Solar power generation power real-time regulation and control method based on cloud cover dynamic monitoring

Abstract

The invention provides a solar power generation power real-time regulation and control method based on cloud cover dynamic monitoring, and belongs to the field of solar power generation power regulation and control, and the method comprises the following steps: observing the position of the sun by using a sun observation and positioning device, and observing the cloud cover dynamic state of a corresponding region by using a microwave radar and a camera according to the position of the sun; an artificial intelligence learning model is set to predict the illumination intensity irradiating the solar panel, the direction of the solar panel is adjusted according to the position of the sun, and meanwhile corresponding stable power output is adjusted and controlled according to the illumination intensity and the power output requirement. By accurately positioning the position of the sun in real time and then detecting the cloud layer of the detection area, the cloud layer information of the sun projected on the solar panel area can be accurately determined, and then the cloud layer information is input into the prediction model for machine learning and prediction, so that the sunlight early intensity obtained by the solar panel can be obtained. And then, according to the power generation parameters of the solar panel, the voltage and current of power generation are calculated, and output is controlled according to the actually required power, so that the real-time power of solar energy is regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the field of solar power generation power regulation, and in particular to a real-time solar power generation power regulation method based on dynamic cloud cover monitoring. Background Art

[0002] At present, solar energy has been widely used as a clean and renewable energy source, but its power generation efficiency is greatly affected by weather conditions, especially cloud cover. Clouds blocking sunlight will cause the light intensity received by photovoltaic panels to drop sharply, which in turn affects the power output. Some existing control measures such as maximum power point tracking (MPPT) and energy storage system integration have alleviated this problem to a certain extent, but they usually rely on fixed algorithms or preset parameters and cannot respond to rapidly changing cloud cover in time5. In addition, although there are some services based on weather forecasts, they are difficult to meet actual needs due to limited prediction accuracy.

[0003] The existing technologies have the following technical defects: Insufficient prediction accuracy: Although the existing short-term weather forecast models can provide cloud cover forecasts within a certain time range, they are weak in capturing minute-level or even second-level changes. Slow response speed: Traditional control strategies often require a long time delay from detection to execution, which is particularly disadvantageous when faced with sudden small-scale clouds. Lack of adaptive mechanism: Most current solutions do not take into account the impact of different geographical locations, seasons and other factors on cloud patterns, resulting in poor versatility and flexibility. Therefore, it is necessary to design a real-time control method for solar power generation based on dynamic cloud monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time control method for solar power generation based on dynamic cloud cover monitoring to solve the technical problems mentioned in the background technology. It realizes accurate perception of cloud cover changes and quickly makes corresponding power adjustments based on them to maintain stable power output.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for real-time control of solar power generation based on dynamic cloud cover monitoring, the method comprising the following steps:

[0007] Step 1: Use a solar observation positioning device to observe the position of the sun;

[0008] Step 2: Use microwave radar and cameras to observe the cloud dynamics in the corresponding area according to the position of the sun;

[0009] Step 3: Set up an AI learning model to predict the light intensity that hits the solar panels;

[0010] Step 4: Adjust the direction of the solar panel according to the position of the sun, and adjust the corresponding stable power output according to the light intensity and power output requirements.

[0011] Furthermore, in step 1, the solar observation and positioning device includes a support rod, a hemisphere and several directional solar intensity sensors. The hemisphere is arranged on the support rod, and the several directional solar intensity sensors are arranged on the hemisphere, and the end of each directional solar intensity sensor is tilted to point to the center of the hemisphere. Then, when the sun shines on the hemispherical solar precise position identification device, the solar intensity data of all directional solar intensity sensors are detected, and the position pointed to by the strongest one or several directional solar intensity sensors is the most precise position of the sun. The hemispherical solar precise position identification device is arranged on the side of the solar panel, and the height of the support rod is consistent with the height of the support plate of the solar panel.

[0012] Furthermore, the top of the directional solar intensity sensor is set to a hemispherical structure, and the hemispherical structure is coated with a sunlight sensing coating. Several directional solar intensity sensors are arranged in contact with each other on a hemispherical surface. When the sun is directly shining on the top of the hemisphere of the directional solar intensity sensor, the sensing intensity of the directional solar intensity sensor is the largest. If it is detected that the values ​​of the solar intensity sensed by more than one directional solar intensity sensor are the same and are the maximum values, the pointing angle of each directional solar intensity sensor with the maximum value is identified. If the directional solar intensity sensors with the maximum values ​​are not adjacent to each other, the pointing directions of all the directional solar intensity sensors with the maximum values ​​are averaged. When there are more than or equal to two solar intensity sensors with the largest sensing values ​​that are adjacent to each other, and the other solar intensity sensors with the maximum values ​​are not adjacent to each other, the solar intensity sensors with the maximum sensing values ​​that are not adjacent to each other are discarded.

[0013] Furthermore, a brush with a semicircular structure is provided on the hemispherical sun precise position identification device, and the hemispherical sun precise position identification device is cleaned regularly to prevent dust from affecting detection.

[0014] Furthermore, in step 2, the center of the observation corresponding area, the center of the solar panel area and the sun are on the same horizontal line, and the sun is projected onto the solar panel area through the observation corresponding area, forming a cloud layer in the observation corresponding area to block the light of the solar panel area. As the sun moves, the observation corresponding area will also move accordingly, and then the microwave radar and the camera are controlled to rotate to illuminate the observation corresponding area.

[0015] Furthermore, in step 2, the microwave radar works by emitting a short pulse laser beam into the atmosphere and receiving the signal reflected by the aerosol or cloud droplets. The distance to the target can be calculated based on the time difference of the signal return; and the signal intensity reflects the surface properties of the target. For cloud detection, the lidar can distinguish different types of particles, including water droplets and ice crystals, and determine the spatial distribution of the particles.

[0016] Furthermore, in step 2, the camera is used to observe the direction of the clouds in real time, and then predict the cloud data that will drift to the corresponding observation area in the next time period based on the forward drifting direction, thereby realizing detection in direction and space and achieving better detection of cloud dynamics.

[0017] Furthermore, in step 3, the cloud cover dynamic data and height data are used as input data of the artificial intelligence learning model, and the sun's position data is used as a coordinated condition, and then the light intensity irradiating the solar panels can be output.

[0018] Furthermore, in step 4, the solar panels are adjusted in real time so that they are perpendicular to the sun, maximizing the absorption of solar energy. The voltage and current data output by the solar panels are predicted based on the intensity of sunlight, and then the required power data is output through a transformer.

[0019] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0020] The present invention can accurately determine the cloud information of the sun projected on the solar panel area by accurately locating the position of the sun in real time and then detecting the cloud layer in the detection area. The cloud layer information is then input into the prediction model for machine learning and prediction. The early intensity of sunlight obtained by the solar panel can be obtained, and then the voltage and current of the generated electricity can be calculated according to the power generation parameters of the solar panel. The output is then controlled according to the actual power required, thereby realizing real-time power regulation of solar energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention;

[0022] Figure 2 It is a schematic diagram of the structure of the solar observation and positioning device of the present invention.

[0023] In the attached drawings, 1-support rod, 2-hemispherical body, 3-directional solar intensity sensor. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are only for the purpose of enabling the reader to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0025] like Figure 1-2 As shown, a method for real-time control of solar power generation based on dynamic cloud cover monitoring comprises the following steps:

[0026] Step 1: Use the solar observation positioning device to observe the position of the sun.

[0027] The solar observation and positioning device includes a support rod 1, a hemisphere 2 and several directional solar intensity sensors 3. The hemisphere 2 is arranged on the support rod 1, and several directional solar intensity sensors 3 are arranged on the hemisphere 2, and the end of each directional solar intensity sensor 3 is tilted to point to the center of the hemisphere 2. Then, when the sun shines on the hemispherical solar precise position identification device, the solar intensity data of all directional solar intensity sensors 3 are detected, and the position pointed to by the strongest one or several directional solar intensity sensors 3 is the most precise position of the sun. The hemispherical solar precise position identification device is arranged on the side of the solar power generation panel, and the height of the support rod 1 is consistent with the height of the support plate of the solar power generation panel.

[0028] The top of the directional solar intensity sensor 3 is set as a hemispherical structure, and the hemispherical structure is coated with a solar light sensing coating. Several directional solar intensity sensors 3 are arranged on a hemispherical surface in a close fit. When the sun is directly shining on the top of the hemisphere of the directional solar intensity sensor 3, the sensing intensity of the directional solar intensity sensor 3 is the largest. If it is detected that the values ​​of the sensing solar intensity of more than a dozen directional solar intensity sensors 3 are the same and the maximum value, the pointing angle of each directional solar intensity sensor 3 of the maximum value is identified. If the directional solar intensity sensors 3 of the maximum value are not adjacent, the pointing directions of all the directional solar intensity sensors 3 of the maximum value are averaged. When there are more than or equal to two solar intensity sensors 3 with the largest sensing values ​​in an adjacent relationship, and the other solar intensity sensors 3 with the maximum values ​​are not adjacent, the solar intensity sensors 3 with the maximum sensing values ​​that are not adjacent are discarded. A brush with a semicircular structure is set on the hemispherical sun precise position identification device, and the hemispherical sun precise position identification device is cleaned regularly to avoid dust affecting the detection. The position of the sun is accurately identified by the hemispherical identification device, and the precise detection of the sun position is achieved.

[0029] Step 2: Use microwave radar and camera to observe the cloud dynamics in the corresponding area according to the position of the sun. The center of the corresponding observation area, the center of the solar panel area and the sun are on the same horizontal line. The sun is projected onto the solar panel area through the corresponding observation area, forming a cloud layer in the corresponding observation area that blocks the light of the solar panel area. As the sun moves, the corresponding observation area will also move with it, and then control the microwave radar and camera to rotate and illuminate the corresponding observation area. Microwave radar works by emitting short pulse laser beams into the atmosphere and receiving signals reflected by aerosols or cloud droplets. The distance of the target can be calculated based on the time difference of the signal return; the signal intensity reflects the surface properties of the target. For cloud detection, laser radar can distinguish different types of particles, including water droplets and ice crystals, and determine the spatial distribution of particles. The camera is used to observe the drifting direction of the clouds in real time, and then predict the cloud data that will drift to the corresponding observation area in the next time period based on the forward drifting direction of the drifting direction, so as to achieve detection in direction and space and better detect the cloud dynamics.

[0030] Step 3: Set up an artificial intelligence learning model to predict the light intensity that hits the solar panels. Use cloud cover dynamics data and height data as input data for the artificial intelligence learning model, and use the sun's position data as a synergistic condition, and then output the light intensity that hits the solar panels. The artificial intelligence learning model is a support vector machine (SVM), random forest (RF), or neural network (NN). The model is trained based on historical data to predict future cloud cover changes and their impact on solar radiation intensity.

[0031] Step 4: Adjust the direction of the solar panel according to the position of the sun, and adjust the corresponding stable power output according to the light intensity and power output requirements. Adjust the solar panel in real time so that the solar panel is perpendicular to the sun, so as to achieve maximum absorption of solar energy, predict the voltage and current data of the solar panel output according to the light intensity of the sun, and then output the corresponding required power data through the transformer. Maximum power point tracking is one of the most commonly used power optimization technologies in photovoltaic systems. When it is detected that the light is weakened due to increased cloud cover, the MPPT controller will adjust the operating voltage so that the photovoltaic array is always in the best working state. Users can also be notified in advance to adjust their power consumption plans based on cloud cover forecasts, such as starting the peak operation of non-critical equipment, thereby reducing the impact of instantaneous power fluctuations caused by changes in cloud cover.

[0032] By accurately locating the position of the sun in real time and then detecting the cloud layer in the detection area, we can accurately determine the cloud layer information of the sun projected on the solar panel area, and then input the cloud layer information into the prediction model for machine learning and prediction. We can get the early intensity of sunlight obtained by the solar panel, and then calculate the voltage and current of the power generation according to the power generation parameters of the solar panel. Then, we can control the output according to the actual power required to realize the real-time power regulation of solar energy.

[0033] Matters not covered by the present invention are known technologies.

[0034] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A real-time control method for solar power generation based on dynamic cloud cover monitoring, characterized in that: The method comprises the following steps: Step 1: Use a solar observation positioning device to observe the position of the sun; Step 2: Use microwave radar and cameras to observe the cloud dynamics in the corresponding area according to the position of the sun; Step 3: Set up an AI learning model to predict the light intensity that hits the solar panels; Step 4: Adjust the direction of the solar panel according to the position of the sun, and adjust the corresponding stable power output according to the light intensity and power output requirements.

2. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 1 is characterized in that: In step 1, the solar observation and positioning device comprises a support rod (1), a hemisphere (2) and a plurality of directional solar intensity sensors (3), the hemisphere (2) is arranged on the support rod (1), the plurality of directional solar intensity sensors (3) are arranged on the hemisphere (2), and the end of each directional solar intensity sensor (3) is tilted to point to the center of the hemisphere (2), and then when the sun shines on the hemispherical solar precise position identification device, the solar intensity data of all directional solar intensity sensors (3) are detected, and the position pointed to by the strongest one or several directional solar intensity sensors (3) is the most precise position of the sun, and the hemispherical solar precise position identification device is arranged on the side of the solar power generation panel, and the height of the support rod (1) is consistent with the height of the support plate of the solar power generation panel.

3. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 2 is characterized in that: The top of the directional solar intensity sensor (3) is set as a hemispherical structure, and the hemispherical structure is coated with a sunlight sensing coating. A plurality of directional solar intensity sensors (3) are arranged in a hemispherical surface in a close relationship. When the sun shines directly on the top of the hemisphere of the directional solar intensity sensor (3), the sensing intensity of the directional solar intensity sensor (3) is the maximum. If it is detected that the values ​​of the solar intensities sensed by more than a dozen directional solar intensity sensors (3) are the same and are the maximum values, the pointing angle of each directional solar intensity sensor (3) with the maximum value is identified. If the directional solar intensity sensors (3) with the maximum values ​​are not adjacent to each other, the pointing directions of all the directional solar intensity sensors (3) with the maximum values ​​are averaged. When there are more than or equal to two solar intensity sensors (3) with the maximum sensing values ​​that are adjacent to each other, and the other solar intensity sensors (3) with the maximum values ​​are not adjacent to each other, the solar intensity sensors (3) with the maximum sensing values ​​that are not adjacent to each other are discarded.

4. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 3 is characterized in that: A semicircular brush is provided on the hemispherical sun precise position identification device, and the hemispherical sun precise position identification device is cleaned regularly to prevent dust from affecting detection.

5. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 1 is characterized in that: In step 2, the center of the observation corresponding area, the center of the solar panel area and the sun are on the same horizontal line. The sun is projected onto the solar panel area through the observation corresponding area, forming a cloud layer in the observation corresponding area to block the light of the solar panel area. As the sun moves, the observation corresponding area will also move with it, and then the microwave radar and the camera are controlled to rotate to illuminate the observation corresponding area.

6. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 5 is characterized in that: In step 2, the microwave radar works by emitting a short-pulse laser beam into the atmosphere and receiving the signal reflected by aerosols or cloud droplets. The distance to the target can be calculated based on the time difference of the signal return; the signal intensity reflects the surface properties of the target. For cloud detection, the lidar can distinguish different types of particles, including water droplets and ice crystals, and determine the spatial distribution of the particles.

7. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 6 is characterized in that: In step 2, the camera is used to observe the direction of the clouds in real time, and then predict the cloud data that will drift to the corresponding observation area in the next time period based on the forward drifting direction, thereby realizing detection in direction and space and achieving better detection of cloud dynamics.

8. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 7 is characterized in that: In step 3, the cloud cover dynamic data and height data are used as input data for the artificial intelligence learning model, and the sun's position data is used as a coordinated condition, and then the light intensity irradiating the solar panels can be output.

9. The method for real-time control of solar power generation based on dynamic cloud cover monitoring according to claim 8 is characterized in that: In step 4, the solar panels are adjusted in real time so that they are perpendicular to the sun, maximizing the absorption of solar energy. The voltage and current data output by the solar panels are predicted based on the intensity of sunlight, and then the required power data is output through the transformer.

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