Power limiting control method and system for photovoltaic power station

Abstract

The application discloses a kind of photovoltaic power station's power control method and system, the present application relates to the technical field of power control method, according to the actual power supply of photovoltaic power station, theoretical power supply and the past power supply data of photovoltaic power station determines the abnormal power supply range of photovoltaic power station, based on abnormal power supply range and the power supply area controlled by photovoltaic power station determines the power control measure of photovoltaic power station, guarantees the accuracy of the power control measure of photovoltaic power station, improves the power control effect of photovoltaic power station.According to the abnormal power supply path in the abnormal power supply path of photovoltaic panel power supply path and abnormal power supply range determine multiple power nodes, and mark the sub-power control measure corresponding to power node;According to the power of power node parameter and the execution progress of sub-power control measure determines power effect parameter, according to the power effect parameter of each power node and the actual power supply of photovoltaic power station triggers the intelligent power supply of photovoltaic power station, realizes the intelligent power supply of photovoltaic power station in each application scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power limiting control method, and particularly relates to a power limiting control method and system of a photovoltaic power station. BACKGROUND

[0002] With the development of science and technology, photovoltaic is gradually applied to people's life and supplies power in the light state. Photovoltaic supplies power in the light state and outputs to the photovoltaic power station. The photovoltaic power station is the power control station of each photovoltaic. Based on the overall power supply of the photovoltaic power station, in the prior art, the actual power supply of the photovoltaic power station is collected, and an abnormal signal is triggered according to the comparison between the actual power supply and the power supply threshold. However, only the single-dimensional abnormal control through the power supply threshold cannot identify the abnormal power supply range of the photovoltaic power station, which affects the accuracy of the power limiting measures of the photovoltaic power station. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art, and provides a power limiting control method and system of a photovoltaic power station.

[0004] The embodiment of the present application provides a power limiting control method of a photovoltaic power station, which comprises the following steps:

[0005] The distribution position of the photovoltaic panel is determined to determine the inspection path of the inspection unmanned aerial vehicle; the inspection unmanned aerial vehicle flies along the inspection path and collects real-time images of the photovoltaic panel;

[0006] The attitude of the photovoltaic panel is determined according to the real-time images of the photovoltaic panel, and the theoretical power supply of the photovoltaic power station is determined based on the attitude of the photovoltaic panel, the light area and the model;

[0007] The abnormal power supply range of the photovoltaic power station is determined according to the actual power supply of the photovoltaic power station, the theoretical power supply and the past power supply data of the photovoltaic power station, and the power limiting measures of the photovoltaic power station are determined based on the abnormal power supply range and the power supply area controlled by the photovoltaic power station;

[0008] In the power limiting measures of the photovoltaic power station, the power supply path of the photovoltaic panel is collected, a plurality of power limiting nodes are determined according to the abnormal power supply path in the power supply path of the photovoltaic panel and the abnormal power supply range, and the sub-power limiting measures corresponding to the power limiting nodes are marked;

[0009] The power limiting effect parameter is determined according to the power parameter of the power limiting node and the execution progress of the sub-power limiting measure, and the intelligent power supply of the photovoltaic power station is triggered according to the power limiting effect parameter of each power limiting node and the actual power supply of the photovoltaic power station.

[0010] The embodiment of the present application provides a power limiting control system of a photovoltaic power station, which is applied to the power limiting control method of the photovoltaic power station, and comprises:

[0011] An image module is configured to determine a patrol path of the patrol UAV according to the distributed positions of the photovoltaic panels, and the patrol UAV flies along the patrol path and collects real-time images of the photovoltaic panels.

[0012] A theoretical power supply module is configured to determine the postures of the photovoltaic panels according to the real-time images of the photovoltaic panels, and determine the theoretical power supply of the photovoltaic power station based on the postures of the photovoltaic panels, the light-illumination areas and the models.

[0013] A power limiting measure module is configured to determine an abnormal power supply range of the photovoltaic power station according to the actual power supply, the theoretical power supply and the past power supply data of the photovoltaic power station, and determine the power limiting measure of the photovoltaic power station based on the abnormal power supply range and the power supply region controlled by the photovoltaic power station.

[0014] A power limiting node module is configured to collect a photovoltaic panel power supply path in the power limiting measure of the photovoltaic power station, determine a plurality of power limiting nodes according to the abnormal power supply path in the photovoltaic panel power supply path and the abnormal power supply range, and mark the sub-power limiting measures corresponding to the power limiting nodes.

[0015] An intelligent power supply module is configured to determine a power limiting effect parameter according to the power parameters of the power limiting nodes and the execution progress of the sub-power limiting measures, and trigger the intelligent power supply of the photovoltaic power station according to the power limiting effect parameters of the power limiting nodes and the actual power supply of the photovoltaic power station.

[0016] Compared with the prior art, the present application has the following advantages:

[0017] In the embodiment of the present application, the theoretical power supply of the photovoltaic power station is determined based on the postures of the photovoltaic panels, the light-illumination areas and the models, the abnormal power supply range of the photovoltaic power station is determined according to the actual power supply, the theoretical power supply and the past power supply data of the photovoltaic power station, the power limiting measure of the photovoltaic power station is determined based on the abnormal power supply range and the power supply region controlled by the photovoltaic power station, the abnormal power supply range is introduced, the overall consideration of the abnormal power supply range and the power supply region controlled by the photovoltaic power station is compatible, the accuracy of the power limiting measure of the photovoltaic power station is ensured, and the power limiting control effect of the photovoltaic power station is improved.

[0018] Therefore, in the power limiting measure of the photovoltaic power station, the photovoltaic panel power supply path is collected, a plurality of power limiting nodes are determined according to the abnormal power supply path in the photovoltaic panel power supply path and the abnormal power supply range, and the sub-power limiting measures corresponding to the power limiting nodes are marked; the power limiting effect parameter is determined according to the power parameters of the power limiting nodes and the execution progress of the sub-power limiting measures, and the intelligent power supply of the photovoltaic power station is triggered according to the power limiting effect parameters of the power limiting nodes and the actual power supply of the photovoltaic power station; the power limiting effect parameter is introduced, the overall control of the power limiting effect parameters of the power limiting nodes and the actual power supply of the photovoltaic power station is performed, the intelligent power supply effect of the photovoltaic power station is ensured, and the intelligent power supply of the photovoltaic power station in various application scenarios is realized. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the power curtailment control method for a photovoltaic power station according to an embodiment of the present invention.

[0020] Figure 2 This is a flowchart illustrating step S11 of the power curtailment control method for a photovoltaic power station in an embodiment of the present invention.

[0021] Figure 3 This is a flowchart illustrating step S12 in the power curtailment control method for a photovoltaic power station according to an embodiment of the present invention.

[0022] Figure 4 This is a flowchart illustrating step S13 of the power curtailment control method for a photovoltaic power station in an embodiment of the present invention.

[0023] Figure 5 This is a flowchart illustrating step S14 of the power curtailment control method for a photovoltaic power station in an embodiment of the present invention.

[0024] Figure 6 This is a flowchart illustrating step S15 of the power curtailment control method for a photovoltaic power station in an embodiment of the present invention.

[0025] Figure 7 This is a schematic diagram of the structural composition of the power curtailment control system for a photovoltaic power station in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] Please see Figures 1 to 7 A power curtailment control method for photovoltaic power plants, applied to power curtailment control scenarios in photovoltaic power plants; the power curtailment control method for photovoltaic power plants includes:

[0028] Step S11: Determine the inspection path of the inspection drone based on the distribution location of the photovoltaic panels; the inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels;

[0029] Step S12: Determine the orientation of the photovoltaic panel based on the real-time image of the photovoltaic panel, and determine the theoretical power supply of the photovoltaic power station based on the orientation, illumination area and model of the photovoltaic panel;

[0030] Step S13: Determine the abnormal power supply range of the photovoltaic power station based on the actual power supply, theoretical power supply, and past power supply data of the photovoltaic power station; and determine the power curtailment measures for the photovoltaic power station based on the abnormal power supply range and the power supply area controlled by the photovoltaic power station.

[0031] Step S14: In the power curtailment measures of the photovoltaic power station, collect the power supply path of the photovoltaic panel, determine multiple power curtailment nodes based on the abnormal power supply path and abnormal power supply range in the power supply path of the photovoltaic panel, and mark the sub-power curtailment measures corresponding to the power curtailment nodes;

[0032] Step S15: Determine the curtailment effect parameters based on the power parameters of the curtailment nodes and the implementation progress of the sub-curtailment measures, and trigger the intelligent power supply of the photovoltaic power station based on the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station.

[0033] refer to Figure 2 In step S11, the inspection path of the inspection drone is determined according to the distribution location of the photovoltaic panels; the inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels.

[0034] In the specific implementation of this invention, the specific steps are as follows:

[0035] S111: Collect the location of the photovoltaic panels, determine the distribution location of multiple photovoltaic panels based on the location detection of the photovoltaic panels, and determine the inspection area of ​​the inspection drone based on the distribution location of multiple photovoltaic panels, the environmental scene in which the multiple photovoltaic panels are located, and the shape of the multiple photovoltaic panels.

[0036] S112: Determine the inspection path of the inspection drone based on the drone's current location and the path planning of the inspection area. The drone acquires the inspection path and flies along the inspection path.

[0037] S113: When the drone is in flight, if the drone detects a photovoltaic panel, it marks the corresponding shooting area. Based on the working status of the camera on the drone, the location of the shooting area, and the distribution of the photovoltaic panel, the shooting mode of the camera on the drone is determined. The camera then shoots along the shooting mode to collect real-time images of the photovoltaic panel.

[0038] In the embodiments of this application, the location of the photovoltaic panel is collected, and the distribution location of multiple photovoltaic panels is determined based on the location detection of the photovoltaic panel location. The inspection area of ​​the inspection drone is determined based on the distribution location of multiple photovoltaic panels, the environmental scene in which the multiple photovoltaic panels are located, and the shape of the multiple photovoltaic panels. This overall consideration of the distribution location of multiple photovoltaic panels, the environmental scene in which the multiple photovoltaic panels are located, and the shape of the multiple photovoltaic panels ensures the accuracy of the inspection area of ​​the inspection drone.

[0039] At this stage, the location of the photovoltaic panels is collected to obtain their specific geographical location information. This information is typically obtained through photovoltaic power plant design drawings, GIS systems, or on-site surveys. During the data collection process, key information such as the latitude and longitude coordinates and altitude of each photovoltaic panel or array needs to be recorded. After obtaining the basic information about the area where the photovoltaic panels are located, researchers need to use this information to determine the specific distribution location of each photovoltaic panel or array. This usually involves detailed measurement and recording of parameters such as the arrangement, spacing, and orientation of the photovoltaic panel arrays. Furthermore, the impact of terrain undulations and obstructions on the photovoltaic panel layout must be considered.

[0040] After determining the distribution location of the photovoltaic panels, the R&D personnel need to comprehensively determine the inspection area of ​​the inspection drone by combining the environmental scene where the photovoltaic panels are located (such as terrain, climate, vegetation, etc.) and the form of the photovoltaic panels (such as size, shape, installation method, etc.). The inspection area should cover all photovoltaic panels and take into account flight obstacles and safety hazards. In addition, the division of the inspection area also needs to be optimized based on the performance parameters of the drone (such as flight speed, endurance, camera field of view, etc.).

[0041] Furthermore, the inspection path of the inspection drone is determined based on the drone's current location and the path planning of the inspection area. The drone acquires the inspection path and flies along it, taking into account both the drone's current location and the path planning of the inspection area, thus ensuring the accuracy of the inspection path.

[0042] At this point, the drone needs to obtain its current precise location through its built-in GPS or other positioning system. This location information will serve as the starting point or reference point for path planning. Before or after takeoff, the drone will perform position calibration to ensure the accuracy of subsequent path planning. For example, suppose the drone is conducting an inspection mission inside a large photovoltaic power station. After takeoff, the drone will first determine its current location, such as latitude and longitude coordinates, through its GPS system. This location information will be recorded and used as the starting point for subsequent path planning.

[0043] Path planning is a crucial step in UAV inspection missions. It requires determining one or more optimal flight paths based on the terrain, obstacles, distribution of solar panels, and the UAV's performance parameters (such as flight speed, endurance, and camera field of view). This path should ensure the UAV efficiently covers all inspected solar panels while avoiding unnecessary flight and energy consumption. Path planning algorithms include, but are not limited to, Dijkstra's algorithm, A* algorithm, and heuristic search algorithms. These algorithms consider various factors such as distance, time, and energy consumption to find the optimal flight path. In this case, the path planning algorithm will consider the distribution density of solar panels, terrain, and other factors. Factors such as the location of photovoltaic panels, trees, or other obstacles are considered. For example, if photovoltaic panels are densely distributed in certain areas of the power plant, the algorithm will plan a denser flight path to ensure that these areas are adequately inspected. At the same time, if there are tall trees or buildings in the power plant, the algorithm will avoid planning a path that flies directly over these obstacles to prevent collisions. Meanwhile, assuming there is an area in the power plant where photovoltaic panels are sparsely distributed and surrounded by trees and buildings, the path planning algorithm will plan a flight path that bypasses the trees and buildings while getting as close as possible to the photovoltaic panels. This path will be slightly winding to ensure that the drone can safely approach each photovoltaic panel for filming.

[0044] Once the path planning is complete, the drone will acquire the inspection path and fly along it. During flight, the drone will use its built-in navigation system and sensors to adjust its flight attitude and position in real time to ensure that it always flies along the planned path. In addition, the drone also has an autonomous obstacle avoidance function, which can adjust its flight path in time when encountering unexpected obstacles to avoid collisions.

[0045] Therefore, when the drone is in flight, if it detects a photovoltaic panel, it marks the corresponding shooting area. Based on the working status of the drone's camera, the location of the shooting area, and the distribution of the photovoltaic panel, the shooting mode of the drone's camera is determined. The camera then shoots along this shooting mode to collect real-time images of the photovoltaic panel. This system takes into account the overall consideration of the working status of the drone's camera, the location of the shooting area, and the distribution of the photovoltaic panel, ensuring the accuracy of the drone's camera shooting mode.

[0046] During the drone's flight, its onboard visual recognition system (such as cameras and image processing algorithms) captures and processes images of the surrounding environment in real time. When the drone flies over or near a photovoltaic panel, the visual recognition system detects the presence of the panel. This detection process is typically based on image feature matching, color recognition, or shape recognition technologies. For example, suppose the drone is flying over a large photovoltaic power station, and its visual recognition system is capturing ground images in real time. When the drone flies directly over a photovoltaic panel, the system detects an object with a specific color (such as dark blue) and shape (such as a rectangle) in the image, which matches the preset photovoltaic panel features. Thus, the system confirms that the photovoltaic panel has been detected.

[0047] Once a solar panel is detected, the drone system immediately marks the corresponding shooting area. This area is typically a rectangular or circular region centered on the solar panel, and its size and shape depend on the drone's flight altitude, the camera's field of view, and the size of the solar panel. The purpose of marking the shooting area is to ensure that the camera can accurately aim at the solar panel for shooting. At this time, when the drone detects the solar panel, the system will mark a rectangular region centered on the solar panel in the image. The size and shape of this rectangular region will be determined based on the drone's flight altitude (e.g., 50 meters) and the camera's field of view (e.g., 45 degrees). After marking, the system will send the information of this region to the camera so that it can perform subsequent shooting operations.

[0048] After determining the shooting area, the drone system will determine the camera's shooting mode based on the camera's operating status (such as resolution, frame rate, exposure time, etc.), the location of the shooting area (such as distance and angle relative to the drone, etc.), and the distribution of the photovoltaic panels. The shooting mode includes the settings of parameters such as the camera's focal length, aperture, and shutter speed, which will directly affect the quality and clarity of the captured image. At the same time, the drone system will determine the camera's shooting mode based on the camera's current operating status (such as 4K resolution and 30fps frame rate) and the location of the shooting area (such as 50 meters away from the drone and an angle of 45 degrees). The system will set the focal length to a level that can clearly capture the details of the photovoltaic panels (such as a focal length of 24mm), set the aperture to an appropriate value to ensure sufficient light intake (such as an aperture of f / 8), and set the shutter speed to a level that can avoid motion blur (such as a shutter speed of 1 / 100 second).

[0049] Once the shooting mode is determined, the camera on the drone will shoot according to that mode, capturing real-time images of the photovoltaic panels. These images will be transmitted to the drone's data storage devices (such as SD cards, internal memory, etc.) for subsequent analysis. Simultaneously, these images are also transmitted in real-time to a ground station or cloud server for remote monitoring and analysis. Furthermore, when the camera shoots according to the determined shooting mode, it captures clear images of the photovoltaic panels. These images will be stored in the drone's internal memory and downloaded and analyzed after the flight. If the drone is equipped with a real-time transmission system, these images are also transmitted in real-time to a ground station or cloud server for maintenance personnel to remotely monitor the operating status of the photovoltaic panels.

[0050] In some embodiments of this application, a shooting mode matching table is collected, as shown in Table 1:

[0051] Table 1 Shooting Mode Matching Table

[0052]

[0053] Assuming the drone is in flight, the camera is functioning normally, it has detected the photovoltaic panels and marked the shooting area; the shooting area is at close range, and the photovoltaic panels are densely distributed; according to the matching table, the camera's functioning is "normal", the shooting area is at "close range", and the photovoltaic panels are densely distributed; therefore, the shooting mode of the camera on the drone is determined to be "high-definition detailed shooting mode"; the camera will shoot along the "high-definition detailed shooting mode" to acquire real-time high-definition images of the photovoltaic panels.

[0054] refer to Figure 3 In step S12, the posture of the photovoltaic panel is determined based on the real-time image of the photovoltaic panel, and the theoretical power supply of the photovoltaic power station is determined based on the posture, illumination area and model of the photovoltaic panel.

[0055] In the specific implementation of this invention, the specific steps are as follows:

[0056] S121: Acquire real-time images of the photovoltaic panel, determine the area to be identified of the photovoltaic panel based on the detection of the real-time images of the photovoltaic panel, determine the positions of multiple attitude nodes of the photovoltaic panel based on the identification of the area to be identified of the photovoltaic panel, and determine the attitude of the photovoltaic panel based on the detection of the positions of multiple attitude nodes of the photovoltaic panel.

[0057] S122: In each photovoltaic panel, a first reference power is determined based on the orientation of the photovoltaic panel and the corresponding irradiated area, and a second reference power is determined based on the model of the photovoltaic panel and the corresponding irradiated area.

[0058] S123: Determine the theoretical power supply of each photovoltaic panel based on the mapping relationship between the first reference power, the second reference power, and the power supply of the photovoltaic panel; determine the theoretical power supply of the photovoltaic power station based on the theoretical power supply of each photovoltaic panel and the energy loss between each photovoltaic panel and the photovoltaic power station.

[0059] In the embodiments of this application, real-time images of the photovoltaic panel are acquired, the area to be identified of the photovoltaic panel is determined based on the detection of the real-time images of the photovoltaic panel, the positions of multiple attitude nodes of the photovoltaic panel are determined based on the identification of the area to be identified of the photovoltaic panel, and the attitude of the photovoltaic panel is determined based on the detection of the multiple attitude node positions of the photovoltaic panel. This approach takes into account the overall consideration of detecting the multiple attitude node positions of the photovoltaic panel, ensuring the accuracy of the photovoltaic panel's attitude.

[0060] At this point, the drone captures real-time images of the photovoltaic panels using its onboard high-definition camera. These images should contain sufficient detail of the photovoltaic panels for subsequent image processing and attitude recognition. The quality of the acquired images is affected by various factors, including the drone's flight altitude, the camera's resolution, and lighting conditions. Let's assume the drone is conducting an inspection mission over a photovoltaic power station at an altitude of 20 meters, equipped with a 40-megapixel camera. When the drone flies directly above a photovoltaic panel, the camera captures a real-time image of that panel. In the image, the edges of the photovoltaic panel are clear and rich in detail, providing a good foundation for subsequent image processing and attitude recognition.

[0061] After acquiring real-time images of the photovoltaic panel, image processing technology is needed to determine the area to be identified. This area is usually the main part of the photovoltaic panel, excluding non-critical parts such as the frame and support. Methods for determining the area to be identified include edge detection, shape matching, and color recognition. At this point, after the drone captures the real-time image of the photovoltaic panel, the image processing system first preprocesses the image, such as denoising and enhancing contrast. Then, edge detection algorithms (such as Canny edge detection) are used to identify the edge information in the image. Next, shape matching algorithms (such as Hough transform) are used to identify the rectangular shape of the photovoltaic panel and determine its boundaries. Finally, the system determines the area to be identified of the photovoltaic panel based on the boundary information, that is, excluding the frame and support parts, and only retaining the effective power generation area of ​​the photovoltaic panel.

[0062] After determining the area to be identified for the photovoltaic panel, a feature point detection algorithm is used to identify the positions of multiple key attitude nodes of the photovoltaic panel. These node positions are usually the corners, center points, or other locations with significant features of the photovoltaic panel. By identifying these node positions, the attitude of the photovoltaic panel in three-dimensional space is further determined. At the same time, after determining the area to be identified for the photovoltaic panel, the system uses a feature point detection algorithm (such as SIFT, SURF, or ORB) to identify multiple key attitude node positions within the area to be identified. These node positions are the four corners, center points, or other significant feature points of the photovoltaic panel. The system records the image coordinates of these nodes and prepares to use them to calculate the attitude of the photovoltaic panel.

[0063] After determining the positions of multiple attitude nodes of the photovoltaic panel, a geometric transformation algorithm is needed to calculate the panel's attitude in three-dimensional space. This attitude typically includes the panel's tilt angle and rotation angle. Methods for calculating attitude include affine transformation, perspective transformation, and 3D reconstruction. Simultaneously, after determining the positions of multiple attitude nodes, the system uses a perspective transformation algorithm to calculate the panel's tilt and rotation angles. First, the system establishes a mathematical model of the photovoltaic panel in three-dimensional space based on the image coordinates of the node positions, the UAV's flight altitude, camera parameters, and other information. Then, through the perspective transformation algorithm, the node positions in the image are mapped onto the 3D model, thereby calculating the panel's tilt and rotation angles. For example, the system calculates a tilt angle of 15 degrees and a rotation angle of 0 degrees, indicating that the photovoltaic panel is tilted relative to the horizontal plane but has not rotated.

[0064] Furthermore, for each photovoltaic panel, a first reference power is determined based on the panel's orientation and corresponding irradiated area, and a second reference power is determined based on the panel's model and corresponding irradiated area. This approach takes into account both the panel's model and corresponding irradiated area, ensuring the accuracy of the second reference power.

[0065] At this point, the orientation of the photovoltaic panel (such as tilt angle and rotation angle) and the corresponding illuminated area are considered to calculate the first reference power. The orientation of the photovoltaic panel will affect the amount of solar radiation it receives, because a tilted photovoltaic panel will capture direct and diffuse sunlight of different intensities. At the same time, the illuminated area is also a key factor affecting the power generation. In order to calculate the first reference power, the power generation efficiency curve or model of the photovoltaic panel is usually used, which describes the power that the photovoltaic panel can generate under different light intensities and temperatures.

[0066] In the specific calculation, it is first necessary to measure or estimate the actual illuminated area of ​​the photovoltaic panel on the tilted surface (taking into account the influence of the tilt angle); then, based on the current light intensity (obtained through on-site measurement or weather forecast data) and temperature (also obtained through on-site measurement or environmental data), find the corresponding power generation efficiency on the photovoltaic panel's power generation efficiency curve; finally, multiply the power generation efficiency by the actual illuminated area to obtain the first reference power. Now, suppose there is a photovoltaic panel, model XYZ, with a tilt angle of 15 degrees, a current light intensity of 1000W / m², and a temperature of 25℃. Through measurement, the actual illuminated area of ​​the photovoltaic panel on the tilted surface is found to be 2m². Consulting the power generation efficiency curve of the XYZ model photovoltaic panel, it is found that under the conditions of light intensity of 1000W / m² and temperature of 25℃, the power generation efficiency of this photovoltaic panel is 16%. Therefore, the first reference power = power generation efficiency × actual illuminated area = 16% × 2m² × 1000W / m² × 1 hour (assuming the illumination lasts for 1 hour) = 320Wh (or 0.32kWh).

[0067] The second reference power is determined based on the model of the photovoltaic panel and the corresponding irradiance area (here, the irradiance area refers to the horizontal projected area of ​​the photovoltaic panel, i.e., without considering the influence of the tilt angle). Unlike the first step, this step does not consider the influence of the tilt angle of the photovoltaic panel on the irradiance area, but is calculated based on the projected area of ​​the photovoltaic panel on the horizontal plane.

[0068] To calculate the second reference power, the photovoltaic panel's power generation efficiency curve or model is also needed. However, here, we assume the photovoltaic panel is horizontally installed (i.e., tilted at 0 degrees), and find the corresponding power generation efficiency on the power generation efficiency curve based on the horizontal projected area and the current light intensity and temperature. Then, multiplying the power generation efficiency by the horizontal projected area yields the second reference power. It's important to note that since photovoltaic panels are usually tilted in actual installations, the second reference power is typically a theoretical value used for comparison and analysis with the first reference power. In this case, we assume the horizontal projected area of ​​the photovoltaic panel is 2.5 m² (note that this differs from the actual light-receiving area on the tilted surface). Similarly, under conditions of 1000 W / m² light intensity and 25°C, consulting the power generation efficiency curve of the XYZ model photovoltaic panel, we find that its power generation efficiency is 15% when horizontally installed (note that this differs from the efficiency when tilted). Therefore, the second reference power = power generation efficiency × horizontal projected area = 15% × 2.5 m² × 1000 W / m² × 1 hour = 375Wh (or 0.375kWh).

[0069] Therefore, the theoretical power supply of each photovoltaic panel is determined based on the mapping relationship between the first reference power, the second reference power, and the power supply of the photovoltaic panel. The theoretical power supply of the photovoltaic power station is determined based on the theoretical power supply of each photovoltaic panel and the energy loss between each photovoltaic panel and the photovoltaic power station. This method takes into account the overall consideration of the theoretical power supply of each photovoltaic panel (ABC) and the energy loss between each photovoltaic panel and the photovoltaic power station, thus ensuring the accuracy of the theoretical power supply of the photovoltaic power station.

[0070] At this point, a mapping relationship is established to link the first reference power, the second reference power, and the actual power output of the photovoltaic panel (if measurable). This mapping relationship helps to more accurately estimate the theoretical power output of the photovoltaic panel under given conditions. Typically, this mapping relationship is an empirical formula, lookup table, or machine learning model trained on historical or experimental data. The accuracy of the mapping relationship depends on the accuracy and completeness of the data, as well as the applicability of the chosen model or formula. For each photovoltaic panel, using its first and second reference power as inputs, the theoretical power output is calculated through the mapping relationship. This theoretical power output is the amount of electricity that the photovoltaic panel can theoretically produce under current light conditions, temperature, and orientation.

[0071] At this point, it is assumed that a mapping relationship has been established, which adjusts the calculation of theoretical power supply based on the ratio of the first reference power and the second reference power. For example, if the ratio of the first reference power to the second reference power is 0.85 (indicating reduced efficiency due to factors such as tilt angle and obstruction), then the second reference power is multiplied by 0.85 to obtain the theoretical power supply.

[0072] The first reference power of the photovoltaic panel is 0.32 kWh, and the second reference power is 0.375 kWh; therefore, the theoretical power supply = second reference power × ratio = 0.375 kWh × 0.85 = 0.31875 kWh (or approximately 0.32 kWh, rounded to two decimal places).

[0073] Considering various energy losses within a photovoltaic (PV) power plant, such as cable losses, inverter efficiency losses, and transformer losses, the overall theoretical power supply of the PV power plant is calculated. For each PV panel, its theoretical power supply has already been calculated. Then, these theoretical power supplies need to be added together to obtain the total theoretical power supply of the PV power plant (ignoring losses). Next, based on the energy loss model or data of the PV power plant, the loss portion of the total theoretical power supply needs to be calculated. Finally, subtracting the loss portion from the total theoretical power supply yields the actual theoretical power supply of the PV power plant. At this point, assuming there are 10 identical PV panels in the PV power plant, each with a theoretical power supply of 0.32 kWh (as shown in the example above), the total theoretical power supply without considering losses = 10 panels × 0.32 kWh / panel = 3.2 kWh. Next, consider the energy losses of the PV power plant; assuming the total energy loss rate of the PV power plant is 10% (this value is usually derived from historical data or power plant design parameters), the loss = total theoretical power supply × loss rate = 3.2 kWh × 10% = 0.32kWh; Finally, the actual theoretical power supply of the photovoltaic power station = total theoretical power supply - loss = 3.2kWh - 0.32kWh = 2.88kWh.

[0074] In some embodiments of this application, a theoretical power supply matching table is collected, as shown in Table 2:

[0075] Table 2 Theoretical Power Supply Matching Table

[0076]

[0077] When the first reference power and the second reference power fall within a certain range, the corresponding theoretical power supply is looked up from the matching table.

[0078] Assuming the total energy loss rate of the photovoltaic power station is 10%, and this loss rate is calculated based on the average theoretical power supply of all photovoltaic panels; if the average theoretical power supply of all photovoltaic panels is 0.35kWh (for example, calculated using the method in step one), then the total energy loss = average theoretical power supply × loss rate = 0.35kWh × 10% = 0.035kWh; then, subtract an average loss amount (or allocate the loss amount proportionally according to the power supply of each photovoltaic panel) from the theoretical power supply of each photovoltaic panel to obtain the contribution of each photovoltaic panel to the actual power supply of the photovoltaic power station; add up the contributions of all photovoltaic panels to obtain the theoretical power supply of the photovoltaic power station.

[0079] refer to Figure 4In step S13, the abnormal power supply range of the photovoltaic power station is determined based on the actual power supply, theoretical power supply and previous power supply data of the photovoltaic power station, and the power curtailment measures of the photovoltaic power station are determined based on the abnormal power supply range and the power supply area controlled by the photovoltaic power station.

[0080] In the specific implementation of this invention, the specific steps are as follows:

[0081] S131: Monitor the photovoltaic power station in real time and collect the actual power supply of the photovoltaic power station. Determine the first power anomaly parameter based on the actual power supply and the theoretical power supply of the photovoltaic power station.

[0082] S132: In a photovoltaic power station, a second power anomaly parameter is determined based on the actual power supply of the photovoltaic power station and the previous power supply data of the photovoltaic power station. The abnormal power supply range of the photovoltaic power station is determined based on the first power anomaly parameter, the second power anomaly parameter and the abnormal power supply mapping relationship.

[0083] S133: Based on the division of abnormal power supply range, multiple abnormal power supply intervals are determined. According to the interval range of multiple abnormal power supply intervals, photovoltaic panels and photovoltaic power stations, corresponding abnormal power supply events are determined. According to each abnormal power supply event and the power supply area controlled by the photovoltaic power station, the power curtailment measures of the photovoltaic power station are determined. The power curtailment measures include power regulation measures, selective shutdown of abnormal photovoltaic panels, external energy storage measures and grid dispatch measures.

[0084] In the embodiments of this application, the photovoltaic power station is monitored in real time, and the actual power supply of the photovoltaic power station is collected. The first power anomaly parameter is determined based on the actual power supply and the theoretical power supply of the photovoltaic power station. This takes into account both the actual power supply and the theoretical power supply of the photovoltaic power station, ensuring the accuracy of the first power anomaly parameter.

[0085] At this time, continuous monitoring of the photovoltaic power station's operating status is required. Typically, a photovoltaic power station is equipped with a monitoring system that can collect real-time operating data of various key equipment within the power station, including but not limited to the output power of the photovoltaic panels, the conversion efficiency of the inverter, and the grid connection status. The monitoring system is built on SCADA (Supervisory Control and Data Acquisition) system and achieves remote monitoring of the power station through sensor networks, data communication technologies, and software platforms.

[0086] The monitoring system periodically (e.g., every second, every minute, or every hour) collects data on the actual power supply of the power station. This data usually comes from the power station's energy metering devices, such as energy meters or power meters, which can accurately measure the energy that the power station delivers to the grid. In order to ensure the accuracy of the data, the energy metering devices need to be calibrated and maintained regularly, and the monitoring system also needs to have data verification and anomaly detection functions.

[0087] The theoretical power supply is obtained through a series of calculations (as described in step S123), reflecting the amount of electricity that the photovoltaic power station can theoretically generate under given conditions; the actual power supply is the amount of electricity that the power station actually transmits to the grid; at the same time, the first power anomaly parameter is the difference or ratio between the actual power supply and the theoretical power supply, used to measure whether the operating status of the power station deviates from the theoretical expectation; if the actual power supply is much lower than the theoretical power supply, it indicates that the power station has a fault or performance degradation; at this time, the first power anomaly parameter is calculated as the difference (actual power supply - theoretical power supply) or the ratio ((actual power supply - theoretical power supply) / theoretical power supply).

[0088] Specifically, suppose that the theoretical power supply of a photovoltaic power station is 300kW at a certain moment (calculated through steps S123), while the actual power supply collected by the monitoring system in real time is 270kW; the monitoring system continuously monitors the operating status of the power station, including the output power of the photovoltaic panels, the conversion efficiency of the inverter, etc.; the monitoring system collects the actual power supply of the power station at this moment as 270kW.

[0089] Actual power supply - Theoretical power supply = 270kW - 300kW = -30kW; Ratio calculation: (Actual power supply - Theoretical power supply) / Theoretical power supply = (-30kW) / 300kW = -0.1 or 10% (indicating that the actual power supply is 10% less than the theoretical power supply); In this example, the first abnormal power parameter is -30kW (difference) or -10% (ratio), indicating that the actual power supply of the power station is lower than the theoretical power supply, indicating a fault or performance degradation; Next, the operation and maintenance personnel will further analyze the cause based on this parameter and take corresponding measures to solve the problem.

[0090] Furthermore, in a photovoltaic power station, a second abnormal power parameter is determined based on the actual power supply and past power supply data of the photovoltaic power station. The abnormal power supply range of the photovoltaic power station is determined based on the first abnormal power parameter, the second abnormal power parameter, and the abnormal power supply mapping relationship. This approach takes into account the overall consideration of the first abnormal power parameter, the second abnormal power parameter, and the abnormal power supply mapping relationship, ensuring the accuracy of the abnormal power supply range of the photovoltaic power station.

[0091] At this point, the actual power supply of the photovoltaic power station is compared with the power supply data of the same period or similar conditions in the past. The past power supply data is usually stored in the power station's database, including historical power generation, average daily power generation, average monthly power generation, etc. At this point, time series analysis, statistical analysis or machine learning methods are used to compare the actual power supply with the past data. The purpose of the comparison is to identify whether the current power supply deviates significantly from the historical average level or expected range. At the same time, the second power anomaly parameter is the difference or ratio between the actual power supply and the past power supply data, which is used to measure the consistency between the current operating status of the power station and the historical data. If the difference is large, it indicates that there is an anomaly or performance fluctuation in the power station.

[0092] The abnormal power supply mapping relationship is a preset rule or model that determines the degree or range of the power station's abnormality based on the values ​​of the first and second abnormal power parameters. This mapping relationship is derived from historical experience, expert knowledge, or data analysis. At the same time, the abnormal power supply range is divided into different levels or categories, such as minor abnormality, moderate abnormality, and severe abnormality. Each level corresponds to a different degree of power abnormality and cause of failure. Based on the specific values ​​of the first and second abnormal power parameters, combined with the abnormal power supply mapping relationship, the current abnormal power supply range of the power station is determined.

[0093] Therefore, multiple abnormal power supply intervals are determined based on the division of abnormal power supply ranges. Corresponding abnormal power supply events are identified based on the interval ranges, photovoltaic panels, and photovoltaic power stations. Power curtailment measures for photovoltaic power stations are determined based on each abnormal power supply event and the power supply area controlled by the photovoltaic power station. These measures include power regulation measures, selective shutdown of abnormal photovoltaic panels, external energy storage measures, and grid dispatch measures. This approach considers all abnormal power supply events and the overall power supply area controlled by the photovoltaic power station, ensuring the accuracy of the power curtailment measures. Furthermore, the introduction of an abnormal power supply range ensures the accuracy of the power curtailment measures and improves the power curtailment control effect of the photovoltaic power station.

[0094] At this point, after determining the abnormal power supply range of the photovoltaic power station (as described in step S132), it is necessary to further subdivide this range into multiple specific abnormal power supply intervals. These intervals are divided based on factors such as the severity, duration, and scope of the power anomaly. At this time, statistical methods (such as cluster analysis), expert systems, or rule-based decision trees are used to divide the abnormal power supply intervals. Each interval should have clear boundary conditions and characteristics to facilitate subsequent analysis and the formulation of countermeasures.

[0095] Abnormal power supply events refer to specific causes or faults that lead to abnormal power supply intervals. These events are related to factors such as performance degradation of photovoltaic panels, inverter failures, cable losses, and changes in weather conditions. In this case, fault diagnosis techniques (such as expert systems, neural networks, and pattern recognition) are used to determine the specific abnormal power supply events by combining the characteristics of the abnormal power supply intervals and the operating data of the photovoltaic power station. In addition, information such as the power station's maintenance records and historical fault data are also considered to assist in the judgment.

[0096] Power curtailment measures refer to a series of actions taken to mitigate the impact of abnormal power supply events on the power grid and users. These measures include power regulation (such as reducing output power), selectively shutting down abnormal photovoltaic panels, activating external energy storage systems to supplement power, and requesting grid dispatch support. In this case, the selection of power curtailment measures should be based on factors such as the severity of the abnormal power supply event, the degree of impact on the power grid, the electricity demand of users, and the power plant's own resources and capabilities. The optimal combination of power curtailment measures should be determined using decision support systems, optimization algorithms, or rule-based reasoning.

[0097] Specifically, assuming that in step S132, a photovoltaic power station is determined to be in a moderate abnormal power supply range, and based on this range, two abnormal power supply intervals are divided: interval A (power abnormality level is -5% to -15%) and interval B (power abnormality level exceeds -15%); according to the first power abnormality parameter (-10%) and the second power abnormality parameter (-3.6%) in step S132, it is determined that the power abnormality level of the current power station is within interval A.

[0098] Based on the power plant's operational data and historical fault records, it was found that the power generation efficiency of several photovoltaic panels has recently decreased significantly, and these panels are located in a specific area of ​​the power plant. Therefore, it is determined that the performance degradation of these photovoltaic panels is the main cause of the current abnormal power supply event. Meanwhile, considering that the current power anomaly has not yet reached a level that seriously affects grid stability and user electricity consumption, the following power curtailment measures have been decided upon:

[0099] Selectively shut down malfunctioning photovoltaic panels: Turn off photovoltaic panels whose performance has significantly deteriorated to reduce the generation of invalid electricity and avoid further impact on the power grid;

[0100] Activate the external energy storage system: Utilize the energy storage battery system equipped in the power station to release stored electrical energy to supplement grid demand when photovoltaic panels generate insufficient power;

[0101] Strengthen monitoring and early warning: Strengthen the monitoring of the power plant's operating status, promptly detect and warn of potential faults or anomalies, so that measures can be taken quickly;

[0102] By implementing these power curtailment measures, the impact of abnormal power supply events on the power grid and users can be effectively mitigated, while ensuring the safe and stable operation of power plants. In practice, power curtailment measures need to be flexibly adjusted according to the specific circumstances of the power plant and the evolution of abnormal power supply events.

[0103] In some embodiments of this application, a power curtailment measure matching table is collected, as shown in Table 3:

[0104] Table 3 Matching Table for Power Restriction Measures

[0105]

[0106] Suppose that the power supply anomaly of a photovoltaic power station is determined to be in range B (-10% to -15%). Based on the matching table, quickly determine whether the abnormal power supply event is an inverter failure or severe cable loss, and take corresponding power curtailment measures: start the external energy storage system to supplement the power, selectively shut down the abnormal photovoltaic panels to reduce the generation of invalid power, and request grid dispatch support to balance grid supply and demand.

[0107] refer to Figure 5 In step S14, in the power curtailment measures of the photovoltaic power station, the power supply path of the photovoltaic panel is collected, and multiple power curtailment nodes are determined according to the abnormal power supply path and abnormal power supply range in the power supply path of the photovoltaic panel, and the sub-power curtailment measures corresponding to the power curtailment nodes are marked.

[0108] In the specific implementation of this invention, the specific steps are as follows:

[0109] S141: The photovoltaic power station performs power curtailment operation along the power curtailment measures, and determines the power supply path of the photovoltaic panels based on the power supply detection of the photovoltaic power station and multiple photovoltaic panels. It determines the abnormal power supply path according to the matching of the photovoltaic panel power supply path and the power curtailment measures. The abnormal power supply path is part of the photovoltaic panel power supply path, and the photovoltaic panel corresponding to the photovoltaic panel power supply path is in an abnormal power supply state.

[0110] S142: Based on the tracing of abnormal power supply paths, determine the abnormal power supply area, mark multiple photovoltaic panels in the abnormal power supply area, monitor the abnormal power supply area in real time, and determine the abnormal power supply range based on the power supply detection of the abnormal power supply area.

[0111] S143: Based on the matching of abnormal power supply range, abnormal power supply area and abnormal power supply path, determine multiple power restriction nodes and mark the location of multiple power restriction nodes. Based on the detection of each power restriction node, determine the corresponding power restriction content. Based on the power restriction content, location and sub-power restriction measure mapping relationship of each power restriction node, determine the sub-power restriction measures corresponding to each power restriction node.

[0112] In the embodiments of this application, the photovoltaic power station performs power curtailment operations along the power curtailment measures, and determines the photovoltaic panel power supply path based on the power supply detection of the photovoltaic power station and multiple photovoltaic panels. Abnormal power supply paths are determined according to the matching of the photovoltaic panel power supply path and the power curtailment measures. As part of the photovoltaic panel power supply path, the photovoltaic panel corresponding to the photovoltaic panel power supply path is in an abnormal power supply state. This takes into account the overall consideration of matching the photovoltaic panel power supply path and the power curtailment measures, and ensures the accuracy of abnormal power supply paths.

[0113] At this point, after the power curtailment measures are determined (as described in step S133), the photovoltaic power station needs to carry out actual operations according to these measures. This includes adjusting the output power of the photovoltaic panels, shutting down some photovoltaic panels, starting the external energy storage system, or requesting grid dispatch support. At this time, the power curtailment operation is usually executed through the power station's control system or remote monitoring system. According to the instructions of the power curtailment measures, the operating parameters of the power station are adjusted to achieve the power curtailment target.

[0114] Photovoltaic power plants are typically equipped with a power supply monitoring system that can monitor the power generation and current path of photovoltaic panels in real time. This system records the complete path of current flowing out of the photovoltaic panels, through inverters, transformers and other equipment, and finally into the power grid. At this time, through the data from the power supply monitoring system, operation and maintenance personnel analyze and determine the power supply path of each photovoltaic panel. These paths are usually displayed graphically on the monitoring screen of the power plant for easy understanding by operation and maintenance personnel.

[0115] After the power curtailment operation is executed, maintenance personnel need to compare the power supply path of the photovoltaic panels with the implementation status of the power curtailment measures. If a photovoltaic panel or device on a certain power supply path experiences an anomaly due to the power curtailment measures (such as a decrease in output power or current interruption), then this path is considered an abnormal power supply path. At the same time, the matching process is automatically completed by the power station's monitoring software, and is also manually analyzed by maintenance personnel. Automatic matching is usually based on preset rules and algorithms, which can monitor and alarm abnormal power supply paths in real time. Manual analysis requires maintenance personnel to make step-by-step comparisons and judgments based on the monitoring data and the specific content of the power curtailment measures.

[0116] Specifically, suppose a photovoltaic power station experiences a decrease in the power generation efficiency of some photovoltaic panels due to weather conditions (such as cloud cover). In order to ensure the stable operation of the power grid, the power station decides to implement power curtailment measures and shut down some photovoltaic panels with lower power generation efficiency. The power station sends shutdown commands to the designated photovoltaic panels through a remote monitoring system. After receiving the commands, these photovoltaic panels gradually reduce their output power and eventually shut down.

[0117] The power supply monitoring system of the power station monitors and records the power supply path of each photovoltaic panel in real time. Under normal circumstances, these paths are clear and stable. However, after the power curtailment operation is implemented, the power supply path of some photovoltaic panels changes (such as current interruption or output power reduction). The operation and maintenance personnel compare the power supply paths before and after the power curtailment measures are implemented through monitoring software. They found that the current on the power supply paths of those photovoltaic panels that were turned off showed obvious interruptions or reductions. Therefore, these paths were identified as abnormal power supply paths. Through this process, the photovoltaic power station can accurately identify abnormal power supply paths caused by power curtailment measures, providing important reference information for subsequent operation and maintenance and fault diagnosis.

[0118] Furthermore, based on the tracing of abnormal power supply paths, abnormal power supply areas are identified, and multiple photovoltaic panels within these areas are marked. The abnormal power supply areas are monitored in real time, and the abnormal power supply range is determined based on the power supply detection of these areas. This approach incorporates the overall considerations for power supply detection in abnormal power supply areas, ensuring the accuracy of the abnormal power supply range.

[0119] At this point, after identifying the abnormal power supply path (as described in step S141), the maintenance personnel need to trace the source of these paths to determine which photovoltaic panels or equipment are located in the abnormal power supply area. This process usually involves a thorough understanding of the power plant layout and power supply path. At this time, tracing is assisted by the power plant's monitoring software or layout diagram. Check the location of the abnormal power supply path on the layout diagram, and then trace upwards along these paths until the source of the problem is found, i.e., the abnormal power supply area.

[0120] Once the abnormal power supply area is identified, maintenance personnel need to mark all photovoltaic panels in that area on the layout diagram or monitoring software. This helps them quickly locate and identify which photovoltaic panels are affected by the abnormal power supply. At the same time, marking is done by using different colors or symbols on the layout diagram, or by setting specific labels or alarm information in the monitoring software. In this way, maintenance personnel can see at a glance which photovoltaic panels are located in the abnormal power supply area.

[0121] After marking the abnormal power supply areas, maintenance personnel need to monitor these areas in real time. This includes monitoring key parameters such as the power generation efficiency, current, and voltage of the photovoltaic panels, as well as fault or abnormal signals. At the same time, real-time monitoring is usually accomplished through the power plant's remote monitoring system. Maintenance personnel set up monitoring software to view parameter changes in abnormal power supply areas in real time and set alarm thresholds so that they can receive timely alerts when parameters exceed the normal range.

[0122] During real-time monitoring, the specific scope of abnormal power supply is further determined based on the data from the power supply detection system. This involves checking multiple photovoltaic panels one by one to determine which panels are indeed affected by abnormal power supply. At this time, the methods for determining the scope of abnormal power supply include comparative analysis, trend prediction, and fault diagnosis. By combining historical data, current parameters, and expert experience, it is possible to determine which photovoltaic panels are in an abnormal power supply state and to determine the specific scope of abnormal power supply.

[0123] Therefore, multiple power curtailment nodes are determined by matching the abnormal power supply range, abnormal power supply area, and abnormal power supply path, and the locations of multiple power curtailment nodes are marked. The corresponding power curtailment content is determined based on the detection of each power curtailment node. The sub-power curtailment measures corresponding to each power curtailment node are determined according to the mapping relationship between the power curtailment content, location, and sub-power curtailment measures of each power curtailment node. This approach takes into account the overall consideration of the power curtailment content, location, and sub-power curtailment measures of each power curtailment node, ensuring the accuracy of the sub-power curtailment measures corresponding to each power curtailment node.

[0124] At this point, based on the matching results of the abnormal power supply range, abnormal power supply area, and abnormal power supply path, several key power curtailment nodes are identified. These nodes are usually critical points in the power supply path, such as connection points of photovoltaic panels, branch points of cables, or interfaces of power conversion equipment. During the matching process, it is necessary to consider whether the range of abnormal power supply covers specific photovoltaic panel groups, cable lines, or equipment, as well as the positional relationship of these elements in the power supply network. Through comprehensive analysis, key nodes that have a direct impact on the abnormal power supply status are identified.

[0125] After identifying the power curtailment points, it is necessary to mark their specific locations. This can be done by marking them on the power grid map, setting up signs on-site, or using the map function of the power monitoring system. When marking the locations, it is important to ensure that the markings are accurate and easy to identify. For large photovoltaic power plants, a high-precision geographic information system (GIS) is required to assist in marking and positioning.

[0126] Based on the detection and analysis of each power curtailment node, the specific content of the power curtailment measures that need to be taken for each node is determined. This includes reducing the power supply, adjusting the voltage or current level, and shutting down some photovoltaic panels. At this time, when determining the power curtailment content, it is necessary to consider the actual power supply status of the node, the severity of abnormal power supply, and the overall operation strategy of the photovoltaic power station. At the same time, it is also necessary to assess the impact of power curtailment measures on grid stability and photovoltaic power station power generation efficiency.

[0127] Based on the curtailment content, location, and predefined sub-curtailment measures mapping relationships of each curtailment node, specific sub-curtailment measures are determined for each curtailment node. These measures include adjusting the orientation of photovoltaic panels to reduce shading, shutting down damaged photovoltaic panels to reduce fault propagation, and activating energy storage systems to balance power supply. Furthermore, when determining sub-curtailment measures, the photovoltaic power plant's operation and maintenance manual, expert experience, and historical data need to be referenced. Additionally, the implementation difficulty, cost-effectiveness, and long-term impact on the photovoltaic power plant must be considered.

[0128] Specifically, suppose a large photovoltaic power station consists of multiple photovoltaic panel groups, each containing dozens of photovoltaic panels; during a certain period of time, the monitoring system detects a sudden drop in the power supply of a certain photovoltaic panel group, and the abnormal power supply range covers part of the photovoltaic panels in the group; by analyzing the abnormal power supply range and power supply path, it is determined that the abnormality occurred at a certain connection point or cable branch point of the photovoltaic panel group, and these points are identified as power curtailment nodes.

[0129] The locations of these power curtailment nodes are marked on the power supply network diagram, and signs are set up on-site to facilitate quick location by maintenance personnel. Through detection and analysis of the power curtailment nodes, a problem of poor contact at a certain connection point leading to unstable power supply was discovered. Therefore, the power curtailment content for this node was determined to be reducing the power supply to the photovoltaic panels connected to that connection point. Based on the predefined sub-curtailment measure mapping relationship and the actual situation of the photovoltaic power station, it was decided to take measures to shut down some damaged photovoltaic panels to reduce the spread of the fault. At the same time, the energy storage system was activated to balance the power supply and ensure the stability of the power grid. Through this process, the photovoltaic power station can accurately locate abnormal power supply situations and take effective power curtailment measures to deal with them, thereby ensuring the stable operation of the power grid and the power generation efficiency of the photovoltaic power station.

[0130] In some embodiments of this application, a sub-power curtailment measure matching table is collected, as shown in Table 4:

[0131] Table 4: Matching Table of Sub-Power Curtailment Measures

[0132]

[0133] This sub-curtailment matching table lists four curtailment nodes. Each node has a corresponding location description, matching status with the abnormal power supply range, area and path, curtailment content and sub-curtailment measures. For example, curtailment node 001 is located at the connection point of photovoltaic panel group A and B. It perfectly matches the abnormal power supply range, area and path. Therefore, the determined curtailment content is to reduce the power supply, and the specific sub-curtailment measure is to shut down the damaged photovoltaic panel B.

[0134] refer to Figure 6In step S15, the power curtailment effect parameters are determined based on the power parameters of the power curtailment nodes and the execution progress of the sub-power curtailment measures. The intelligent power supply of the photovoltaic power station is triggered based on the power curtailment effect parameters of each power curtailment node and the actual power supply of the photovoltaic power station.

[0135] In the specific implementation of this invention, the specific steps are as follows:

[0136] S151: Monitor the sub-power curtailment measures implemented by the power curtailment nodes in real time, determine the implementation progress of the sub-power curtailment measures based on the implementation content of the power curtailment nodes relative to the sub-power curtailment measures, and at the same time, monitor the power parameters of the power curtailment nodes in real time, and determine the power curtailment effect parameters based on the power parameters of the power curtailment nodes, the implementation progress of the sub-power curtailment measures, and the location of the power curtailment nodes.

[0137] S152: Determine the first intelligent power supply parameters of the photovoltaic power station based on the power curtailment effect parameters of each power curtailment node and the relative position of two adjacent power curtailment nodes; determine the second intelligent power supply parameters based on the power curtailment effect parameters of each power curtailment node and the actual power supply of the photovoltaic power station.

[0138] S153: Determine the intelligent power supply event of the photovoltaic power station based on the first intelligent power supply parameter, the second intelligent power supply parameter and the intelligent power supply mapping relationship, and trigger the intelligent power supply of the photovoltaic power station along the intelligent power supply event.

[0139] In the embodiments of this application, the sub-power curtailment measures implemented by the power curtailment nodes are monitored in real time. The execution progress of the sub-power curtailment measures is determined based on the execution content of the power curtailment nodes relative to the sub-power curtailment measures. At the same time, the power parameters of the power curtailment nodes are monitored in real time. The power curtailment effect parameters are determined based on the power parameters of the power curtailment nodes, the execution progress of the sub-power curtailment measures, and the location of the power curtailment nodes. This approach takes into account the overall consideration of the power parameters of the power curtailment nodes, the execution progress of the sub-power curtailment measures, and the location of the power curtailment nodes, ensuring the accuracy of the power curtailment effect parameters.

[0140] At this time, the sub-power-limiting measures implemented by the power-limiting nodes are monitored in real time. Details: monitoring is carried out through remote sensors, automated control systems, or manual inspections. It is important to ensure the real-time nature and accuracy of the monitoring so as to promptly detect and address any deviations or faults.

[0141] Based on the monitored power rationing nodes and their implementation of sub-power rationing measures, the progress of these measures is assessed. This includes determining whether the measures have started, the speed of implementation, and the estimated completion time. The determination of progress relies on specific algorithms or logic that calculate metrics such as percentage of progress and remaining time based on monitoring data. In addition, delay factors such as equipment failure and weather conditions must be considered.

[0142] While monitoring the implementation of power curtailment measures, it is also necessary to monitor the power parameters of the curtailment nodes in real time, such as voltage, current, and power factor. These parameters provide important information about the grid status and the effectiveness of power curtailment. At this time, the monitoring of power parameters is usually achieved through sensors or measuring devices installed in the grid. These devices send real-time data to a central monitoring system, which analyzes the data to evaluate the grid performance and the effectiveness of power curtailment measures.

[0143] Based on the monitored power parameters, the implementation progress of the sub-power curtailment measures, and the location information of the power curtailment nodes, power curtailment effect parameters are determined. These parameters are used to quantitatively evaluate the impact and effect of power curtailment measures on the power grid. At this time, power curtailment effect parameters include voltage fluctuation range, current reduction percentage, power factor improvement degree, etc. The calculation of these parameters depends on specific mathematical models or algorithms, which convert monitoring data into meaningful performance indicators.

[0144] Specifically, suppose a photovoltaic power plant needs to implement power curtailment measures due to grid overload; based on grid analysis, several key curtailment nodes are identified, and specific sub-curtailment measures are implemented at these nodes, such as reducing the output power of photovoltaic panels; through a remote monitoring system, the power plant operators can view the status of the equipment that reduces the output power at each curtailment node in real time; for example, they see that the output power of a certain photovoltaic panel has been reduced from 100% to the predetermined 80%.

[0145] Based on monitoring data, operators calculated the implementation progress of the power curtailment measures. For example, they found that the output power reduction tasks at all curtailment nodes had been 80% completed within the scheduled time, and the remaining tasks were expected to be completed in the next few minutes. At the same time, the monitoring system continued to collect power parameters for each curtailment node, such as voltage and current. Operators observed that as the output power decreased, the voltage fluctuation range in the grid decreased, and the current also decreased accordingly. Based on the monitored power parameters and implementation progress, operators calculated the power curtailment effect parameters. For example, they found that the voltage fluctuation range decreased from ±5% to ±3%, and the current decreased by about 10%, indicating that the power curtailment measures had achieved significant results.

[0146] Furthermore, the first intelligent power supply parameters of the photovoltaic power station are determined based on the curtailment effect parameters of each curtailment node and the relative positions of two adjacent curtailment nodes. The second intelligent power supply parameters are determined based on the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station. This approach takes into account both the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station, ensuring the accuracy of the second intelligent power supply parameters.

[0147] At this point, the first intelligent power supply parameters of the photovoltaic power station are determined based on the power curtailment effect parameters of each power curtailment node and the relative positions of two adjacent power curtailment nodes. These parameters aim to reflect the power supply stability and efficiency of the local power grid within the power station. Then, the power curtailment effect parameters of each power curtailment node, such as voltage fluctuations and current changes, are collected and analyzed. Next, the positional relationship between adjacent power curtailment nodes is considered, which is achieved through a power grid topology map or a geographic information system (GIS). Combining this information, the first intelligent power supply parameters, such as voltage stability index and current balance, are calculated.

[0148] The second intelligent power supply parameters are determined based on the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station. These parameters aim to reflect the overall power supply performance and efficiency of the power station. At this time, the curtailment effect parameters of each curtailment node are collected and analyzed. Then, these parameters are combined with the actual power supply of the power station (such as total power generation, grid-connected power, etc.) and the second intelligent power supply parameters such as power supply efficiency and power factor are calculated through specific algorithms or models. These parameters can comprehensively evaluate the power supply performance and efficiency of the power station and provide an important basis for the optimized operation of the power station.

[0149] Specifically, suppose a photovoltaic power station consists of multiple photovoltaic panels and inverters, some of which need to be subject to power curtailment due to weather conditions; during the power curtailment process, the power curtailment effect parameters of each curtailment node are collected, and the positional relationship of adjacent nodes is considered.

[0150] Suppose there are two adjacent power-canceling nodes, A and B, located at the output terminals of photovoltaic panel group 1 and photovoltaic panel group 2, respectively. Through monitoring and analysis, it is found that the voltage fluctuation of node A is small, while the current change of node B is large. Combining the relative positions of nodes A and B and their power-canceling effect parameters, the first intelligent power supply parameters such as voltage stability index and current balance are calculated. For example, it is found that the voltage stability of photovoltaic panel group 1 is good, while the current distribution of photovoltaic panel group 2 has problems.

[0151] Assuming the actual power supply of the power station is 1000kW, of which 800kW is grid-connected, by collecting and analyzing the curtailment effect parameters of each curtailment node, the power supply efficiency and power factor of the power station are calculated as second intelligent power supply parameters. For example, it is found that due to the implementation of curtailment measures for some photovoltaic panels, the overall power supply efficiency of the power station has slightly decreased, but the power factor remains at a high level. This indicates that the power quality of the power station is still good, but further optimization of the curtailment strategy is needed to improve the power supply efficiency. Through this process, based on the curtailment effect parameters and relative position relationships of the curtailment nodes, as well as the actual power supply of the power station, intelligent power supply parameters reflecting the local and overall power supply performance and efficiency of the power station are determined. These parameters provide important basis for the operation and management of the power station, helping to optimize the curtailment strategy and improve the power supply quality and efficiency.

[0152] Therefore, the intelligent power supply event of the photovoltaic power station is determined based on the first intelligent power supply parameter, the second intelligent power supply parameter, and the intelligent power supply mapping relationship. The intelligent power supply of the photovoltaic power station is triggered along the intelligent power supply event. This method takes into account the overall consideration of the first intelligent power supply parameter, the second intelligent power supply parameter, and the intelligent power supply mapping relationship, ensuring the accuracy of the intelligent power supply event of the photovoltaic power station. At the same time, the power curtailment effect parameter is introduced to control the power curtailment effect parameter of each power curtailment node and the actual power supply of the photovoltaic power station as a whole, ensuring the intelligent power supply effect of the photovoltaic power station and realizing intelligent power supply of the photovoltaic power station in various application scenarios.

[0153] At this point, the first intelligent power supply parameter (such as voltage stability index, current balance, etc.) and the second intelligent power supply parameter (such as power supply efficiency, power factor, etc.) are compared with the predefined intelligent power supply mapping relationship to determine the intelligent power supply event currently faced by the photovoltaic power station. At this point, the intelligent power supply mapping relationship defines the correspondence between different parameter combinations and intelligent power supply events. For example, when the voltage stability index is lower than a certain threshold, the "voltage instability" event is triggered; when the power supply efficiency continues to decline and the power factor is also low, the "low power supply efficiency" event is triggered.

[0154] Once a smart power supply event is identified, the system will trigger corresponding smart power supply strategies or measures based on the event. These strategies or measures are designed to respond quickly and resolve power supply issues, ensuring the stable operation and efficient power supply of the power plant. At this time, smart power supply strategies include adjusting power curtailment measures, optimizing the grid structure, and activating backup power sources or energy storage systems. The triggering process involves an automated control system, which automatically selects and executes appropriate strategies or measures based on the type and severity of the smart power supply event. In addition, the triggering process also includes sending alarms or notifications to power plant operators so that they can be informed of the situation in a timely manner and take necessary responses.

[0155] Specifically, assuming a photovoltaic power station is in operation and the first and second smart power supply parameters have been determined according to step S152, smart power supply events will now be determined based on these parameters and the smart power supply mapping relationship, and the corresponding smart power supply will be triggered.

[0156] Suppose that the first intelligent power supply parameter collected shows that the voltage stability index of a certain area is lower than the preset threshold, indicating that there is a voltage instability problem in that area; at the same time, the second intelligent power supply parameter shows that the overall power supply efficiency of the power station has decreased slightly, but is still within an acceptable range, while the power factor remains at a high level; combined with the intelligent power supply mapping relationship, the current intelligent power supply event is determined to be "local voltage instability".

[0157] Once a "local voltage instability" event is identified, the system will automatically trigger corresponding intelligent power supply strategies. For example, the system will adjust the power curtailment measures in the area to reduce the load impact on the power grid, thereby improving voltage stability. In addition, the system will send alarms to the power plant operators, informing them of the current voltage instability problem and providing suggested solutions or operating procedures. Based on the suggestions provided by the system, the operators will further adjust the power grid structure or activate backup power sources to ensure the stable operation and efficient power supply of the power plant.

[0158] In some embodiments of this application, a smart power supply event matching table is collected, as shown in Table 5:

[0159] Table 5 Smart Power Supply Event Matching Table

[0160]

[0161] Assume the collected first smart power supply parameter (voltage stability index) is 0.80 and the second smart power supply parameter (power supply efficiency) is 0.88; according to the matching table, the voltage stability index 0.80 is less than 0.85 and the power supply efficiency 0.88 is greater than 0.90 (but in this case, we mainly focus on the voltage stability index because it has already triggered the event condition); therefore, the current smart power supply event is determined to be a "low voltage stability event".

[0162] Once a smart power supply event is identified, the system automatically triggers corresponding countermeasures. For example, for a "low voltage stability event," the system: adjusts the power grid structure and optimizes voltage distribution; activates reactive power compensation equipment to improve voltage stability; and sends an alarm to operators, recommending further inspection and maintenance measures.

[0163] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of the power curtailment control system for a photovoltaic power station according to an embodiment of the present invention; the power curtailment control system for the photovoltaic power station includes:

[0164] Image module 21 is used to determine the inspection path of the inspection drone based on the distribution location of the photovoltaic panels; the inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels.

[0165] The theoretical power supply module 22 is used to determine the posture of the photovoltaic panel based on the real-time image of the photovoltaic panel, and to determine the theoretical power supply of the photovoltaic power station based on the posture, illumination area and model of the photovoltaic panel.

[0166] The power curtailment measures module 23 is used to determine the abnormal power supply range of the photovoltaic power station based on the actual power supply, theoretical power supply and past power supply data of the photovoltaic power station, and to determine the power curtailment measures of the photovoltaic power station based on the abnormal power supply range and the power supply area controlled by the photovoltaic power station.

[0167] The power curtailment node module 24 is used to collect the power supply path of the photovoltaic panel in the power curtailment measures of the photovoltaic power station, determine multiple power curtailment nodes based on the abnormal power supply path and abnormal power supply range in the power supply path of the photovoltaic panel, and mark the sub-power curtailment measures corresponding to the power curtailment nodes.

[0168] The intelligent power supply module 25 is used to determine the power curtailment effect parameters based on the power parameters of the curtailment nodes and the implementation progress of the sub-curtailment measures, and to trigger the intelligent power supply of the photovoltaic power station based on the power curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station.

[0169] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A power curtailment control method for a photovoltaic power station, characterized in that, include: The inspection path of the inspection drone is determined based on the distribution location of the photovoltaic panels; The inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels; The orientation of the photovoltaic panel is determined based on real-time images of the photovoltaic panel, and the theoretical power supply of the photovoltaic power station is determined based on the orientation, illuminated area and model of the photovoltaic panel. The abnormal power supply range of a photovoltaic (PV) power station is determined based on its actual power supply, theoretical power supply, and historical power supply data. Power curtailment measures are then determined based on the abnormal power supply range and the power supply area controlled by the PV power station. This includes: real-time monitoring of the PV power station and collecting its actual power supply data; determining a first power anomaly parameter based on the actual and theoretical power supply data; determining a second power anomaly parameter based on the actual and historical power supply data; determining the abnormal power supply range based on the first and second power anomaly parameters and the abnormal power supply mapping relationship; identifying multiple abnormal power supply intervals based on the division of the abnormal power supply range; determining corresponding abnormal power supply events based on the interval ranges, PV panels, and the PV power station; and determining power curtailment measures based on each abnormal power supply event and the power supply area controlled by the PV power station. These power curtailment measures include power regulation measures, selective shutdown of abnormal PV panels, external energy storage measures, and grid dispatch measures. In the power curtailment measures for photovoltaic (PV) power plants, the power supply path of PV panels is collected. Multiple curtailment nodes are determined based on abnormal power supply paths and ranges within these paths, and the corresponding sub-curtailment measures are marked. This includes: the PV power plant performing curtailment operations along the curtailment measures; determining the PV panel power supply path based on power supply detection between the PV power plant and multiple PV panels; determining abnormal power supply paths based on the matching of PV panel power supply paths and curtailment measures; the abnormal power supply path being part of the PV panel power supply path, with the corresponding PV panel in an abnormal power supply state; determining abnormal power supply areas based on tracing abnormal power supply paths, marking multiple PV panels within these areas, real-time monitoring of abnormal power supply areas, and determining the abnormal power supply range based on power supply detection within these areas; determining multiple curtailment nodes based on the matching of abnormal power supply ranges, areas, and paths, marking the locations of these nodes; determining the corresponding curtailment content based on the detection of each curtailment node; and determining the sub-curtailment measures corresponding to each curtailment node based on the curtailment content, location, and sub-curtailment measure mapping relationship of each node. The power curtailment effect parameters are determined based on the power parameters of the curtailment nodes and the implementation progress of the sub-curtailment measures. The intelligent power supply of the photovoltaic power station is triggered based on the power curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station.

2. The power curtailment control method for photovoltaic power plants according to claim 1, characterized in that, The inspection path of the inspection drone is determined based on the distribution location of the photovoltaic panels; The inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels, including: The system collects data on the location of photovoltaic panels, determines the distribution of multiple photovoltaic panels based on their location detection, and then determines the inspection area of ​​the inspection drone based on the distribution of multiple photovoltaic panels, the environmental scene in which they are located, and the shape of the multiple photovoltaic panels. The inspection path of the inspection drone is determined based on the drone's current location and the path planning of the inspection area. The drone acquires the inspection path and flies along the inspection path. When the drone is in flight, if it detects a photovoltaic panel, it marks the corresponding shooting area. Based on the working status of the camera on the drone, the location of the shooting area, and the distribution of the photovoltaic panel, the shooting mode of the camera on the drone is determined. The camera then shoots along the shooting mode to collect real-time images of the photovoltaic panel.

3. The power curtailment control method for photovoltaic power plants according to claim 1, characterized in that, The process of determining the photovoltaic panel's orientation based on real-time images, and determining the theoretical power supply of the photovoltaic power station based on the panel's orientation, illuminated area, and model, includes: Real-time images of the photovoltaic panel are acquired, the area to be identified on the photovoltaic panel is determined based on the detection of the real-time images, the positions of multiple attitude nodes of the photovoltaic panel are determined based on the identification of the area to be identified, and the attitude of the photovoltaic panel is determined based on the detection of the positions of multiple attitude nodes. In each photovoltaic panel, a first reference power is determined based on the orientation of the photovoltaic panel and the corresponding irradiated area, and a second reference power is determined based on the model of the photovoltaic panel and the corresponding irradiated area. The theoretical power supply of each photovoltaic panel is determined based on the mapping relationship between the first reference power, the second reference power, and the power supply of the photovoltaic panel. The theoretical power supply of the photovoltaic power station is determined based on the theoretical power supply of each photovoltaic panel and the energy loss between each photovoltaic panel and the photovoltaic power station.

4. The power curtailment control method for photovoltaic power plants according to claim 1, characterized in that, The process of determining the curtailment effect parameters based on the power generation parameters of the curtailment nodes and the implementation progress of sub-curtailment measures, and triggering the intelligent power supply of the photovoltaic power station based on the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station, includes: The system monitors the sub-power curtailment measures implemented by the power curtailment nodes in real time, determines the implementation progress of the sub-power curtailment measures based on the implementation content of the power curtailment nodes relative to the sub-power curtailment measures, and simultaneously monitors the power parameters of the power curtailment nodes in real time, determining the power curtailment effect parameters based on the power parameters of the power curtailment nodes, the implementation progress of the sub-power curtailment measures, and the location of the power curtailment nodes.

5. The power curtailment control method for a photovoltaic power station according to claim 4, characterized in that, The process of determining the curtailment effect parameters based on the power parameters of the curtailment nodes and the implementation progress of sub-curtailment measures, and triggering the intelligent power supply of the photovoltaic power station based on the curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station, further includes: The first intelligent power supply parameters of the photovoltaic power station are determined based on the power curtailment effect parameters of each power curtailment node and the relative position of two adjacent power curtailment nodes. The second intelligent power supply parameters are determined based on the power curtailment effect parameters of each power curtailment node and the actual power supply of the photovoltaic power station. The intelligent power supply event of the photovoltaic power station is determined based on the first intelligent power supply parameter, the second intelligent power supply parameter, and the intelligent power supply mapping relationship, and the intelligent power supply of the photovoltaic power station is triggered along the intelligent power supply event.

6. A power curtailment control system for a photovoltaic power station, characterized in that, The power curtailment control system of the photovoltaic power station is applied to the power curtailment control method of the photovoltaic power station as described in any one of claims 1-5, and the power curtailment control system of the photovoltaic power station includes: The image module is used to determine the inspection path of the inspection drone based on the distribution location of the photovoltaic panels; the inspection drone flies along the inspection path and collects real-time images of the photovoltaic panels. The theoretical power supply module is used to determine the orientation of the photovoltaic panel based on real-time images of the photovoltaic panel, and to determine the theoretical power supply of the photovoltaic power station based on the orientation, illumination area and model of the photovoltaic panel. The power curtailment module is used to determine the abnormal power supply range of a photovoltaic (PV) power station based on its actual power supply, theoretical power supply, and historical power supply data. Based on the abnormal power supply range and the power supply area controlled by the PV power station, it determines the power curtailment measures for the PV power station. These measures include: real-time monitoring of the PV power station and collecting its actual power supply data; determining a first power anomaly parameter based on the actual and theoretical power supply data; determining a second power anomaly parameter based on the actual and historical power supply data; determining the abnormal power supply range based on the first and second power anomaly parameters and the abnormal power supply mapping relationship; determining multiple abnormal power supply intervals based on the division of the abnormal power supply range; determining corresponding abnormal power supply events based on the interval ranges, PV panels, and the PV power station; and determining power curtailment measures for the PV power station based on each abnormal power supply event and the power supply area controlled by the PV power station. These power curtailment measures include power regulation measures, selective shutdown of abnormal PV panels, external energy storage measures, and grid dispatch measures. The power curtailment node module is used to collect the power supply path of photovoltaic panels in the power curtailment measures of photovoltaic power plants. It identifies multiple power curtailment nodes based on abnormal power supply paths and abnormal power supply ranges within these paths, and marks the corresponding sub-curtailment measures for each node. This includes: the photovoltaic power plant performing power curtailment operations along the specified curtailment measures; determining the photovoltaic panel power supply path based on power supply detection between the photovoltaic power plant and multiple photovoltaic panels; identifying abnormal power supply paths based on the matching of photovoltaic panel power supply paths and curtailment measures; the abnormal power supply path being part of the photovoltaic panel power supply path, with the corresponding photovoltaic panel in an abnormal power supply state; identifying abnormal power supply areas based on tracing abnormal power supply paths, marking multiple photovoltaic panels within these areas, monitoring abnormal power supply areas in real time, and determining the abnormal power supply range based on power supply detection within these areas; identifying multiple power curtailment nodes based on the matching of abnormal power supply ranges, abnormal power supply areas, and abnormal power supply paths, marking the locations of these nodes; determining the corresponding curtailment content based on the detection of each power curtailment node; and determining the sub-curtailment measures corresponding to each power curtailment node based on the curtailment content, location, and sub-curtailment measure mapping relationship of each node. The intelligent power supply module is used to determine the power curtailment effect parameters based on the power parameters of the curtailment nodes and the implementation progress of the sub-curtailment measures, and to trigger the intelligent power supply of the photovoltaic power station based on the power curtailment effect parameters of each curtailment node and the actual power supply of the photovoltaic power station.

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