A Simulation Evaluation Method and System for New Energy Power Stations Based on Multi-Data Fusion

Abstract

A simulation evaluation method and system for new energy power plants based on multi-data fusion, relating to the field of data processing technology, is disclosed. The method includes: acquiring environmental parameters of the area where the target new energy power plant is located and layout parameters of multiple power generation units within the target new energy power plant; constructing a three-dimensional simulation interface of the target new energy power plant based on the layout parameters of each power generation unit, and generating a first power generation simulation curve in the three-dimensional simulation interface based on the rated power and environmental parameters of each power generation unit; receiving power generation data from the target new energy power plant and generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; combining the first and second power generation simulation curves to generate a power-to-capacity (HVC) simulation evaluation curve for the target new energy power plant, and displaying the HVC simulation evaluation curve on the three-dimensional simulation interface. Implementing the technical solution provided in this application achieves the effect of improving the accuracy of new energy power plant simulation evaluation.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a simulation evaluation method and system for new energy power stations based on multi-data fusion. Background Technology

[0002] With the growing global demand for renewable energy, the planning, construction, and operation and maintenance of new energy power plants, especially wind and solar power plants, have become increasingly important. To optimize the design of these plants and improve their operational efficiency, simulation and evaluation technology for new energy power plants has emerged. This technology aims to predict and verify the commissioning performance of power plants under different application conditions through high-precision simulations, thereby guiding actual construction and adjustment strategies.

[0003] Currently, existing simulation evaluation methods for renewable energy power plants typically involve collecting historical power generation data from these plants over a past period to simulate their operational status and presenting the results in a report. However, in practical applications, the operational status of renewable energy power plants is influenced by various conditions. Considering only a single dimension of power generation data for simulation evaluation often fails to reflect the actual operational status of the power plants, leading to biased simulation evaluation results and reducing the accuracy of the simulation evaluation. Summary of the Invention

[0004] This application provides a simulation evaluation method, system, electronic device, and storage medium for new energy power plants based on multi-data fusion, which can improve the accuracy of simulation evaluation of new energy power plants.

[0005] Firstly, this application provides a simulation evaluation method for new energy power plants based on multi-data fusion, including:

[0006] Obtain environmental parameters of the area where the target new energy power station is located and layout parameters of multiple power generation units in the target new energy power station;

[0007] Based on the layout parameters of each power generation unit, a three-dimensional simulation interface for the target new energy power station is constructed, and a first power generation simulation curve is generated in the three-dimensional simulation interface based on the rated power of each power generation unit and the environmental parameters.

[0008] Receive the power generation data of the target new energy power station and generate a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface;

[0009] By combining the first power generation simulation curve and the second power generation simulation curve, a power generation ratio simulation evaluation curve for the target new energy power station is generated, and the power generation ratio simulation evaluation curve is displayed on the three-dimensional simulation interface.

[0010] A second aspect of this application provides a simulation and evaluation system for new energy power plants based on multi-data fusion, the system comprising:

[0011] The parameter acquisition module is used to acquire the environmental parameters of the area where the target new energy power station is located and the layout parameters of multiple power generation units in the target new energy power station;

[0012] The first power generation simulation curve determination module is used to construct a three-dimensional simulation interface of the target new energy power station based on the layout parameters of each power generation unit, and generate a first power generation simulation curve in the three-dimensional simulation interface based on the rated power of each power generation unit and the environmental parameters.

[0013] The second power generation simulation curve determination module is used to receive the power generation data of the target new energy power station and generate the second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface.

[0014] The commissioning ratio simulation evaluation module is used to combine the first power generation simulation curve and the second power generation simulation curve to generate the commissioning ratio simulation evaluation curve of the target new energy power station, and display the commissioning ratio simulation evaluation curve on the three-dimensional simulation interface.

[0015] A third aspect of this application provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, which, when loaded and executed by the processor, implements a simulation evaluation method for new energy power stations based on multi-data fusion.

[0016] In a fourth aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a simulation evaluation method for new energy power stations based on multi-data fusion.

[0017] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0018] By adopting the above technical solution, environmental parameters of the target new energy power station area and layout parameters of multiple power generation units within the station are obtained, comprehensively considering various factors affecting the efficiency of new energy power generation. First, the method constructs a three-dimensional simulation interface based on the layout parameters of the power generation units, realistically recreating the spatial layout of the station and ensuring consistency between the simulation model and the actual site. Then, combining the rated power of the power generation units and environmental parameters, a first power generation simulation curve is generated, accurately simulating the theoretical power generation under ideal conditions. Simultaneously, actual power generation data is received and a second power generation simulation curve is generated, enabling the simulation evaluation to reflect the actual power generation situation during operation. By combining the first and second power generation simulation curves, a production ratio simulation evaluation curve is generated, which not only intuitively displays the difference between theoretical and actual power generation but also quantitatively evaluates the actual operating efficiency of the station. Finally, the production ratio simulation evaluation curve is displayed on the three-dimensional simulation interface, providing intuitive and visual evaluation results, facilitating comprehensive analysis and decision-making by users. Through the fusion of multi-dimensional data and three-dimensional simulation display, the limitations of traditional single-data-dimensional evaluation methods are overcome, providing a more comprehensive evaluation means and thus improving the accuracy of new energy power station simulation evaluation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a simulation evaluation method for new energy power stations based on multi-data fusion, provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the structure of a new energy power station simulation and evaluation system based on multi-data fusion provided in an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0023] To enable those skilled in the art 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] This application provides a simulation and evaluation method for new energy power plants based on multi-data fusion. In one embodiment, please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the simulation and evaluation method for new energy power plants based on multi-data fusion provided in this application embodiment. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone tool application. The method can also be implemented using a microcontroller or run on a new energy power plant simulation and evaluation system based on multi-data fusion and the von Neumann architecture. Specifically, the method may include the following steps:

[0027] Step 101: Obtain the environmental parameters of the area where the target new energy power station is located and the layout parameters of multiple power generation units in the target new energy power station.

[0028] Among them, the target new energy power station refers to a centralized power generation station that uses renewable energy to generate electricity. Specifically, it can be a solar photovoltaic power station, a wind power station, or a hybrid power station of both. The target new energy power station includes multiple power generation units.

[0029] Environmental parameters refer to the quantitative indicators of external environmental factors that affect the power generation efficiency of the target new energy power station, mainly including data on light intensity, temperature, and wind speed.

[0030] A power generation unit refers to the basic functional unit in a target new energy power station that can convert renewable energy into electrical energy. Depending on the type of new energy, it may include solar photovoltaic modules and wind turbine generators.

[0031] Layout parameters refer to geometric parameters that describe the spatial distribution characteristics of each power generation unit in the target new energy power station, mainly including parameters such as spatial coordinates, orientation angle, and spacing between power generation units.

[0032] Specifically, since the power generation efficiency of renewable energy power plants is comprehensively affected by various environmental and layout factors, to achieve accurate evaluation of a target renewable energy power plant, it is first necessary to obtain the environmental parameters of the area where the target renewable energy power plant is located and the layout parameters of multiple power generation units within the target renewable energy power plant. Multiple environmental monitoring devices are deployed at different locations within the target renewable energy power plant to collect environmental parameters in real time, including data on light intensity, temperature, and wind speed. These environmental monitoring devices include light sensors, temperature sensors, and wind speed sensors. These sensors continuously collect environmental data at a preset sampling period and transmit the collected environmental data to the data processing unit via a data acquisition module. Simultaneously, the layout parameters of the power generation units are extracted from the engineering design documents of the target renewable energy power plant, including the spatial coordinates, orientation angle, and spacing of each power generation unit. These layout parameters reflect the specific installation location and relative positional relationship of the power generation units within the power plant, directly affecting the mutual shading effect between power generation units and the overall power generation efficiency. Obtaining these environmental and layout parameters provides fundamental data support for subsequently constructing a 3D simulation interface and generating power generation simulation curves, and also provides a basis for evaluating the actual power generation efficiency of the power generation units and optimizing the layout scheme. This method, based on multidimensional data acquisition, can comprehensively reflect the key factors affecting the power generation efficiency of new energy power plants, effectively improving the accuracy of subsequent simulation evaluations.

[0033] Step 102: Based on the layout parameters of each power generation unit, construct a three-dimensional simulation interface for the target new energy power station, and generate the first power generation simulation curve in the three-dimensional simulation interface based on the rated power and environmental parameters of each power generation unit.

[0034] Among them, the three-dimensional simulation interface refers to a virtual digital scene constructed based on the actual layout of the target new energy power station. This interface uses a three-dimensional coordinate system to display the spatial layout characteristics and operating status of the power station.

[0035] Rated power refers to the maximum electrical power that a power generation unit can stably output under standard test conditions, reflecting the basic power generation capacity of the power generation unit.

[0036] The first power generation simulation curve refers to the theoretical power output curve calculated based on the rated power of the power generation unit and environmental parameters. This curve reflects the ideal power generation performance of the target new energy power station under specific environmental conditions.

[0037] Specifically, to intuitively display the spatial layout of the target renewable energy power plant and evaluate its theoretical power generation performance, a three-dimensional simulation interface needs to be constructed and a first power generation simulation curve generated. First, the initial layout position is determined based on the spatial coordinates in the layout parameters of each power generation unit. Then, the shading influence coefficient of each power generation unit is calculated based on its orientation angle. During this process, the solar azimuth and elevation angles at multiple standard time points are acquired to generate a solar trajectory curve. Combined with the orientation angle of the power generation units, the shadow length and projection direction of each power generation unit at different time points are calculated. This shadow information is mapped to the planar coordinate system of the target renewable energy power plant to form a shadow overlap matrix, thereby determining the cumulative shading duration of each power generation unit. The corresponding shading influence coefficient is then determined using a pre-set coefficient mapping table. Subsequently, the initial layout position of each power generation unit is optimized and adjusted based on the spacing between power generation units and the shading influence coefficient to obtain the target layout position, thus constructing a three-dimensional simulation interface that accurately reflects the spatial distribution characteristics of the power plant. When generating the first power generation simulation curve, the system extracts light intensity, temperature, and wind speed data from environmental parameters. It then determines the corresponding environmental influencing factors using a pre-defined environmental parameter mapping table, corrects the calibrated conversion efficiency of the power generation units, and obtains the target power conversion efficiency. The rated power of each power generation unit is multiplied by its corresponding target power conversion efficiency to calculate the theoretical power output value. These output values ​​are then mapped to the coordinate system of the 3D simulation interface, ultimately generating the first power generation simulation curve. This simulation method based on multi-parameter optimization accurately reflects the power generation performance of the power station under ideal conditions, providing a reference benchmark for subsequent comparative analysis with actual power generation data.

[0038] Based on the above embodiments, as an optional embodiment, step 102: constructing a three-dimensional simulation interface for the target new energy power station according to the layout parameters of each power generation unit, this step may further include the following steps:

[0039] Step 201: Determine the spatial coordinates, orientation angle, and spacing between power generation units in each layout parameter; determine the corresponding initial layout position based on the spatial coordinates of each power generation unit, and calculate the shading influence coefficient of each power generation unit based on the orientation angle of each power generation unit.

[0040] The spatial coordinates are represented by a three-dimensional Cartesian coordinate system (X, Y, Z), where the X and Y axes define the position of the power generation unit in the station plane, and the Z axis represents the installation height of the power generation unit. The spatial coordinates are used to determine the precise position of each power generation unit in the station and are the basic data for constructing the three-dimensional simulation interface.

[0041] Orientation angle describes the spatial orientation characteristics of a power generation unit, and the orientation angle directly affects the efficiency of the power generation unit in receiving renewable energy.

[0042] The spacing between power generation units refers to the minimum distance requirement between adjacent power generation units, including row spacing and column spacing.

[0043] Specifically, to accurately construct the 3D simulation interface of the target renewable energy power station and optimize the layout of power generation units, it is first necessary to clarify the specific spatial location and installation method of each power generation unit. Layout parameters for each power generation unit are extracted from the power station design documents, including defining the spatial coordinates (X, Y, Z) of the power generation unit's position in 3D space, determining the orientation angle of the power generation unit's renewable energy reception direction, and the minimum spacing requirements to ensure normal operation of the power generation units. For solar photovoltaic modules, the orientation angle includes the azimuth angle (usually with due south as the 0-degree reference) and the tilt angle (the angle with the horizontal plane); for wind turbine generators, the windward direction of the rotor is mainly considered. Based on this spatial coordinate information, the system first marks the initial layout position of each power generation unit in the 3D coordinate system. Subsequently, considering that mutual shading between power generation units will affect power generation efficiency, it is necessary to calculate the shading impact coefficient based on the orientation angle of each power generation unit. By combining parameters such as the solar azimuth angle and altitude angle to generate a solar trajectory curve, the shadow projection of the power generation units at different time points is analyzed to determine the shadow length and projection direction of each power generation unit at the standard time point. By mapping this shadow information onto the station's planar coordinate system and constructing a shadow overlap matrix, the cumulative shading duration of each power generation unit can be calculated. The corresponding shading impact coefficient is then determined using a pre-defined coefficient mapping table. This layout analysis method, based on spatial geometry and illumination patterns, can effectively assess the mutual influence between power generation units.

[0044] Based on the above embodiments, as an optional embodiment, step 201, which calculates the shading impact coefficient of each power generation unit based on the orientation angle of each unit, may further include the following steps:

[0045] Step 211: Obtain the solar azimuth and elevation angles at multiple standard time points, and generate solar trajectory curves based on the solar azimuth and elevation angles at each standard time point; determine the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angle of each power generation unit and the solar trajectory curve.

[0046] The solar azimuth angle is the angle between the projection of sunlight onto the horizontal plane and the direction of true north. With true north as the reference direction, values ​​are negative to the east and positive to the west. At noon, when the sun is due south, the solar azimuth angle is 0 degrees. This angle reflects the relative direction of the sun on the horizontal plane and directly affects the direction of the shadow cast by the power generation unit.

[0047] The solar altitude angle is the angle between the sun's rays and the horizontal plane, ranging from 0 to 90 degrees. The altitude angle is 0 degrees when the sun is above the horizon and 90 degrees when it is at the zenith. The solar altitude angle varies with time and season, reaching its maximum on the summer solstice and its minimum on the winter solstice, directly affecting the length of the shadow cast by the power generation unit.

[0048] The solar trajectory curve is a spatial curve that describes the apparent motion of the sun in the sky. This curve is formed by connecting the solar position data of multiple standard time points and reflects the sun's movement pattern from sunrise to sunset.

[0049] The shadow length refers to the actual length of the shadow area formed by a power generation unit on the ground or other power generation unit surfaces under sunlight.

[0050] The projection direction refers to the spatial direction in which the shadowed area extends, and it is directly related to the solar azimuth and the orientation angle of the power generation unit. The projection direction is usually expressed as the angle with true north, and it changes dynamically with the solar azimuth.

[0051] Specifically, to accurately assess the shading effects between power generation units, it is necessary to first simulate the sun's trajectory across the sky and calculate the shadow cast by the power generation units accordingly. Specifically, based on the geographical coordinates (longitude and latitude) of the target renewable energy power station, the system selects typical dates of key solar terms such as the spring equinox, summer solstice, autumn equinox, and winter solstice, setting standard time points at fixed time intervals (e.g., 1 hour) on each typical date. For each standard time point, astronomical algorithms are used to calculate the sun's azimuth (the angle between the sun's projection and true north) and altitude (the angle between the sun and the horizon). These angular data reflect the sun's spatial position at a specific moment. The azimuth and altitude data of all standard time points are continuously mapped onto a spherical coordinate system, forming a solar trajectory curve describing the sun's all-weather movement. Subsequently, combining the orientation angles (including azimuth and tilt angles) of each power generation unit, based on geometric optics principles, the angle of incidence of sunlight on the plane of the power generation unit is calculated, and the shadow length of the power generation unit at each standard time point is derived based on its physical dimensions. Simultaneously, the projection direction of the shadow is determined based on the solar azimuth angle and the spatial relationship between the power generation units. This shadow analysis method based on astronomical geometry can accurately simulate the shading characteristics of power generation units at different times of the year, providing basic data for the subsequent construction of the shadow overlap matrix, and ultimately achieving a quantitative assessment of the shading impact.

[0052] Step 221: Map the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy power station to obtain the shadow overlap matrix.

[0053] Among them, the shadow overlap matrix is ​​a two-dimensional data structure used to characterize the shading relationship between power generation units in the target new energy power station.

[0054] Specifically, to quantitatively assess the shading relationships between power generation units, it is necessary to convert the shadow projection features in three-dimensional space into overlap analysis on a two-dimensional plane. First, a planar coordinate system for the target renewable energy power station is established. Using the feature points of the station boundary as a reference, an XOY planar projection mesh is constructed, with the mesh density determined based on the size characteristics of the power generation units. For each standard time point, the system maps the shadow length and projection direction of each power generation unit to this planar coordinate system through geometric projection transformation. The transformation process considers the influence of terrain undulations on the shadow shape, and elevation correction ensures accurate positioning of the shadow area. In the planar coordinate system, the shadow area of ​​each power generation unit is represented as a polygon that changes over time, with its vertex coordinates calculated from the shadow length and projection direction. The shadow polygons from all standard time points are superimposed onto the same planar coordinate system to construct an M×N shadow overlap matrix, where M represents the number of power generation units and N represents the number of standard time points. Each element in the matrix records whether the shadow of a power generation unit overlaps with the positions of other power generation units at the corresponding time point, and the area ratio of the overlapping region. This matrix analysis method based on planar projection can efficiently identify the mutual shading relationships between power generation units, providing data support for calculating the shading impact coefficient. By analyzing the temporal characteristics of the shadow overlap matrix, the shading status of any power generation unit within the site at different times can be clearly understood, providing accurate quantitative basis for subsequent layout optimization. In particular, this matrix can also be visualized in a 3D simulation interface, helping operation and maintenance personnel intuitively understand the spatial distribution characteristics of shading impact.

[0055] Step 231: Based on the shadow overlap matrix, determine the cumulative shading duration of each power generation unit, and determine the shading impact coefficient corresponding to each cumulative shading duration in the preset coefficient mapping table.

[0056] The cumulative shading duration refers to the total duration during which a power generation unit is affected by shading from other power generation units within the selected standard time range. When the element value in the shadow overlap matrix exceeds the preset shading threshold, it indicates that there is effective shading at that time point. The time intervals of these time points are weighted and summed to obtain the cumulative shading duration.

[0057] The pre-defined coefficient mapping table is a data structure that establishes the correspondence between cumulative occlusion duration and occlusion impact coefficient, used to convert time-dimensional occlusion characteristics into dimensionless impact coefficients. This mapping table is based on historical operational data statistics and uses piecewise functions or lookup tables to define the occlusion impact coefficients corresponding to the cumulative occlusion duration in different intervals.

[0058] Specifically, to convert the shadow overlap matrix into a quantitative indicator usable for layout optimization, it is necessary to calculate the cumulative shading duration for each power generation unit and establish a mapping relationship with the shading impact coefficient. First, a time-series analysis is performed on the shadow overlap matrix to statistically analyze the time periods during which each power generation unit is affected by shading at all standard time points. When the value of a matrix element is greater than a preset shading threshold, such as an area overlap ratio exceeding 10%, it is considered that there is effective shading at that time point. By accumulating the time intervals of all effective shading time points, the cumulative shading duration for each power generation unit is obtained. Considering the different degrees of shading impact at different times, the system weights the shading duration, giving higher weights to key periods (such as periods with high solar radiation intensity) and lower weights to peripheral periods (such as early morning and evening). Subsequently, the weighted cumulative shading duration is input into a preset coefficient mapping table. This mapping table, established based on historical operating data and expert experience, defines a non-linear correspondence between shading duration and impact coefficient. The shading impact coefficient for each power generation unit is obtained through table lookup or interpolation. This method, based on time-series accumulation and mapping transformation, simplifies complex shading phenomena into a single evaluation index, facilitating quantitative comparison and decision-making during layout optimization. Specifically, the coefficient mapping table is designed to consider the shading sensitivity of different types of power generation units, employing different mapping rules for solar photovoltaic modules and wind turbine generators to ensure that the calculated shading impact coefficients accurately reflect actual power generation performance losses.

[0059] Step 202: Combine the spacing between each power generation unit and the shading influence coefficient to adjust the initial layout position of each power generation unit to obtain the corresponding target layout position.

[0060] The shading impact coefficient is a dimensionless parameter that quantifies the degree of mutual shading between power generation units. Its value typically ranges from 0 to 1, where 0 represents no shading impact and 1 represents complete shading. Specifically, the shading impact coefficient is a comprehensive index calculated by analyzing the shadow projection of power generation units at different time points and combining it with the cumulative shading duration.

[0061] The initial layout location refers to the installation location of each power generation unit in three-dimensional space, which is initially determined based on the spatial coordinates in the site design documents.

[0062] Specifically, to reduce the mutual shading impact between power generation units and improve the overall power generation efficiency of the power plant, the initial layout of the power generation units needs to be optimized. First, based on the shading impact coefficient calculated in the previous steps, combinations of power generation units with severe shading impact are identified. For these power generation units, their spatial positions are adjusted through iterative optimization while meeting the minimum power generation unit spacing constraint. During the adjustment process, the system uses the power generation unit spacing as a hard constraint to ensure that the distance between adjacent power generation units is not less than the design requirement. Simultaneously, the shading impact coefficient is used as the optimization objective; by changing the spatial coordinates of the power generation units, the system seeks a layout scheme that minimizes the overall shading impact. For solar photovoltaic modules, the main consideration is adjusting the row and column spacing, especially in areas with significant terrain undulations, where comprehensive optimization based on terrain characteristics is also necessary. For wind turbine generators, the focus is on the spacing between units in the prevailing wind direction to avoid significant wake interference from upwind units to downwind units. Through multiple rounds of iterative optimization, the target layout position for each power generation unit is finally determined, minimizing the overall shading loss of the power plant. This layout optimization method based on multiple constraints can not only improve the theoretical power generation efficiency of the power station, but also provide a more reasonable layout scheme for actual engineering construction, ensuring that the power station can achieve the expected power generation performance after completion.

[0063] Step 203: Based on the target layout location of each power generation unit, generate a three-dimensional simulation interface for the target new energy power station.

[0064] The target layout location refers to the optimal installation location of the power generation unit, which is finally determined after optimizing and adjusting the initial layout location and taking into account the spacing between power generation units and the shading impact coefficient.

[0065] Specifically, to showcase the spatial layout characteristics of the target new energy power station and provide a visualized operation monitoring platform, a 3D simulation interface needs to be built based on the optimized target layout location. First, a basic terrain model of the station is established in the 3D modeling environment, integrating the site's terrain elevation data and surface feature information into the coordinate system. Then, according to the target layout location of each power generation unit, a 3D model of the power generation unit is constructed at the corresponding spatial coordinate points. For solar photovoltaic modules, the support structure and module tilt angle need to be accurately reproduced; for wind turbine generators, the geometric features of key components such as the rotor, nacelle, and tower need to be represented. After completing the basic model construction, the system configures independent data tags for each power generation unit to display real-time operating parameters, including power output values, environmental parameters, and equipment status. Simultaneously, interactive control functions are integrated into the 3D simulation interface, supporting operations such as scene roaming, perspective switching, and target tracking, facilitating operation and maintenance personnel to observe the station layout from different angles. In particular, the system can also overlay and display power generation simulation curves and environmental parameter distribution cloud maps in the 3D scene, achieving spatial visualization of the data. This simulation interface, based on 3D visualization technology, not only helps maintenance personnel intuitively understand the spatial layout of the site, but also serves as an important tool for operation monitoring and efficiency evaluation, providing strong support for the daily operation and maintenance management of the site. By updating operational data and status information in real time within the 3D scene, maintenance personnel can quickly identify anomalies, promptly diagnose and handle faults, and improve the efficiency of site operation and maintenance.

[0066] Based on the above embodiments, as an optional embodiment, step 102: generating a first power generation simulation curve in the three-dimensional simulation interface according to the rated power and environmental parameters of each power generation unit, this step may further include the following steps:

[0067] Step 204: For each power generation unit, extract the light intensity data, temperature data, and wind speed data from the environmental parameters, and determine the environmental impact factors corresponding to the light intensity data, temperature data, and wind speed data according to the preset environmental parameter mapping table.

[0068] The preset environmental parameter mapping table is a multi-dimensional data structure used to establish a quantitative relationship between environmental parameters such as light intensity, temperature, and wind speed and their corresponding influencing factors.

[0069] Specifically, to accurately assess the impact of environmental factors on the performance of power generation units, key environmental parameters need to be extracted and transformed. The system first extracts irradiance, temperature, and wind speed data for each power generation unit location from the site monitoring data. For solar photovoltaic modules, the focus is on the variation characteristics of irradiance, including the components of direct and diffuse radiation, and the impact of module surface temperature on power generation efficiency. For wind turbines, the focus is primarily on wind speed data at hub height and its spatiotemporal distribution characteristics. From the environmental parameter mapping table, the corresponding environmental impact factors are obtained by searching or interpolating based on the actual values ​​of different parameters. Specifically, irradiance data is mapped to an irradiance impact factor through a radiation conversion model, reflecting the deviation between actual irradiance conditions and standard test conditions; temperature data is mapped to a temperature impact factor through a temperature characteristic curve, reflecting the degree of influence of temperature changes on power generation efficiency; and wind speed data is mapped to a wind speed impact factor through a power curve, characterizing the changes in power generation performance under actual wind conditions. This multi-parameter mapping-based environmental impact assessment method can transform complex environmental conditions into standardized impact factors, facilitating comprehensive calculations in power generation efficiency assessment. In particular, the calculation of environmental impact factors takes into account the coupling effects between parameters, such as the correlation between temperature and light intensity, and the correlation between wind speed and temperature, to ensure that the assessment results are more consistent with actual operating characteristics. This quantitative processing of environmental parameters can provide a basis for correction in subsequent power generation efficiency assessments, improving the accuracy of power plant performance evaluations.

[0070] Step 205: Correct the calibrated conversion efficiency of the power generation unit based on various environmental impact factors to obtain the target power conversion efficiency; multiply the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain the theoretical power output value of each power generation unit.

[0071] The rated conversion efficiency refers to the energy conversion ratio of a power generation unit to electrical energy under standard test conditions.

[0072] The target power conversion efficiency refers to the energy conversion efficiency of a power generation unit under actual operating conditions after adjustments for environmental factors. This efficiency value is obtained by comprehensively adjusting the calibrated conversion efficiency with environmental factors such as illumination, temperature, and wind speed, reflecting the comprehensive impact of actual environmental conditions on power generation performance.

[0073] The theoretical power output value refers to the expected output power of a power generation unit calculated based on its rated power and target power conversion efficiency under current environmental conditions. This value is obtained by multiplying the rated power of the power generation unit by the target power conversion efficiency adjusted for environmental factors, and reflects the ideal power generation capacity of the equipment under actual operating conditions.

[0074] Specifically, to accurately predict the power generation performance of power generation units under actual environmental conditions, environmental factor corrections are needed to the rated conversion efficiency. First, the rated conversion efficiency of each power generation unit is obtained; this efficiency value is the baseline efficiency measured under standard test conditions. For solar photovoltaic modules, the rated conversion efficiency is typically measured under standard light intensity and standard temperature conditions; for wind turbine generators, it is measured under standard air density and rated wind speed conditions. Then, the light influence factor, temperature influence factor, and wind speed influence factor obtained in the previous steps are comprehensively calculated, and the rated conversion efficiency is corrected using a product form to obtain the target power conversion efficiency reflecting actual operating conditions. This correction process considers the coupling effect of environmental parameters and can more accurately reflect the actual performance state of the power generation unit. Finally, the rated power of each power generation unit is multiplied by the corresponding target power conversion efficiency to calculate the theoretical power output value, which reflects the expected power generation capacity of the power generation unit under current environmental conditions. This environmentally corrected power prediction method, by converting performance parameters under standard conditions into output characteristics under actual operating conditions, provides a reliable theoretical basis for power plant power generation efficiency assessment and operation optimization. In particular, by displaying the theoretical power output value in the 3D simulation interface, maintenance personnel can intuitively compare the difference between the actual power generation and the theoretical prediction value, promptly identify performance anomalies, and carry out targeted maintenance.

[0075] Step 206: Map the theoretical power output value of each power generation unit to the coordinate system of the three-dimensional simulation interface to generate the first power generation simulation curve.

[0076] Specifically, to demonstrate the theoretical power generation performance of the power generation unit, the calculated theoretical power output value needs to be visualized. The system first constructs a standardized coordinate system in the 3D simulation interface. The horizontal axis represents the time dimension, with selectable time scales such as hours, days, and months; the vertical axis represents the power output value. For each power generation unit, its theoretical power output value at different time points is converted into discrete data points on a curve through coordinate mapping. These data points are then connected using smoothing methods such as spline interpolation or Bézier curves to form a continuous first power generation simulation curve. The curve generation process considers the temporal characteristics of the data to ensure that the curve trend accurately reflects the dynamic changes in power output. For solar photovoltaic modules, the first power generation simulation curve typically exhibits obvious diurnal variation characteristics, with the peak occurring at noon; for wind turbine generators, the curve shape is mainly affected by wind conditions and may show significant fluctuations. In the 3D simulation interface, the first power generation simulation curves of different power generation units can be distinguished by different colors or line types for easy comparison and analysis. Through this visual representation, operation and maintenance personnel can quickly grasp the theoretical power generation performance of each power generation unit in the station, identify the time distribution characteristics of power output, and provide intuitive data support for operation and management decisions.

[0077] Step 103: Receive the power generation data of the target new energy power station and generate the second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface.

[0078] Among them, power generation data refers to the set of electrical parameters collected through the station monitoring system that reflect the actual operating status of the power generation unit.

[0079] The second power generation simulation curve refers to the power output change curve generated based on the measured power generation data of the target new energy power station. This curve is formed by mapping the actual active power data of the power generation unit to the coordinate system of the three-dimensional simulation interface in a time series, thus realistically reflecting the changes in the power generation performance of the equipment during operation.

[0080] Specifically, to achieve a comparative analysis of theoretical power generation performance and actual power generation, the measured power generation data of the power plant needs to be visualized. The system receives real-time actual power output data from each power generation unit within the target renewable energy power plant through data acquisition equipment. The data includes electrical parameters such as active power, reactive power, voltage, and current. The received power generation data is first preprocessed, including outlier filtering, data completion, and timestamp alignment, to ensure data continuity and reliability. Subsequently, the processed power generation data is mapped according to the same coordinate system rules as the first power generation simulation curve to generate a second power generation simulation curve characterizing the actual power generation performance. For solar photovoltaic modules, the second power generation simulation curve reflects various losses and efficiency changes during the actual photoelectric conversion process; for wind turbine generators, it reflects the power output characteristics during actual operation. In the 3D simulation interface, the second power generation simulation curve and the first power generation simulation curve use different display styles, facilitating a direct comparison of the differences between theoretical and measured values. Through this visualization method of hyperbolic comparison, maintenance personnel can quickly identify power generation performance anomalies, assess power generation efficiency losses, and promptly formulate targeted optimization measures.

[0081] Based on the above embodiments, as an optional embodiment, step 103, which generates a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface, may further include the following steps:

[0082] Step 301: Divide the power generation data into segments according to preset time intervals to obtain data samples for multiple time windows; based on the data samples of each time window, determine the cumulative value of power generation within each time window.

[0083] Specifically, to achieve a refined evaluation of power generation performance, it is necessary to divide and statistically analyze continuous power generation data over time. The system first sets appropriate time intervals based on performance evaluation requirements, such as 15 minutes, 1 hour, or 24 hours. The continuously collected power generation data is then segmented according to these time intervals, forming multiple time windows of fixed duration. Within each time window, the active power data of the power generation unit is integrated over time, converting the power value into an energy value to obtain the cumulative power generation value for that period. For solar photovoltaic modules, this segmented statistical method reflects the power generation distribution characteristics at different times, facilitating the analysis of the impact of solar radiation conditions on power generation efficiency; for wind turbine generators, it helps evaluate power generation performance under different wind conditions. During data processing, the system performs integrity checks on the data samples of each time window, using interpolation or labeling for periods with missing data to ensure the accuracy of the statistical results. Through this time-window-based cumulative statistical method, the continuous power generation process can be transformed into discrete evaluation indicators, providing more detailed data support for subsequent performance evaluation and optimization.

[0084] Step 302: Calculate the actual power output value of each time window based on the increment of the cumulative value between each time window and the adjacent time window and the preset time interval.

[0085] Specifically, to accurately assess the actual power generation performance of a power generation unit in different time windows, it is necessary to convert the cumulative power generation into power output characteristics. The system first calculates the difference in cumulative power generation between adjacent time windows, i.e., the power generation increment, which reflects the actual power generation of the unit within the current time window. Then, this power generation increment is divided by a preset time interval (converting the time unit to hours) to obtain the average actual power output value for that time window. For solar photovoltaic modules, this incremental power calculation method can eliminate the influence of the cumulative effect and more accurately reflect the actual power generation capacity in each time period; for wind turbine generators, it can reflect the average power generation level in different time periods, facilitating comparison with theoretical power output values. During the calculation process, the system verifies the validity of the data, eliminating abnormal incremental values ​​caused by factors such as equipment start-up, shutdown, and malfunctions, ensuring the reliability of the calculation results. Through this incremental analysis method, discrete power generation statistics can be converted into continuous power change characteristics, providing a more intuitive metric for power generation efficiency assessment. By analyzing the changing trends of actual power output values ​​in different time windows, it is also possible to identify equipment performance degradation or malfunction symptoms, providing a basis for predictive maintenance.

[0086] Step 303: Map each actual power output value to the coordinate system of the three-dimensional simulation interface to generate the second power generation simulation curve.

[0087] Specifically, to visualize actual power generation performance, the calculated actual power output value needs to be converted into an intuitive graphical display. The system first maps discrete data points onto the coordinate system of the 3D simulation interface, using the center moment of the time window as the horizontal axis and the corresponding actual power output value as the vertical axis. Then, a cubic spline interpolation algorithm is used to smoothly connect these discrete data points, forming a continuous second power generation simulation curve. This curve accurately reflects the power output variation characteristics of the power generation unit during actual operation. For solar photovoltaic modules, the second power generation simulation curve typically exhibits a clear diurnal periodic fluctuation, with the fluctuation amplitude significantly affected by weather conditions; for wind turbine generators, the curve shape is mainly determined by wind conditions and the unit's control strategy. In the 3D simulation interface, the second power generation simulation curve and the first power generation simulation curve are distinguished by different colors and line types, facilitating a direct comparison between theoretical expectations and actual operation. Through this curve visualization method, maintenance personnel can quickly identify power generation performance anomalies and assess actual power generation efficiency.

[0088] Step 104: Combine the first power generation simulation curve and the second power generation simulation curve to generate the commissioning ratio simulation evaluation curve of the target new energy power station, and display the commissioning ratio simulation evaluation curve on the three-dimensional simulation interface.

[0089] The commissioning ratio simulation evaluation curve is a performance evaluation curve plotted over time by calculating the ratio of actual power output to theoretical power output. The vertical axis of the curve represents the commissioning ratio of the power generation unit at each time point, reflecting the degree of conformity between the actual power generation efficiency and the theoretical expectation.

[0090] Specifically, the system first pairs the power values ​​of the first and second power generation simulation curves at the same time point, calculating the ratio of the actual power output to the theoretical power output to obtain the commissioning ratio data reflecting power generation efficiency. During the calculation process, the system verifies the validity of the power values, eliminating abnormal data caused by equipment failure or external interference to ensure the accuracy of the commissioning ratio calculation results. Subsequently, the calculated commissioning ratio data is mapped to the coordinate system of the 3D simulation interface in a time series, and a continuous commissioning ratio simulation evaluation curve is generated using a smooth interpolation method. For solar photovoltaic modules, this curve reflects the deviation between the actual photoelectric conversion efficiency and the theoretical efficiency, and can be used to evaluate module performance degradation and maintenance effectiveness; for wind turbine generators, it reflects the impact of actual operating strategies on power generation efficiency. The system sets a standard reference range for the commissioning ratio in the 3D simulation interface, using different colors to indicate when the curve falls into different ranges, facilitating quick assessment of equipment operating status by maintenance personnel. Through this visual analysis method of the commissioning ratio, power generation efficiency loss can be accurately quantified, performance optimization space can be identified, and data support can be provided for operation and maintenance decisions.

[0091] Based on the above embodiments, as an optional embodiment, step 104: generating a simulation evaluation curve for the commissioning ratio of the target new energy power station by combining the first power generation simulation curve and the second power generation simulation curve, may further include the following steps:

[0092] Step 401: Extract the theoretical power peak points of the first power generation simulation curve in multiple sampling periods and the actual power peak points of the second power generation simulation curve in each sampling period.

[0093] Specifically, to evaluate the performance of a power generation unit under optimal operating conditions, it is necessary to extract and analyze the peak characteristics of power output. The system first sets reasonable sampling periods based on power generation characteristics; for example, for solar photovoltaic modules, the period with the strongest sunlight is typically selected, while for wind turbines, the period with the best wind conditions is chosen. Within each sampling period, the system uses a peak detection algorithm to scan the first and second power generation simulation curves respectively, identifying the theoretical power peak point and the actual power peak point. To ensure the accuracy of peak detection, the system sets dynamic thresholds and time window constraints to avoid misidentifying short-term fluctuations as peak points. For solar photovoltaic modules, the peak point usually occurs around noon, reflecting the maximum photoelectric conversion efficiency; for wind turbines, the peak point corresponds to the period when the wind speed reaches the rated value. During peak extraction, the system preprocesses the data, including noise filtering and outlier removal, to ensure the accuracy of the extraction results. Through this peak-based analysis method, the maximum power generation capacity of the power generation unit can be focused on, evaluating the performance differences of the equipment under optimal operating conditions.

[0094] Step 402: Calculate the time offset and amplitude offset between the theoretical power peak point and the actual power peak point in each sampling period.

[0095] Specifically, to accurately quantify the actual performance deviation of a power generation unit, a multi-dimensional analysis of the difference between theoretical and actual power peak points is required. The system first calculates the time offset between the actual power peak point and the theoretical power peak point within each sampling period. This offset reflects the temporal differences in the power generation response. For solar photovoltaic modules, the time offset may be caused by factors such as module orientation deviation and shading; for wind turbine generators, it may stem from the influence of generator start-up characteristics and control strategies. Simultaneously, the system calculates the power amplitude offset between the two peak points, which directly reflects the degree of power generation capacity loss. During the calculation, the system employs a standardization method to convert the amplitude offset into a percentage relative to the theoretical peak, facilitating comparisons between devices of different capacities. To improve calculation accuracy, the system interpolates and fits data around the peak points to ensure the continuity and reliability of the offset. Through this two-dimensional offset analysis, the performance anomalies of the power generation unit can be comprehensively evaluated, and the specific manifestations of efficiency loss can be identified.

[0096] Step 403: Based on the time offset and amplitude offset within each sampling period, determine the production ratio coefficient for each sampling period; perform time-series superposition of the production ratio coefficients for each sampling period to obtain the production ratio simulation evaluation curve.

[0097] Specifically, the power generation ratio coefficient is a performance evaluation index calculated based on a combination of time offset and amplitude offset. This coefficient is obtained by weighted fusion of the time response difference and power output difference between the theoretical power peak point and the actual power peak point, reflecting the comprehensive performance level of the power generation unit within a specific sampling period.

[0098] Specifically, to comprehensively transform multi-dimensional performance deviations into a unified evaluation index, a production-to-input ratio (P / I ratio) calculation model based on offsets needs to be established. Specifically, the system first sets weighting coefficients for time offset and amplitude offset based on power generation characteristics, and then calculates the P / I ratio coefficient for each sampling period through weighted fusion. For solar photovoltaic modules, a large time offset indicates a misalignment in power generation timing, which reduces the P / I ratio coefficient; a significant amplitude offset directly reflects a loss in power generation efficiency. For wind turbine generators, the system dynamically adjusts the weighting configuration based on wind conditions to more accurately assess performance deviations. After calculating the P / I ratio coefficient, the system combines the coefficients from each sampling period in chronological order and uses a cubic spline interpolation algorithm to smooth the discrete P / I ratio coefficients, generating a continuous P / I ratio simulation evaluation curve. To ensure the reliability of the evaluation results, the system sets an effective value range for the P / I ratio coefficient and corrects or marks outliers. Through this comprehensive evaluation method based on multi-dimensional offsets, a continuous evaluation curve reflecting the overall performance level of the power generation unit can be obtained.

[0099] Based on the above embodiments, as an optional embodiment, step 403, which determines the production ratio coefficient for each sampling period based on the time offset and amplitude offset within each sampling period, may further include the following steps:

[0100] Step 413: Divide the time offset in each sampling period by the standard time interval to obtain the corresponding time offset coefficient; divide the amplitude offset in each sampling period by the theoretical power peak value to obtain the corresponding amplitude offset coefficient.

[0101] Specifically, to eliminate the influence of different time scales and power levels on the evaluation results, the time offset and amplitude offset need to be standardized. The system first sets a standard time interval based on power generation characteristics. For solar photovoltaic modules, the duration of the peak power generation period during the day is typically selected, while for wind turbines, the characteristic cycle of wind condition changes is considered. The time offset within each sampling period is divided by this standard time interval to obtain a dimensionless time offset coefficient, which reflects the degree of power generation response delay relative to the reference time. Simultaneously, the system divides the amplitude offset within each sampling period by the theoretical peak power of the corresponding period to obtain an amplitude offset coefficient, which characterizes the proportion of power loss relative to theoretical power generation capacity. This standardization process converts the offset characteristics of different dimensions into comparable relative quantities, facilitating subsequent comprehensive evaluation. For solar photovoltaic modules, the standardized offset coefficient better reflects the change in module performance over time; for wind turbines, it helps evaluate power generation efficiency under different wind conditions. In particular, this standardization method can also be used for performance comparison between different types of power generation units, providing a unified evaluation benchmark for overall site optimization.

[0102] Step 423: Perform a weighted summation of the time offset coefficient and amplitude offset coefficient for each sampling period to obtain the corresponding input-output ratio coefficient.

[0103] Specifically, to reasonably integrate performance deviations in both time and amplitude dimensions into a single evaluation index, a weighted comprehensive calculation model needs to be established. The system first sets weighting factors for the time offset coefficient and amplitude offset coefficient based on the characteristics and operational requirements of the power generation unit. For solar photovoltaic modules, since power generation efficiency is closely related to sunshine duration, the weight of the time offset coefficient is usually higher; for wind turbine generators, considering that power output directly reflects wind energy utilization efficiency, the weight of the amplitude offset coefficient may be greater. Within each sampling period, the system multiplies the time offset coefficient by its corresponding weight and the amplitude offset coefficient by its corresponding weight, then adds the two weighted results to obtain the commissioning ratio coefficient for that period. To ensure the rationality of the calculation results, the system dynamically optimizes the weight configuration, adjusting the weight ratio in a timely manner according to environmental conditions and operating status. Through this weighted fusion method, a comprehensive evaluation index reflecting power generation performance can be obtained. In particular, the calculation process of the commissioning ratio coefficient also considers the nonlinear characteristics of the impact of different types of deviations on power generation efficiency, using correction coefficients to calibrate the calculation results and improve the accuracy of the evaluation.

[0104] It should be noted that wind farms are an important type of power generation facility among new energy power plants. The commissioning efficiency of a wind farm is affected not only by factors such as generator layout and environmental parameters, but also by micro-site selection and turbine location. Reasonable micro-site selection helps to utilize high-quality wind sources, while appropriate turbine location can reduce the wind-solar attenuation effect, thereby maximizing the power generation potential of the wind farm.

[0105] The evaluation method for wind farms includes: First, acquiring environmental parameters such as wind speed data for the area where the target wind farm is located, and collecting layout parameters such as the spatial coordinates and orientation angles of the wind turbines. Second, when constructing a 3D simulation interface for the wind farm based on the wind turbine layout parameters, it is necessary to determine the spatial coordinates, orientation angles, and spacing between each wind turbine, and calculate the shading impact coefficient of each turbine based on these parameters, thereby adjusting the final layout position of the turbines.

[0106] The specific steps for calculating the shading impact coefficient involve acquiring wind direction data at multiple standard time points and generating wind trajectory curves based on these wind directions. Then, according to the orientation angle and wind trajectory of each turbine, the shadow length and projection direction at each time point are determined and mapped to the site's planar coordinate system to obtain the shadow overlap matrix. Finally, based on the shadow overlap matrix, the cumulative shading duration of each turbine is determined, and the corresponding shading impact coefficient is looked up in a pre-defined mapping table. Through this series of calculations, the layout of wind turbines can be reasonably optimized, avoiding excessive array shading.

[0107] On the other hand, in addition to generating theoretical power curves for wind farms using layout parameters, this method also deploys a certain number of sensors, such as wind measurement towers, to collect actual wind speed data. Using this measured data, combined with the power curves, the actual power output of the wind farm can be accurately predicted, generating a simulation curve of the wind farm's actual power generation.

[0108] Finally, by comparing the theoretical and actual power generation curves of wind farms, the time and power offsets are calculated to obtain evaluation parameters such as the commissioning ratio, which are then visualized in a three-dimensional simulation scenario, providing an intuitive decision-making basis for the operation and maintenance optimization of wind farms.

[0109] This method integrates multiple factors, including micro-site selection of wind farms, turbine layout optimization, measured wind speed data, and power prediction, enabling a more accurate and comprehensive evaluation of the actual operational performance of wind farms. It not only improves the accuracy and reliability of the evaluation but also provides an intuitive 3D visualization interface that facilitates scheme analysis and optimization decisions, offering strong technical support for the efficient planning, construction, and refined operation and maintenance of wind farms.

[0110] Reference Figure 2 This application provides a simulation and evaluation system for new energy power plants based on multi-data fusion. The system includes: a parameter acquisition module, a first power generation simulation curve determination module, a second power generation simulation curve determination module, and a production ratio simulation and evaluation module, wherein:

[0111] The parameter acquisition module is used to acquire the environmental parameters of the area where the target new energy power station is located and the layout parameters of multiple power generation units in the target new energy power station;

[0112] The first power generation simulation curve determination module is used to construct a three-dimensional simulation interface of the target new energy power station based on the layout parameters of each power generation unit, and generate the first power generation simulation curve in the three-dimensional simulation interface based on the rated power and environmental parameters of each power generation unit.

[0113] The second power generation simulation curve determination module is used to receive the power generation data of the target new energy power station and generate the second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface.

[0114] The commissioning ratio simulation evaluation module is used to combine the first power generation simulation curve and the second power generation simulation curve to generate the commissioning ratio simulation evaluation curve of the target new energy power station, and display the commissioning ratio simulation evaluation curve on the three-dimensional simulation interface.

[0115] Based on the above embodiments, the first power generation simulation curve determination module is also used to determine the spatial coordinates, orientation angles, and spacing between power generation units in each layout parameter; determine the corresponding initial layout position based on the spatial coordinates of each power generation unit, and calculate the shading influence coefficient of each power generation unit based on the orientation angle of each power generation unit; adjust the initial layout position of each power generation unit by combining the spacing between each power generation unit and each shading influence coefficient to obtain the corresponding target layout position; and generate a three-dimensional simulation interface of the target new energy power station based on the target layout position of each power generation unit.

[0116] Based on the above embodiments, the first power generation simulation curve determination module is also used to obtain the solar azimuth and elevation angles at multiple standard time points, generate a solar trajectory curve based on the solar azimuth and elevation angles at each standard time point; determine the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angle of each power generation unit and the solar trajectory curve; map the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy power station to obtain the shadow overlap matrix; determine the cumulative shading duration of each power generation unit based on the shadow overlap matrix, and determine the shading influence coefficient corresponding to each cumulative shading duration in a preset coefficient mapping table.

[0117] Based on the above embodiments, the first power generation simulation curve determination module is further used to extract light intensity data, temperature data, and wind speed data from environmental parameters for each power generation unit, and determine the environmental impact factors corresponding to the light intensity data, temperature data, and wind speed data according to a preset environmental parameter mapping table; correct the calibration conversion efficiency of the power generation unit based on each environmental impact factor to obtain the target power conversion efficiency; multiply the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain the theoretical power output value of each power generation unit; and map the theoretical power output value of each power generation unit to the coordinate system of the three-dimensional simulation interface to generate the first power generation simulation curve.

[0118] Based on the above embodiments, the second power generation simulation curve determination module is further used to segment the power generation data according to a preset time interval to obtain data samples of multiple time windows; based on the data samples of each time window, determine the cumulative value of power generation in each time window; based on the increment of the cumulative value between each time window and the adjacent time window and the preset time interval, calculate the actual power output value of each time window; and map each actual power output value to the coordinate system of the three-dimensional simulation interface to generate the second power generation simulation curve.

[0119] Based on the above embodiments, the commissioning ratio simulation evaluation module is also used to extract the theoretical power peak points of the first power generation simulation curve in multiple sampling periods and the actual power peak points of the second power generation simulation curve in each sampling period; calculate the time offset and amplitude offset between the theoretical power peak points and the actual power peak points in each sampling period; determine the commissioning ratio coefficient for each sampling period based on the time offset and amplitude offset in each sampling period; and perform time-series superposition of the commissioning ratio coefficients for each sampling period to obtain the commissioning ratio simulation evaluation curve.

[0120] Based on the above embodiments, the production ratio simulation evaluation module is also used to divide the time offset in each sampling period by the standard time interval to obtain the corresponding time offset coefficient; divide the amplitude offset in each sampling period by the theoretical power peak value to obtain the corresponding amplitude offset coefficient; and perform a weighted summation of the time offset coefficient and amplitude offset coefficient in each sampling period to obtain the corresponding production ratio coefficient.

[0121] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0122] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. 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.

[0123] The communication bus 302 is used to enable communication between these components.

[0124] 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.

[0125] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0126] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0127] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, 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 touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program based on a multi-data fusion-based simulation and evaluation method for new energy power stations.

[0128] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 that is a simulation evaluation method for new energy power stations based on multi-data fusion. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0130] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0134] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0135] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A new energy station simulation evaluation method based on multi-data fusion, characterized in that, The method comprises: obtaining environmental parameters of a region where a target new energy station is located and layout parameters of a plurality of power generation units in the target new energy station; constructing a three-dimensional simulation interface of the target new energy station according to the layout parameters of each power generation unit, and generating a first power generation simulation curve in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters; receiving power generation data of the target new energy station, and generating a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; combining the first power generation simulation curve and the second power generation simulation curve to generate a production ratio simulation evaluation curve of the target new energy station, and displaying the production ratio simulation evaluation curve in the three-dimensional simulation interface; the construction of the three-dimensional simulation interface of the target new energy station according to the layout parameters of each power generation unit comprises: determining the spatial coordinates, orientation angles and power generation unit spacings in each layout parameter; determining the initial layout positions corresponding to the spatial coordinates of each power generation unit, and calculating the shadowing influence coefficients of each power generation unit based on the orientation angles of each power generation unit; adjusting the initial layout positions of each power generation unit in combination with the power generation unit spacings and the shadowing influence coefficients to obtain the target layout positions corresponding to each power generation unit; generating the three-dimensional simulation interface of the target new energy station based on the target layout positions of each power generation unit; the generation of the first power generation simulation curve in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters comprises: for each power generation unit, extracting the light intensity data, temperature data and wind speed data from the environmental parameters, and determining the environmental influence factors corresponding to the light intensity data, temperature data and wind speed data respectively according to a preset environmental parameter mapping table; correcting the rated conversion efficiency of each power generation unit based on the environmental influence factors to obtain the target power conversion efficiency; multiplying the rated power of each power generation unit by the corresponding target power conversion efficiency to obtain the theoretical power output value of each power generation unit; mapping the theoretical power output value of each power generation unit to the coordinate system of the three-dimensional simulation interface to generate the first power generation simulation curve.

2. The new energy field station simulation evaluation method based on multi-data fusion according to claim 1, characterized in that, the calculation of the shadowing influence coefficients of each power generation unit based on the orientation angles of each power generation unit comprises: obtaining the solar azimuth and altitude angles of a plurality of standard time points, generating a solar trajectory curve based on the solar azimuth and altitude angles of each standard time point; determining the shadow length and projection direction of each power generation unit at each standard time point according to the orientation angles of each power generation unit and the solar trajectory curve; mapping the shadow length and projection direction of each power generation unit at each standard time point to the plane coordinate system of the target new energy station to obtain a shadow overlap matrix; determining the cumulative shadowing time of each power generation unit based on the shadow overlap matrix, and determining the shadowing influence coefficients corresponding to each cumulative shadowing time in a preset coefficient mapping table.

3. The new energy field station simulation evaluation method based on multi-data fusion according to claim 1, characterized in that, The generating, in the three-dimensional simulation interface, of a second power generation simulation curve corresponding to the power generation data includes: segmenting the power generation data according to a preset time interval to obtain data samples of multiple time windows; determining, based on the data samples of each of the time windows, a cumulative value of power generation within each of the time windows; calculating, based on an increment of the cumulative values between each of the time windows and an adjacent time window and the preset time interval, an actual power output value of each of the time windows; mapping each of the actual power output values to a coordinate system of the three-dimensional simulation interface to generate a second power generation simulation curve.

4. The new energy field station simulation evaluation method based on multi-data fusion according to claim 1, characterized in that, The generating, in combination of the first power generation simulation curve and the second power generation simulation curve, of a production ratio simulation evaluation curve of the target new energy station includes: extracting theoretical power peak points of the first power generation simulation curve within multiple sampling periods and actual power peak points of the second power generation simulation curve within each of the sampling periods; calculating a time offset and an amplitude offset between the theoretical power peak points and the actual power peak points within each of the sampling periods; determining, based on the time offset and the amplitude offset within each of the sampling periods, a production ratio coefficient of each of the sampling periods; superimposing, in time sequence, the production ratio coefficients of each of the sampling periods to obtain a production ratio simulation evaluation curve.

5. The new energy field station simulation evaluation method based on multi-data fusion according to claim 4, characterized in that, The determining, based on the time offset and the amplitude offset within each of the sampling periods, of the production ratio coefficient of each of the sampling periods includes: dividing the time offset within each of the sampling periods by a standard time interval to obtain a corresponding time offset coefficient; dividing the amplitude offset within each of the sampling periods by a theoretical power peak value to obtain a corresponding amplitude offset coefficient; performing weighted summation on the time offset coefficient and the amplitude offset coefficient of each of the sampling periods to obtain a corresponding production ratio coefficient.

6. A new energy station simulation evaluation system based on multi-data fusion, characterized in that, The system includes: a parameter acquisition module configured to acquire environmental parameters of a region in which a target new energy station is located and layout parameters of multiple power generation units in the target new energy station; a first power generation simulation curve determination module configured to construct a three-dimensional simulation interface of the target new energy station according to the layout parameters of each of the power generation units, and generate a first power generation simulation curve in the three-dimensional simulation interface according to rated powers of each of the power generation units and the environmental parameters; a second power generation simulation curve determination module configured to receive power generation data of the target new energy station and generate a second power generation simulation curve corresponding to the power generation data in the three-dimensional simulation interface; a production ratio simulation evaluation module configured to generate a production ratio simulation evaluation curve of the target new energy station in combination of the first power generation simulation curve and the second power generation simulation curve, and display the production ratio simulation evaluation curve in the three-dimensional simulation interface; The constructing, according to the layout parameters of each of the power generation units, of the three-dimensional simulation interface of the target new energy station includes: determining spatial coordinates, orientation angles, and power generation unit spacings in each of the layout parameters; According to the spatial coordinates of each power generation unit, a corresponding initial layout position is determined, and based on the orientation angle of each power generation unit, a shielding influence coefficient of each power generation unit is calculated; Combined with the distance between each power generation unit and the shielding influence coefficient, the initial layout position of each power generation unit is adjusted to obtain a corresponding target layout position; Based on the target layout position of each power generation unit, a three-dimensional simulation interface of the target new energy station is generated; The first power generation simulation curve is generated in the three-dimensional simulation interface according to the rated power of each power generation unit and the environmental parameters, including: For each power generation unit, the illumination intensity data, temperature data and wind speed data are extracted from the environmental parameters, and the corresponding environmental influence factors of the illumination intensity data, temperature data and wind speed data are determined according to a preset environmental parameter mapping table; Based on the environmental influence factors, the calibrated conversion efficiency of the power generation unit is corrected to obtain a target power conversion efficiency; The rated power of each power generation unit is multiplied by the corresponding target power conversion efficiency to obtain a theoretical power output value of each power generation unit; The theoretical power output value of each power generation unit is mapped to the coordinate system of the three-dimensional simulation interface to generate a first power generation simulation curve.

7. An electronic device, comprising: The electronic device 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 to enable the electronic device to perform the new energy station simulation evaluation method based on multi-data fusion according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions which, when executed, perform the new energy station simulation evaluation method based on multi-data fusion according to any one of claims 1-5.

Images