Photovoltaic power prediction method, system and equipment of photovoltaic power station and medium

Abstract

The invention discloses a photovoltaic power prediction method, system and device of a photovoltaic power station and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring spatial position information of a plurality of photovoltaic power stations in a target area, and dividing each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial position information; receiving power change data and meteorological data of the upstream power station group in the first time period; when the power change data and the meteorological data meet preset extreme weather judgment conditions, extreme weather movement parameters are calculated based on the meteorological data, and an extreme weather propagation time sequence is determined according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; and predicting the photovoltaic power of the downstream power station group in the second time period in combination with the power change data and the extreme weather propagation time sequence. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of photovoltaic power prediction of the photovoltaic power station is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a photovoltaic power prediction method, system, device and medium for a photovoltaic power station. Background Art

[0002] As the global demand for renewable energy grows, solar photovoltaic power generation has been widely used and developed as a clean energy technology. Photovoltaic power stations can convert sunlight into electrical energy, which is of great significance to reducing dependence on fossil energy and reducing environmental pollution. Therefore, how to accurately predict power has become an urgent problem to be solved.

[0003] At present, the existing photovoltaic power prediction methods of photovoltaic power stations mainly rely on analyzing the historical power data of photovoltaic power stations to predict the photovoltaic power in the future period. However, in practical applications, since the actual operation of photovoltaic power stations will be affected by weather changes, it is often difficult to consider the impact of extreme weather when predicting the photovoltaic power of photovoltaic power stations using only historical data, resulting in deviations in the prediction results, thereby reducing the accuracy of photovoltaic power prediction of photovoltaic power stations. Summary of the invention

[0004] The present application provides a photovoltaic power prediction method, system, electronic device and storage medium for a photovoltaic power station, which can improve the accuracy of photovoltaic power prediction for the photovoltaic power station.

[0005] In a first aspect, the present application provides a photovoltaic power prediction method for a photovoltaic power station, comprising: Acquire spatial location information of a plurality of photovoltaic power stations in a target area, and divide each of the photovoltaic power stations into an upstream power station group and a downstream power station group based on the spatial location information; Receiving power change data and meteorological data of the upstream power station group in a first period; When the power change data and the meteorological data meet the preset extreme weather determination conditions, the extreme weather movement parameters are calculated based on the meteorological data, and the extreme weather propagation timing is determined according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; The power variation data and the extreme weather propagation time series are combined to predict the photovoltaic power of the downstream power station group in the second time period.

[0006] In a second aspect of the present application, a photovoltaic power prediction system for a photovoltaic power station is provided, the system comprising: A power station group division module, used to obtain spatial location information of multiple photovoltaic power stations in a target area, and divide each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial location information; A data receiving module, used for receiving power change data and meteorological data of the upstream power station group in a first period; a propagation timing determination module, configured to calculate extreme weather movement parameters based on the meteorological data when the power change data and the meteorological data meet the preset extreme weather determination conditions, and determine the extreme weather propagation timing according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; The photovoltaic power prediction module is used to combine the power change data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second time period.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a photovoltaic power prediction method for a photovoltaic power station when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a photovoltaic power prediction method for a photovoltaic power station.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, the photovoltaic power station is divided into an upstream power station group and a downstream power station group based on the acquired spatial position information. This spatial division method enables the system to effectively capture the propagation law of extreme weather and provides spatial dimension support for power prediction. By analyzing the power change data and meteorological data of the upstream power station group, the system establishes an extreme weather judgment mechanism, which can timely identify weather systems that may have a significant impact on photovoltaic power generation. After confirming the extreme weather conditions, the system calculates extreme weather movement parameters based on meteorological data. These parameters, combined with the relative position information of the upstream and downstream power station groups, enable the system to accurately predict the propagation process of extreme weather and form a complete propagation time series. The system combines the power change characteristics of the upstream power station group with the extreme weather propagation time series, and realizes the accurate prediction of the power change of the downstream power station group by considering the attenuation effect of the propagation distance. By considering the dimension of extreme weather impact and the dimension of power data change, the photovoltaic power of the photovoltaic power station is comprehensively predicted, reducing the deviation caused by weather impact, thereby improving the accuracy of the photovoltaic power prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a photovoltaic power prediction method of a photovoltaic power station provided in an embodiment of the present application; Figure 2It is a structural schematic diagram of a photovoltaic power prediction system of a photovoltaic power station provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a photovoltaic power prediction method for a photovoltaic power station. In one embodiment, please refer to Figure 1 , Figure 1 The present invention provides a flow chart of a photovoltaic power prediction method for a photovoltaic power station provided by an embodiment of the present invention. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run in a photovoltaic power prediction system for a photovoltaic power station based on a von Neumann system. Specifically, the method can include the following steps: Step 101: obtaining spatial location information of multiple photovoltaic power stations in a target area, and dividing each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial location information.

[0016] The spatial location information refers to the distribution characteristics of the photovoltaic power station in the geographic space, including the geographic coordinates, altitude and dominant wind direction of the photovoltaic power station. The spatial location information can be obtained through the geographic information collection equipment installed in the photovoltaic power station, or it can be retrieved from the geographic information system database.

[0017] The upstream power station group refers to a group of photovoltaic power stations in the target area, divided based on geographical correlation and dominant wind direction. Specifically, it refers to photovoltaic power stations whose geographical correlation is greater than the correlation threshold and whose dominant wind direction is upwind. These power stations are usually located at the front end of the weather system propagation path and are the first to be affected by weather changes.

[0018] The downstream power station group refers to the target power station group to be predicted in the target area. Due to its special geographical location characteristics, the photovoltaic power stations in the downstream power station group will be affected by weather similar to that of the upstream power station group after a certain time delay.

[0019] Specifically, the altitude and dominant wind direction of each photovoltaic power station are extracted from the spatial position information, wherein the dominant wind direction includes upwind and downwind. Based on each altitude, the height difference between each photovoltaic power station is determined, and according to each position, the distance difference between each photovoltaic power station is determined. Subsequently, the height difference and distance difference between each photovoltaic power station are weighted and summed to obtain the geographical correlation between each photovoltaic power station. The geographical correlation can comprehensively reflect the spatial relationship between photovoltaic power stations and provide a basis for subsequent grouping. After obtaining the geographical correlation, multiple photovoltaic power stations whose geographical correlation is greater than the correlation threshold and whose dominant wind direction is upwind are divided into an upstream power station group, and photovoltaic power stations whose geographical correlation with each photovoltaic power station in the upstream power station group is greater than the correlation threshold and whose dominant wind direction is downwind are divided into a downstream power station group. This grouping method based on geographical correlation and wind direction can accurately reflect the propagation order of the influence of the weather system and provide a reliable spatial reference for subsequent power prediction. By acquiring this spatial location information and grouping the power stations, the spatial correlation between photovoltaic power stations is established, so that the system can predict the power change trend of the downstream power station group according to the power change of the upstream power station group, thereby improving the accuracy of photovoltaic power prediction. At the same time, this grouping method also provides a basis for subsequent analysis of the impact of extreme weather, so that the system can better cope with power generation fluctuations caused by weather changes.

[0020] Based on the above embodiment, as an optional embodiment, in step 101: dividing each photovoltaic power station into an upstream power station group and a downstream power station group based on spatial location information, this step may also include the following steps: Step 201: extracting the altitude and prevailing wind direction of each photovoltaic power station from the spatial position information, the prevailing wind direction including upwind direction and downwind direction; determining the height difference between each photovoltaic power station based on each altitude, and determining the distance difference between each photovoltaic power station based on each position.

[0021] Among them, the altitude refers to the vertical height of the photovoltaic power station relative to the mean sea level. The dominant wind direction refers to the most common or longest-lasting wind direction in a certain area within a specific observation period (such as month or quarter), usually expressed in angle values, measured clockwise with due north as the reference (0 degrees or 360 degrees). Altitude and dominant wind direction can be understood as key parameters describing the geographical environment characteristics of photovoltaic power stations. Altitude reflects the spatial distribution of photovoltaic power stations in the vertical direction, and power stations at different altitudes may face different meteorological conditions; the dominant wind direction characterizes the main characteristics of the atmospheric circulation in the area, where the upwind direction indicates the direction of the airflow, and the downwind direction indicates the direction of the airflow. These two parameters jointly determine the propagation characteristics of the weather system in the area.

[0022] Specifically, the altitude data of each photovoltaic power station is first obtained through the geographic information system, and the wind direction data of the location of each photovoltaic power station is obtained from the meteorological monitoring station. Among them, the dominant wind direction refers to the wind direction that appears most frequently in a specific time period, which can be obtained by statistical analysis of historical wind direction data. The wind direction data is marked as upwind and downwind according to the direction of the airflow. This classification method helps to determine the propagation path of the weather system in the future. After obtaining the altitude data, the altitude difference between any two photovoltaic power stations is calculated to obtain the height difference between the photovoltaic power stations. At the same time, based on the geographic coordinates of each photovoltaic power station, the actual distance between the photovoltaic power stations is calculated using the spherical distance calculation formula to obtain the distance difference. These difference data constitute the basic parameters for describing the spatial relationship of photovoltaic power stations. The height difference and distance difference data obtained in this way provide basic data support for the subsequent calculation of geographic correlation. These data can objectively reflect the spatial position relationship between photovoltaic power stations, which helps the system to accurately determine the impact range and propagation path of the weather system.

[0023] Step 202: performing weighted summation on the height differences and distance differences between the photovoltaic power stations to obtain the geographic correlation between the photovoltaic power stations.

[0024] Among them, geographic correlation refers to a quantitative index of spatial relationship obtained by weighted summation of the height difference and distance difference between photovoltaic power stations, which is used to characterize the degree of spatial correlation between different photovoltaic power stations.

[0025] Specifically, firstly, weight coefficients α and β are set for the height difference and distance difference respectively, and these weight coefficients can be determined according to actual observation data and historical experience. The geographical correlation between any two photovoltaic power stations is calculated by the formula: geographical correlation = α × height difference + β × distance difference. Among them, the value range of weight coefficients α and β is between 0 and 1, and α + β = 1. In practical applications, the value of weight coefficients can be dynamically adjusted according to the terrain characteristics and meteorological conditions of the target area to obtain the optimal calculation result. Through this weighted summation method, the obtained geographical correlation can fully reflect the spatial relationship between photovoltaic power stations. A higher geographical correlation indicates that the two power stations are closely connected in spatial location, and the influence of the weather system will propagate in a similar way; a lower geographical correlation indicates that the spatial correlation between the two power stations is weak, and the propagation characteristics of weather influence may be significantly different. This quantitative correlation calculation method provides an objective evaluation standard for the subsequent power station grouping, which helps to improve the scientificity and rationality of the grouping, thereby providing a more reliable spatial reference basis for power prediction.

[0026] Step 203: divide multiple photovoltaic power stations whose geographical correlation is greater than the correlation threshold and whose dominant wind direction is upwind into an upstream power station group, and divide the photovoltaic power stations whose geographical correlation with each photovoltaic power station in the upstream power station group is greater than the correlation threshold and whose dominant wind direction is downwind into a downstream power station group.

[0027] Specifically, photovoltaic power stations are grouped and divided based on geographical correlation and dominant wind direction. The purpose of this grouping method is to identify photovoltaic power station groups with a sequential relationship on the propagation path of the weather system, so as to establish an effective early warning and prediction mechanism. Since the weather system usually propagates along the dominant wind direction, and the weather impacts on power stations with similar spatial locations are more similar, the geographical correlation and dominant wind direction are used as the main basis for grouping. In the specific implementation, the correlation threshold is first set, which can be determined by analyzing historical data and actual operation experience. The system first screens out photovoltaic power stations with the dominant wind direction being upwind, and calculates the geographical correlation between these power stations. When the geographical correlation between some power stations is greater than the correlation threshold, these power stations are divided into the upstream power station group. These upstream power stations are located at the front end of the propagation path of the weather system and can first perceive the impact of weather changes. Subsequently, the system calculates the geographical correlation between each power station in the upstream power station group and other ungrouped power stations. For those photovoltaic power stations whose geographical correlation with any power station in the upstream power station group is greater than the correlation threshold and whose dominant wind direction is downwind, they are divided into the downstream power station group. This grouping method based on correlation transfer ensures that there is a stable spatial correlation between the power stations in the downstream power station group and the upstream power station group. Through this grouping method, the system establishes a prediction framework based on spatial propagation characteristics. The power changes of the upstream power station group can be used as early warning signals, and the power changes of the downstream power station group can be predicted by analyzing the data of the upstream power station group. This grouping method not only takes into account the spatial distance relationship between power stations, but also takes into account the wind direction factor, making the grouping results more in line with the actual weather impact propagation law, thereby improving the accuracy of power prediction.

[0028] Based on the above embodiment, as an optional embodiment, in step 101: based on the spatial location information, each photovoltaic power station is divided into an upstream power station group and a downstream power station group. After this step, the following steps may be further included: Step 204: The photovoltaic power stations in the target area that are not in the upstream power station group and the downstream power station group are divided into a reference power station group.

[0029] Specifically, the system first identifies all photovoltaic power stations in the target area, and then excludes the power stations that have been divided into the upstream power station group and the downstream power station group. For the remaining power stations, the system analyzes them according to their geographical location, operating characteristics and degree of weather impact. These power stations that are not directly affected by extreme weather are in similar geographical and climatic environments, and their power change characteristics can reflect the power generation performance under normal meteorological conditions. When determining the benchmark power station group, the system considers the spatial distribution characteristics of the power stations to ensure that the power stations in the benchmark power station group have a certain geographical correlation with the upstream and downstream power station groups, but are not directly affected by the target extreme weather system. This selection method ensures that the benchmark power station group can reflect the regional meteorological characteristics and serve as a control group for power changes. The benchmark power station group divided in this way has a strong reference value. It can not only be used to verify the accuracy of the prediction method, but also help identify abnormal power changes caused by extreme weather. By comparing the power change characteristics of the benchmark power station group and the affected power station group, the system can more accurately assess the actual impact of extreme weather and improve the accuracy of the prediction results.

[0030] Step 205: Obtain the reference photovoltaic power of each photovoltaic power station in the reference power station group; when there is a photovoltaic power station in the reference power station group whose reference photovoltaic power is greater than the photovoltaic power threshold, generate warning information and send the warning information to the terminal of the person in charge of the photovoltaic power station in the target area.

[0031] Specifically, the system first collects the power data of each photovoltaic power station in the benchmark power station group in real time, compares the actual power collected with the nominal power of the power station, and calculates the power deviation. These benchmark photovoltaic power data reflect the actual operating status of the power station that is not directly affected by extreme weather. The system compares the calculated power deviation with the preset photovoltaic power threshold. When the deviation exceeds the threshold, it indicates that the power station may be affected by unexpected weather or other abnormal conditions exist. After the abnormal situation is found, the system automatically generates early warning information. The early warning information contains key information such as the location information, current power value, and degree of abnormality of the abnormal power station, and sends this information to the terminal of the person in charge of the photovoltaic power station in the target area through the preset communication channel. This timely information transmission mechanism ensures that the operation and management personnel can quickly learn about the abnormal situation and take necessary countermeasures in time. Through this monitoring and early warning mechanism based on the benchmark power station group, the system can effectively identify and respond to potential risks. This early warning method not only expands the monitoring range of the prediction system, but also provides additional security. The timely push of early warning information enables the power station operator to more comprehensively grasp the weather impact in the area, and improves the operational reliability and safety of the entire photovoltaic power generation system.

[0032] Step 102: Receive power variation data and meteorological data of an upstream power station group in a first period of time.

[0033] The first period refers to the time interval during which the upstream power station group collects power change data and meteorological data, and is usually set to a continuous observation time before the current moment.

[0034] Power change data refers to the dynamic change characteristic data of the photovoltaic power generation power of the upstream power station group in the first period, mainly including two key parameters: power change amplitude and change duration. Among them, the power change amplitude indicates the degree of deviation of the power station output power relative to the nominal power, and the change duration indicates the duration of the power fluctuation. These data are collected in real time by the power monitoring system of the photovoltaic power station.

[0035] Meteorological data refers to the set of meteorological parameters in the area where the upstream power station group is located in the first period, mainly including wind speed, cloud cover and cloud movement trajectory data. Wind speed indicates the speed of air flow; cloud cover indicates the degree of cloud coverage in the sky, usually expressed as a percentage; cloud movement trajectory data records the direction and position change information of the cloud movement in space. These data are monitored and recorded in real time by professional equipment at the meteorological monitoring station.

[0036] Specifically, the power change data and meteorological data of the upstream power station group in the first period are received. This is because the upstream power station group is first affected by the weather system due to its special geographical location, and its power output changes and local meteorological conditions can provide important references for predicting the power changes of the downstream power station group. By analyzing the power change data and meteorological data at the same time, the impact degree and propagation characteristics of the weather system can be accurately judged. In specific implementation, the system collects the power output data of each photovoltaic power station in the upstream power station group through a real-time monitoring device. The power change data includes the power change amplitude and the duration of the change, which are obtained by calculating the power difference between adjacent time points. At the same time, the meteorological data of the area where the upstream power station group is located is obtained from the meteorological monitoring station, including wind speed, cloud cover and cloud movement trajectory data. Among them, wind speed and cloud cover data are used to determine whether extreme weather conditions occur, while cloud movement trajectory data are used to analyze the movement characteristics of the weather system. The system collects and stores these data according to a preset time window. The first period is usually selected as a continuous period before the current moment, and the time length can be adjusted according to actual needs. After preliminary processing, these data are formed into a standardized data format for subsequent analysis and processing. The data obtained in this way are highly timely and relevant, and can promptly reflect the actual impact of weather systems on photovoltaic power stations.

[0037] Step 103: When the power change data and meteorological data meet the preset extreme weather judgment conditions, the extreme weather movement parameters are calculated based on the meteorological data, and the extreme weather propagation timing is determined according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group.

[0038] Among them, the preset extreme weather judgment conditions refer to a set of multi-dimensional judgment criteria for identifying extreme weather, mainly including power change rate threshold, wind speed threshold and cloud cover threshold.

[0039] Extreme weather movement parameters refer to key indicators that describe the spatial movement characteristics of weather systems, mainly including two parameters: average movement direction and average movement speed. The average movement direction is expressed as an angle value, calculated clockwise from the north direction; the average movement speed represents the displacement distance of the weather system per unit time. These parameters are calculated by analyzing the cloud movement trajectory data in meteorological data.

[0040] The extreme weather propagation time series refers to the time sequence and arrival time of the weather system from the upstream power station group to each photovoltaic power station in the downstream power station group. This time series data is obtained by calculating the projection distance of the weather system in the moving direction and combining it with the average moving speed to form a time series containing the expected affected time of each downstream power station.

[0041] Specifically, it is first necessary to determine whether the extreme weather determination conditions are met. The system calculates the power change rate of the upstream power station group. When the power change rate exceeds the preset change rate threshold, and the wind speed observed at the same time exceeds the wind speed threshold or the cloud cover exceeds the cloud cover threshold, it is determined that extreme weather conditions occur. This multi-dimensional determination method can effectively identify weather systems that have a significant impact on photovoltaic power generation. When extreme weather conditions are confirmed, the system calculates extreme weather movement parameters based on the cloud movement trajectory data in the meteorological data. Specifically, it includes two key parameters: average movement direction and average movement speed. The average movement direction is obtained by analyzing the position change of the cloud mass center at continuous time points, and the average movement speed is determined by calculating the displacement of the cloud in unit time. These parameters describe the movement characteristics of the weather system in space. Subsequently, the system determines the extreme weather propagation sequence based on the obtained extreme weather movement parameters and the relative position relationship between the upstream power station group and the downstream power station group. First, the distance matrix between the upstream and downstream power station groups is constructed, and then the projection distance of the extreme weather in the moving direction is calculated, and combined with the average movement speed, the estimated arrival time of the extreme weather from the upstream power station group to each photovoltaic power station in the downstream power station group is calculated. The extreme weather propagation sequence determined in this way can accurately reflect the time sequence of the weather system's impact. This not only provides a time reference for predicting power changes in downstream power stations, but also can correct the power change amplitude according to the propagation distance, thereby improving the accuracy of the prediction.

[0042] Based on the above embodiment, as an optional embodiment, in step 103: when the power change data and the meteorological data meet the preset extreme weather determination conditions, this step may further include the following steps: Step 301: Obtain the total power change rate of the power change data and the wind speed and cloud cover in the meteorological data; when the absolute value of the power change rate is greater than the change rate threshold, and the wind speed is greater than the wind speed threshold or the cloud cover is greater than the cloud cover threshold, determine that the power change data and the meteorological data meet the preset extreme weather judgment conditions.

[0043] Specifically, the power change rate of the upstream power station group is calculated first. The system analyzes the power data of continuous sampling points in the first period, calculates the ratio of the power change amplitude per unit time to the nominal power, and obtains the power change rate. At the same time, wind speed and cloud cover data are extracted from meteorological data. Wind speed data reflects the intensity of air flow movement, and cloud cover data indicates the degree of cloud cover in the sky.

[0044] During the judgment process, the system first compares the calculated absolute value of the power change rate with the preset change rate threshold (such as 15% per minute). A large power change rate usually means that the output of the photovoltaic power station has fluctuated significantly. Subsequently, the system compares the monitored wind speed with the wind speed threshold (such as 10 meters per second) and the cloud cover with the cloud cover threshold (such as 80%). When the absolute value of the power change rate exceeds the change rate threshold, and at the same time satisfies any of the conditions that the wind speed exceeds the wind speed threshold or the cloud cover exceeds the cloud cover threshold, the system determines that the extreme weather judgment condition is met. This judgment method based on multi-parameter thresholds has strong reliability. The judgment of the power change rate ensures that only weather changes that have a substantial impact on power generation will be identified as extreme weather; while the judgment of wind speed and cloud cover provides meteorological support, avoiding the misjudgment of power fluctuations caused by non-weather factors as extreme weather. At the same time, the parameters of this judgment method can be dynamically adjusted according to actual operating experience, which improves the adaptability and accuracy of the system.

[0045] Based on the above embodiment, as an optional embodiment, in step 103: calculating extreme weather movement parameters based on meteorological data, this step may also include the following steps: Step 302: Obtain cloud movement trajectory data from meteorological data; calculate the average moving direction and average moving speed of the cloud movement trajectory based on a preset time window and the cloud movement trajectory data; and use the average moving direction and average moving speed as extreme weather movement parameters.

[0046] Specifically, firstly, cloud movement trajectory data is extracted from meteorological data. These data contain spatial coordinate information of the cloud mass center at different time points. The system sets a preset time window (such as 30 minutes) and tracks and analyzes the movement of clouds within this time window. The selection of the time window needs to consider the statistical significance of the data and the changing characteristics of the weather system, ensuring that the amount of data is sufficient for effective analysis and that the calculation results can reflect the movement characteristics of the current weather system. After obtaining the cloud position data within the time window, the system first calculates the average moving direction. By analyzing the changes in the position of the cloud mass center at consecutive time points, the moving direction at each time point is calculated, and then these direction values ​​are weighted averaged to obtain the average moving direction. The direction is expressed as an angle value, based on the true north direction (0 degrees or 360 degrees), and calculated clockwise. At the same time, the system calculates the displacement distance of the cloud between adjacent time points, and calculates the moving speed at each time point in combination with the time interval, and obtains the average moving speed by weighted average. The calculated average moving direction and average moving speed are used as extreme weather movement parameters, which fully describe the movement characteristics of the weather system. This parameter calculation method based on actual observation data is highly scientific and reliable. The system uses weighted average calculations to not only smooth out the impact of short-term fluctuations, but also highlight the importance of the latest data, making the calculation results more representative. The extreme weather movement parameters obtained in this way provide a data basis for the subsequent prediction of the spread of weather systems.

[0047] Based on the above embodiment, as an optional embodiment, in step 103: determining the extreme weather propagation sequence according to the extreme weather movement parameter and the relative position between the upstream power station group and the downstream power station group, this step may also include the following steps: Step 303: constructing a distance matrix between the upstream power station group and the downstream power station group based on the relative position between the upstream power station group and the downstream power station group.

[0048] Specifically, the geographic coordinate information of each photovoltaic power station in the upstream power station group and the downstream power station group is first obtained, including the longitude and latitude data of each power station. In order to improve the calculation accuracy, the system adopts the great circle distance calculation method, which takes into account the curvature characteristics of the earth's surface and can obtain the actual distance between two geographic coordinate points. In the distance calculation process, the system constructs a matrix structure. The number of rows of the matrix corresponds to the number of power stations in the upstream power station group, and the number of columns corresponds to the number of power stations in the downstream power station group. For each element in the matrix, the system calculates the great circle distance between the corresponding upstream power station and the downstream power station. In this way, each element in the matrix accurately records the actual geographic distance between a pair of upstream and downstream power stations. The distance matrix constructed in this way has the characteristics of completeness and accuracy. Each element of the matrix contains accurate distance information, which provides an important basis for the subsequent calculation of extreme weather propagation timing. At the same time, the design of the matrix structure facilitates the system to quickly access and process distance data, thereby improving the calculation efficiency. This matrix construction method based on actual geographic distance not only takes into account the influence of the earth's curvature and improves the accuracy of distance calculation, but also provides a reliable spatial reference for the subsequent calculation of propagation timing in combination with extreme weather movement parameters. Through this systematic description of spatial relationships, the time process of extreme weather propagating from upstream power plant groups to downstream power plant groups can be predicted more accurately.

[0049] Step 304: Based on the average moving speed and average moving direction in the extreme weather moving parameters, the effective propagation speed of the extreme weather in the projection direction of the distance matrix is ​​calculated.

[0050] Specifically, the system first calculates the direction of the connection between each pair of upstream and downstream power stations according to the geographical location information of the upstream and downstream power stations in the distance matrix. For each distance value in the distance matrix, the system calculates the angle between the corresponding power station connection line and the due north direction as the power station connection direction angle. Then, the average moving direction in the extreme weather movement parameter is compared with the power station connection direction, and the angle between the two directions is calculated. This angle reflects the degree of deviation between the movement path of the weather system and the power station connection line. After determining the direction angle, the system uses the average moving speed for projection calculation. By multiplying the average moving speed by the cosine value of the direction angle, the speed component of the extreme weather in the power station connection direction, that is, the effective propagation speed, is obtained. This calculation method takes into account the influence of directional deviation on the propagation effect. When the moving direction of the weather system is exactly the same as the power station connection direction, the effective propagation speed is the largest; when there is a directional deviation, the effective propagation speed will decrease accordingly. The effective propagation speed calculated in this way is more in line with the actual physical process. It not only reflects the movement characteristics of the weather system, but also takes into account the influence of the spatial position relationship on the propagation effect, making the subsequent propagation timing prediction more accurate. For each distance value in the distance matrix, the system calculates the corresponding effective propagation speed to form a complete speed projection data set, which provides a reliable speed basis for the subsequent calculation of extreme weather propagation timing. This speed calculation method that takes directional factors into account improves the accuracy of propagation timing prediction.

[0051] Step 305: Determine the estimated arrival time of the extreme weather from the upstream power station group to each photovoltaic power station in the downstream power station group based on the effective propagation speed and the distance matrix; sort the estimated arrival times in time sequence to obtain the extreme weather propagation time sequence.

[0052] Specifically, the system first uses the distance data between each pair of upstream and downstream power stations recorded in the distance matrix, combined with the calculated effective propagation speed, to calculate the estimated arrival time of extreme weather from each power station in the upstream power station group to each power station in the downstream power station group. The calculation process takes into account multiple propagation paths from different upstream power stations. For each power station in the downstream power station group, the earliest estimated arrival time is selected as the impact time of the power station. After obtaining the estimated arrival time of all downstream power stations, the system sorts these times. The starting time point of the sorting is the initial moment when the extreme weather affects the upstream power station group, and the end time point is the estimated arrival time of the last downstream power station. Through this sorting, the system establishes a complete time series, which clearly shows the time sequence of extreme weather affecting each power station in the downstream power station group in turn. The extreme weather propagation time series determined in this way is systematic and practical. It not only considers the influence of spatial distance and propagation speed, but also ensures the accuracy of time series prediction through multi-path comparison. This time series calculation method based on physical characteristics can provide a reliable time reference for power prediction of downstream power station groups.

[0053] Step 104: Combine the power variation data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second period.

[0054] Among them, the second period refers to the time range when the extreme weather system affects the downstream power station group, specifically the entire time period from when the weather system begins to affect the first downstream power station to when it completely leaves the last downstream power station.

[0055] Specifically, the system first determines the time range of the second period based on the extreme weather propagation sequence. The start time of the second period is set to the estimated arrival time of the earliest affected downstream power station, and the end time is determined according to the estimated arrival time of the last downstream power station and the expected duration of the impact. During this period, the system will predict the power changes of each downstream power station in sequence according to the propagation sequence. During the prediction process, the system analyzes the power change data of the upstream power station group in the first period and extracts the key features of the power change, including the power change amplitude and the duration of the change. Considering the attenuation effect of the weather system during the propagation process, the system makes a distance correction to the power change amplitude according to the distance between the upstream and downstream power stations. The farther the distance, the smaller the expected power change amplitude is. This correction takes into account the natural attenuation characteristics of the weather system intensity with the propagation distance. At the same time, the system also needs to consider the actual characteristics of the downstream power station group, such as power station capacity, component type and installation method, and make appropriate adjustments to the prediction results. This prediction method that considers the characteristics of the power station can better adapt to the actual conditions of different power stations and improve the accuracy of the prediction. According to the propagation sequence, the system applies the corrected power change characteristics to each downstream power station in sequence, thereby obtaining the power prediction results of each power station in the second period. Through this forecasting method combined with time series, the system can accurately reflect the impact of extreme weather on downstream power stations. The forecast results not only include the amplitude information of power changes, but also accurately describe the time when the changes occur, providing comprehensive decision support for power station operation and management.

[0056] Based on the above embodiment, as an optional embodiment, in step 104: combining the power change data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second period, this step may also include the following steps: Step 401: extract the power change amplitude and change duration of the upstream power station group in the first period from the power change data; determine the time interval for the extreme weather to reach each photovoltaic power station in the downstream power station group according to the extreme weather propagation sequence.

[0057] Specifically, the system first extracts the power change characteristics of the upstream power station group in the first period from the power change data. By analyzing the power curve, the system identifies the moment when the power begins to change significantly and the moment when it returns to normal, and calculates the duration of the change. At the same time, by calculating the ratio of the maximum deviation of the power change to the nominal power, the power change amplitude is obtained. These characteristic parameters reflect the intensity and persistence of the impact of extreme weather on photovoltaic power stations. When determining the time interval, the system calculates the time difference from the impact of extreme weather on the upstream power station group to each photovoltaic power station in the downstream power station group based on the extreme weather propagation time series. Specifically, the system calculates the time difference of the estimated time of arrival at each downstream power station based on the initial moment when the extreme weather affects the upstream power station group. This time interval calculation method based on the propagation time series takes into account the actual propagation characteristics of the weather system. This feature extraction and time interval calculation method has strong practicality. The extraction process of power change characteristics takes into account the complete process of power change, including not only the amplitude of change but also the duration, so that the prediction results can more comprehensively reflect the impact of extreme weather. At the same time, the time interval calculated based on the propagation time series provides an accurate time reference for subsequent power prediction, so that the system can accurately predict the specific moment when extreme weather affects each downstream power station.

[0058] Step 402: performing distance correction on the power variation amplitude based on each time interval, and the target power variation amplitude corresponding to each photovoltaic power station in the downstream power station group.

[0059] Specifically, the system first calculates the actual distance that the extreme weather system propagates to each downstream power station based on the time interval and the effective propagation speed of extreme weather. This distance reflects the actual distance that the weather system travels from the upstream power station group to each downstream power station, and is an important basis for distance correction. When making distance correction, the system uses the attenuation function to correct the power change amplitude of the upstream power station group. The attenuation function takes into account the natural attenuation characteristics of the intensity of the weather system with the propagation distance. The longer the distance, the smaller the corrected power change amplitude. At the same time, the system also takes into account the influence of the terrain characteristics and meteorological conditions on the propagation path on the attenuation effect, and adapts to different propagation environments by adjusting the attenuation coefficient. For each power station in the downstream power station group, the system calculates the corresponding attenuation coefficient according to its corresponding propagation distance, and multiplies this coefficient by the original power change amplitude to obtain the target power change amplitude of the power station. This correction method based on physical characteristics can better reflect the spatial variation law of the intensity of extreme weather impact. The target power change amplitude obtained by this distance correction method is more in line with the actual situation. It not only takes into account the initial impact intensity of the weather system, but also reflects the attenuation effect during the propagation process, making the prediction results more accurate.

[0060] Step 403: For each photovoltaic power station in the downstream power station group, sum the target power change amplitude and the nominal power corresponding to the photovoltaic power station to obtain the photovoltaic power of the photovoltaic power station in the second time period; and take the average power of the photovoltaic power of each photovoltaic power station in the downstream power station group in the second time period as the photovoltaic power of the downstream power station group in the second time period.

[0061] Specifically, the system first processes the power forecast of each photovoltaic power station in the downstream power station group. For each power station, its nominal power is algebraically summed with the target power change amplitude calculated in the previous period to obtain the predicted power of the power station in the second period. This calculation method takes into account the power deviation caused by extreme weather. The target power change amplitude may be positive or negative, representing an increase or decrease in power, respectively. After obtaining the predicted power of each power station, the system calculates the overall photovoltaic power of the downstream power station group. By taking the arithmetic average of the predicted power of all power stations in the downstream power station group, the comprehensive photovoltaic power of the entire power station group in the second period is obtained. This average calculation method can smooth the extreme changes of a single power station and provide a more stable prediction result. This power calculation method has strong practicality and reliability. It not only takes into account the individual characteristics of each power station, but also reflects the overall performance of the entire power station group through average calculation. The prediction result not only includes the impact of extreme weather, but also maintains the correlation with the actual power generation capacity of the power station, making the prediction more accurate.

[0062] Reference Figure 2 , a photovoltaic power prediction system for a photovoltaic power station provided in an embodiment of the present application, the system comprises: a power station group division module, a data receiving module, a transmission timing determination module, and a photovoltaic power prediction module, wherein: A power station group division module is used to obtain spatial location information of multiple photovoltaic power stations in a target area, and divide each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial location information; A data receiving module, used for receiving power change data and meteorological data of an upstream power station group in a first period; A propagation timing determination module is used to calculate extreme weather movement parameters based on meteorological data when power change data and meteorological data meet preset extreme weather determination conditions, and determine the extreme weather propagation timing according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; The photovoltaic power prediction module is used to combine the power change data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second period.

[0063] On the basis of the above-mentioned embodiment, the power station group division module is also used to extract the altitude and the dominant wind direction of each photovoltaic power station from the spatial position information, the dominant wind direction including the upwind direction and the downwind direction; based on each altitude, determine the height difference between each photovoltaic power station, and according to each position, determine the distance difference between each photovoltaic power station; perform weighted summation on the height difference and the distance difference between each photovoltaic power station to obtain the geographical correlation between each photovoltaic power station; divide a plurality of photovoltaic power stations whose geographical correlation is greater than the correlation threshold and whose dominant wind direction is the upwind direction into an upstream power station group, and divide a photovoltaic power station whose geographical correlation with each photovoltaic power station in the upstream power station group is greater than the correlation threshold and whose dominant wind direction is the downwind direction into a downstream power station group.

[0064] Based on the above embodiments, the propagation timing determination module is also used to obtain the total power change rate of the power change data and the wind speed and cloud cover in the meteorological data; when the absolute value of the power change rate is greater than the change rate threshold, and the wind speed is greater than the wind speed threshold or the cloud cover is greater than the cloud cover threshold, it is determined that the power change data and the meteorological data meet the preset extreme weather judgment conditions.

[0065] Based on the above embodiment, the propagation timing determination module is also used to obtain cloud movement trajectory data in meteorological data; based on a preset time window and cloud movement trajectory data, the average moving direction and average moving speed of the cloud movement trajectory are calculated; and the average moving direction and average moving speed are used as extreme weather movement parameters.

[0066] On the basis of the above embodiments, the propagation timing determination module is also used to construct a distance matrix between the upstream power station group and the downstream power station group based on the relative position between the upstream power station group and the downstream power station group; based on the average moving speed and the average moving direction in the extreme weather movement parameters, calculate the effective propagation speed of the extreme weather in the projection direction of the distance matrix; determine the estimated arrival time of the extreme weather from the upstream power station group to each photovoltaic power station in the downstream power station group according to the effective propagation speed and the distance matrix; and sort the estimated arrival times in time to obtain the extreme weather propagation timing.

[0067] On the basis of the above embodiments, the photovoltaic power prediction module is also used to extract the power change amplitude and change duration of the upstream power station group in the first time period from the power change data; determine the time interval for the extreme weather to reach each photovoltaic power station in the downstream power station group according to the extreme weather propagation sequence; perform distance correction on the power change amplitude based on each time interval, and obtain the target power change amplitude corresponding to each photovoltaic power station in the downstream power station group; for each photovoltaic power station in the downstream power station group, sum the target power change amplitude and the nominal power corresponding to the photovoltaic power station to obtain the photovoltaic power of the photovoltaic power station in the second time period; and use the average power of the photovoltaic power of each photovoltaic power station in the downstream power station group in the second time period as the photovoltaic power of the downstream power station group in the second time period.

[0068] Based on the above embodiment, the power station group division module is also used to divide the photovoltaic power stations in the target area that are not in the upstream power station group and the downstream power station group into a reference power station group; obtain the reference photovoltaic power of each photovoltaic power station in the reference power station group; when there is a photovoltaic power station in the reference power station group whose reference photovoltaic power is greater than the photovoltaic power threshold, generate early warning information and send the early warning information to the terminal of the person in charge of the photovoltaic power station in the target area.

[0069] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0070] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0071] The communication bus 302 is used to realize the connection and communication between these components.

[0072] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0073] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0074] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0075] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a photovoltaic power prediction method for a photovoltaic power station.

[0076] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a photovoltaic power prediction method of a photovoltaic power station in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0077] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0081] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0082] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0083] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A photovoltaic power prediction method for a photovoltaic power station, characterized in that: include: Acquire spatial location information of a plurality of photovoltaic power stations in a target area, and divide each of the photovoltaic power stations into an upstream power station group and a downstream power station group based on the spatial location information; Receiving power change data and meteorological data of the upstream power station group in a first period; When the power change data and the meteorological data meet the preset extreme weather determination conditions, the extreme weather movement parameters are calculated based on the meteorological data, and the extreme weather propagation timing is determined according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; The power variation data and the extreme weather propagation time series are combined to predict the photovoltaic power of the downstream power station group in the second time period.

2. The photovoltaic power prediction method of a photovoltaic power station according to claim 1, characterized in that: The step of dividing each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial position information includes: Extracting the altitude and the prevailing wind direction of each photovoltaic power station from the spatial position information, wherein the prevailing wind direction includes an upwind direction and a downwind direction; Based on the altitudes, determining the height difference between the photovoltaic power stations, and according to the locations, determining the distance difference between the photovoltaic power stations; Performing weighted summation on the height difference and the distance difference between the photovoltaic power stations to obtain the geographical correlation between the photovoltaic power stations; A plurality of photovoltaic power stations whose geographical correlation is greater than a correlation threshold and whose dominant wind direction is upwind are divided into an upstream power station group, and a photovoltaic power station whose geographical correlation with each photovoltaic power station in the upstream power station group is greater than the correlation threshold and whose dominant wind direction is downwind is divided into a downstream power station group.

3. The photovoltaic power prediction method of a photovoltaic power station according to claim 1, characterized in that: When the power change data and the meteorological data meet the preset extreme weather determination conditions, the method includes: Obtaining a total power change rate of the power change data and a wind speed and cloud cover in the meteorological data; When the absolute value of the power change rate is greater than the change rate threshold, and the wind speed is greater than the wind speed threshold or the cloud cover is greater than the cloud cover threshold, it is determined that the power change data and the meteorological data meet the preset extreme weather determination condition.

4. The photovoltaic power prediction method of a photovoltaic power station according to claim 1, characterized in that: The step of calculating extreme weather movement parameters based on the meteorological data includes: Obtaining cloud movement trajectory data from the meteorological data; Based on the preset time window and the cloud movement trajectory data, the average moving direction and average moving speed of the cloud movement trajectory are calculated; The average moving direction and the average moving speed are used as extreme weather movement parameters.

5. The photovoltaic power prediction method of a photovoltaic power station according to claim 1, characterized in that: Determining the extreme weather propagation time sequence according to the extreme weather movement parameter and the relative position between the upstream power station group and the downstream power station group includes: Based on the relative position between the upstream power station group and the downstream power station group, construct a distance matrix between the upstream power station group and the downstream power station group; Based on the average moving speed and the average moving direction in the extreme weather moving parameters, calculating the effective propagation speed of the extreme weather in the projection direction of the distance matrix; Determine, according to the effective propagation speed and the distance matrix, an estimated arrival time of the extreme weather from the upstream power station group to each photovoltaic power station in the downstream power station group; The estimated arrival times are sorted in time series to obtain the extreme weather propagation time series.

6. The photovoltaic power prediction method of a photovoltaic power station according to claim 1, characterized in that: The combining the power variation data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second period includes: Extracting the power change amplitude and change duration of the upstream power station group in the first time period from the power change data; Determining the time interval for the extreme weather to reach each photovoltaic power station in the downstream power station group according to the extreme weather propagation time sequence; Performing distance correction on the power variation amplitude based on each of the time intervals, and the target power variation amplitude corresponding to each photovoltaic power station in the downstream power station group; For each photovoltaic power station in the downstream power station group, summing the target power change amplitude and the nominal power corresponding to the photovoltaic power station to obtain the photovoltaic power of the photovoltaic power station in the second time period; The average power of the photovoltaic power of each photovoltaic power station in the downstream power station group in the second time period is used as the photovoltaic power of the downstream power station group in the second time period.

7. The photovoltaic power prediction method of a photovoltaic power station according to claim 2, characterized in that: After dividing each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial position information, the method further includes: The photovoltaic power stations in the target area that are not in the upstream power station group and the downstream power station group are divided into a reference power station group; Obtaining a reference photovoltaic power of each photovoltaic power station in the reference power station group; When there is a photovoltaic power station in the reference power station group whose reference photovoltaic power is greater than the photovoltaic power threshold, an early warning message is generated and sent to a terminal of a person in charge of the photovoltaic power station in the target area.

8. A photovoltaic power prediction system for a photovoltaic power station, characterized in that: The system comprises: A power station group division module, used to obtain spatial location information of multiple photovoltaic power stations in a target area, and divide each photovoltaic power station into an upstream power station group and a downstream power station group based on the spatial location information; A data receiving module, used for receiving power change data and meteorological data of the upstream power station group in a first period; a propagation timing determination module, configured to calculate extreme weather movement parameters based on the meteorological data when the power change data and the meteorological data meet the preset extreme weather determination conditions, and determine the extreme weather propagation timing according to the extreme weather movement parameters and the relative position between the upstream power station group and the downstream power station group; The photovoltaic power prediction module is used to combine the power change data and the extreme weather propagation time series to predict the photovoltaic power of the downstream power station group in the second time period.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the photovoltaic power prediction method for a photovoltaic power station as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the photovoltaic power prediction method for a photovoltaic power station according to any one of claims 1 to 7 is executed.

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