Photovoltaic building abnormity control method and system based on intelligent algorithm

Abstract

The invention discloses a photovoltaic building abnormity control method and system based on an intelligent algorithm, and relates to the technical field of photovoltaic building abnormity control, and the method comprises the following steps: building a building curved surface grid coordinate system according to photovoltaic module arrangement characteristics and building structure parameters, and carrying out the irradiation region division of the surface of a photovoltaic building; constructing an impedance matching model, and calculating an optimal power tracking curve corresponding to different building surface curvatures in the divided area; generating a power correction value set based on the optimal power tracking curve according to a power grid frequency modulation demand; the shadow shielding area is judged based on the power correction value set, avoidance attitude adjustment is carried out on the shadow of the photovoltaic panel, and the problem that the existing photovoltaic system control method does not fully consider the influence of the building curved surface features on illumination distribution, and consequently the power tracking efficiency is low in the complex building environment is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal control of photovoltaic buildings. More specifically, the present invention relates to a method and system for abnormal control of photovoltaic buildings based on intelligent algorithms. Background Art

[0002] With the rapid development of renewable energy technologies, photovoltaic power generation has become an important part of global green energy. Photovoltaic buildings can not only provide green electricity but also have the advantages of energy conservation and carbon emission reduction. However, in actual operation, photovoltaic systems are often affected by factors such as changes in light, fluctuations in environmental temperature, equipment aging, and pollution, which may lead to a decrease in system efficiency or equipment failure.

[0003] For example, an abnormal detection method, device, and system for a photovoltaic power station control system disclosed in the invention patent announcement with the publication number CN116483056B obtain the weather perception data of each sensor and its neighboring sensors during a detection period; determine whether the weather perception data of the sensor is abnormal according to the weather perception data of the sensor and its neighboring sensors during the detection period; if the weather perception data of the sensor is abnormal in multiple consecutive detection periods, it is determined that the sensor is operating abnormally, and relevant fault warnings are given, realizing online detection of the abnormal operation of the sensor, improving the accuracy of tracker abnormal detection, and improving the comprehensiveness of the whole station abnormal detection.

[0004] For example, a photovoltaic control system, a control method, and a device for a photovoltaic control system disclosed in the invention patent announcement with the publication number CN108931973B. The control method of the photovoltaic control system includes: determining whether the control module that is controlling the photovoltaic system has an abnormality; in the case of determining an abnormality, switching to another control module to control the photovoltaic system, solving the technical problem of low reliability of the control system of the photovoltaic system that provides electrical energy for a smart home system in the related art.

[0005] In photovoltaic buildings, photovoltaic modules are usually installed on building surfaces with different angles and curvatures. Affected by the building form, surrounding environment, and weather changes, the irradiation distribution is highly uneven, resulting in a reduction in the overall power generation efficiency of the photovoltaic system. Usually, based on a fixed power tracking algorithm, the influence of the building surface curvature characteristics on the light distribution is not fully considered, resulting in low power tracking efficiency in complex building environments.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a photovoltaic building anomaly control method and system based on an intelligent algorithm, which optimize the power generation of a photovoltaic building through the intelligent algorithm and improve the power tracking accuracy, so as to solve the problem that the existing photovoltaic system control method fails to fully consider the influence of the building surface characteristics on the light distribution, resulting in low power tracking efficiency in a complex building environment.

[0008] To achieve the above object, the present invention provides the following technical solutions: A photovoltaic building anomaly control method based on an intelligent algorithm, comprising the following steps: constructing a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and the building structure parameters, and dividing the irradiation area on the surface of the photovoltaic building; constructing an impedance matching model, and calculating the optimal power tracking curve corresponding to different building surface curvatures in the divided area; generating a set of power correction values based on the optimal power tracking curve according to the power grid frequency modulation requirements; and judging the shadow occlusion area based on the set of power correction values, and performing an avoidance attitude adjustment on the shadow of the photovoltaic panel.

[0009] In a preferred embodiment, constructing a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and the building structure parameters, and dividing the irradiation area on the surface of the photovoltaic building, specifically: obtaining the three-dimensional point cloud data and the solar azimuth angle of the photovoltaic building, and extracting the surface normal vector and the curvature parameters; dividing grid cells based on the surface normal vector and the curvature parameters to obtain a building surface grid coordinate system; dynamically adjusting the building surface grid coordinate system according to the solar azimuth angle, and dividing the irradiation area on the surface of the photovoltaic building.

[0010] In a preferred embodiment, dynamically adjusting the building surface grid coordinate system according to the solar azimuth angle, and dividing the irradiation area on the surface of the photovoltaic building, specifically: calculating the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell based on the solar azimuth angle; calculating the standard deviation of the irradiation intensity of the grid area based on the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell; and dividing the building surface into several sub-regions that satisfy that the standard deviation of the irradiation intensity of the grid area is less than a preset first threshold.

[0011] In a preferred embodiment, constructing an impedance matching model, and calculating the optimal power tracking curve corresponding to different building surface curvatures in the divided area, specifically: dividing each sub-region into several curvature regions, and measuring the output impedance spectrum of the photovoltaic modules in each curvature region; performing Fourier transform on the output impedance spectrum of the photovoltaic modules in each curvature region to obtain the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue; constructing a curvature-impedance matching function for each curvature region based on the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue; and generating the optimal power tracking curve for each sub-region according to the curvature-impedance matching function of each curvature region.

[0012] In a preferred embodiment, according to the power grid frequency regulation requirements, a set of power correction values is generated based on the optimal power tracking curve. Specifically: based on the optimal power tracking curve of each sub-region, the first power value of each sub-region is obtained; a power grid frequency regulation instruction is received, and the frequency deviation amount and the regulation rate demand value are calculated; based on the frequency deviation amount and the regulation rate demand value, a fuzzy PID algorithm is used to calculate the power correction coefficient of each sub-region; the first power value of each sub-region is multiplied by the power correction coefficient of each sub-region to obtain a set of power correction values.

[0013] In a preferred embodiment, a shadow occlusion area is judged based on the set of power correction values, and an avoidance attitude adjustment is made for the shadow of the photovoltaic panel. Specifically: when the power correction value of the sub-region meets the preset judgment condition, the sub-region is determined as a shadow occlusion area; a shadow movement prediction model is constructed, and the adjustment angle of the photovoltaic panel in the shadow occlusion area is calculated; based on the adjustment angle, an avoidance attitude adjustment is made for the shadow of the photovoltaic panel.

[0014] The technical effects and advantages of the photovoltaic building anomaly control method and system based on intelligent algorithms of the present invention: 1. Through the arrangement characteristics of photovoltaic modules and building structure parameters, the present invention constructs a building curved surface grid coordinate system, realizes the accurate division of the irradiance area on the surface of the photovoltaic building, and improves the utilization rate of solar energy by the photovoltaic system; secondly, by dynamically adjusting the building curved surface grid coordinate system, calculating the irradiance intensity according to the solar azimuth angle, and optimizing the area division based on the standard deviation of the irradiance intensity, the photovoltaic building can adapt to the solar irradiance changes in different time periods and improve the power generation stability; in addition, this method adopts an impedance matching model to calculate the optimal power tracking curve according to the curvature of different building surfaces, ensuring that the photovoltaic modules can still achieve efficient power output under complex building structures. By combining the power grid frequency regulation requirements and using a fuzzy PID algorithm to calculate a set of power correction values, the photovoltaic system can flexibly adjust the output power, improve the adaptability and response speed of the power grid frequency regulation, and thus enhance the grid connection stability. Finally, this method also has the ability of intelligent shadow avoidance, can identify the shadow occlusion area based on the power correction value, and avoid the shadow impact by adjusting the attitude of the photovoltaic panel, effectively reducing the occlusion loss and further improving the overall power generation efficiency of the photovoltaic building. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flow chart of the photovoltaic building anomaly control method based on intelligent algorithms of the present invention.

[0016] Figure 2 It is a schematic structural diagram of the photovoltaic building anomaly control system based on intelligent algorithms of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1, Figure 1 A photovoltaic building anomaly control method based on an intelligent algorithm of the present invention is provided, including the following steps: S1, constructing a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and building structure parameters, and dividing the irradiation area on the surface of the photovoltaic building; In this example, constructing a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and building structure parameters, and dividing the irradiation area on the surface of the photovoltaic building is specifically as follows: Obtaining the three-dimensional point cloud data and solar azimuth angle of the photovoltaic building, and extracting the surface normal vector and curvature parameters; Dividing grid cells based on the surface normal vector and curvature parameters to obtain a building surface grid coordinate system; Dynamically adjusting the building surface grid coordinate system according to the solar azimuth angle, and dividing the irradiation area on the surface of the photovoltaic building.

[0019] It should be noted that in the photovoltaic building anomaly control method based on an intelligent algorithm, constructing a building surface grid coordinate system and dividing the irradiation area are key steps to optimize the photovoltaic power generation efficiency. This method first obtains the three-dimensional point cloud data of the photovoltaic building and combines the solar azimuth angle information to accurately depict the geometric characteristics of the building surface. By extracting the surface normal vector and curvature parameters, the spatial form of the building surface is further analyzed, and based on this, grid cells are divided to construct a building surface grid coordinate system. This coordinate system can accurately describe the arrangement characteristics of photovoltaic modules and be dynamically adjusted according to the building structure parameters to adapt to complex and changeable building forms.

[0020] At the same time, according to the change of the solar azimuth angle, the building surface grid coordinate system is dynamically adjusted in real time, making the division of the irradiation area on the surface of the photovoltaic building more accurate, ensuring that each grid cell can reasonably allocate light resources. The main advantages of this method are that by accurately obtaining data and intelligent analysis, the ability of photovoltaic modules to capture solar energy is improved, maximizing the photovoltaic utilization rate of the building surface. At the same time, dynamically adjusting the grid coordinate system can effectively cope with the solar irradiation changes in different time periods, enabling the photovoltaic modules to maintain the best working state under various lighting conditions. In addition, this method enhances the adaptability of the photovoltaic system to complex building structures, enabling it to be widely applied to various special-shaped buildings and ensuring efficient and stable power generation performance.

[0021] In this example, the building surface grid coordinate system is dynamically adjusted according to the solar azimuth angle to divide the irradiation area of the photovoltaic building surface. Specifically: Based on the solar azimuth angle, calculate the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell; Based on the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell, calculate the standard deviation of the irradiation intensity of the grid area; Divide the building surface into several sub-regions where the standard deviation of the irradiation intensity of the grid area is less than a preset first threshold.

[0022] It should be noted that based on the change of the solar azimuth angle, the irradiation intensity of each grid cell is calculated, and at the same time, the average irradiation intensity of the grid cell is obtained, so as to obtain the light distribution of different regions. Subsequently, by analyzing the difference between the irradiation intensity of each grid cell and the average irradiation intensity, the standard deviation of the irradiation intensity of the entire grid area is calculated to measure the uniformity of the light distribution. According to the calculation results, the building surface is divided into multiple sub-regions where the standard deviation of the irradiation intensity is less than the preset first threshold, so as to ensure that the light conditions within each sub-region are relatively uniform, which is beneficial to the stable power generation of the photovoltaic modules. The main advantage of this method is that by dynamically adjusting the grid coordinate system, the photovoltaic modules can accurately adapt to the change of the sun's position, thereby optimizing the light distribution and improving the overall photovoltaic conversion efficiency.

[0023] In addition, by calculating the standard deviation of the irradiation intensity and dividing the region, it helps to reduce the power generation performance fluctuation caused by uneven light in the local area and make the operation of the photovoltaic system more stable. At the same time, this method can adapt to complex building structure forms and make the photovoltaic building system maintain the best working state under different environmental conditions. Generally speaking, this method uses intelligent algorithms to achieve the precise division and dynamic adjustment of the irradiation area of the photovoltaic building, providing strong support for improving the energy management efficiency and power generation stability of the photovoltaic building.

[0024] S2. Construct an impedance matching model and calculate the optimal power tracking curve corresponding to different building surface curvatures in the divided regions; In this example, construct an impedance matching model and calculate the optimal power tracking curve corresponding to different building surface curvatures in the divided regions. Specifically: Divide each sub-region into several curvature regions, Measure the output impedance spectrum of the photovoltaic modules in each curvature region; Perform Fourier transform on the output impedance spectrum of the photovoltaic modules in each curvature region to obtain the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue; Based on the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue, construct a curvature-impedance matching function for each curvature region; Generate the optimal power tracking curve for each sub-region according to the curvature-impedance matching function of each curvature region.

[0025] In this example, based on the real part frequency domain eigenvalue of impedance and the imaginary part frequency domain eigenvalue of impedance, construct the curvature-impedance matching function of each curvature region. The specific calculation formula is as follows:

[0026] Where, is the curvature corresponding impedance matching degree, is the curvature of the building surface, is the real part frequency domain eigenvalue of impedance, is the frequency of the impedance spectrum, is the minimum frequency of the impedance spectrum, is the maximum frequency of the impedance spectrum, is the imaginary part frequency domain eigenvalue of impedance.

[0027] It should be noted that each sub-region of the photovoltaic building surface is further divided into multiple curvature regions to fully consider the influence of the complex shape of the building surface on the performance of photovoltaic modules. On this basis, measure the output impedance spectrum of photovoltaic modules in each curvature region to obtain the electrical characteristic data of photovoltaic modules under different building surface forms. Subsequently, through Fourier transform of the output impedance spectrum, extract the frequency domain eigenvalues of the real part and imaginary part of the impedance, so as to accurately characterize the impedance characteristics of photovoltaic modules in different curvature regions. Based on these eigenvalues, further construct the curvature-impedance matching function to describe the relationship between the curvature of the building surface and the output impedance of the photovoltaic module. Finally, according to the curvature-impedance matching function of each curvature region, calculate and generate the maximum power point tracking (MPPT) curve of each sub-region, so as to ensure that the photovoltaic module can achieve the most efficient power output under complex building surface conditions.

[0028] The main advantages of this method are as follows: through precise impedance matching analysis, the adaptability of photovoltaic modules under different building curvature conditions is improved, enabling them to adapt to special-shaped building structures to the greatest extent and avoiding energy losses caused by mismatched power tracking strategies. At the same time, Fourier transform is used to extract the frequency domain eigenvalues of impedance, enabling the photovoltaic system to more accurately identify the electrical characteristics of different regions and improving the accuracy of impedance matching. In addition, by establishing the curvature-impedance matching function, this method can dynamically adjust the power tracking strategy of photovoltaic modules, making them always in the best working state, and ensuring the efficient operation of the photovoltaic system whether the building surface is flat or curved. Most importantly, this method optimizes the power tracking curve, enabling the photovoltaic building system to quickly respond to external environmental changes and improving the stability and reliability of photovoltaic power generation.

[0029] S3. Generate a set of power correction values based on the best power tracking curve according to the power grid frequency regulation requirements; In this example, generating a set of power correction values based on the best power tracking curve according to the power grid frequency regulation requirements is specifically as follows: Based on the best power tracking curve of each sub-region, obtain the first power value of each sub-region; Receive the power grid frequency regulation instruction, and calculate the frequency deviation amount and the adjustment rate demand value; Based on the frequency deviation amount and the adjustment rate demand value, use the fuzzy PID algorithm to calculate the power correction coefficient of each sub-region; Multiply the first power value of each sub-region by the power correction coefficient of each sub-region to obtain a set of power correction values.

[0030] It should be noted that for the power grid frequency regulation requirements, generating a set of power correction values based on the maximum power point tracking (MPPT) curve ensures that the photovoltaic system can flexibly respond to the power grid requirements and improve the grid connection stability. First, this method is based on the best power tracking curve of each sub-region to calculate its corresponding first power value, that is, the optimal output power of the photovoltaic module under the current environmental conditions. Subsequently, the system receives the frequency regulation instruction from the power grid and calculates the current frequency deviation amount of the power grid and the adjustment rate demand value in real time to evaluate the frequency stability and power adjustment demand of the power grid.

[0031] In order to accurately adjust the output power of the photovoltaic system, this method introduces the fuzzy PID (Proportional-Integral-Derivative) control algorithm. According to the calculated frequency deviation amount and the adjustment rate demand value, it dynamically calculates the power correction coefficient of each sub-region. The fuzzy PID algorithm combines the fast response characteristics of the traditional PID control and the adaptive adjustment ability of the fuzzy control, and can achieve more intelligent power adjustment under different power grid fluctuation conditions. Finally, the system multiplies the first power value of each sub-region by the calculated power correction coefficient, thereby generating a set of power correction values, enabling the photovoltaic system to accurately match the frequency regulation requirements of the power grid and ensuring the stability and reliability of the power output. The specific advantages are as follows: First, by calculating the first power value based on the MPPT curve, it can ensure that the photovoltaic module always maintains the most efficient power output under different light conditions, and at the same time provides an accurate reference power for subsequent frequency regulation control. Secondly, the introduction of the fuzzy PID control algorithm makes the power adjustment process more intelligent and adaptive. Compared with the traditional PID control, it can optimize and adjust the parameters in real time according to different power grid fluctuation conditions, and improve the response speed of the photovoltaic system to the power grid frequency change. In addition, this method enables the photovoltaic building to dynamically adjust the output power by calculating a set of power correction values, so as to better meet the power grid frequency regulation requirements and enhance the stability of the power grid.

[0032] S4. Determine the shadow occlusion area based on the set of power correction values, and perform an avoidance attitude adjustment on the shadow of the photovoltaic panel.

[0033] In this example, determining the shadow occlusion area based on the set of power correction values and performing an avoidance attitude adjustment on the shadow of the photovoltaic panel is specifically as follows: When the power correction value of a sub-region meets the preset judgment condition, the sub-region is determined as a shadow occlusion area; Construct a shadow movement prediction model and calculate the adjustment angle of the photovoltaic panel in the shadow occlusion area; Perform an avoidance attitude adjustment on the shadow of the photovoltaic panel based on the adjustment angle.

[0034] Among them, the formula for the preset judgment condition is as follows: 、 Among them, is the power correction value of the i-th sub-region, is the preset second threshold, is the initial time point, is the preset time interval.

[0035] In this example, establish a shadow movement prediction model and calculate the adjustment angle of the photovoltaic panel in the shadow occlusion area. The specific calculation formula is as follows: The calculation formula for the azimuth adjustment angle of the photovoltaic panel in the shadow occlusion area is as follows:

[0036] The calculation formula for the tilt adjustment angle of the photovoltaic panel in the shadow occlusion area is as follows:

[0037] Among them, is the azimuth adjustment angle of the photovoltaic panel in the shadow occlusion area, and are the displacement components of the shadow in the x-axis and y-axis directions, is the shadow movement speed, is the adjustment time, is the length of the photovoltaic panel, is the tilt adjustment angle of the photovoltaic panel in the shadow occlusion area, is the height of the photovoltaic panel, is the solar altitude angle, is the remaining time predicted by the shadow movement prediction model for the shadow to completely cover the photovoltaic panel.

[0038] It should be noted that judging the shadow occlusion area through the power correction value set and making an intelligent avoidance attitude adjustment for the photovoltaic panels are the key technologies to improve the photovoltaic power generation efficiency and system stability. First, this method uses the power correction value set generated during the power grid frequency modulation process to monitor the power output of the photovoltaic modules in real time. When the power correction value of a certain sub-region meets the preset judgment condition, that is, its power output shows a significant decrease compared with the normal state, the system determines this sub-region as the shadow occlusion area and identifies the photovoltaic modules affected by the shadow.

[0039] On this basis, this method further constructs a shadow movement prediction model to accurately calculate the optimal adjustment angle of the photovoltaic panels. The change of the shadow is affected by the position of the sun, atmospheric conditions, and the surrounding building structure. Therefore, the prediction model needs to comprehensively consider the solar azimuth angle, the three-dimensional information of the building environment, and the historical illumination data to construct a dynamic shadow movement trajectory. Based on this model, the system can predict the movement direction and occlusion trend of the shadow, calculate the optimal adjustment angle of the photovoltaic panels, and thus reduce the impact of the shadow on the photovoltaic power generation.

[0040] According to the calculated adjustment angle, this method makes an intelligent avoidance attitude adjustment for the photovoltaic panels. Through the adjustable bracket or rotating mechanism of the photovoltaic modules, the orientation of the photovoltaic panels is automatically optimized to avoid the current or upcoming shadow occlusion area, ensuring that the solar radiation is received to the greatest extent and improving the light energy utilization rate. This adjustment process can combine the real-time illumination data and the grid demand to dynamically optimize the angle adjustment strategy of the photovoltaic panels to balance the maximum power output and grid connection stability.

[0041] The technical advantages of this solution are mainly reflected in the following aspects: First, judging the shadow occlusion area based on the power correction value enables the system to accurately identify the abnormal illumination conditions of the photovoltaic panels. Compared with the traditional fixed threshold judgment method, this method is more intelligent and adaptable. Second, the introduction of the shadow movement prediction model enables the system to predict the shadow occlusion situation in advance, rather than only making passive adjustments after the shadow appears, thereby reducing power generation losses and improving the overall photovoltaic power generation efficiency. In addition, the avoidance attitude adjustment strategy of this method enables the photovoltaic modules to actively adapt to environmental changes, avoid being in the shadow-affected area for a long time, extend the service life of the photovoltaic system, and improve the overall energy conversion efficiency.

[0042] Embodiment 2 Figure 2 The photovoltaic building abnormal control system based on the intelligent algorithm of the present invention is given, including a region division module, an impedance matching module, a frequency modulation response module, and a shadow adjustment module: The region division module is used to construct a building surface grid coordinate system according to the arrangement characteristics of the photovoltaic modules and the building structure parameters, and divide the irradiation area of the photovoltaic building surface; An impedance matching module, which is used to construct an impedance matching model and calculate the optimal power tracking curve corresponding to the curvature of different building surfaces in the divided area; A frequency modulation response module, which is used to generate a set of power correction values based on the optimal power tracking curve according to the power grid frequency modulation requirements; A shadow adjustment module, which is used to judge the shadow occlusion area based on the set of power correction values and perform an avoidance attitude adjustment on the shadow of the photovoltaic panel.

[0043] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0044] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0045] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0046] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0047] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0048] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photovoltaic building anomaly control method based on intelligent algorithms, characterized in that, Including the following steps: Construct a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and building structure parameters, and divide the irradiation area on the surface of the photovoltaic building; Construct an impedance matching model and calculate the optimal power tracking curve corresponding to the curvature of different building surfaces in the divided area; Generate a set of power correction values based on the optimal power tracking curve according to the power grid frequency modulation requirements; Judge the shadow occlusion area based on the set of power correction values, and perform an avoidance attitude adjustment on the shadow of the photovoltaic panel.

2. The abnormal control method for a photovoltaic building based on an intelligent algorithm according to claim 1, wherein, The constructing a building surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and building structure parameters, and dividing the irradiation area on the surface of the photovoltaic building is specifically as follows: Obtain the three-dimensional point cloud data and solar azimuth angle of the photovoltaic building, and extract the surface normal vector and curvature parameters; Divide grid cells based on the surface normal vector and curvature parameters to obtain a building surface grid coordinate system; Dynamically adjust the building surface grid coordinate system according to the solar azimuth angle, and divide the irradiation area on the surface of the photovoltaic building.

3. The photovoltaic building anomaly control method based on an intelligent algorithm according to claim 2, characterized in that The dynamically adjusting the building surface grid coordinate system according to the solar azimuth angle, and dividing the irradiation area on the surface of the photovoltaic building is specifically as follows: Based on the solar azimuth angle, calculate the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell; Based on the irradiation intensity of each grid cell and the average irradiation intensity of the grid cell, calculate the standard deviation of the irradiation intensity of the grid area; Divide the building surface into several sub-regions that satisfy the standard deviation of the irradiation intensity of the grid area being less than a preset first threshold.

4. The photovoltaic building anomaly control method based on an intelligent algorithm according to claim 3, characterized in that The constructing an impedance matching model and calculating the optimal power tracking curve corresponding to the curvature of different building surfaces in the divided area is specifically as follows: Divide each sub-region into several curvature regions; Measure the output impedance spectrum of the photovoltaic modules in each curvature region; Perform Fourier transform on the output impedance spectrum of the photovoltaic modules in each curvature region to obtain the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue; Based on the impedance real part frequency domain eigenvalue and the impedance imaginary part frequency domain eigenvalue, construct a curvature-impedance matching function for each curvature region; Generate the optimal power tracking curve for each sub-region according to the curvature-impedance matching function of each curvature region.

5. The photovoltaic building abnormal control method based on an intelligent algorithm according to claim 4, wherein The generating a set of power correction values based on the optimal power tracking curve according to the power grid frequency modulation requirements is specifically as follows: Based on the optimal power tracking curve of each sub-region, obtain the first power value of each sub-region; Receive the power grid frequency modulation instruction, and calculate the frequency deviation amount and the regulation rate demand value; Based on the frequency deviation amount and the regulation rate demand value, use the fuzzy PID algorithm to calculate the power correction coefficient of each sub-region; Multiply the first power value of each sub-region by the power correction coefficient of each sub-region to obtain a set of power correction values.

6. The photovoltaic building anomaly control method based on an intelligent algorithm according to claim 5, wherein The judging the shadow occlusion area based on the set of power correction values, and performing an avoidance attitude adjustment on the shadow of the photovoltaic panel is specifically as follows: When the power correction value of the sub-region meets the preset judgment condition, determine the sub-region as the shadow occlusion area; Construct a shadow movement prediction model and calculate the adjustment angle of the photovoltaic panel in the shadow occlusion area; Perform an avoidance attitude adjustment on the shadow of the photovoltaic panel based on the adjustment angle.

7. The abnormal control method for a photovoltaic building based on an intelligent algorithm according to claim 6, wherein Based on the real - part frequency - domain eigenvalue of impedance and the imaginary - part frequency - domain eigenvalue of impedance, construct the curvature - impedance matching function for each curvature region, and the specific calculation formula is as follows: Among them, is the curvature corresponding impedance matching degree, is the building surface curvature, is the real part frequency domain eigenvalue of the impedance, is the frequency of the impedance spectrum, is the minimum frequency of the impedance spectrum, is the maximum frequency of the impedance spectrum, is the imaginary part frequency domain eigenvalue of the impedance.

8. The method for abnormal control of photovoltaic buildings based on intelligent algorithms according to claim 7, wherein, The formula for the preset judgment condition is as follows: 、 Wherein, is the power correction value of the i-th sub-region, is the preset second threshold, is the initial time point, is the preset time interval.

9. The photovoltaic building anomaly control method based on an intelligent algorithm according to claim 8, wherein, Construct the shadow movement prediction model and calculate the adjustment angle of the photovoltaic panel in the shadow - occluded area. The specific calculation formula is as follows: The calculation formula for the azimuth adjustment angle of the photovoltaic panel in the shadow - occluded area is as follows: The calculation formula for the tilt - angle adjustment angle of the photovoltaic panel in the shadow - occluded area is as follows: Among them, is the azimuth adjustment angle of the photovoltaic panel in the shadow occlusion area, and are the displacement components of the shadow in the x-axis and y-axis directions, is the shadow movement speed, is the adjustment time, is the length of the photovoltaic panel, is the tilt adjustment angle of the photovoltaic panel in the shadow occlusion area, is the height of the photovoltaic panel, is the solar altitude angle, is the remaining time predicted by the shadow movement prediction model for the shadow to completely cover the photovoltaic panel.

10. A photovoltaic building anomaly control system based on an intelligent algorithm, which is applied to the photovoltaic building anomaly control method based on an intelligent algorithm according to any one of claims 1-9, and is characterized in that, It includes a region - division module, an impedance - matching module, a frequency - modulation response module, and a shadow - adjustment module: The region - division module is used to construct a building - surface grid coordinate system according to the arrangement characteristics of photovoltaic modules and building - structure parameters, and divide the irradiation area of the photovoltaic building surface; The impedance - matching module is used to construct an impedance - matching model and calculate the optimal power - tracking curve corresponding to different building - surface curvatures in the divided regions; The frequency - modulation response module is used to generate a set of power correction values based on the optimal power - tracking curve according to the power - grid frequency - modulation requirements; The shadow - adjustment module is used to judge the shadow - occluded area based on the set of power correction values and perform an avoidance - attitude adjustment on the shadow of the photovoltaic panel.

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