Generating capacity calculation method and system based on wind and light resources and equipment data

Abstract

The invention relates to the technical field of new energy power generation, and discloses a generating capacity calculation method and system based on wind and light resources and equipment data. The method comprises the following steps: acquiring and fusing wind and light resource data and equipment performance parameters to construct a standardized data set; based on the data set, a resource-equipment dynamic coupling potential field is established, and the potential field accurately represents the comprehensive power generation potential in the space-time dimension by fusing the resource space gradient and the equipment response characteristics; carrying out triple integral calculation on the potential field, and controlling an error by adopting a self-adaptive algorithm; and finally, realizing self-calibration of the system through model verification and parameter optimization. According to the method, the defects of insufficient data fusion, model staticization, contradiction between calculation precision and efficiency and the like of a traditional method are overcome, and the accuracy, the adaptive capacity and the engineering practical value of generating capacity calculation are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy power generation, and particularly relates to a power generation calculation method and system based on wind and light resources and equipment data. BACKGROUND

[0002] With the increasing demand for renewable energy worldwide, wind and solar energy as important clean energy, the accuracy of its power generation prediction is of great significance for the stable operation of the power grid, power dispatching optimization and energy transaction decision-making; however, wind and solar power generation has significant intermittency, volatility and uncertainty, and its power output is affected by multiple complex factors such as weather conditions and equipment performance, which brings great challenges to accurate estimation.

[0003] The existing wind and light power generation calculation method has obvious limitations: most models lack data fusion, and cannot integrate multi-source heterogeneous meteorological and equipment data; and most models use static or quasi-static assumptions, which are difficult to describe the coupling relationship between resource space-time fluctuations and equipment dynamic response, resulting in a decline in accuracy in complex scenarios; in addition, the existing method describes the key physical processes such as resource field gradient and equipment response delay roughly, and the integral calculation error is large, and also lacks an effective adaptive optimization mechanism to cope with equipment aging and climate change.

[0004] In summary, the existing technology has deficiencies in data fusion, dynamic coupling modeling, calculation accuracy and model adaptability, and is difficult to meet the engineering needs of high-precision power generation evaluation. Therefore, there is an urgent need for a new calculation method that can deeply integrate multi-source data, accurately describe dynamic coupling processes and have online optimization capabilities to support efficient consumption of renewable energy and safe and stable operation of the power grid. SUMMARY

[0005] The technical problem to be solved by the present application is the shortcomings in the prior art, and therefore we propose a power generation calculation method and system based on wind and light resources and equipment data.

[0006] In order to achieve the above object, the application adopts the following technical scheme: The power generation calculation method based on wind and light resources and equipment data includes the following steps: Step one: data acquisition and fusion processing: acquire the wind and light resource data of the target area, including the spatial and temporal distribution data of wind speed, wind direction, solar irradiance, and environmental temperature; simultaneously acquire the performance parameter data of the power generation equipment, including the wind turbine power curve, photovoltaic module efficiency characteristics, and equipment geographic location information; pre-process the acquired data to form a standardized data set; Step two: dynamic coupling potential field construction: based on the standardized data set, a resource-equipment dynamic coupling potential field is constructed through a dynamic coupling algorithm, which is a continuous field quantity in space and time, used to represent the comprehensive power generation potential at any spatial location and time point; the dynamic coupling algorithm fuses resource fluctuation characteristics and equipment response characteristics to establish a power generation capacity evaluation model considering the space-time correlation; Step three: power generation potential field integral calculation and verification: triple integral operation is performed on the dynamic coupling potential field in the target spatial region and calculation period, and the total power generation is solved through numerical calculation method; an integral error compensation mechanism is established, and an adaptive step algorithm is used to improve the calculation accuracy, and finally the calibrated total power generation estimation value is output; Step four: model verification and optimization: select the power generation data of a typical time period for model verification, establish the model to determine the optimization priority and update the model parameters in real time.

[0007] Preferably, the data acquisition and fusion processing specifically includes: wind and light resource data acquisition: acquire gridized resource data through meteorological monitoring stations, satellite remote sensing equipment, and numerical weather prediction systems, with a time resolution of not less than 1 hour and a spatial resolution of not less than 1 kilometer; equipment parameter acquisition: acquire detailed technical parameters of power generation equipment from equipment monitoring systems and manufacturer databases, including rated power, efficiency curve, and operating limit conditions; data preprocessing specifically includes: quality control of resource data, elimination of obvious outliers, and filling of missing data using Kriging interpolation method; standardization processing of equipment parameters, unification of data format and unit; establishment of space-time index to ensure the space-time matching of resource data and equipment data.

[0008] Preferably, the processing of wind and light resource data in step one further includes: establishing a space-time correlation analysis model of resource data, extracting main space-time characteristic patterns using empirical orthogonal function decomposition method; establishing a Weibull distribution model for wind speed data and a Beta distribution model for irradiance data to quantify the uncertainty characteristics of resources; using wavelet transform and Fourier analysis method to study the time-frequency characteristics of resources and identify the fluctuation rules of different time scales; establishing quality control standards for resource data, including data integrity, accuracy, and consistency check specifications.

[0009] Preferably, the processing of the equipment parameter data in step one further comprises: smoothing the fan power curve, using S-shaped function to fit the transition characteristics of the cut-in, rated and cut-out wind speed intervals; establishing a temperature correction model for the photovoltaic module efficiency, considering the influence of irradiance and ambient temperature on conversion efficiency; establishing an equipment aging model, dynamically adjusting equipment performance parameters according to operating time and maintenance records; designing an equipment parameter verification process, ensuring parameter accuracy through comparison and analysis of measured data and theoretical parameters.

[0010] Preferably, the dynamic coupling potential field construction in step two specifically comprises: a potential field initialization step: calculating the theoretical power generation of each grid point based on standardized data as the initial value of the potential field; a time-space coupling calculation step: considering the time-space correlation of resources and the dynamic response characteristics of equipment, correcting the initial potential field through coupling algorithm; a potential field optimization step: smoothing the potential field distribution using regularization method to eliminate abnormal fluctuations and ensure the physical rationality of the potential field.

[0011] Preferably, the dynamic coupling potential field is defined by the following formula: ; wherein is a spatial position vector, is a time variable; is the theoretical power generation at position and time ; is the spatial gradient module of the resource field; is the response time constant of the equipment; is the coupling strength coefficient; the theoretical power generation is calculated according to the type of equipment: for wind power equipment: , wherein is the fan power curve function, is the wind speed; for photovoltaic power generation equipment: , wherein is the irradiance, is the efficiency function, is the temperature; the spatial gradient module of the resource field is calculated using central difference method, considering wind speed gradient for wind power and irradiance gradient for photovoltaic; the response time constant of the equipment is determined according to the type of equipment, taking 5-15 seconds for the fan and 1-5 seconds for the photovoltaic inverter; the coupling strength coefficient is obtained by training historical data, with a value range of 0.1-1.0.

[0012] ​Preferably, the power generation potential field integration calculation in step three specifically includes: a numerical integration step: using Gaussian quadrature for spatial integration and Runge-Kutta method for time integration to ensure calculation accuracy and stability; an error control step: establishing an integral error estimation model, and adaptively adjusting the integral step size according to the smoothness of the potential field; processing the boundary effect, and using the mirror continuation method to reduce the integral error; a result verification step: collecting historical power generation data to establish a verification data set; calculating the deviation statistics of the predicted value and the actual value; and establishing an error correction function to systematically correct the integral results.

[0013] Preferably, the numerical calculation method for solving the total power generation is specifically: ; wherein is a spatial integration region; is a time integration region; is a weight function, considering equipment density and grid access conditions; the integral calculation uses a hierarchical sampling method to increase sampling points in high gradient areas; represents a spatial region a time period triple integration is performed on a spatial differential element, a time differential element, and together constitute the infinitesimal of space-time integration, and the product of the resource-equipment coupling potential field and the weight function is continuously added up at each location within the target spatial range and at each time within the target time range, thereby directly obtaining the total power generation; the calculation method of the weight function is: ; wherein, is the equipment spatial distribution density; is a time weight coefficient, considering day and night and seasonal changes; is a grid access capability factor, reflecting the local grid's consumption capacity.

[0014] Preferably, the model verification and optimization specifically includes: a model verification step: selecting a historical typical period, comparing the difference between the calculated value and the actual power generation, calculating statistical indicators such as root mean square error and average absolute percentage error, and evaluating the model accuracy; a parameter optimization step: using a genetic algorithm to optimize model parameters, minimizing prediction errors, establishing a parameter sensitivity analysis model, identifying key parameters and performing key optimization; a model updating step: establishing an online learning mechanism, dynamically updating model parameters according to new data, regularly retraining the model, and adapting to the effects of equipment aging and climate change.

[0015] The application provides another technical solution: a power generation amount calculation system based on wind and light resources and equipment data, comprising: a data acquisition module: which comprises: a resource data acquisition unit, which integrates multi-source meteorological data and satellite remote sensing data to realize all-around acquisition of wind and light resource data; an equipment data acquisition unit, which acquires the running state and performance parameters of the equipment in real time; a data quality monitoring unit, which automatically detects data abnormalities and triggers an alarm and processing mechanism, and records data quality problems and processing results; a data storage management unit: which optimizes the data organization structure, adopts a hierarchical storage strategy, and improves the data access efficiency; a core calculation module: which comprises: a potential field calculation acceleration unit, which uses GPU parallel computing technology to improve the calculation efficiency; an integral optimization unit, which realizes an adaptive integral algorithm and dynamically adjusts the integral strategy according to the calculation accuracy requirement; a memory management unit, which uses the memory pool technology to optimize the memory allocation and release in the large-scale data processing process; and a result output module: which provides multiple output formats, including data reports, trend charts and spatial distribution maps, and is also used for result export and interface calling.

[0016] The technical effects and advantages of the application: in the application, the wind and light resource data and the performance parameter data of the power generation equipment are collected to form standardized multi-source data, the resource fluctuation characteristics and the equipment response characteristics are fused through a dynamic coupling algorithm, a power generation capacity evaluation model considering the space-time correlation is established, the complex dynamic relationship between the resources and the equipment is more truly reflected, and the accuracy of the power generation potential evaluation is improved; the triple integral operation is used to solve the dynamic coupling potential field, an integral error compensation mechanism is established, the calculation accuracy is improved through the adaptive step algorithm, the calibrated total power generation estimation value is finally output, the calculation error is effectively reduced, and the robustness of the result is improved; in the data acquisition, potential field construction and integral calculation process, the meteorological factors such as wind speed, wind direction, irradiance and temperature, and the equipment and system factors such as fan power curve, photovoltaic component efficiency, equipment aging and grid access capacity are comprehensively considered, so that the power generation estimation is more close to the actual situation; through the model verification and optimization steps, the model can adapt to the influence of equipment aging and climate change, and maintain long-term high accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0017] The disclosed content of the application is explained with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the protection scope of the application. In the drawings, the same reference signs are used to refer to the same parts:

[0018] Fig. 1 It is a flowchart of the application; Fig. 2 It is a module topology diagram of the application. DETAILED DESCRIPTION

[0019] It is easy to understand that, according to the technical solutions of the present application, a person skilled in the art can propose various structures and implementation modes that can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solutions of the present application, and should not be regarded as the whole or as the limitation or restriction of the technical solutions of the present application.

[0020] Referring to Figs. 1-2 As shown in the drawings, the present application provides a power generation calculation method and system based on wind and light resources and equipment data, aiming to solve the problems of insufficient wind and light power generation prediction accuracy and poor model robustness in the prior art. The method realizes high-precision estimation of wind and light power generation through multi-source data fusion, dynamic coupling potential field construction and fine integration calculation.

[0021] Embodiment one: this embodiment describes the specific steps of the power generation calculation method of the present application in detail.

[0022] Step one: data acquisition and fusion processing: acquire comprehensive, accurate and standardized input data: specifically including: wind and light resource data acquisition: acquire wind and light resource data of the target area through multiple channels, use ground observation data provided by meteorological monitoring stations, combine satellite remote sensing equipment to acquire large-scale, high spatial resolution irradiance, cloud amount and other data; and use numerical weather prediction system to acquire gridded weather forecast data for a period of time in the future, including wind speed, wind direction, solar irradiance, ambient temperature, etc. In order to ensure the calculation accuracy, the time resolution of these resource data is not less than 1 hour, and the spatial resolution is not less than 1 kilometer; equipment parameter acquisition: acquire real-time equipment operation state data from the monitoring system of the power generation equipment, such as the speed of the fan, the angle of the blade, the output power, the current and voltage of the photovoltaic module, etc. At the same time, acquire detailed technical parameters of the power generation equipment from the manufacturer database or equipment nameplate, including the rated power, power curve, cut-in / cut-out wind speed of the fan, the rated power, efficiency curve, temperature coefficient, maximum output power, minimum operating temperature of the photovoltaic module, and the accurate geographic location information of the equipment.

[0023] Quality control and standardization of raw data: Resource data quality control: Perform quality checks on the collected wind and solar resource data, remove outliers that are obviously beyond the physical range or statistically abnormal, such as negative wind speed, irradiance exceeding the theoretical maximum, etc. For missing data, use spatial interpolation techniques such as Kriging interpolation to fill in, to ensure the continuity and integrity of the data; Equipment parameter standardization: Standardize equipment parameters of different sources and formats, unify data format and unit, ensure data consistency, unify all power units to MW, and temperature units to Celsius; Spatio-temporal index establishment: Establish the spatio-temporal index of resource data and equipment data to ensure accurate spatio-temporal matching. For any time point and spatial location, the corresponding resource data and equipment data can be accurately found.

[0024] In a preferred embodiment, the processing of wind and solar resource data further includes: Spatio-temporal correlation model establishment: Establish a spatio-temporal correlation model for resource data, use empirical orthogonal function decomposition method to extract the main spatio-temporal characteristics of the resource field, which helps to capture the overall evolution trend and spatial structure of wind field and irradiance field, providing a deeper understanding for subsequent dynamic coupling; Resource uncertainty quantification: Establish Weibull distribution model for wind speed data and Beta distribution model for irradiance data to quantify the uncertainty of resources, which can better describe the random fluctuation characteristics of wind speed and irradiance; Time-frequency characteristic analysis: Use wavelet transform method to analyze the time-frequency characteristics of resources, identify the periodic fluctuation rules of resources such as daily cycle, seasonal cycle, etc., which is convenient for understanding the internal mechanism of resource change and providing periodic feature input for prediction model.

[0025] In another preferred embodiment, the processing of equipment parameter data further includes: Wind turbine power curve smoothing: Smooth the wind turbine power curve, use S-shaped function to fit the transition characteristics of cut-in, rated, and cut-out wind speed intervals, more accurately describe the output power of wind turbine at different wind speeds, especially the dynamic response when wind speed changes sharply; Photovoltaic module efficiency temperature correction: Establish a temperature correction model for photovoltaic module efficiency, considering the influence of irradiance and ambient temperature on conversion efficiency, for example, efficiency decreases with temperature rise, this model can accurately quantify this influence; Establishment of equipment aging model: Establish an equipment aging model, dynamically adjust equipment performance parameters according to operating time and maintenance records, for example, the efficiency of photovoltaic modules gradually decreases with operating time, the wear and tear of mechanical parts of wind turbine affects its power output, this model can reflect these long-term changes.

[0026] Step two: dynamic coupling potential field construction: deeply integrate resource fluctuation characteristics and device response characteristics to form a time-space continuous comprehensive power generation potential field, which specifically includes: potential field initialization step: based on the standardized data set, calculate the theoretical power generation power of each grid point or the location where the device is located at each time point. For wind power generation equipment, the theoretical power generation power is calculated according to the wind turbine power curve function , where is the wind speed at position and time . For photovoltaic power generation equipment, the theoretical power generation power is calculated according to the irradiance and efficiency function , where is the ambient temperature. These theoretical power values are used as the initial values of the dynamic coupling potential field; time-space coupling step: considering the time-space correlation of resources and the dynamic response characteristics of devices, the initial potential field is corrected by a dynamic coupling algorithm that integrates the spatial gradient of the resource field, device response time and other factors to more realistically reflect the power generation potential.

[0027] In a preferred embodiment, the dynamic coupling potential field is defined by the following formula: ; where: is the spatial position vector, is the time variable; is the theoretical power generation power at position and time , which is calculated according to the type of equipment; is the spatial gradient module of the resource field, which represents the degree of change of the resource in space. For wind power, the wind speed gradient is mainly considered, and for photovoltaic power, the irradiance gradient is mainly considered. The gradient module is calculated using the central difference method; is the device response time constant, which reflects the response speed of the device to resource changes, such as wind turbine blade adjustment, converter response, etc. It is determined according to the type of equipment. The wind turbine usually takes 5-15 seconds, and the photovoltaic inverter usually takes 1-5 seconds; is the coupling strength coefficient, which is a parameter that adjusts the degree of influence of resource gradient and device response on the potential field. The coefficient is obtained by training historical data, and the value range is 0.1-1.0 to ensure that the model can adapt to different regions and device characteristics; potential field optimization sub-step: use regularization method to smooth the potential field distribution, eliminate abnormal fluctuations caused by data noise or local instability of the model, and ensure the physical rationality of the potential field, such as using Gaussian smoothing, Laplace smoothing, etc.

[0028] Step three: integral calculation of power generation potential field: the potential of the dynamic coupling potential field in a specific spatio-temporal range is accumulated to obtain the final total power generation estimate, which includes: numerical integral step: triple integral operation is performed on the dynamic coupling potential field in the target spatial region and the calculation period to solve the total power generation.

[0029] In a preferred embodiment, the total power generation is calculated by the integral formula: ; wherein: is the spatial integral region, i.e. the geographical range where the wind-solar power plant is located; is the time integral region, i.e. the period for which the power generation needs to be calculated, such as a day, a week or a month; is the weight function, which takes into account factors such as device density and grid access conditions; the integral calculation uses a hierarchical sampling method, with more sampling points in high gradient areas, specifically, the spatial integral uses the Gaussian quadrature method, which has high precision given the number of sampling points, and the time integral uses the Runge-Kutta method, which has good stability and precision when dealing with time series integration; represents the triple integral in the spatial region and the time period , is the spatial differential element, is the time differential element, and together constitute the infinitesimal of spatio-temporal integration, the product of the resource-device coupling potential field and the weight function is continuously accumulated at each location in the target spatial range and at each time in the target time range, thus directly obtaining the total power generation.

[0030] In a preferred embodiment, the weight function is calculated as: ; wherein: is the device spatial distribution density, representing the number or capacity of devices per unit area near the spatial location , reflecting the power generation potential density of the region; is the time weight coefficient, considering the influence of day and night and seasonal changes on power generation, for example, the weight coefficient of photovoltaic power generation will decrease at night or in winter; is the grid access capability factor, reflecting the local grid's ability to absorb, in areas where the grid's ability to absorb is limited, even if the power generation potential is high, the actual output will be limited, and this factor is used to quantify this limitation.

[0031] Error compensation step: Establish an integral error estimation model, and adaptively adjust the integral step size according to the smoothness of the potential field. In areas with drastic changes in potential field, smaller step size is used to capture details; in areas with gentle potential field, larger step size is used to improve calculation efficiency; boundary effects are handled, and mirror extension method is used to reduce integral error, which helps to avoid errors caused by data truncation at the boundary of the integral region.

[0032] Result calibration step: Based on historical data, establish an error correction function to correct systematic deviations in the integral results. By comparing historical calculation values with actual power generation, identify and quantify systematic deviations, and apply them to future prediction results to improve accuracy.

[0033] Example two: This example describes the process of verifying and continuously optimizing the above-mentioned power generation calculation method.

[0034] Model verification step: Select historical typical periods, such as high wind speed, low wind speed, high irradiance, low irradiance, seasonal alternation, etc., compare the difference between calculated value and actual power generation; calculate the root mean square error, mean absolute percentage error and other statistical indicators to evaluate the precision and stability of the model, and quantify the deviation between the model prediction value and the actual value.

[0035] Parameter optimization step: Use intelligent optimization algorithms such as genetic algorithm, particle swarm optimization, etc. to optimize key parameters in the model, such as coupling strength coefficient , coefficients in weight function, etc. to minimize prediction error; establish parameter sensitivity analysis model to identify key parameters that have the greatest impact on model output, and focus on optimizing and fine-tuning these parameters.

[0036] Model update step: Establish an online learning mechanism to dynamically update model parameters based on new real-time data and historical operation data. The model can continuously learn from new data over time, adapt to equipment aging, climate change, and changes in power grid structure; periodically retrain the model to adapt to the effects of equipment aging and climate change, for example, every certain period of time, use the latest historical data to retrain the entire model to ensure the long-term effectiveness of the model.

[0037] Embodiment three: the application provides a power generation calculation system based on wind and light resources and equipment data, characterized in that it comprises: a data acquisition module, which comprises: a resource data acquisition unit, which integrates multi-source meteorological data and satellite remote sensing data to realize all-around acquisition of wind and light resource data; an equipment data acquisition unit, which acquires equipment running state and performance parameters in real time; a data quality monitoring unit, which automatically detects data abnormalities and triggers alarm and processing mechanism, records data quality problems and processing results; a data storage management unit: optimizing data organization structure, adopting hierarchical storage strategy, improving data access efficiency; a core calculation module, which comprises: a potential field calculation acceleration unit, which adopts GPU parallel computing technology to improve calculation efficiency; an integral optimization unit, which realizes adaptive integral algorithm, dynamically adjusts integral strategy according to calculation precision requirement; a memory management unit, which adopts memory pool technology, optimizes memory allocation and release in large-scale data processing process; a result output module: providing various output formats, including data report, trend chart and spatial distribution diagram, also used for result export and interface calling, the data report can provide detailed power generation values, the trend chart can show the time sequence change of power generation, and the spatial distribution diagram can directly display the power generation potential of different regions.

[0038] The technical scope of the application is not limited to the content in the above description, and those skilled in the art can make various modifications and changes to the above embodiments without departing from the technical idea of the application, and these modifications and changes should all belong to the protection scope of the application.

Claims

1. A method for calculating power generation based on wind and solar resource and equipment data, characterized in that, The method comprises the following steps: step The method comprises the following steps: step 1: data acquisition and fusion processing: acquiring wind and light resource data of a target area, including the spatial and temporal distribution data of wind speed, wind direction, solar irradiance and environmental temperature; synchronously acquiring performance parameter data of power generation equipment, including wind turbine power curve, photovoltaic module efficiency characteristics and equipment geographic location information; pre-processing the acquired data to form a standardized data set; step 2: dynamic coupling potential field construction: based on the standardized data set, a resource-equipment dynamic coupling potential field is constructed through a dynamic coupling algorithm, the potential field is a time and space continuous field quantity, and is used to represent the comprehensive power generation potential at any spatial position and time point; the dynamic coupling algorithm fuses resource fluctuation characteristics and equipment response characteristics to establish a power generation capacity evaluation model considering the time and space correlation; step 3: power generation potential field integral calculation and verification: performing triple integral operation on the dynamic coupling potential field in the target spatial area and calculation period, and solving the total power generation through a numerical calculation method; an integral error compensation mechanism is established, an adaptive step algorithm is used to improve the calculation precision, and finally the calibrated total power generation estimation value is output; step 4: model verification and optimization: selecting power generation data of a typical time period for model verification, establishing a model, determining an optimization priority and updating model parameters in real time.

2. The method for calculating the power generation capacity based on wind and light resources and equipment data according to claim 1, characterized in that: The data acquisition and fusion processing specifically comprises: wind and light resource data acquisition: acquiring grid-based resource data through a meteorological monitoring station, satellite remote sensing equipment and a numerical weather prediction system, the time resolution is not less than 1 hour, and the spatial resolution is not less than 1 km; parameter acquisition of the equipment: acquiring detailed technical parameters of the power generation equipment from an equipment monitoring system and a manufacturer database, including rated power, efficiency curve and operation limitation condition; the data pre-processing specifically comprises: performing quality control on the resource data, removing obvious outliers and filling in missing data by using a Kriging interpolation method; performing standardized processing on the equipment parameters, unifying data format and unit; and establishing a time and space index to ensure the time and space matching of the resource data and the equipment data.

3. The method for calculating the power generation capacity based on wind and light resources and equipment data according to claim 2, characterized in that: The processing of the wind and light resource data in step 1 further comprises: establishing a time and space correlation analysis model of the resource data, and extracting main time and space characteristic modes by using an empirical orthogonal function decomposition method; establishing a Weibull distribution model for wind speed data and a beta distribution model for irradiance data to quantize the uncertainty characteristics of the resources; studying the time-frequency characteristics of the resources by using wavelet transform and Fourier analysis method to identify the fluctuation rules of different time scales; and establishing quality control standards of the resource data, including data integrity, accuracy and consistency check specifications.

4. The method for calculating the power generation capacity based on wind and light resources and equipment data according to claim 2, characterized in that: The processing of the equipment parameter data in step 1 further comprises: performing smoothing processing on the wind turbine power curve, and using an S-shaped function to fit the transition characteristics of the cut-in, rated and cut-out wind speed intervals; establishing a temperature correction model for the photovoltaic module efficiency to consider the influence of irradiance and environmental temperature on the conversion efficiency; establishing an equipment aging model to dynamically adjust the equipment performance parameters according to the operation time and maintenance records; and designing an equipment parameter verification process to ensure the accuracy of the parameters by comparing and analyzing the measured data and the theoretical parameters.

5. The method for calculating the power generation amount based on wind and light resources and equipment data according to claim 1, characterized in that: The dynamic coupling potential field construction in the step two specifically comprises: a potential field initialization step: calculating the theoretical power generation of each grid point based on the standardized data as the initial value of the potential field; a space-time coupling calculation step: considering the space-time correlation of resources and the dynamic response characteristics of equipment, the initial potential field is corrected by a coupling algorithm; a potential field optimization step: using a regularization method to smooth the potential field distribution, eliminating abnormal fluctuations, and ensuring the physical rationality of the potential field.

6. The method for calculating the power generation amount based on wind and light resources and equipment data according to claim 5, characterized in that: The dynamic coupling potential field is defined by the following formula: ; wherein is a spatial position vector, is a time variable; is the theoretical power generation at position and time ; is the spatial gradient module of the resource field; is the equipment response time constant; is the coupling strength coefficient; the theoretical power generation is calculated according to the type of equipment respectively: for wind power equipment: , wherein is the fan power curve function, is the wind speed; for photovoltaic power generation equipment , wherein is the irradiance, is the efficiency function, is the temperature; the spatial gradient module of the resource field is calculated by the central difference method, considering the wind speed gradient for wind power and the irradiance gradient for photovoltaic; the equipment response time constant is determined according to the type of equipment, taking 5-15 seconds for the fan and 1-5 seconds for the photovoltaic inverter, the coupling strength coefficient is obtained by training historical data, with a value range of 0.1-1.

0.

7. The method of claim 6, wherein the method further comprises: The power generation potential field integral calculation in the step three specifically comprises: a numerical integral step: using Gaussian quadrature method for spatial integration and Runge-Kutta method for time integration to ensure calculation accuracy and stability; an error control step: establishing an integral error estimation model, and adaptively adjusting the integral step according to the smoothness of the potential field; processing the boundary effect, using the mirror continuation method to reduce the integral error; a result verification step: collecting historical power generation data to establish a verification data set; calculating the deviation statistics of the predicted value and the actual value; establishing an error correction function to systematically correct the integral results. 8.The method of claim 7, wherein: The numerical calculation method is specifically for solving total power generation: ; wherein is a spatial integration region; is a time integration region; is a weight function, considering device density and power grid access conditions; the integral calculation adopts a hierarchical sampling method, and sampling points are encrypted in a high gradient region; represents a spatial region a time period triple integration is performed on a spatial differential element, a time differential element, and constitute a space-time integration element, and the product of the resource-device coupling potential field and the weight function is continuously added to the contribution of each position in the target spatial range and each time in the target time range, so that the total power generation is directly obtained. Weight function The calculation method is: ; wherein, is the device spatial distribution density; is the time weight coefficient, considering day and night and seasonal changes; is the grid access capacity factor, reflecting the local grid's accommodation capacity. 9.The method of claim 1, wherein: The model verification and optimization specifically comprises: a model verification step: selecting a typical historical period, comparing the difference between the calculated value and the actual power generation, calculating statistical indicators such as root mean square error and average absolute percentage error, and evaluating the model accuracy; a parameter optimization step: using genetic algorithm to optimize the model parameters, minimizing the prediction error, establishing a parameter sensitivity analysis model, identifying key parameters and performing key optimization; a model updating step: establishing an online learning mechanism, dynamically updating the model parameters according to new data, regularly retraining the model, and adapting to the effects of equipment aging and climate change.

10. A system for calculating power generation based on wind and solar resource and equipment data for implementing the method for calculating power generation based on wind and solar resource and equipment data according to any one of claims 1 to 9, characterized in that: Comprise: A data acquisition module comprising: a resource data acquisition unit integrating multi-source meteorological data and satellite remote sensing data to realize all-around acquisition of wind and light resource data; an equipment data acquisition unit for real-time acquisition of equipment operating status and performance parameters; a data quality monitoring unit for automatic detection of data anomalies and triggering of alarm and processing mechanism, recording data quality problems and processing results; a data storage management unit for optimizing data organization structure and adopting hierarchical storage strategy to improve data access efficiency; a core calculation module comprising: a potential field calculation acceleration unit using GPU parallel computing technology to improve calculation efficiency; an integral optimization unit for realizing adaptive integral algorithm and dynamically adjusting integral strategy according to calculation accuracy requirements; a memory management unit using memory pool technology to optimize memory allocation and release in large-scale data processing; a result output module providing multiple output formats including data report, trend chart and spatial distribution map, and also used for result export and interface calling.

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