentration, pollutant type and composition, particle size distribution, surface coverage, light reflectivity changes and humidity; The sensors include a dust sensor, a pollutant sensor, a particle size distribution sensor, a surface coverage sensor, a light reflectivity sensor, a humidity sensor and an ambient light intensity sensor.

3. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 2, characterized in that: The analyzing weather forecast data includes preprocessing the weather forecast data and identifying key meteorological parameters that affect the tilt adjustment of photovoltaic modules, and integrating the key meteorological parameters and historical output power data of the photovoltaic power generation system into a multidimensional data set; Use correlation analysis to evaluate the linear relationship between each key meteorological parameter and PV power generation efficiency, and determine the degree of influence of these key meteorological parameters on the output of the PV power generation system; Predicting environmental changes in the next few hours involves combining the ARIMA model with the LSTM model, using ARIMA to process the linear part of the time series and using LSTM to process the nonlinear relationship in the data to predict rainfall within one hour. The formula is expressed as: Among them, P rain represents the probability of rainfall in the next hour, σ represents the Sigmoid activation function, ω i represents the model parameter weight, φ j Y t-j represents the autoregressive term of the ARIMA model, θ k ∈ t-k represents the moving average term of the ARIMA model, α l h t-l Represents the hidden state weight of the LSTM model; Considering the nonlinear effects of multiple factors such as cloud cover, sunshine intensity, wind speed and light intensity, an efficiency model is constructed, and the formula is expressed as follows: Among them, η PV represents the power generation efficiency of the photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 and λ2 are environmental parameters representing the influence coefficient of the photovoltaic system, cloud(t) represents the cloud cover percentage at time t, cos(θ(t)) represents the influence of the light angle on the photovoltaic module, θ(t) represents the inclination angle of the module, wind(t) represents the wind speed at time t, S max (t) represents the maximum sunshine intensity; P rain The value range of is 0 to 1, and the closer the value is to 1, the more likely it is to rain; η PV The value range is from 0 to 1. The closer the value is to 1, the closer the power generation efficiency of the photovoltaic system under the current environmental conditions is to the ideal state.

4. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 3, characterized in that: The determination of whether the pollution degree affects the power generation efficiency includes evaluating the pollution degree in four dimensions: pollution coverage, pollution thickness, pollution type and environmental factors, and constructing a quantitative model to quantify its potential impact on the power generation efficiency. The pollution coverage rate includes analyzing the coverage area of ​​the pollutants on the surface of the component; The pollution thickness includes that the thickness of the pollution directly affects the transmittance of light; The pollution types include: different types of pollutants have different light blocking effects on the surface of photovoltaic modules, and different evaluation standards are set; The environmental factors include, combining environmental conditions, incorporating dynamic factors into the evaluation system; The formula of the quantitative model is expressed as: Among them, η eff represents the power generation efficiency of the quantified photovoltaic system, S(t) represents the sunshine intensity at time t, λ1 represents the influence coefficient of the pollution type, Type(t) represents the pollutant type at time t, α i Represents the weight coefficient of pollution coverage, Cov(t i ) represents the time t i The pollution coverage rate at the time, Thick(t i ) represents the time t i The pollution thickness at time t, ∈ represents a small positive number to prevent the denominator from being zero, tanh represents the hyperbolic tangent function, σ represents the Sigmoid function, Env(t) represents the environmental factor at time t, Env max Indicates the maximum value of environmental factors, S max (t) represents the maximum sunshine intensity at time t; η eff The value range of is 0 to 1. When ηeff = 1, it means that pollution has no effect on power generation efficiency; When η eff When =0, it means that pollution has seriously affected the power generation efficiency.

5. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 4, characterized in that: The method of dynamically adjusting the inclination angle of the photovoltaic module according to the prediction results and the pollution degree to optimize the power generation efficiency under the upcoming lighting conditions includes: When the predicted rainfall probability P rain When ≥0.7, it is adjusted to the contaminated self-cleaning mode; The system adjusts the inclination of the PV panels to an angle that is conducive to rainwater cleaning, maximizing the self-cleaning effect, and no manual cleaning operations are performed during this period; When P rain ≤0.7, and η eff When ≤0.85, it is adjusted to the polluted manual cleaning mode to ensure the operating efficiency of the photovoltaic system; The perspective of rainwater cleaning needs to consider the type of pollution, rainfall intensity and wind speed; Different types of contaminants have different adhesion to the surface of PV modules and different self-cleaning difficulties. For lighter contaminants, the tilt angle adjustment does not need to be too large, while for heavier or highly adhesive contaminants, the tilt angle adjustment needs to be larger; In the case of heavy rainfall, a larger inclination angle can more effectively utilize the flushing effect of rainwater and help pollutants fall off the surface of photovoltaic modules. In the case of light rainfall, the tilt angle should be adjusted to a smaller value to keep the rainwater on the surface of the module longer and increase the cleaning effect; When the wind speed is high, a larger inclination angle can make it easier for the wind to carry away pollutants. When the wind speed is low, the inclination angle should be adjusted to a smaller value to avoid insufficient wind to disperse pollutants. The angle formula of rainwater cleaning is expressed as: Among them, θ base Indicates the current photovoltaic module power generation optimization inclination, Rain intensity Indicates the real-time rainfall intensity, Rain max Indicates the maximum rainfall intensity, Wind speed Indicates real-time wind speed, Wind max Indicates the maximum wind speed, Type factor It represents the influencing factor of pollution type, and k1, k2, and k3 represent adjustment coefficients.

6. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 5, characterized in that: The method of dynamically adjusting the inclination angle of the photovoltaic module to optimize the power generation efficiency under the upcoming lighting conditions includes: When the power generation efficiency η PV When it is >0.9, it is in high efficiency state, the system keeps the existing inclination angle unchanged and maintains efficient power generation; When the power generation efficiency is 0.7<η PV When ≤0.9, it is a medium efficiency state, and the system will make slight tilt adjustments; The minor adjustments include, based on the real-time cloud cover, the system fine-tuning the tilt angle to maximize the scattered light; When the power generation efficiency η PV When ≤0.7, it is a low efficiency state and the system performs substantial adjustments to optimize scattered light absorption.

7. The method for adaptively adjusting the tilt angle of photovoltaic modules based on environmental conditions according to claim 6, characterized in that: The dynamically adjusting the inclination angle of the photovoltaic module to optimize the power generation efficiency under the upcoming lighting conditions also includes the system recording the change in power generation efficiency after each adjustment and comparing it with the previous efficiency value; When the efficiency is significantly improved after adjustment, the system records the adjusted parameters for future reference; When there is no significant improvement in efficiency after adjustment, the system adjusts parameter settings through a feedback mechanism and tries new adjustment strategies; By accumulating historical data, the system will gradually optimize the parameters and adjustment strategies in the model to ensure that the system's power generation efficiency always remains at the best state under various complex environments.

8. An adaptive photovoltaic module tilt adjustment system based on environmental conditions using the method according to any one of claims 1 to 7, characterized in that: Environmental prediction module, which obtains short-term weather forecast data through the meteorological data interface, analyzes the weather forecast data, and predicts environmental changes in the next few hours; The pollution monitoring module uses sensors to monitor the pollution data on the surface of photovoltaic modules in real time to determine whether the pollution level affects the power generation efficiency; The optimization module dynamically adjusts the inclination of the photovoltaic modules according to the prediction results and the pollution level to optimize the power generation efficiency under the upcoming lighting conditions.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method for adaptively adjusting the tilt angle of photovoltaic components based on environmental conditions are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for adaptively adjusting the tilt angle of a photovoltaic assembly based on environmental conditions are implemented.

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