A variable frequency dust cleaning device for improving photovoltaic power generation efficiency

Abstract

The application discloses a variable frequency dust cleaning device for improving photovoltaic power generation efficiency, and the control system can dynamically adjust the output frequency and amplitude of the signal generator according to the cleaning requirement. In particular, when cleaning different types of dust, the system can automatically adjust the vibration frequency, so that the cleaning process is more accurate. Larger particles of dust are effectively removed by low-frequency and large-amplitude vibration, while smaller particles are finely cleaned by high-frequency micro-vibration. The dynamic adjustment of the frequency and amplitude ensures that each type of dust can be completely removed, avoiding the poor effect caused by the fixed frequency in the traditional cleaning method.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic dust removal, specifically, it relates to a frequency conversion dust removal device for improving the efficiency of photovoltaic power generation. Background Technology

[0002] Dust accumulation in the air significantly impacts the energy conversion of photovoltaic (PV) panels. PV panels convert sunlight into electricity; dust accumulation reduces the light transmittance of the panels, thus affecting their power generation efficiency. This problem is particularly severe in dry, low-rainfall regions, leading to a decrease in power generation. Therefore, dust is one of the key factors affecting the power generation of photovoltaic power plants.

[0003] Extensive research has been conducted both domestically and internationally on cleaning accumulated dust in large-scale photovoltaic power plants. The main methods for cleaning dust from photovoltaic panel surfaces include: manual cleaning, compressed air blowing, mechanical vibration, and dust-suppressing coatings.

[0004] While manual cleaning is the most intuitive and traditional cleaning method, typically using brushes, cleaning cloths, or other tools to manually remove surface dust, its advantages include the ability to remove stubborn dust and the ability to perform detailed cleaning on each photovoltaic module. However, manual cleaning is inefficient, labor-intensive, and in large-scale photovoltaic power plants, it incurs high costs and significant risks, especially in high-temperature or high-altitude environments.

[0005] Compressed air purging uses high-pressure air jets to quickly blow away dust from photovoltaic panels. Its advantages include ease of operation and speed, making it particularly suitable for lightly dusted surfaces. While compressed air purging avoids surface scratches that can occur with contact cleaning, it is less effective for heavily accumulated or strongly adhered dust. Furthermore, air purging can sometimes cause secondary dust pollution, especially in windy areas.

[0006] Mechanical rapping uses vibration to loosen dust from the surface of photovoltaic panels, causing it to fall off. This method is effective for cleaning large areas of accumulated dust, removing most of the surface dust with high efficiency. However, mechanical rapping may cause some wear and tear on the surface of the photovoltaic panels, especially for high-efficiency panels. Long-term use may result in minor damage, affecting the lifespan of the panels.

[0007] Dust-suppressing coatings are applied to the surface of photovoltaic panels using a special anti-dust coating to reduce dust adhesion. This coating has self-cleaning properties, preventing dust accumulation to some extent and reducing the frequency of cleaning. However, dust-suppressing coatings are relatively expensive, and their effectiveness is affected by weather conditions and usage time; over time, the coating's dust-proofing effect gradually decreases.

[0008] Dust removal in photovoltaic power plants directly affects power generation efficiency and equipment lifespan. While existing cleaning methods can remove dust to some extent, they suffer from low efficiency, high cost, and potential damage to equipment.

[0009] In view of this, the present invention is proposed. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a frequency conversion dust removal device for improving the efficiency of photovoltaic power generation, thereby solving the problems mentioned in the background art.

[0011] To solve the above-mentioned technical problems, the basic concept of this invention is as follows:

[0012] A frequency conversion dust removal device for improving photovoltaic power generation efficiency includes: a signal generator for emitting frequency conversion signals;

[0013] A power amplifier, connected to a signal generator, is used to amplify the frequency conversion signal to the required power.

[0014] Fixed bracket;

[0015] A transducer is fixedly mounted on the surface of a flat or curved plate via a fixed bracket and connected to a power amplifier. It is used to convert power signals into vibrations and apply them to the surface of a photovoltaic or photothermal plate.

[0016] A controller with a control system is used to adjust the output frequency of the signal generator and control the working state of the transducer. The control system can gradually reduce the frequency of the signal generator within a certain period of time to complete one dust removal process.

[0017] Optionally, the signal generator can generate a frequency-converted signal with a frequency of 10kHz to 20kHz; the power amplifier can amplify the power of the frequency signal output by the signal generator to 50W to 200W; and the control system can control the frequency of the signal generator to gradually decrease from 20kHz to 10kHz within 30 seconds.

[0018] Optionally, a visual monitoring device is also included for real-time monitoring of dust distribution on the surface of a photovoltaic or solar thermal panel. The visual monitoring device detects dust in uncleaned areas on the panel surface using image processing algorithms and transmits feedback signals to the control system. The control system automatically adjusts the vibration frequency and amplitude based on the feedback signals to ensure complete dust removal. The steps are as follows:

[0019] Visual monitoring equipment is used to scan the surface of photovoltaic or solar thermal panels in real time to obtain images of dust distribution on the panel surface. Then, image processing algorithms are used to analyze the acquired images, identify areas of dust accumulation, and generate dust distribution data. Based on the dust distribution data, the cleanliness level is assessed to determine which areas need further cleaning.

[0020] Based on the dust distribution detection results, the visual monitoring equipment transmits relevant feedback signals to the control system. The feedback signals include the location and area of ​​the uncleaned dust area, as well as the degree of dust accumulation that requires further cleaning.

[0021] After receiving the feedback signal, the control system analyzes the areas that are not cleaned properly and automatically adjusts the vibration frequency and amplitude to ensure that the dust in the uncleaned areas can be completely removed. Specifically, it automatically selects a suitable frequency based on the size and adhesion of the dust particles. Larger dust particles may require a lower frequency and a larger amplitude to remove, while smaller dust particles are cleaned by high-frequency micro-vibrations. For stubborn dust areas that are difficult to remove, the control system can increase the amplitude to make the transducer vibrate more strongly, thereby activating the dust particles and moving them to the standing wave nodes.

[0022] Optionally, the steps of analyzing the acquired image using image processing algorithms to identify dust accumulation areas and generate dust distribution data are as follows:

[0023] The input image is I(x,y), where x and y represent the pixel coordinates in the image. Each pixel in the image can have three components: red, green, and blue. Then, through the grayscale process, the image is converted into a single-channel image G(x,y). The calculation formula is as follows: G(x,y)=0.299·R(x,y)+0.587·G(x,y)+0.114·B(x,y), where G(x,y) are the red, green, and blue components of the (x,y)th pixel in the image, respectively.

[0024] Thresholding segmentation is used to separate dust regions from the background in an image. An appropriate threshold T is chosen to convert the grayscale image into a binary image B(x, y), where dust regions are marked as 1 and background as 0. For each pixel G(x, y), if its grayscale value is greater than the threshold T, it is considered a dust region; otherwise, it is considered background. The binarization formula is:

[0025] By extracting features from dusty areas, the dust accumulation region is determined, and the edges of dusty areas in the image are detected using the Sobel operator, the expression of which is: G x (x,y)=B(x+1,y)-B(x-1,y), G y(x,y)=B(x,y+1)-B(x,y-1) is the gradient of the image in the x and y directions, and C(x,y) represents the edge intensity of the dust region;

[0026] The image after edge detection is analyzed using the connected component labeling method to calculate the clustering degree A of dust regions. First, let D be the set of detected dust regions, where each connected region R... i satisfy: The aggregation degree A can be represented by the ratio of the region area to the edge density: The calculations for the area and perimeter of the boundary are as follows: Representing region R i The boundary;

[0027] Based on the above results of aggregation degree and connected region labeling, a dust distribution map D is generated. map (x, y), whose value represents the dust density of each pixel, is expressed as: Let (x, y) be an indicator function, if (x, y) belongs to the dust region R i If it is 1, then it is 1; otherwise it is 0. i For region R i The degree of aggregation.

[0028] Optionally, when assessing the cleanliness of each area, first calculate the total area A of the dusty areas. total and the total area A of the photovoltaic panels panel The overall cleanliness is obtained through comparison. Then, for each region R... i Calculate its cleanliness: in, and The introduction of this feature is to reduce the weight of areas with high thickness and strong adhesion, so that the system can automatically adjust the cleaning strategy.

[0029] Based on the cleanliness assessment results, areas requiring further cleaning are marked. All areas with a cleanliness level below a preset threshold (R) are also included. i Areas that will be selected as key areas for cleaning will have their cleaning strategies adjusted based on this information by the control system.

[0030] Optionally, the control system can adjust the vibration frequency and amplitude according to the size of the dust particles and the cleaning requirements. For larger dust particles, the system increases the amplitude and vibration intensity; for smaller dust particles, the system adjusts the frequency to achieve fine cleaning. The steps are as follows:

[0031] A particle image of the dust region is obtained from the acquired image. Then, the area A of the dust region is determined. i Set a threshold A threshold To classify particles, the expression is: Particles are classified into large and small particles using threshold classification. Then, the frequency and amplitude of vibration are adjusted according to the size of the dust particles.

[0032] Based on the adhesion of the particles, the frequency and amplitude of the vibration are finely adjusted, and its expression is: A adjusted =A new ·(1+γ·C i ),f adjusted =f new ·(1-δ·C i ), where γ and δ are adjustment coefficients that control the increase or decrease of amplitude and frequency in areas with strong adhesion, respectively. A adjusted and f adjusted These are the adjusted frequency and amplitude, respectively.

[0033] Once all dusty areas have been removed, the system calculates the total dust area A. total Determine whether to stop the cleaning process.

[0034] Optionally, when adjusting the vibration frequency and amplitude according to the size of dust particles, the vibration amplitude is increased for larger dust particles, and the expression is as follows: The expression for adjusting the vibration frequency for small dust particles is: Among them, f base It is the fundamental frequency; β is the adjustment factor; A i It is the area of ​​the current dust region; A max It is the maximum area of ​​the large particles, A base It is the fundamental amplitude; α is the amplification factor; A max It is the maximum area of ​​the large particles; A i It represents the area of ​​the current dust region.

[0035] A variable frequency dust removal device for improving photovoltaic power generation efficiency also includes an environmental sensor for monitoring ambient temperature, humidity, and wind speed; the control system dynamically adjusts the working state of the dust removal device, including vibration frequency, amplitude, and cleaning cycle, according to the monitored environmental data to adapt to different climatic conditions; when the temperature is too high or the humidity is too high, the control system automatically adjusts the cleaning intensity to avoid excessive dust adhesion or damage.

[0036] After adopting the above-described technical invention, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all of the following advantages at the same time:

[0037] 1. The control system of this invention can dynamically adjust the output frequency and amplitude of the signal generator according to cleaning needs. Especially when cleaning different types of dust, the system can automatically adjust the vibration frequency, making the cleaning process more precise. Larger dust particles are effectively removed through low-frequency, high-amplitude vibration, while smaller particles are finely cleaned through high-frequency micro-vibration. This dynamic adjustment of frequency and amplitude ensures that each type of dust can be thoroughly removed, avoiding the poor results caused by fixed frequencies in traditional cleaning methods.

[0038] 2. By converting power signals into vibrations through a transducer and applying them to the surface of photovoltaic or solar thermal panels, the device can efficiently remove accumulated dust from the surface of photovoltaic or solar thermal panels in a short time. Compared to traditional cleaning methods, this frequency conversion dust removal device, designed to improve photovoltaic power generation efficiency, significantly enhances cleaning efficiency. The gradually decreasing frequency of the frequency conversion signal concentrates dust at the standing wave nodes, forming an effective dust removal path and ensuring thorough cleaning.

[0039] 3. A frequency conversion dust removal device for improving photovoltaic power generation efficiency amplifies the frequency conversion signal to the required power through a power amplifier to ensure that the vibration energy is accurately transferred to the cleaning surface. The device's efficient vibration output ensures cleaning effectiveness while avoiding unnecessary energy waste. Compared with other cleaning methods, this device can provide the same or more efficient cleaning effect while saving energy.

[0040] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0041] The accompanying drawings described below are merely some embodiments. Those skilled in the art can obtain other drawings based on these drawings without any creative effort. In the drawings:

[0042] Figure 1 This is a schematic diagram showing the frequency variation with the period of a variable frequency dust removal device used to improve the efficiency of photovoltaic power generation.

[0043] Figure 2 A variable frequency dust removal device for improving photovoltaic power generation efficiency is placed on the front view of the plate.

[0044] Figure 3 A side view of a variable frequency dust removal device for improving photovoltaic power generation efficiency placed on a plate.

[0045] Figure 4 A comparison curve of cleaning effects in a frequency conversion dust removal device used to improve the efficiency of photovoltaic power generation;

[0046] Figure 5This is a diagram showing the effect of particle size on cleaning performance in a variable frequency dust removal device used to improve photovoltaic power generation efficiency.

[0047] Figure 6 This is one of the three-dimensional structural diagrams of the handheld dust removal device in Example 2;

[0048] Figure 7 This is the second three-dimensional structural diagram of the handheld dust removal device in Example 2.

[0049] The attached diagram lists the components represented by each number as follows:

[0050] 1. Transducer; 2. Mounting bracket; 3. Plate surface; 4. Control box; 5. Hand lever; 6. Connecting part; 7. Display screen; 8. Placement slot; 9. Plate body; 10. Camera.

[0051] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0052] The invention will now be described in further detail with reference to the accompanying drawings.

[0053] Example 1: Please refer to Figure 1-2 As shown, this embodiment provides a frequency conversion dust removal device for improving photovoltaic power generation efficiency, including a signal generator for emitting frequency conversion signals;

[0054] A power amplifier, connected to a signal generator, is used to amplify the frequency conversion signal to the required power.

[0055] Fixed bracket 2;

[0056] Transducer 1 is fixedly installed on the surface of the flat or curved plate 3 via a fixed bracket 2 and connected to a power amplifier to convert power signals into vibrations and apply them to the surface of the photovoltaic or photothermal plate 3.

[0057] The controller 4 is equipped with a control system. The control system is used to adjust the output frequency of the signal generator and control the working state of the transducer 1. The control system can gradually reduce the frequency of the signal generator within a certain period of time to complete one dust removal process.

[0058] This invention is mainly applied to cleaning dust from flat or curved surfaces such as photovoltaic or solar thermal panels 3, glass mirrors, and pipe walls. The invention primarily includes a signal generator, a power amplifier, a transducer 1, and a corresponding control system. The signal generator sends a frequency-converted signal to the power amplifier, which then outputs power to the transducer 1, which is fixedly mounted on the flat or curved surface. Based on the Crani diagram principle, vibrations acting on a flat or curved surface cause particles to accumulate at the nodes of a standing wave. As the frequency of the transducer 1 gradually decreases, the period lengthens with decreasing vibration frequency. While the wave velocity remains constant due to its material dependence, the wavelength increases, resulting in a sparser Crani diagram. This manifests as particles moving away from the vibration source; this is the principle of frequency conversion dust removal. Using this principle to clean dust from photovoltaic or solar thermal panels 3 and other surfaces is simple, convenient, and easy to maintain.

[0059] In this embodiment, the signal generator can generate a frequency-converted signal with a frequency of 10kHz to 20kHz; the power amplifier can amplify the power of the frequency signal output by the signal generator to 50W to 200W; and the control system can control the frequency of the signal generator to gradually decrease from 20kHz to 10kHz within 30 seconds. Controlling the signal generator frequency to decrease from 20kHz to 10kHz within 30 seconds completes one dust removal cycle. Multiple dust removal cycles are reasonably set according to the size of the photovoltaic or solar thermal panel 3 to complete the dust cleaning work on the entire surface of the panel 3.

[0060] Please see Figure 6-7 As shown, Example 2: A handheld dust removal device includes a handheld handle 5, an upper connecting part (6) of the handheld handle 5, a display screen 7 and multiple control buttons above the connecting part 6, a placement groove 8 in the middle of one side of the connecting part 6, two plates 9 connected to its edge, a transducer 1 fixedly connected to the lower part of the uppermost plate 9, a controller 4 inside the placement groove 9, and a signal line connecting the transducer 1 and the controller 4. A camera 10 is also fixedly connected to one side of the plate 9. The use of the handheld dust removal device allows personnel to clamp the dust removal device at different positions on the plate surface as needed. The plate surface is located between the transducer 1 and the lowermost plate 9. The handheld design also facilitates subsequent adjustment of its position by personnel, and data can be monitored in real time through the display screen 7.

[0061] This embodiment also includes a visual monitoring device for real-time monitoring of dust distribution on the surface of a photovoltaic or solar thermal panel. The visual monitoring device detects dust in uncleaned areas on the panel surface using an image processing algorithm and transmits feedback signals to the control system. The control system automatically adjusts the vibration frequency and amplitude based on the feedback signals to ensure complete dust removal. The steps are as follows:

[0062] Visual monitoring devices such as cameras, infrared sensors, or laser scanners are used to scan the surface of photovoltaic or solar thermal panels in real time to obtain images of dust distribution on the panel surface. Then, image processing algorithms are used to analyze the acquired images, identify areas of dust accumulation, and generate dust distribution data. Based on the dust distribution data, the cleanliness level is assessed to determine which areas need further cleaning.

[0063] Based on the dust distribution detection results, the visual monitoring equipment transmits relevant feedback signals to the control system. The feedback signals include information such as the location and area of ​​the uncleaned dust area and the degree of dust accumulation that needs further cleaning.

[0064] After receiving the feedback signal, the control system analyzes the areas that are not cleaned properly and automatically adjusts the vibration frequency and amplitude to ensure that the dust in the uncleaned areas can be completely removed. Specifically, it automatically selects a suitable frequency based on the size and adhesion of the dust particles. Larger dust particles may require a lower frequency and a larger amplitude to remove, while smaller dust particles are cleaned by high-frequency micro-vibrations. For stubborn dust areas that are difficult to remove, the control system can increase the amplitude, so that the transducer 1 generates stronger vibrations, thereby activating the dust particles and moving them to the standing wave nodes.

[0065] In this embodiment, the steps of analyzing the acquired image using an image processing algorithm, identifying areas of dust accumulation, and generating dust distribution data are as follows:

[0066] The input image is I(x,y), where x and y represent the pixel coordinates in the image. Each pixel in the image can have three components: red, green, and blue (RGB). Then, through a grayscale conversion process, the image is converted into a single-channel image G(x,y). The calculation formula is as follows: G(x,y)=0.299·R(x,y)+0.587·G(x,y)+0.114·B(x,y), where G(x,y) are the red, green, and blue components of the (x,y)th pixel in the image, respectively.

[0067] A thresholding method is used to separate dust regions from the background in an image. An appropriate threshold T is chosen, and the grayscale image is converted into a binary image B(x, y), where dust regions are marked as 1 and the background as 0. For each pixel G(x, y), if its grayscale value is greater than the threshold... T If the pixel is positive, it indicates that the pixel is a dust area; otherwise, it is the background. The binarization formula is:

[0068] By extracting features from dusty areas, the dust accumulation region is determined, and the edges of dusty areas in the image are detected using the Sobel operator, the expression of which is: G x (x,y)=B(x+1,y)-B(x-1,y),Gy (x,y)=B(x,y+1)-B(x,y-1) is the gradient of the image in the x and y directions, and C(x,y) represents the edge intensity of the dust region;

[0069] The image after edge detection is analyzed using the connected component labeling method to calculate the clustering degree A of dust regions. First, let D be the set of detected dust regions, where each connected region R... i satisfy: And clustering degree A This can be represented by the ratio of the region area to the edge density: The calculations for the area and perimeter of the boundary are as follows: Representing region R i The boundary;

[0070] Based on the above results of aggregation degree and connected region labeling, a dust distribution map D is generated. map (x, y), whose value represents the dust density of each pixel, is expressed as: Let (x, y) be an indicator function, if (x, y) belongs to the dust region R i If it is 1, then it is 1; otherwise it is 0. i For region R i The degree of aggregation.

[0071] In this embodiment, when assessing the cleanliness of each area, the first step is to calculate the total area A of the dusty areas. total and the total area A of the photovoltaic panels panel The overall cleanliness is obtained through comparison. Then, for each region R... i Calculate its cleanliness: in, and The introduction of this feature is to reduce the weight of areas with high thickness and strong adhesion, so that the system can automatically adjust the cleaning strategy.

[0072] Based on the cleanliness assessment results, areas requiring further cleaning are marked. All areas with a cleanliness level below a preset threshold (R) are also included. i Areas that will be selected as key areas for cleaning will have their cleaning strategies adjusted based on this information by the control system.

[0073] In this embodiment, the control system can adjust the vibration frequency and amplitude according to the size of the dust particles and the cleaning requirements. For larger dust particles, the system increases the amplitude and vibration intensity; for smaller dust particles, the system adjusts the frequency to achieve fine cleaning. The steps are as follows:

[0074] A particle image of the dust region is obtained from the acquired image. Then, the area A of the dust region is determined. i Set a threshold Athreshold To classify particles, the expression is: Particles are classified into large and small particles using threshold classification. Then, the frequency and amplitude of vibration are adjusted according to the size of the dust particles.

[0075] Based on the adhesion of the particles, the boundary complexity C i The frequency and amplitude of the vibration are evaluated and fine-tuned, and their expression is: A adjusted =A new ·(1+γ·C i ),f adjusted =f new ·(1-δ·C i ), where γ and δ are adjustment coefficients that control the increase or decrease of amplitude and frequency in areas with strong adhesion, respectively. A adjusted and f adjusted These are the adjusted frequency and amplitude, respectively.

[0076] Once all dusty areas have been removed, the system calculates the total dust area A. total Determine whether to stop the cleaning process.

[0077] In this embodiment, when adjusting the vibration frequency and amplitude according to the size of dust particles, the expression for increasing the vibration amplitude for larger dust particles is as follows: The expression for adjusting the vibration frequency for small dust particles is: Among them, f base It is the fundamental frequency; β is the adjustment factor; A i It is the area of ​​the current dust region; A max It is the maximum area of ​​the large particles, A base It is the fundamental amplitude; α is the amplification factor; A max It is the maximum area of ​​the large particles; A i It represents the area of ​​the current dust region.

[0078] A variable frequency dust removal device for improving photovoltaic power generation efficiency also includes an environmental sensor for monitoring external environmental parameters such as ambient temperature, humidity, and wind speed; the control system dynamically adjusts the working state of the dust removal device, including vibration frequency, amplitude, and cleaning cycle, according to the monitored environmental data to adapt to different climatic conditions; when the temperature is too high or the humidity is too high, the control system automatically adjusts the cleaning intensity to avoid excessive dust adhesion or damage.

[0079] Existing cleaning technologies include methods such as manual cleaning, mechanical slapping, and compressed air blowing. While these methods can remove dust, they all have limitations in terms of precise adjustment, particularly in their poor adaptability to particle size and dust adhesion. Specifically:

[0080] Manual cleaning: Cleaning by manually brushing or wiping with a cloth can be highly precise, but it is labor-intensive, inefficient, and can cause significant damage to the surface of the photovoltaic panels.

[0081] Mechanical rapping: Mechanical rapping devices remove dust by applying vibration to the surface, but their vibration frequency and amplitude are usually fixed and cannot be dynamically adjusted according to different particle sizes and adhesion, which may lead to unsatisfactory cleaning results, especially for fine particles or areas that are difficult to clean.

[0082] Compressed air purging: Compressed air is suitable for cleaning light dust, but when there is a lot of dust, the purging effect is not obvious and it cannot handle dust with strong adhesion.

[0083] Electromagnetic vibration cleaning: This method uses electromagnetic vibration to clean tiny particles. Although it is effective for fine particles, the frequency and amplitude are usually fixed and lack the ability to be adjusted according to actual needs.

[0084] To ensure fairness in the comparative experiments, all experiments were conducted under identical environmental conditions. All experiments used the same type of photovoltaic panels, and the same amount of dust accumulation was simulated through standardized methods. The experimental time for all cleaning methods was set to 30 minutes, and the photovoltaic panel surfaces were pre-treated before cleaning to ensure a uniform amount of dust accumulation. In the experiments, we measured the area of ​​dust removed by each cleaning method and quantitatively analyzed the cleaning effect using image processing techniques. Furthermore, all methods were conducted indoors, with the temperature maintained at 25°C and humidity at approximately 60% to eliminate the influence of the external environment on the experimental results.

[0085] Depend on Figure 4 As shown, after 30 minutes of cleaning time, the present invention demonstrates significantly superior cleaning performance compared to other existing technologies. By dynamically adjusting the frequency and amplitude of vibration through a control system, the present invention can remove 85% of dust within the same time frame, significantly outperforming traditional cleaning methods.

[0086] This invention removes 85% of the dust, leaving only 15% of the dusty area after cleaning.

[0087] Manual cleaning: Removes only 55% of the dust, leaving 45% of the dusty area after cleaning.

[0088] Mechanical vibration: Removes 50% of the dust, leaving 50% of the dusty area after cleaning.

[0089] Compressed air: Removes 45% of the dust, leaving 55% of the dusty area after cleaning.

[0090] Electromagnetic vibration cleaning: Removes 40% of the dust, leaving 60% of the dusty area remaining after cleaning.

[0091] Within the same time frame, this invention can more effectively remove dust from the surface of photovoltaic panels or solar thermal panels, and the cleaning effect is significantly better than traditional methods. Traditional methods, such as manual cleaning, mechanical vibration, and compressed air blowing, not only have limited cleaning effects but also are poor at removing stubborn dust or fine particles.

[0092] Depend on Figure 5 As shown, this experiment also compared the cleaning effects of different particle sizes. Large dust particles: This invention can remove 85% of large dust particles, while mechanical rapping and compressed air have effects of 70% and 45%, respectively. Although mechanical rapping can remove some large particles, its cleaning effect is inferior because it cannot precisely adjust the vibration frequency and amplitude.

[0093] Small dust particles: The cleaning effect of this invention reaches 90%, mechanical vibration 65%, and compressed air 55%. Smaller dust particles require more precise vibration adjustment. The control system can automatically adjust high-frequency micro-vibration to ensure that these fine particles are effectively removed.

[0094] Particulate matter: This invention achieves a cleaning efficiency of up to 92% for fine particles, significantly higher than mechanical rapping (60%) and compressed air (58%). Fine particles typically have strong adhesion, requiring precise vibration control. This invention achieves thorough removal of fine particles through precise adjustment of frequency and amplitude.

[0095] The control system of this invention can dynamically adjust the vibration frequency and amplitude during the cleaning process according to the size of different particles, ensuring efficient cleaning of different types of dust. Compared with traditional methods, this invention has a stronger adaptability to both small and large particles, and can provide more precise cleaning under different environmental conditions.

[0096] This invention is not limited to the embodiments described above. Anyone should understand that structural changes made under the guidance of this invention, and any technical inventions that are the same as or similar to this invention, fall within the protection scope of this invention. Technical aspects, shapes, and structures not described in detail in this invention are all prior art.

Claims

1. A frequency conversion dust removal device for improving photovoltaic power generation efficiency, characterized in that, include: A signal generator is used to generate frequency conversion signals; A power amplifier, connected to a signal generator, is used to amplify the frequency conversion signal to the required power. Fixed bracket (2); The transducer (1) is fixedly installed on the surface of the plate (3) plane or curved surface via a fixed bracket (2) and connected to a power amplifier to convert the power signal into vibration and act on the surface of the photovoltaic or photothermal plate (3). The controller (4) with the control system is used to adjust the output frequency of the signal generator and control the working state of the transducer (1). The control system can gradually reduce the frequency of the signal generator within a certain period of time to complete a dust removal process. The signal generator can generate a frequency-converted signal with a frequency of 10kHz to 20kHz; the power amplifier can amplify the power of the frequency signal output by the signal generator to 50W to 200W; the control system can control the frequency of the signal generator to gradually decrease from 20kHz to 10kHz within 30 seconds. It also includes visual monitoring equipment for real-time monitoring of dust distribution on the surface of photovoltaic or solar thermal panels; the visual monitoring equipment detects dust in uncleaned areas on the panel surface using image processing algorithms and transmits feedback signals to the control system; the control system automatically adjusts the vibration frequency and amplitude based on the feedback signals to ensure complete dust removal, the steps of which are as follows: Visual monitoring equipment is used to scan the surface of photovoltaic or solar thermal panels in real time to obtain images of dust distribution on the panel surface. Then, image processing algorithms are used to analyze the acquired images, identify areas of dust accumulation, and generate dust distribution data. Based on the dust distribution data, the cleanliness level is assessed to determine which areas need further cleaning. Based on the dust distribution detection results, the visual monitoring equipment transmits relevant feedback signals to the control system. The feedback signals include the location and area of ​​the uncleaned dust area, as well as the degree of dust accumulation that requires further cleaning. After receiving the feedback signal, the control system analyzes the areas that are not cleaned properly and automatically adjusts the vibration frequency and amplitude to ensure that the dust in the uncleaned areas can be completely removed. The steps for analyzing the acquired image using image processing algorithms, identifying areas of dust accumulation, and generating dust distribution data are as follows: The input image is I(x, y), where x and y represent the pixel coordinates in the image. Each pixel in the image can have three components: red, green, and blue (RGB). Then, through the grayscale process, the image is converted into a single-channel image G(x, y), and the calculation formula is as follows: G(x, y) = 0.299 · R(x, y) + 0.587 · G(x, y) + 0.114 · B(x, y), where G(x, y) are the red, green, and blue components of the (x, y)th pixel in the image, respectively. Thresholding segmentation is used to separate dust regions from the background in an image. An appropriate threshold T is selected to convert the grayscale image into a binary image B(x, y), where dust regions are marked as 1 and background as 0. For each pixel G(x, y), if its grayscale value is greater than the threshold T, it is considered a dust region; otherwise, it is considered background. The binarization formula is as follows: ； By extracting features from dusty areas, the dust accumulation region is determined, and the edges of dusty areas in the image are detected using the Sobel operator, the expression of which is: G x (x,y)=B(x+1,y)_B(x_1,y),G y (x, y) = B(x, y+1) - B(x, y_1) is the gradient of the image in the x and y directions, and C(x, y) represents the edge intensity of the dust region; The image after edge detection is analyzed using the connected component labeling method to calculate the clustering degree A of dust regions. First, let D be the set of detected dust regions, where each connected region R... i satisfy: The aggregation degree A can be represented by the ratio of the region area to the edge density: The calculations for the area and perimeter of the boundary are as follows: Representing region R i The boundary; Based on the above clustering and connectivity labeling results, a dust distribution map D is generated. Its value represents the dust density of each pixel, and its expression is: Let (x, y) be an indicator function, if (x, y) belongs to the dust region R i If it is 1, then it is 1; otherwise it is 0. i For region R i The degree of aggregation.

2. The frequency conversion dust removal device for improving photovoltaic power generation efficiency according to claim 1, characterized in that, When assessing the cleanliness of each area, firstly, calculate the total area A of the dusty areas. total and the total area A of the photovoltaic panels p a n e l The overall cleanliness is obtained through comparison. Then, for each region R... i Calculate its cleanliness: in, and The introduction of this feature is to reduce the weight of areas with high thickness and strong adhesion, so that the system can automatically adjust the cleaning strategy. Based on the cleanliness assessment results, areas requiring further cleaning are marked, and all areas with a cleanliness level below a preset threshold (R) are also marked. i Areas that will be selected as key areas for cleaning will have their cleaning strategies adjusted based on this information by the control system.

3. The frequency conversion dust removal device for improving photovoltaic power generation efficiency according to claim 1, characterized in that, The control system can adjust the vibration frequency and amplitude according to the size of the dust particles and the cleaning requirements. For large dust particles, the system increases the amplitude and vibration intensity; for small dust particles, the system adjusts the frequency to achieve fine cleaning. The steps are as follows: A particle image of the dust region is obtained from the acquired image. Then, the area A of the dust region is determined. i Set a threshold Value A threshold To classify particles, the expression is: Particles are classified into large and small particles using threshold classification. Then, the frequency and amplitude of vibration are adjusted according to the size of the dust particles. Based on the boundary complexity C i The particle adhesion force, as characterized, is adaptively fine-tuned to the frequency and amplitude of vibration, and its expression is: A adjusted =A new · (1+ γ ·C i ), f adjusted =f new · (1_δ ·C i ), where γ and δ are adjustment coefficients that control the increase or decrease of amplitude and frequency in the region of strong adhesion, respectively. A adjusted and f adjusted These are the adjusted frequency and amplitude, respectively. Once all dusty areas have been removed, the system calculates the total dust area A. total Determine whether to stop the cleaning process.

4. The frequency conversion dust removal device for improving photovoltaic power generation efficiency according to claim 1, characterized in that, When adjusting the vibration frequency and amplitude according to the size of dust particles, the expression for increasing the vibration amplitude for larger dust particles is: The expression for adjusting the vibration frequency for small dust particles is: Among them, f base It is the fundamental frequency, β is the adjustment factor, and A is the base frequency. i It is the surface of the current dust area. Product, A max It is the maximum area of ​​the large particles, A base A is the fundamental amplitude, α is the amplification factor, and A is the fundamental amplitude. max It is the maximum area of ​​the large particles, A i It represents the area of ​​the current dust region.

5. A frequency conversion dust removal device for improving photovoltaic power generation efficiency according to claim 1, characterized in that, It also includes environmental sensors to monitor ambient temperature, humidity, and wind speed; the control system dynamically adjusts the working status of the dust removal device, including vibration frequency, amplitude, and cleaning cycle, based on the monitored environmental data to adapt to different climatic conditions; when the temperature is too high or the humidity is too high, the control system automatically adjusts the cleaning intensity to avoid excessive dust adhesion or damage.

Images