A method and system for tracing green electricity consumption

Abstract

This invention relates to the field of new energy management technology, and in particular to a method and system for tracing the source of green electricity consumption. The method includes the following steps: obtaining wind turbine design drawings and constructing a wind power generation model; performing wind power generation simulation based on the model to obtain wind power generation data; detecting gear meshing anomalies in the model based on the data to obtain gear meshing anomaly data; identifying bearing cracks based on the anomaly data to obtain bearing crack data; locating crack defects based on the data; designing a fatigue-resistant wind power structure based on the crack defect location; integrating the fatigue-resistant structure into the model and predicting wind power output; and generating green electricity digital certificates based on the wind power output. This invention improves the reliability of wind power structures, the accuracy of fault detection, and the credibility of green electricity traceability based on new energy management technology, comprehensively enhancing the efficiency of green electricity consumption and management transparency.

Description

Technical Field

[0001] This invention relates to the field of new energy management technology, and in particular to a method and system for tracing the source of green electricity consumption. Background Technology

[0002] In terms of green electricity consumption, wind power generation presents challenges. During peak periods, abundant wind resources in some regions ensure sufficient green electricity supply, alleviating the problem of "green electricity shortage" for users. However, during off-peak periods, due to unstable wind conditions, a large amount of green electricity cannot be fully utilized, resulting in significant resource waste. Health monitoring and fault detection of wind power equipment rely on single data sources or detection methods, easily overlooking subtle anomalies during operation. This limits the timeliness of fault identification and early warning, thus affecting the accuracy of green electricity consumption. The lack of precise correlation between actual wind power output and data leads to low accuracy and credibility of green electricity digital certificates, negatively impacting the reliability of green electricity trading and traceability. The processing of user grid load data does not consider dynamic changes, resulting in a lack of real-time and accurate green electricity consumption analysis, failing to effectively address grid load fluctuations and the uncertainty of green electricity supply. The construction and management of the green electricity traceability chain are relatively simplistic, failing to fully utilize advanced information technologies such as blockchain and digital signatures. The lack of a reliable and transparent traceability mechanism prevents the green electricity consumption and traceability process from forming an effective closed loop, hindering the promotion and application of renewable energy. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method and system for tracing the source of green electricity consumption in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for tracing the source of green energy consumption includes the following steps:

[0005] Step S1: Obtain the design drawings of the wind turbine and build a wind power generation model; perform wind power generation simulation based on the wind power generation model to obtain wind power generation data;

[0006] Step S2: Detect gear meshing anomalies in the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identify bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; locate the crack defect location based on the bearing crack data.

[0007] Step S3: Design a fatigue-resistant wind power structure based on the location of crack defects; integrate the fatigue-resistant wind power structure into the wind power generation model and predict the wind power output; generate green electricity digital vouchers based on the wind power output.

[0008] Step S4: Obtain user grid load data; map green electricity digital vouchers to user grid load data to obtain user green electricity load data; perform green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; construct a green electricity traceability link diagram based on green electricity consumption data and upload it to the green electricity management platform to execute the green electricity consumption traceability task.

[0009] This invention addresses the issue of green electricity consumption through wind power generation. While peak-season wind resources are abundant in some areas, ensuring sufficient green electricity supply and alleviating the "green electricity shortage" problem for users, during off-seasons, unstable winds result in significant resource waste as a large amount of green electricity cannot be fully utilized. Furthermore, the reliance on a single data source or detection method for monitoring the health status and detecting faults in wind power equipment can easily overlook subtle anomalies during operation, limiting the timeliness of fault identification and early warning, thus affecting the accuracy of green electricity consumption. The lack of precise correlation between actual wind power output and data leads to low accuracy and reliability in generating green electricity digital certificates, negatively impacting the credibility of green electricity trading and traceability. The processing of user grid load data does not consider dynamic changes, resulting in a lack of real-time and accurate green electricity consumption analysis, failing to effectively address grid load fluctuations and the uncertainty of green electricity supply. Finally, the construction and management of the green electricity traceability chain is relatively simplistic, failing to fully utilize advanced information technologies such as blockchain and digital signatures, lacking a reliable and transparent traceability mechanism. This prevents the green electricity consumption and traceability process from forming an effective closed loop, hindering the promotion and application of renewable energy. By integrating fatigue-resistant structural design into the wind power generation model, not only is the equipment's adaptability to fatigue and unexpected events improved, but the prediction of wind power output is also optimized, reducing the deviation between wind power output and actual grid load. This has a positive effect on the generation of green electricity digital certificates, as more accurate power output ensures the authenticity and credibility of digital certificates, enhancing the transparency of green electricity trading and the trust of market participants. By reflecting fluctuations in user grid load in real time, the relationship between green electricity digital certificates and user demand can be effectively mapped, making green electricity consumption analysis results more accurate and timely. This effectively addresses grid load fluctuations, preventing green electricity waste or shortages and ensuring that every unit of green electricity can be consumed to the maximum extent. By constructing a green electricity traceability chain diagram and uploading it to the green electricity management platform, a transparent and credible traceability mechanism can be formed. The application of technologies such as blockchain ensures that every step from green electricity generation to consumption is traceable, preventing cheating or data tampering in green electricity trading, making green electricity traceability and management more efficient and reliable. This link diagram not only enhances the traceability of the green electricity consumption process, but also provides support for subsequent policy formulation and the improvement of industry standards, promoting the wider application of renewable energy.

[0010] Preferably, this specification also provides a green energy consumption traceability system for executing the green energy consumption traceability method described above, the green energy consumption traceability system comprising:

[0011] The wind power generation simulation module acquires wind turbine design drawings and constructs a wind power generation model; it then performs wind power generation simulation based on the model to obtain wind power generation data.

[0012] The bearing crack identification module performs gear meshing anomaly detection on the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identifies bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; and locates the crack defect location based on the bearing crack data.

[0013] The green electricity digital certificate generation module designs fatigue-resistant wind power structures based on the location of crack defects; integrates the fatigue-resistant wind power structures into the wind power generation model and predicts wind power output; and generates green electricity digital certificates based on the wind power output.

[0014] The green electricity traceability module acquires user grid load data; maps green electricity digital certificates to user grid load data to obtain user green electricity load data; performs green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; constructs a green electricity traceability link diagram based on green electricity consumption data and uploads it to the green electricity management platform to execute green electricity consumption traceability tasks.

[0015] The present invention relates to a green energy consumption traceability system. This system can implement any of the green energy consumption traceability methods of the present invention. It serves as a medium for the operation and signal transmission between various modules to complete the green energy consumption traceability method. The internal modules of the system cooperate with each other to improve the reliability of wind power structures, the accuracy of fault detection, and the reliability of green energy traceability, thereby comprehensively improving the efficiency of green energy consumption and management transparency. Attached Figure Description

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0017] Figure 1 This is a schematic diagram of the steps in the green energy consumption traceability method of the present invention;

[0018] Figure 2 This is a detailed flowchart of step S1 in the present invention;

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0021] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method for tracing the source of green energy consumption, the method comprising the following steps:

[0024] Step S1: Obtain the design drawings of the wind turbine and build a wind power generation model; perform wind power generation simulation based on the wind power generation model to obtain wind power generation data;

[0025] In this embodiment, two-dimensional and three-dimensional design drawings of the wind turbine are imported into the engineering drawing software SolidWorks. The drawings must clearly indicate structural data such as turbine blade length (unit: meters, accuracy not less than 0.01m), hub central shaft diameter (unit: mm), main bearing position coordinates (expressed in the central coordinate system, unit: mm), number of gear teeth, and gear module (unit: mm). Using a multibody dynamics simulation platform, such as ADAMS or RecurDyn, a complete wind turbine mechanical system model is constructed from the drawings. The model needs to define rotating components (such as blades and main shaft), stationary components (such as support structures), and their mutual constraints. Constraint types include revolute joints, fixed joints, and gear pairs. Wind loading is simulated using CFD software (such as ANSYS Fluent) to simulate the external flow field. Boundary conditions are set as follows: incoming wind speed 10m / s to 25m / s, wind direction angle variation range 0° to 30°, and a turbulence model (SST k-ω) is used to simulate turbulence. The simulation time is 60s with a time step of 0.01s. During the simulation, blade rotation speed, main shaft torque, and power output values ​​are extracted and output as wind power generation data at a frequency of 10Hz.

[0026] Step S2: Detect gear meshing anomalies in the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identify bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; locate the crack defect location based on the bearing crack data.

[0027] In this embodiment, the wind power generation data from step S1 is input into the vibration analysis system. The gear meshing frequency and high-frequency impact signal are extracted using a combination of envelope demodulation and Hilbert transform. The sampling frequency of the acquired raw vibration signal is set to 48kHz. Measurement points are selected on both sides of the gearbox input and output ends. A spectrum analysis tool is used to compare the theoretical meshing frequency (calculated from the number of gear teeth and rotational speed) with the actual signal. If additional frequency components exist within ±0.5Hz, they are marked as abnormal. After extracting the abnormal frequency signals, wavelet packet decomposition (using the db6 wavelet basis, with a decomposition level of 4) is used to obtain the characteristic energy distribution of the high-frequency signal. The portion of the high-frequency signal energy whose mean exceeds a set threshold (referring to 1.8 times the average value under normal gear meshing conditions) is classified as abnormal gear meshing data. By using a joint criterion based on Kurtosis value and RMSE (root mean square error), sudden impact signals are screened from abnormal data and compared with the natural frequency of the bearing raceway to identify bearing cracks. After confirming the crack data, the crack coordinates are back-derived using the finite element local mesh refinement method. The crack coordinate accuracy is required to be less than ±0.2mm, with the impeller center as the coordinate origin in three-dimensional space.

[0028] Step S3: Design a fatigue-resistant wind power structure based on the location of crack defects; integrate the fatigue-resistant wind power structure into the wind power generation model and predict the wind power output; generate green electricity digital vouchers based on the wind power output.

[0029] In this embodiment, the spatial coordinates of the crack defect obtained in step S2 are used as constraints to create a local structural design space for the wind turbine main shaft and hub area in the CATIA environment. The crack location is designated as a stress concentration area, and the ANSYS Static Structural module is used for stress concentration assessment. The material parameters are set as Q345D low-alloy steel, with an elastic modulus of 210 GPa, Poisson's ratio of 0.3, and a yield strength of 345 MPa. Based on the stress concentration factor at the crack (obtained through local mesh analysis, with a threshold of K≥2.0), the axial flange thickness is set to be 20% thicker than the original design (if the original thickness is 30 mm, it is adjusted to 36 mm). The flange connection angle is adjusted by ±5° from the original design (the specific angle is determined based on the off-center load direction). Then, the main shaft rigidity (unit: N·mm²) is calculated using finite element analysis. Combined with the simulation results of the meshing off-center load stress dispersion (solving for the uniformity of stress distribution at the nodes through simulated load action), the optimal diameter of the fatigue-resistant connection flange is derived, set within the range of 600 mm to 800 mm (based on the equivalent stress distribution). The structural modification results were imported into the system-level model in IGES format, and a 20-second isotropic wind speed simulation was performed to predict wind power output, with the predicted power change rate not exceeding 2%. The output power value and wind speed data were integrated, and the hourly power generation was statistically analyzed according to the national standard GB / T 31838-2015, serving as the basis for subsequent green electricity digital certificates.

[0030] Step S4: Obtain user grid load data; map green electricity digital vouchers to user grid load data to obtain user green electricity load data; perform green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; construct a green electricity traceability link diagram based on green electricity consumption data and upload it to the green electricity management platform to execute the green electricity consumption traceability task.

[0031] In this embodiment, grid load data recorded in user-side smart meters is collected with a granularity of no less than 15 minutes, in kWh units, covering a time interval of ±24 hours from the predicted wind power output period. The wind power generation time period identifier (e.g., 20250410_0900_1000) is aligned with the user's electricity consumption timestamp. A mapping matrix method is used to establish a green electricity load mapping relationship for the user, where the matrix elements represent the ratio between wind power output and user load power, with a precision control of 0.01. Based on this ratio, green electricity usage load data within the corresponding time period is extracted from the user side. The green electricity power allocation is then time-weighted and integrated. Combined with the user-side energy storage device capacity (e.g., 500 kWh battery capacity) and its current state of charge (SOC value of 80%), the maximum amount of green electricity that can be stored is calculated. The amount of electricity consumed and stored is uniformly encoded as green electricity consumption data, formatted using a JSON structure, and a directed graph structure is constructed with user identity ID, green electricity certificate number, and time stamp as nodes. Each node in the graph contains the consumption action (such as "consumption" / "storage") and a timestamp. The graph structure is generated using the Neo4j graph database and then uploaded to the central server of the power grid green electricity management platform to realize the graph management of the green electricity consumption traceability link. The upload interface uses the MQTT protocol for encrypted communication, and the message message structure is fixed in three parts: identity header information, graph body content, and transmission check code (CRC-32).

[0032] Preferably, step S1 specifically includes:

[0033] Step S11: Obtain the design drawings of the wind turbine and extract the blade structure data and gearbox structure data;

[0034] In this embodiment, wind turbine design drawings are obtained. The drawing files are opened using AutoCAD software. The drawings must specify the geometric parameters of the blades, such as blade length (in meters, with an accuracy of at least 0.01m), thickness (in millimeters, with an accuracy of at least 0.1mm), and angle (in degrees, with an accuracy of at least 0.1°). The aerodynamic shape features of the blades are also extracted. Gearbox design drawings should include parameters such as the gear module (in millimeters), number of teeth (dimensionless), gear pressure angle (in degrees), and the length and diameter of the gear shaft. For the gearbox's three-dimensional structural data, a three-dimensional model is created using SolidWorks or CATIA, and the structural data, including the distribution of the gear sets and the load distribution on the gear shaft, is exported. When obtaining blade and gearbox structural data, it is ensured that the accuracy of all parameters is at least as high as the design requirements. The gearbox's transmission ratio should be verified and extracted according to the specific requirements in the design drawings.

[0035] Step S12: Acquire wind data and perform blade aerodynamic response detection on the blade structure data to obtain blade aerodynamic response data;

[0036] In this embodiment, wind data is acquired, specifically within the range of 3 m / s to 25 m / s, collected hourly. Wind speed data provided by meteorological stations is used, or wind speed and direction for a specific area are calculated using a wind prediction model. Based on this, the aerodynamic response of the blades is detected. Wind simulation software, such as ANSYS Fluent or OpenFOAM, is used to simulate the aerodynamic characteristics of the blades under different wind speeds. The aerodynamic response of the blades is simulated using a finite element model. Aerodynamic load data for the blades is extracted, including lift coefficient (C1), drag coefficient (Cd), and aerodynamic torque (unit: Nm), and analyzed hourly by wind speed and direction. Input parameters required for aerodynamic response detection include wind speed (3 m / s to 25 m / s), airflow density (1.225 kg / m³), and blade angle of attack (0° to 10° range). These simulation data are used to generate aerodynamic response data of the blades under different wind speeds, describing the law of force variation on the blades.

[0037] Step S13: Perform power transmission simulation on gearbox structure data based on blade aerodynamic response data, where the input torque is set to 40000-70000Nm and the input speed is set to 10-20rpm to obtain power transmission data;

[0038] In this embodiment, based on the blade aerodynamic response data obtained in step S12, the power transmission of the gearbox is simulated using gear transmission system simulation software (such as Simulink, MATLAB, or ADAMS). During this process, the input torque range is set to 40,000 Nm to 70,000 Nm, and the speed range is set to 10 rpm to 20 rpm. These input parameters are set based on the actual operating conditions of the wind turbine generator. The input torque of the gearbox is calculated through the interaction force between the wind turbine blades and the air, while the input speed is determined by the relationship between the blade speed and the gear ratio. The gearbox parameters, including the gear module, number of teeth, and pressure angle, are input according to the design drawings. The power transmission simulation process obtains power transmission data by calculating the torque transmission, load distribution, and gear meshing state within the gear system. This data should include the speed of each gear in the gearbox, the transmitted torque, and the stress distribution of the bearings. The simulation time is set to twice the simulation cycle, i.e., each operating condition is simulated for approximately 60 seconds, and the simulation results are saved as a data file.

[0039] Step S14: Determine the power generation energy based on the power transmission data;

[0040] In this embodiment, the generated energy is calculated based on the power transmission data obtained in step S13 and the power calculation formula for wind turbine generation. The generated energy of the wind turbine can be calculated using the following formula:

[0041] ;

[0042] in, Power generation capacity (unit: W). The density is the air density (set to 1.225 kg / m³). V represents the blade sweep area (in m²), and v represents the wind speed (in m / s). The efficiency of the wind turbine is typically set to 0.35. Based on the input wind speed and aerodynamic response data, the power output for each time period is calculated step by step, and the mechanical transmission efficiency is determined according to the gearbox transmission characteristics. The power output for each hour is integrated to obtain the total power generation for that time period (unit: kWh). Parameters required for this process include wind speed data, blade swept area, and efficiency value.

[0043] Step S15: Construct a wind power generation model based on power transmission data and generated energy;

[0044] In this embodiment, a dynamic model of the wind power generation system is built using Simulink or MATLAB. The main components of this model include wind speed input, blade aerodynamic response, gearbox transmission, and generator output. Based on the parameters mentioned in the preceding steps, such as the blade aerodynamic response, gearbox input torque and speed range, and efficiency, various physical models of the system are set. Using wind data as external input, a time-series simulation of the entire wind power generation system is performed to obtain the power output at different times. This model can be used to evaluate the power generation performance under different wind speeds and operating conditions.

[0045] Step S16: Perform wind power generation simulation based on the wind power generation model to obtain wind power generation data.

[0046] In this embodiment, wind power generation simulation is performed using a wind power generation simulation platform (such as WindSim or Simulink) based on the wind power generation model constructed in step S15. During the simulation, the input wind speed range is 3 m / s to 25 m / s, and the simulation time is set to 24 hours. Hourly wind power generation data is gradually obtained through model calculations. Wind speed and blade aerodynamic response data are used as inputs in the simulation, while gearbox torque transmission efficiency and power output are used as constraints. Hourly wind power generation data includes power generation (unit: kWh), system efficiency, output voltage, etc., and the output data is stored in an Excel spreadsheet or database for subsequent analysis and processing. In this step, multiple simulations are conducted to ensure the stability and efficiency of wind turbine generation under different climatic conditions.

[0047] Preferably, step S16 specifically includes:

[0048] Step S161: Import the wind power generation model into the simulation software;

[0049] In this embodiment, when importing the wind power generation model into the simulation software, it is first necessary to ensure that the simulation software version is compatible with the model. For this step, the imported wind power generation model needs to include the main design parameters of the wind turbine, such as blade length, gearbox transmission ratio, and generator efficiency. Based on the design drawings and preliminary calculation data, all relevant parameters should be prepared in advance to ensure that the model can be loaded correctly in the simulation software. Simulation software such as ANSYS and Simulink will prompt the user to select the type of wind turbine generator and the wind turbine data model used during the import process, ensuring that the geometry, aerodynamic parameters, and mechanical transmission characteristics of the wind turbine are accurately described. After import, preliminary model verification is required to ensure that the model parameters are correct and that the software can recognize all inputs.

[0050] Step S162: In the simulation software, set the wind speed range to 3-25m / s, the air density to 1.225kg / m³, and the wind speed angle to 0–360°;

[0051] In this embodiment, when setting the wind speed range to 3-25 m / s in the simulation software, precise settings for the wind speed are required based on different wind conditions. The 3-25 m / s range is set according to the actual wind power generation environment in practical applications, encompassing different operating conditions from low to high wind speeds. The wind speed angle is set from 0° to 360°, representing the directional change of the wind speed, ensuring the simulation software can perform reasonable aerodynamic response simulations based on wind speed data from different directions. The air density value is typically set to 1.225 kg / m³, representing the air density of the standard atmosphere; this value directly affects the energy calculation of wind power generation. These fixed values ​​need to be input through the simulation software interface to ensure that various wind conditions are considered during the simulation. All these settings affect the final aerodynamic response of the wind turbine and the predicted power generation.

[0052] Step S163: In the simulation software, set the blade length to 40-60m and the blade angle to 0-45°;

[0053] In this embodiment, when the blade length is set to 40-60m and the blade angle to 0-45° in the simulation software, blade length and blade angle are important parameters affecting wind turbine performance. Blade length affects the swept area of ​​the wind turbine, which is directly related to the wind turbine's power generation capacity. Blade angle determines the angle of the blade relative to the wind direction, which affects the wind turbine's operating efficiency at different wind speeds. When setting these parameters, the corresponding blade parameters need to be input according to the wind turbine's design requirements. The blade length should be set between 40m and 60m, a range that covers common parameters for medium to large wind turbines. The blade angle should be set between 0° and 45°, a range applicable to typical wind turbine operating conditions, where 0° indicates the wind direction is parallel to the blade, and 45° is the blade's maximum angle of attack. After inputting these values ​​into the simulation software, the software automatically calculates the blade's aerodynamic response and its interaction with wind speed and direction.

[0054] Step S164: In the simulation software, set the gearbox transmission ratio to 1:10-1:20 and the generator efficiency to 90%-98%;

[0055] In this embodiment, when the gearbox transmission ratio is set to 1:10-1:20 and the generator efficiency to 90%-98% in the simulation software, the gearbox transmission ratio and generator efficiency are key mechanical transmission parameters in the wind power generation system. The gearbox transmission ratio determines the relationship between the input shaft speed and the output shaft speed, affecting the wind turbine's speed and torque. A transmission ratio set to 1:10-1:20 means that the wind turbine's input speed is typically within a low range, and the gearbox transmission system increases the speed to meet the generator's operating requirements. This range of transmission ratios is suitable for most commercial wind turbine generator sets. The generator efficiency is set to 90%-98%. Efficiency values ​​within this range represent the generator's efficiency in converting mechanical energy into electrical energy, and the efficiency setting directly affects the final power generation calculation. In the simulation software, the user should input these values ​​to ensure that the software can perform accurate dynamic simulations based on these mechanical characteristics.

[0056] Step S165: Run the wind power generation simulation program in the simulation software and output the wind power generation data.

[0057] In this embodiment, when running the wind power generation simulation program in the simulation software, the software will perform comprehensive calculations based on the aforementioned data such as wind speed, blade parameters, gearbox transmission ratio, and generator efficiency. The simulation process requires ensuring that all input parameters are accurately entered, and that the software can generate aerodynamic response, speed, torque, and power generation for each time period during the wind power generation process based on this input data. During the simulation, changes in wind speed and direction will affect the blade angle adjustment and aerodynamic response, thereby affecting the generator's speed and power output. The simulation software will output wind power generation data according to the set time periods. This data typically includes power output, speed changes, wind speed data, efficiency changes, etc., and will be used for subsequent power generation calculations, energy assessments, and the implementation of green energy consumption tasks.

[0058] Preferably, the gear meshing abnormality detection in step S2 includes:

[0059] Extract gear vibration signals from wind power generation data and convert the gear vibration signals into gear vibration spectra;

[0060] In this embodiment, vibration signals from the wind turbine gear system are collected using vibration sensors or accelerometers. The sensors should be installed in appropriate locations on the gearbox, typically the bearing area, to ensure that the acquired vibration signals represent the dynamic characteristics of the gear meshing process. The vibration signal acquisition time interval should be within 0.01 seconds, and the sampling frequency should be set to 5000Hz to ensure that the details of high-speed rotation in the gear system can be captured. Subsequently, the time-domain vibration signal is converted to a frequency-domain signal using a Fourier transform method to obtain the gear vibration spectrum. The results of spectrum analysis typically reveal different frequency components related to gear meshing, including important information such as the meshing frequency and gear harmonics.

[0061] The meshing harmonic frequencies are statistically analyzed based on the gear vibration spectrum, and the third meshing harmonic frequency is calculated based on the meshing harmonic frequencies.

[0062] In this embodiment, the gear meshing frequency is extracted. The meshing frequency is typically the frequency at which the gear teeth contact each other per revolution, calculated as: Gear meshing frequency = Rotational speed × Number of teeth ÷ 60. Then, the first, second, and third meshing harmonic frequencies are identified through the harmonic components in the spectrum. The third meshing harmonic frequency is three times the meshing frequency and usually corresponds to higher-order vibrations generated during gear meshing. These harmonic frequencies typically appear as multiple frequency peaks in the spectrum. Calculating the third meshing harmonic frequency requires a precise frequency resolution, typically set to 0.1 Hz, to ensure high-precision identification of relevant components in the spectrum.

[0063] Based on the third meshing harmonic frequency, the three meshing gear segments were screened, and the gear thermal load of the three meshing gear segments was analyzed to obtain gear thermal load data;

[0064] In this embodiment, gear segments with the strongest vibrations near the third harmonic frequency are selected. These gear segments typically correspond to the parts of the gearbox that experience high stress and severe wear. During the selection process, the actual gear segment is located by calibrating the peak frequency in the spectrum, and its generated heat load is calculated. The gear heat load calculation requires inputting the gear's rotational speed, meshing force, and material properties. Specifically, the formula for calculating the gear heat load is: Heat Load = Meshing Force × Gear Tooth Surface Contact Area × Coefficient of Friction. In this process, the coefficient of friction can be selected based on the material surface properties, typically ranging from 0.05 to 0.15. The rotational speed and meshing force are obtained from actual wind turbine operating data. This analysis identifies gear segments with high heat loads, providing a basis for subsequent lubricant film thickness measurement.

[0065] The high heat load period of the gear is statistically analyzed based on gear heat load data; the gear lubricating oil film thickness is measured based on the high heat load period of the gear; abnormal thickness of the gear lubricating oil film is identified according to the preset gear lubricating oil film thickness threshold, and abnormal thickness data of gear lubricating oil film is obtained.

[0066] In this embodiment, when analyzing the high heat load periods of the gears based on gear heat load data, it is necessary to perform time window analysis on the heat load data for each period to determine which periods exceed a set threshold. This threshold is typically set based on the design standards and heat resistance of the gear system, with a common heat load threshold being 1000 W / m². By segmenting the gear heat load data over time, if the heat load within a certain period consistently exceeds this threshold, it is identified as a high heat load period. These high heat load periods are critical for overheating and poor lubrication of the gear system and require close monitoring. During the high heat load periods, the thickness of the lubricating oil film is monitored in real time using a lubricating oil film thickness sensor. The measurement of lubricating oil film thickness typically employs optical sensing or ultrasonic technology, with an accuracy at the micrometer level. Depending on equipment requirements, the sensor should maintain good contact with the gear meshing area to ensure measurement accuracy. The measurement results are fed back to the monitoring system in real time and compared with the preset lubricating oil film thickness threshold. The standard threshold for lubricating oil film thickness is typically set based on the gear's operating conditions, generally between 20μm and 100μm. Exceeding this range indicates poor lubrication or oil film rupture. By monitoring gear lubricating oil film thickness data in real time and comparing it to the preset threshold, gear segments with abnormal lubricating oil film thickness can be identified. When the oil film thickness is below the set lower limit or above the set upper limit, the system will mark it as abnormal. Specifically, if the oil film thickness is below 20μm, it is considered insufficient lubrication, posing a risk of wear; if the oil film thickness is above 100μm, it indicates over-lubrication or a risk of oil film tearing. In these cases, the abnormal data needs to be stored and the corresponding gear segment marked for further analysis and early warning.

[0067] Abnormal lubricating oil film thickness of gear segments in wind power generation model based on abnormal gear lubricating oil film thickness data;

[0068] In this embodiment, after identifying abnormal thickness, the abnormal lubricating oil film thickness data is used to mark gear segments with abnormal lubricating oil film thickness in the wind power generation model. These gear segments are usually the parts that experience problems during operation, leading to decreased equipment efficiency or gear wear. The system feeds back the information of these gear segments to the operation control module of the wind power generation system for subsequent maintenance and adjustment. During marking, the operating status, vibration data, and changes in lubricating oil film thickness of the gear segments are usually used to determine which gear segments have problems.

[0069] Based on the abnormal lubricating oil film thickness of the gear segment and the three meshing gear segments, the gear meshing intersection calculation is performed to obtain the abnormal gear meshing data.

[0070] In this embodiment, during the intersection operation, the overlapping portion between the two conditions is calculated to determine which gear segments simultaneously meet both conditions. These gear segments are considered to be the most critical and require further inspection and maintenance. This data can be used in the wind turbine's condition monitoring and fault early warning system to ensure timely maintenance and repair of the equipment under abnormal conditions.

[0071] Preferably, the identification of bearing cracks in step S2 includes:

[0072] Gear rotation data is obtained by simulating gear rotation based on gear meshing anomaly data;

[0073] In this embodiment, the gear's geometric parameters (such as the number of teeth, pitch, module, pressure angle, etc.) and operating parameters (such as rotational speed, load, etc.) are input into the gear rotation simulation system. The gear rotation simulation employs numerical calculation methods, typically using the finite element method (FEM) or multibody dynamics (MBD) to simulate the rotation process of the gear system and obtain rotational data of the gear at different times. Key parameters of the rotational data include rotation angle, rotational speed, relative velocity at the gear contact point, and gear meshing force, which will serve as the basis for subsequent load calculations and analyses. It is particularly important to note that the gear rotation simulation should consider gear contact stiffness and frictional characteristics; therefore, the friction coefficient used in the simulation should be between 0.05 and 0.15, with the specific value determined based on the actual gear material and operating conditions. During the simulation, the time step should be set to within 10 ms to ensure simulation accuracy.

[0074] The gear load is statistically analyzed based on gear rotation data; gear shaft data is obtained; load transmission simulation is performed on the gear shaft data based on the gear load to obtain the gear shaft load;

[0075] In this embodiment, the gear load is calculated based on the distribution of meshing force during gear rotation. Specifically, the gear load calculation can be based on the meshing force, tooth surface contact area, and contact stress distribution during gear rotation. The load calculation formula is: Load = Sum of contact forces at each tooth surface contact point, where the contact force can be calculated using meshing stiffness, rotational angular velocity, and friction. The magnitude of the gear load is usually affected by the gear meshing frequency and vibration characteristics; therefore, special attention should be paid to recording the peak load moment during calculation. Statistical analysis of load data typically employs time-series analysis to obtain the load changes of the gear during rotation, including instantaneous load and average load. Structural parameters of the gear shaft are extracted from the wind turbine gearbox, including shaft diameter, length, rotational speed, and material properties (such as Young's modulus, Poisson's ratio, etc.). Data acquisition of the gear shaft can be achieved using displacement sensors, temperature sensors, and strain sensors installed on the shaft. The data collected by the sensors will reflect the shaft's deformation, temperature changes, and stress distribution in real time. When acquiring data, the clearance parameters between the bearing and the gear shaft should be set (typically 0.2mm to 1.0mm), while ensuring that the rotational speed of the gear shaft is synchronized with the system's operating state. Load transfer simulation of the gear shaft is then performed based on the gear load. This requires using the finite element method (FEM) to model and calculate the stress and load transfer process of the gear shaft. The load transfer simulation process requires coupling the gear shaft data (such as material properties, geometry, and load distribution) with the gear load data. The load transfer calculation is based on the following formula: Bearing load transfer force = Gear load × Load distribution factor. The setting of the load transfer factor depends on the material and geometric properties of the gear shaft, such as shaft stiffness and moment of inertia. The simulation results include stress and load distribution at different locations on the gear shaft and can help identify overload areas on the gear shaft.

[0076] Based on gear shaft load, gear load types are classified to obtain helical gear load data and spiral gear load data;

[0077] In this embodiment, the loads are grouped according to gear type (such as helical gears, spiral gears, etc.), and load data for each type of gear is extracted. Helical gears and spiral gears exhibit different load characteristics during meshing. Helical gear loads typically manifest as higher axial loads, while spiral gear loads are larger radial loads. The load type classification is based on the gear meshing angle and the direction of force during rotation. For example, helical gear load data is extracted by analyzing the direction of the meshing force, and the axial load is usually more significant; while spiral gear load data is calculated based on the gear's helix angle and contact surface, and the radial load is more prominent.

[0078] Extract axial load data of helical gears from helical gear load data; perform stress detection on the raceway of thrust bearings based on the axial load data of helical gears to obtain the stress on the raceway of thrust bearings; identify the concentrated area of ​​stress on the raceway of thrust bearings.

[0079] In this embodiment, the changes in the axial component during gear meshing are identified. Through torque balance and load distribution algorithms, the force distribution in the axial direction during gear meshing can be calculated. Axial load data for helical gears can typically be obtained through stress analysis methods, particularly using finite element analysis to simulate the contact stress on the gear meshing surface. The formula for calculating axial load data is typically: Axial load = Total load × Axial force coefficient of the helical gear tooth surface. The axial force coefficient is calculated based on the gear's geometric characteristics (such as tooth pitch, pressure angle, gear module, etc.). The thrust bearing's function is to bear axial loads and transmit them to the supporting structure; therefore, the stress distribution of the bearing raceway is crucial to the bearing's service life. Stress detection of the thrust bearing raceway is typically performed by establishing a three-dimensional finite element model of the bearing. This analysis requires inputting parameters such as axial load, bearing material properties, and rolling element dimensions. During the detection process, the simulated stress should pay particular attention to the contact pressure of the bearing raceway to determine which areas bear higher pressure. A common stress threshold is that the bearing raceway contact stress should not exceed 500 MPa to avoid premature fatigue failure. Stress testing of the thrust bearing raceway can identify areas of stress concentration. These areas are typically caused by uneven gear load distribution or manufacturing defects in the bearing. Stress concentration areas can be identified using stress distribution maps, with regions where stress values ​​exceed a threshold typically selected as concentration areas. By setting a stress threshold (e.g., bearing raceway stress exceeding 400 MPa), critical high-stress areas of the raceway can be identified; these areas are prone to fatigue damage during bearing operation.

[0080] Extract helical gear eccentric load data from helical gear load data; construct a rolling bearing load transfer model based on the helical gear eccentric load data; simulate bearing load transfer based on the rolling bearing load transfer model to obtain bearing load transfer data;

[0081] In this embodiment, the influence of gear manufacturing and assembly errors on load distribution is considered. Eccentric loads mainly manifest as uneven load distribution during gear meshing due to the eccentricity of the gear and bearing. By analyzing the load variation during helical gear meshing, the eccentric load distribution can be calculated. Eccentric load calculation typically involves vector decomposition of the load distribution and calculation based on geometric deviations and mechanical properties. After obtaining the helical gear eccentric load data, the next step is to construct a load transfer model for the rolling bearing. This model simulates the interaction between the rolling elements and raceways to calculate the load distribution among different rolling elements. The transfer model needs to include the bearing's geometric parameters (such as the number of rolling elements, raceway radius, raceway width, etc.) and external loads (such as radial loads, eccentric loads, etc.). Based on these data, the finite element method is used to simulate the bearing's force process, obtaining the load transfer distribution. When simulating bearing load transfer using the rolling bearing load transfer model, the load transfer process within the bearing is calculated by inputting the force distribution of the rolling elements and raceways. The results of bearing load transfer simulations typically include the loads borne by each rolling element within the bearing and the load distribution transmitted to the outer and inner rings. During the simulation, the number and size of the rolling elements, as well as the angles of force contact, need to be precisely set. Commonly used load transfer simulation software includes ABAQUS or ANSYS, which employ the finite element method for numerical calculations.

[0082] Calculate the load ellipse offset based on bearing load transfer; identify high-offset bearing raceway regions based on load ellipse offset;

[0083] In this embodiment, based on the bearing load transfer data, the calculation of the load elliptic offset is used to describe the asymmetric distribution of load on the bearing raceway. Typically, the elliptic offset of the load distribution is obtained by calculating the contact point displacement of the inner and outer rings of the bearing. The offset calculation formula is: Offset = (Load distribution center - Raceway geometric center). This calculation method considers the asymmetry of the contact between the rolling elements and the raceway and provides accurate offset data through numerical simulation. Based on the load elliptic offset, high-offset bearing raceway regions can be identified. These regions typically bear larger loads and are prone to early fatigue damage. High-offset regions are identified through statistical analysis of the offset; regions with offsets exceeding a set threshold are considered high-offset regions. A commonly used offset threshold is set to regions with offsets greater than 0.2 mm; raceway regions exceeding this value should be given special attention and monitoring.

[0084] The bearing fatigue zone is determined based on the concentrated area of ​​the bearing raceway and the high offset bearing raceway area.

[0085] In this embodiment, the fatigue region of the bearing can be determined by combining the stress concentration area of ​​the bearing raceway with the high offset bearing raceway region. These regions are typically caused by localized fatigue cracks generated under repeated loads, which easily lead to bearing failure. By using a fatigue analysis model, combined with the material's fatigue strength and load distribution, the location and extent of the fatigue region can be determined. This analysis typically relies on SN curves (stress-life curves) for calculation, and fatigue life prediction is obtained by simulating the number of load cycles.

[0086] Based on the bearing fatigue region, the probability of bearing cracks is predicted, and bearing crack data is obtained.

[0087] In this embodiment, the crack probability is calculated using fatigue cumulative damage theory based on crack probability prediction in the bearing fatigue region. This step requires inputting information such as the material's fatigue strength, stress concentration factors, and bearing operating environment parameters to calculate the probability of bearing crack occurrence. Common crack prediction models include the Miners rule; combined with simulated load cycles, the probability and time of bearing crack occurrence can be obtained.

[0088] Preferably, the location of the crack defect in step S2 includes:

[0089] Based on bearing crack data acquisition, bearing crack signals are obtained.

[0090] In this embodiment, a high-frequency sensor is used to acquire the vibration signal of the bearing. The sensor is installed near the outer or inner ring of the bearing to capture the high-frequency vibration signal generated by cracks during bearing operation. An accelerometer with a high sampling frequency of at least 10kHz is selected as the vibration sensor to ensure accurate capture of high-frequency crack signals. During sampling, an appropriate time window needs to be set, typically a frequency of 10,000 samples per second, to obtain crack signals with high precision. Each time a signal is acquired, the sensor output signal needs to be filtered to remove low-frequency noise and retain the high-frequency components relevant to the crack. The filter bandwidth is set to 2-20kHz, which typically includes the vibration frequency range generated by the crack. The vibration signal data is stored in the data acquisition system for further analysis.

[0091] Extracting the high-frequency response crack band of bearing crack signal;

[0092] In this embodiment, the acquired vibration signal is subjected to a Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal. In the frequency-domain signal, the high-frequency response caused by the crack typically manifests as strong peaks in one or more frequency bands. Based on the bearing's operating conditions and the characteristics of the crack, the frequency band is set to 2kHz to 20kHz. By filtering the frequency-domain signal, the high-frequency response crack band is extracted. The key to this step is accurately setting the frequency band range and selecting an appropriate frequency resolution. The frequency resolution is generally set to 1Hz to ensure accurate location of the crack signal. Within this frequency band, obvious peaks in the spectrum are searched to determine the characteristic frequencies and intensities of the crack; these frequency characteristics will serve as the basis for subsequent analysis.

[0093] Identifying abnormal areas in bearing raceways based on high-frequency response crack bands;

[0094] In this embodiment, the time-domain signal is converted into a frequency-domain signal using a Fast Fourier Transform (FFT) to obtain the spectrum of the crack signal. Then, the signal characteristics of different frequency bands in the spectrum are analyzed, particularly the frequency band occupied by the crack signal. In the frequency domain signal, cracks often exhibit significant energy peaks within a specific frequency range. Depending on the operating conditions of different bearings and the type of crack, the frequency range is generally set to 2kHz to 20kHz, and the frequency components of the crack usually fall within this range. Through careful screening of the frequency domain signal, the high-frequency response crack band is extracted. Within the high-frequency response crack band, by superimposing and analyzing the spectral signals, abnormal regions related to the contact points on the bearing raceway surface can be identified. At this point, the spectral signal needs to be compared with the geometric model of the bearing raceway. The geometric features of the bearing raceway surface (such as surface roughness, wear condition, etc.) directly affect the propagation characteristics of the crack signal; therefore, accurate geometric data of the bearing is required, which is usually obtained through 3D scanning or numerical modeling. To determine whether a certain area is abnormal, a threshold for the spectral signal can be set. A common method is to determine the threshold by calculating the standard deviation of the spectral signal, typically set to three times the standard deviation. If the amplitude of a frequency component in the spectrum exceeds the set threshold, the crack signal in that frequency band is considered to belong to an abnormal region. By setting this threshold, noise signals can be effectively eliminated, retaining only those signals reflecting defects in the bearing raceway. Then, time series analysis is performed on the signals from these abnormal regions to confirm whether they are related to specific areas of the bearing raceway. This analysis needs to be combined with the geometric model of the bearing raceway to ensure that the identified abnormal regions are consistent with the actual raceway surface.

[0095] The crack coordinates are obtained by inverting the crack coordinates in the abnormal region of the bearing raceway based on the high-frequency response crack band.

[0096] In this embodiment, the crack coordinates are inverted by combining the identified high-frequency response crack band with the geometric model of the bearing raceway. Establishing the bearing raceway model requires precise geometric data, typically obtained using a laser scanner or 3D measuring instrument to acquire the three-dimensional geometry of the bearing raceway. The crack signal from the high-frequency response band is registered with the corresponding position in the bearing raceway model, and the crack location is inverted based on the frequency characteristics of the crack signal. A certain inversion accuracy needs to be set during the inversion process, generally 1 mm is chosen as the accuracy standard to ensure the accuracy of the crack location. The inversion process uses the relationship between the crack frequency and the contact point of the bearing raceway, and the specific coordinates of the crack on the raceway are determined through numerical inversion methods.

[0097] The crack coordinates are mapped to the abnormal area of ​​the bearing raceway to obtain the location of the crack defect.

[0098] In this embodiment, the crack coordinates obtained from the inversion are mapped onto the actual bearing raceway surface. The mapping process first requires an accurate bearing raceway surface model, which is divided into multiple small regions, each representing a contact point. Specifically, the raceway surface is divided into grids at 0.5mm intervals. Based on the inverted crack coordinates, the grid region containing the crack coordinates is determined, and the location of the crack defect is marked within that region. Coordinate transformation technology is used to match the two-dimensional coordinates of the crack with the three-dimensional coordinate system of the raceway surface. By mapping the crack coordinates to the raceway surface, the location of the crack defect can be accurately pinpointed, forming a spatial distribution map of the crack defect. This map can be used for subsequent fault diagnosis and life prediction analysis.

[0099] Preferably, step S3 specifically includes:

[0100] Step S31: Set the axial flange thickness based on the location of the crack defect; evaluate the spindle rigidity based on the axial flange thickness;

[0101] In this embodiment, the location of the crack defect is typically obtained through high-frequency response crack band analysis, and the specific coordinates of the crack in the bearing raceway are obtained through a positioning algorithm. This data can be used for subsequent adjustment of structural parameters. The axial flange thickness is then set. The thickness of the axial flange is directly related to the spindle rigidity, and its design must consider the influence of the crack defect area. Specifically, the determination of the axial flange thickness should be optimized based on the crack location. Generally, the crack defect location is in the contact area of ​​the bearing; therefore, the thickness setting should ensure that the stress distribution in the crack area does not cause instability to the spindle structure. The specific value of the axial flange thickness can be calculated through mechanical analysis. Typically, finite element analysis (FEA) is used to simulate the bearing's stress state to determine a suitable flange thickness. Common thickness ranges are 5mm to 20mm, with the specific value determined based on the crack location, spindle material, and expected load conditions. Once the axial flange thickness is determined, the spindle rigidity is evaluated based on this thickness. Spindle rigidity is typically evaluated by calculating the spindle's bending stiffness, using the formula: Where L is the shaft length, E is the Young's modulus of the material, and I is the second moment of the shaft section. By inputting parameters such as shaft length, material properties, and flange thickness, the rigidity data of the spindle can be obtained.

[0102] Step S32: Adjust the hub connection angle based on the location of the crack defect; detect the off-center load stress dispersion force based on the hub connection angle;

[0103] In this embodiment, the hub connection angle is adjusted based on the location of the crack defect. The hub connection angle has a significant impact on force transmission and load distribution. Crack defects lead to uneven load distribution, thus affecting the stress state of the hub. By simulating the hub stress at different angles, an optimal connection angle is determined. This angle is determined through a combination of mechanical simulation and experimental data. The simulation results are typically calculated using the finite element method (FEM), and the angle is adjusted according to the force distribution between the hub and the shaft. A common angle range is 15° to 45°. Next, based on the adjusted hub connection angle, the off-center stress dispersion force is detected. Off-center stress dispersion force is caused by stress concentration or uneven distribution due to an unreasonable hub connection angle. Through finite element analysis, the stress distribution at different angles is calculated to identify the off-center stress dispersion caused by an inappropriate hub connection angle. This data helps optimize subsequent structural design and reduce the impact of uneven loads.

[0104] Step S33: Design the diameter of the connecting flange based on the spindle rigidity and the stress dispersion force of the off-center load;

[0105] In this embodiment, the diameter of the connecting flange directly affects the spindle's load-bearing capacity and fatigue resistance. The design of the connecting flange must ensure that it does not undergo excessive deformation or damage under long-term load. By analyzing the spindle's rigidity, a lower limit for the flange diameter is determined to ensure that it does not deform excessively under a given load. Then, based on the off-center load stress dispersion force, the flange diameter is further optimized to ensure that the connecting flange can effectively disperse stress under the most unfavorable conditions (e.g., extreme loads, crack defect areas). The flange diameter is usually obtained through optimization design calculations, and its value is generally between 100mm and 300mm, depending on the dimensions of the bearings and spindle, the location of cracks, and operating conditions.

[0106] Step S34: Construct a fatigue-resistant wind power structure based on the connecting flange diameter and axial flange thickness;

[0107] In this embodiment, fatigue-resistant design requires consideration of factors such as material fatigue strength, temperature variations in the working environment, and periodic load variations. When constructing the structure, the dynamic load response of the wind turbine structure is first simulated using finite element analysis based on the material's fatigue resistance (e.g., using alloy steel with a high fatigue limit). A wind turbine structure model is constructed based on the flange diameter and thickness, and fatigue analysis is performed to determine the fatigue life of key load-bearing components. The structural design must ensure that, during long-term operation, the stability of the wind power generation system is not affected by fatigue failure in any critical component. Important parameters in this process include flange diameter, thickness, material fatigue strength, and the wind turbine's operating load. The design utilizes fatigue strength calculation formulas and fatigue life prediction models.

[0108] Step S35: Integrate the fatigue-resistant wind power structure into the wind power generation model and predict the wind power output;

[0109] In this embodiment, the fatigue-resistant wind turbine structure is integrated with other components of the wind power generation system (such as generators, pitch control systems, etc.). The integration process is based on the working principle of the wind power generation system, calculating the output power of the system through dynamic simulation. Predicting wind power output requires consideration of factors such as wind speed, wind direction, and blade angle. Using historical wind speed data, turbine efficiency, and system power curves, the output power of the turbine under different operating conditions is calculated. Output power data can be predicted using mathematical formulas in the wind power generation model, generating a time series of wind power output.

[0110] Step S36: Generate green electricity digital vouchers based on wind power output.

[0111] In this embodiment, wind power output data should be accessed. This data is obtained by summing the real-time power generation data recorded during the operation of the wind power structure and the operating time of the wind turbine, with the unit being kilowatt-hours (kWh). After acquiring this data, it is filtered based on the green electricity certification standards set in the grid renewable energy grid-connected monitoring platform (e.g., according to the national standard GB / T 37244-2018 "Technical Rules for Issuance and Trading of Green Electricity Certificates," the electricity must be produced from non-fossil energy sources and included in the scope of renewable energy subsidies). Only the portions that meet the production requirements are retained. For example, the effective output of green electricity is set to require an average wind speed greater than 3.5 m / s, an equipment load rate of not less than 75%, and an annual cumulative on-grid electricity of not less than 1 million kWh. Data recording segments that meet these three technical parameter thresholds are then selected. Subsequently, a data encapsulation program is invoked to reorganize the fields of the filtered data, generating a digital data packet containing the following information: a unique wind farm code (e.g., CN-WF-20250411-001), equipment number, start and end times of power generation (recorded in ISO8601 time format, e.g., 2025-04-01T00:00:00Z to 2025-04-01T23:59:59Z), total wind power output (e.g., 2750.5kWh), metering agency code, and metering data signature (data digest encrypted using the national cryptographic algorithm SM3). Next, using a blockchain smart contract interface, this data packet is written into a pre-defined consortium blockchain system. This blockchain system should have a multi-node endorsement mechanism built on the Hyperledger Fabric framework to ensure record consistency. During the on-chain operation, the data verification logic is first executed via chaincode to check the integrity of data fields, the legality of the source, and the validity of the signature. After successful verification, the "Green Electricity Certificate Minting" function is executed to write the data onto the chain and generate a corresponding green electricity digital certificate token. This token has a unique number (TokenID) on the chain and supports encapsulation into a non-fungible token (NFT) according to the ERC-721 standard format, which is then bound to the wind power company's account address. Subsequently, all public information of the token can be accessed through the query interface of the green electricity traceability platform, including the geographical location of the wind farm from which the green electricity originated (latitude and longitude accurate to six decimal places), the production period, certification body information, and audit status. If circulation is required, the ownership of the token can be transferred to a green energy-consuming enterprise through a smart contract, and the hash, transaction time, and recipient address of the transaction are recorded to form a traceable green electricity consumption path. The data acquisition equipment involved in the entire process is a smart electricity metering terminal, whose acquisition accuracy must reach the national standard level of 0.5S or higher. Data transmission uses a dedicated VPN channel to ensure that the data is not tampered with during transmission. During the data signing process, key exchange and digital digest encapsulation are completed using nationally certified digital certificates (such as those issued by CFCA).The final generated green electricity digital certificate data is generally no less than 2KB in length, and has uniqueness, immutability and verifiability. It can be directly connected to the National Energy Administration's green certificate platform or used in regional green electricity trading markets.

[0112] Preferably, step S36 specifically includes:

[0113] Step S361: Perform wind power output credibility processing based on wind power output to obtain credible wind power output data;

[0114] In this embodiment, the core of the wind power output credibility processing lies in verifying, cleaning, and enhancing the credibility of the raw wind power output data. First, raw wind power output data is collected, with the data unit being kilowatt-hours (kWh) and a sampling period of 10 minutes. The data originates from the wind farm's smart energy metering terminal, and the metering accuracy should meet the 0.5S level requirement in the national standard GB / T17215.321-2008. For this data, a data verification module is used to perform boundary detection and timestamp consistency analysis, eliminating records with power values ​​below the minimum power threshold (e.g., below 10 kWh) or abnormal sampling intervals (exceeding ±15 seconds deviation). Subsequently, a hash verification based on the SHA-256 digest algorithm is performed on the retained data. The verification fields include the device ID, output quantity, and acquisition time, forming an anti-tampering verification chain. To achieve trusted processing, the verified data is encrypted using the national standard SM4 symmetric encryption algorithm in conjunction with a hardware encryption module. The encryption key is generated locally by the wind farm and bound to a key certificate issued by the CFCA certification authority. Each piece of data corresponds to a set of trusted data structures containing encrypted output, timestamp, and device code, ultimately forming trusted wind power output data. The data format is uniformly JSON structure, with a storage length of no less than 512 bytes.

[0115] Step S362: Obtain the geographical location identifier of the wind farm and bind the spatiotemporal tags to the trusted wind power output data to obtain the unique spatiotemporal identifier of wind power generation;

[0116] In this embodiment, the wind farm's geographic location identifier is collected via a BeiDou / GPS dual-mode positioning module, with a positioning accuracy within 5 meters. Geographic information is represented in three-dimensional coordinates of longitude, latitude, and elevation, with latitude and longitude retained to six decimal places, using the WGS-84 coordinate system. For each piece of trusted wind power output data, spatiotemporal synchronization is performed based on its acquisition time. A standard time source from a synchronization server (e.g., the China Time Service Center's time API) is invoked, and the time is represented using the ISO8601 standard format, such as "2025-04-11T10:00:00Z". Subsequently, the geographic location code (e.g., GEO-CN-GD-JZ-001, representing a wind farm in Jiangmen City, Guangdong Province) is bound to this time information and linked to the trusted output, constructing a unique spatiotemporal identifier for wind power generation. This identifier structure contains the fields: "wind_id" (device ID), "geo_pos" (latitude and longitude coordinates), "time_stamp" (time stamp), "power_out" (encrypted output), and "source_hash" (SHA-256 hash value generated in step S361). This spatiotemporal identifier structure is bound by structured tags, and the output is a single unique record, possessing global uniqueness and data consistency.

[0117] Step S363: Generate the original structure data of green electricity certificates based on the unique spatiotemporal identifier of wind power generation;

[0118] In this embodiment, the original structure data of the green electricity certificate consists of a fixed data structure. All field content is extracted and expanded from the unique spatiotemporal identifier of wind power generation in step S362. The original structure includes the following fields: "Certificate Number" field (generated from region code, equipment ID, and timestamp, format such as ZC-GD-001-20250411T100000), "Power Generation" field (unit kWh, derived from the encrypted field in the trusted data), "Geographic Information" field (latitude and longitude coordinates), "Time Period" field (start and end time of power generation), "Power Generation Equipment" field (equipment model, rated power, and operating status code), "Signature Digest" field (inherited from the hash digest value in step S361), "Metrology Agency" field (third-party metering company number), and "Verification Identifier" field (empty value used for subsequent digital signature writing). The generation process uses a fixed data template and is automatically completed by the data encapsulation engine through Python scripts. All fields are encoded according to the JSON structure, and the size of a single structure data is no less than 1KB. This structure data serves as the source data for the green electricity certificate, providing the basic data carrier for subsequent authentication and blockchain writing.

[0119] Step S364: Perform digital signature authentication processing based on the original structure data of the green electricity certificate to obtain the original structure data of the authenticated green electricity certificate;

[0120] In this embodiment, the digital signature authentication process employs the SM2 asymmetric encryption algorithm approved by the State Cryptography Administration. First, the national cryptographic signature device, using a private key issued by the CFCA certification center, performs digest processing on the original green electricity certificate structure data generated in step S363. The digest algorithm is SM3, generating a 256-bit digest value. Then, the local private key is used to encrypt this digest value using SM2, generating a digital signature field, which is inserted into the "verification identifier field" of the original structure data, forming the authenticated green electricity certificate structure. This process uses a hardware security module (HSM) for key retrieval and signature encapsulation, ensuring that the key cannot be exported and the signature cannot be replayed. The final output data structure remains consistent with the original certificate, only adding a "signature" field, containing a Base64-encoded signature string with a length of no less than 512 characters. After authentication, the certificate structure undergoes a final integrity check, using SHA-256 to regenerate a hash for the entire structure, facilitating subsequent on-chain verification.

[0121] Step S365: Write the original structure data of the certified green electricity certificate into the blockchain node to generate a green electricity digital certificate.

[0122] In this embodiment, the original structure data of the certified green electricity certificate is uploaded to a designated blockchain node deployed in the Hyperledger Fabric consortium blockchain architecture via an API interface. Before writing, the certificate verification function (chaincode_verify_cert) in the chaincode is called to verify the signature field, including signature validity verification (decryption using the SM2 public key and comparison with the SM3 digest) and structure field integrity check. After successful verification, the "certificate on-chain function" chaincode_issue_cert is called to persistently write the data structure to the ledger in Key-Value format, where the Key value is the certificate number (e.g., ZC-GD-001-20250411T100000) and the Value is a complete JSON structure. After successful writing, the blockchain node returns the transaction hash value (TransactionID) and records the write transaction in the current block. Each green electricity digital certificate has a unique record position in the ledger and is bound to an on-chain timestamp, node signature information, and a multi-replica storage path. The blockchain system requires at least four endorsing nodes and employs the Raft consensus mechanism for data consistency verification. Success is considered achieved only after the certificate has been written and stored on a majority of nodes. The final certificate token can be retrieved via an on-chain query interface, containing complete information such as block hash, power generation information, signature content, original certificate data, and wind turbine ID, enabling complete traceability and confirmation of every unit of green electricity.

[0123] Preferably, step S4 specifically includes:

[0124] Step S41: Obtain user power grid load data;

[0125] In this embodiment, user grid load data is acquired through smart meters and centralized load monitoring devices deployed at the end of the distribution network. The meters must comply with national standard GB / T 17215.321-2008 (0.5S level or higher accuracy), with a sampling period of 15 minutes and units in kilowatts (kW). Each sampling record includes "user ID," "timestamp," "current value (A)," "voltage value (V)," and "active power (kW)." All data is transmitted to the regional data control platform via the DLMS / COSEM communication protocol. To ensure data accuracy, an NTP protocol time synchronization server is used to align all sampling times, allowing an error of no more than ±1 second. Collected data undergoes format verification and missing value completion; missing values ​​are replaced by the historical average value for the same time period. User data missing for more than two consecutive sampling periods is directly marked as unusable. The final grid load data structure is output in CSV file format, with fields including "user_id," "timestamp," "active_power," "reactive_power," "voltage," and "current," used for subsequent green electricity mapping and absorption processing.

[0126] Step S42: Map the green electricity digital certificate to the user's grid load data to obtain the user's green electricity load data;

[0127] In this embodiment, the mapping operation uses timestamps and regional grid codes for binding and matching. The green electricity digital certificate contains a generation timestamp field (format ISO8601) and a geographic location code corresponding to the power generation equipment (e.g., CN-JZ-002). The user's grid load data also contains an electricity consumption timestamp and a user's region number field (e.g., USER-JZ-001). Time alignment is used to match the generation time period of the green electricity certificate with the user's electricity consumption time period, with an allowable deviation range of ±5 minutes. Geographic matching is based on the power supply links recorded in the distribution network topology, comparing whether the user's branch contains the green power node through the power supply path database. After a successful match, the output quantity field in the green electricity certificate is allocated to eligible users according to the user's electricity consumption ratio, forming a mapping relationship table. The mapping table records "certificate number", "user number", "allocated green electricity (kWh)" and "matching time period", stored in JSON structure as user green electricity load data for subsequent green electricity transmission and distribution analysis.

[0128] Step S43: Determine the amount of green electricity to be transmitted and distributed based on user green electricity load data;

[0129] In this embodiment, the absorption capacity is statistically analyzed based on the portion of user electricity consumption that can be covered by green electricity matching. Specifically, the "allocated green electricity" field in the user's green electricity load data is aggregated to calculate the total green electricity absorption value for all users within each 15-minute time period. Simultaneously, the real-time power transmission and distribution data platform is accessed to obtain the current time period's transmission capacity threshold, in megawatts (MW). The data originates from the dispatch center's SCADA system, with an accuracy requirement of ±0.1MW and a data refresh cycle of 5 minutes. If the total allocated green electricity exceeds the current transmission capacity threshold, the allocation is proportionally reduced to the maximum transmission and distribution capacity. The reduction rule is "dispatch available capacity / total requested capacity." All user green electricity load data are adjusted according to this ratio to form the actual transmission and distribution green electricity absorption data structure, with fields including "user number," "adjusted green electricity," "timestamp," and "transmission and distribution capacity source number." Finally, the transmission and distribution green electricity absorption is summarized daily to form a time-of-use absorption data table in kWh units.

[0130] Of particular importance, step S43 includes the following steps:

[0131] Step S431: Extract cable data and user access meter address based on user green electricity load data;

[0132] In this embodiment, in the green electricity consumption and dispatch system, the green electricity load data is a structured table containing information such as user ID, timestamp, load power (unit kW), access node number, and voltage level. Based on the "user ID" field of this data, the corresponding user's meter equipment number and installation address are retrieved from the power asset management database. Further, based on the "access node number" field, the distribution cable number connected to the user's access point is extracted from the power grid asset equipment database. This cable number must uniquely correspond to a cable segment in a 10kV or lower branch circuit, and include cable specifications (including cable cross-sectional area, conductor material, cable type, laying method, length, insulation resistance, etc.).

[0133] Step S432: Identify the cable flow path of cable data based on the user's access meter address;

[0134] In this embodiment, based on the user access cable number extracted in step S431, the connection relationship between cables is obtained by tracing back to the substation or feeder outgoing point. By reading the cable path layer and topology connection table in the GIS system, the connection relationship between cable numbers is analyzed segment by segment, and the connection links are established step by step according to the path hierarchy of "meter address → junction box → distribution box → branch line → main line → transformer → busbar". The direction of each cable path is determined by the incoming and outgoing ends shown in the installation drawings; the incoming end is defined as the power supply side, and the outgoing end is defined as the load side. If the access cable is bidirectional, the current flow direction is determined using real-time load flow analysis data from the power distribution automation system. Finally, a cable flow path table arranged according to the actual physical direction is output, labeling cable segment numbers, start and end nodes, cable length, direction, conductor resistivity, and other attributes.

[0135] Step S433: Construct the power grid topology based on the cable flow path and the user's connected electricity meter address;

[0136] In this embodiment, based on the cable flow path output in S432, each node in the path (including transformers, feeder branch points, junction boxes, low-voltage switchgear, and meter access points) is used as a vertex in the graph structure, and each cable segment is used as an edge to establish a physical topology model of the power grid. The topology graph is stored using an adjacency list structure, and each node contains attributes such as voltage level (unit: kV), bus number, connected device ID, electrical distance (unit: m), and branch resistance and reactance values ​​(unit: Ω). The above data is imported using a distribution network modeling platform (such as DIgSILENT PowerFactory or PSS®E), and the complete network topology of the low-voltage distribution network is constructed and visualized according to the structural rules. The topology graph should ensure that it does not contain loops and has a clear directionality (flowing from the power source side to the load side), and the structure should meet the requirements of single radial power supply or dual power supply switching structure in the distribution regulations.

[0137] Step S434: Map the cable flow path to the power grid topology to obtain the power grid flow topology;

[0138] In this embodiment, based on the power grid topology generated in S433, the cable flow paths identified in step S432 are mapped one-to-one with the cable numbers according to the node numbers. During the mapping process, the cable number serves as the unique identifier of the edge, and the node number serves as the vertex of the topology graph. If there are multiple path intersection points, branch processing nodes need to be introduced into the topology graph, and the control method of the branch nodes (such as automatic reclosing, fuse isolation) needs to be confirmed. After mapping, a directed graph structure with a clear direction is generated, forming a power grid flow topology graph. Each path has a direction attribute, cable impedance attribute, and load distribution label. This structure is stored in a structured graph database, such as the Neo4j graph database, to facilitate subsequent path tracing and power loss calculation.

[0139] Step S435: Detect the transmission and distribution path impedance based on the power grid flow topology; calculate the transmission and distribution path current based on the total impedance of the transmission and distribution path; calculate the transmission and distribution energy loss based on the transmission and distribution path impedance and the transmission and distribution path current;

[0140] In this embodiment, within the constructed power grid topology, the impedance of the transmission and distribution path is first measured based on the specific technical parameters of each cable segment. The data for each cable segment includes its resistance (in ohms) and reactance (in ohms), obtained by retrieving construction data and equipment technical parameter tables registered by the cable installation unit. The resistance value is related to the cable material, cross-sectional area, length, and operating temperature; specific values ​​can be obtained by consulting construction as-built drawings and cable product technical documents. The reactance value is extracted from relevant State Grid cable design manuals based on the cable type, insulation structure, and laying method. The impedance calculation of the transmission and distribution path follows the directed connection relationship in the topology, starting from the wind power access point and ending at the access point corresponding to the end user's meter. The resistance and reactance values ​​of each cable segment are accumulated sequentially according to the order of the cable segments connected in the path. In practice, a graph traversal algorithm (e.g., depth-first traversal) is used to extract all cable segment numbers involved in the wind power transmission path. Then, the impedance parameters corresponding to these cable segments are extracted from the database and calculated sequentially to obtain the total impedance of each path. If multiple parallel branches exist, the impedance data of each branch is recorded separately. After obtaining the total impedance, the wind power transmission data (in kilowatts) for that period is extracted from the wind farm dispatch system. If the dispatch data has a 15-minute resolution, the transmission power value corresponding to the timestamp is extracted. Simultaneously, the real-time voltage value of the wind farm injection point is read from the power monitoring device, in kilovolts. To standardize the calculation method, it is assumed that the system's operating power factor is 0.95, which is the typical value for wind farm grid-connected power factor specified in the State Grid operation standards. Using these parameters, the line current of the corresponding transmission path is calculated. This calculation is performed by the power analysis module, which is part of the dispatch master station system and has real-time power, voltage, and current calculation functions. After the current data is determined, the power loss calculation begins. The required inputs are the aforementioned path current value and the resistance value of each cable segment. The system inputs the current value to the cable parameter processing module, which matches the current and resistance and performs power loss calculation, in kilowatts. To obtain energy loss, the transmission time period must also be considered. For example, if the standard dispatch cycle is 15 minutes, a time coefficient of 0.25 hours is used to calculate the energy loss value for the corresponding time period, in kilowatt-hours. This processing is completed by the energy loss statistics module. The module's output format includes path number, user, cable segment number, total path resistance, current value, power loss value, and energy loss value, facilitating subsequent summary analysis. To ensure the accuracy of the results, all ineffective power supply branches should be excluded during processing, i.e., only the flow path corresponding to the wind farm's power supply should be retained. When a user's access meter address involves multiple branch paths, the loss should be calculated and aggregated separately for each path.The final result is a table showing the correspondence between transmission and distribution paths and losses. Each record clearly lists: wind power access point number, user's meter address, grid topology path number, total path impedance, current value, active power loss, energy loss value, and the corresponding time period. This table serves as the data foundation for subsequent consumption calculation and traceability.

[0141] Step S436: Obtain the total power transmission from the wind farm;

[0142] In this embodiment, the total power transmitted by the wind farm is derived from data from wind power metering devices installed at the wind farm's outlet. These devices record the output power (in kWh) at 5-minute or 15-minute intervals using independent meters. This power data is synchronized with the EMS system via the main station system, with timestamps accurate to the minute, and includes fields such as: time, power, transmission voltage, transmission current, and total power transmitted. The historical records are exported in CSV format, and the power transmission data for each time period within the current transmission and distribution cycle are summed to obtain the total power transmitted by the wind farm in the current cycle.

[0143] Step S437: Determine the amount of green electricity consumed by the wind farm based on the total power transmission from the wind farm and the power transmission and distribution losses.

[0144] In this embodiment, the total power transmission data (unit: kWh) for a specified period is retrieved from the wind farm. This data is recorded by the metering device at the wind farm's outgoing line. Then, the transmission and distribution energy loss value calculated in step S435 is retrieved. The two must strictly correspond in fields such as timestamp, meter number, and grid path number. The data processing module deducts the corresponding transmission and distribution losses from the total power transmission volume to obtain the actual green electricity received by the user side, which is the green electricity consumption amount. The result is rounded to two decimal places. Each record includes fields such as timestamp, wind farm number, access node ID, meter number, cable path number, topology path number, power transmission volume, loss amount, and consumption amount, and is uniformly stored in the green electricity consumption database. Before data is stored, it is checked using a path link consistency algorithm to ensure that the path number, meter number, and topology structure correspond one-to-one. This data provides accurate and stable data support for the subsequent construction of a green electricity traceability map and green electricity consumption verification.

[0145] Step S44: Determine the amount of green electricity stored based on the amount of green electricity consumed by transmission and distribution;

[0146] In this embodiment, the green electricity storage capacity is calculated from the green electricity that has not been timely dispatched and used by the transmission and distribution system. The total amount of reliable wind power output is calculated in kWh, and then the actual amount of green electricity consumed by transmission and distribution is subtracted to obtain the remaining storable electricity. The energy storage system parameters need to be obtained through EMS (Energy Management System). The energy storage capacity of each energy storage unit should be greater than 200kWh, the charging and discharging efficiency should not be less than 90%, and the minimum charging unit threshold is set to 20kWh. To determine whether green electricity is suitable for storage, two conditions must be met simultaneously: (1) the remaining green electricity in a single transaction is not less than 20kWh; (2) the target energy storage device is in an idle state, and the SOC (State of Charge) is less than 80%. Data that meets the conditions is written into the energy storage scheduling module, and the energy storage unit number, storage time, and electricity value are recorded to form a green electricity storage structure. The fields include "storage unit number", "storage amount", "corresponding green electricity certificate number", "time stamp", and "equipment status". All records are written into the local database for subsequent retrieval and scheduling.

[0147] Of particular importance, step S44 includes the following steps:

[0148] Step S441: Extract the green energy storage device number based on the green energy transmission and distribution consumption;

[0149] In this embodiment, available energy storage device information is extracted from green electricity consumption records. This step requires calling the device registry and transmission and distribution consumption data table in the green electricity dispatch database, and performing a joint query based on the transmission and distribution node ID, transmission and distribution time period (e.g., 10:00–10:15 on April 11, 2025), and the corresponding green electricity consumption amount (unit: kWh). For each transmission and distribution consumption record, its corresponding transmission and distribution node ID is read, and then the energy storage device number that is active and physically connected to the node is retrieved from the "Static Configuration List of Energy Storage Devices" based on this ID. The filtering conditions must meet the following requirements: the device is not offline in the current time period, the status code is marked as "1" (online), the battery temperature is below 50°C, and the state of charge (SOC) is below 95%. If a transmission and distribution node corresponds to multiple energy storage device numbers, all of them need to be extracted to form a set of energy storage device numbers for data retrieval in subsequent steps. The execution of this step is implemented using SQL structured query language, and the parameter fields include node ID, device number, connection status, last reported time, current SOC, etc. Equipment numbers such as “BESS-ZH-001” and “BESS-ZH-002” are regularly reported and synchronized through the energy storage station’s automatic monitoring system (such as a DCS platform).

[0150] Step S442: Determine the charging capacity of the energy storage unit based on the green energy storage device number;

[0151] In this embodiment, after extracting the energy storage device number, the charging capacity parameters of each energy storage unit need to be extracted from the real-time status database. Each number corresponds to an energy storage unit, and the charging capacity of this unit is determined by three parameters: maximum allowable charging power P_max (kW), remaining rechargeable capacity E_remain (kWh), and allowable charging time T_max (hours). P_max is directly read from the rated parameters in the device manual. For example, for a lithium battery pack with model "BESS-ZH-001", its P_max is 250kW. E_remain is calculated based on the current SOC (e.g., 45%) and the total capacity (e.g., 1000kWh), calculated as E_remain = (100% - SOC) × total capacity = 550kWh. T_max is determined by the platform's current grid operation strategy, generally ranging from 1 to 4 hours. The current strategy is specified by the regional load control and dispatching system and is 2 hours. Thus, the maximum instantaneous charging power and maximum acceptable capacity of each energy storage unit are finally obtained. The above parameters need to be retrieved in real time through the SCADA system or EMS platform, and all data should have timestamps consistent with the consumption period. For example, if the consumption period is 10:00–10:15, then the equipment status value at 10:00 should be retrieved, and historical averages or discrete values ​​should not be used.

[0152] Step S443: Perform power distribution simulation based on the charging capacity of the energy storage unit and the amount of green electricity consumed in transmission and distribution to obtain power distribution data;

[0153] In this embodiment, energy storage units are sorted from largest to smallest by E_remain. During the current allocation cycle, power is preferentially allocated to devices with larger E_remain. The allocation ratio is calculated based on the combined weight of the device's P_max and E_remain. For example, if the P_max of three devices are 250kW, 150kW, and 100kW, and their E_remain are 550kWh, 300kWh, and 200kWh, respectively, then the weight can be set as Wi = P_max × E_remain. After obtaining the weight coefficient for each device, the total power consumption is allocated according to the weight ratio. During the allocation cycle, the allocated power must be controlled to not exceed the device's allowed instantaneous power limit, and the allocated energy must be controlled to not exceed its remaining capacity limit. If these limits are exceeded, the remaining portion is allocated to the next priority device. The final data includes the power received by each energy storage unit in the current cycle (in kWh), the allocation start and end timestamps, the device number, and the control command execution record. The allocation results are stored in the "Electricity Allocation Data Table". The table structure fields include: equipment number, allocation period, allocated electricity (rounded to two decimal places), execution time, success or failure flag, etc.

[0154] Step S444: Calculate the green energy storage capacity based on the power distribution data and the amount of green energy consumed in transmission and distribution.

[0155] In this embodiment, all device allocation records that have successfully received electricity in the current cycle are extracted from the power allocation data table, and all allocated electricity is accumulated. To ensure accuracy, the actual charging feedback data of the devices in the SCADA system is retrieved for comparison. If a device fails to complete the charging operation due to communication abnormalities or malfunctions, it is not included in the storage calculation. The actual green electricity storage (unit: kWh) is obtained by summing the actual electricity generated by each device completing the charging operation within the same time cycle. This value must not exceed the green electricity consumption amount determined in S437; otherwise, abnormal data should be removed. All calculation results, along with the corresponding transmission and distribution node ID, energy storage device number, cycle start and end time, and record timestamp, are written into the "Green Electricity Storage Details Table." The table structure includes fields such as cycle number, consumed electricity, actual allocation amount, green electricity storage amount, and difference value (used for traceability error analysis), all stored using double-precision floating-point numbers. Finally, the green electricity storage data will serve as the quantitative input parameter for the "stored green electricity path segment" in the subsequent traceability map, which will be read and called by the map generation module.

[0156] Step S45: Integrate the green electricity consumption and storage capacity to obtain green electricity consumption data;

[0157] In this embodiment, the integration operation is achieved by constructing a unified structure, merging the "green electricity consumption data" output in step S43 and the "green electricity storage structure" output in step S44 into a unified data record. Each data record contains the following fields: "Green Electricity Certificate Number", "User Number (if consuming) / Storage Unit Number (if storing)", "Timestamp", "Green Electricity Amount (kWh)", "Consumption Type" (field value is 'transmission and distribution' or 'storage'), "Equipment Number", and "Confirmation Code (used for tamper-proof SHA-256 verification)". The system performs a unified sorting and deduplication process after the end of each daily scheduling cycle to avoid duplicate records, generating a unique hash value for all fields as a verification identifier to ensure data uniqueness and integrity. The integrated green electricity consumption data structure is stored in JSON format, and copies are retained in both local and remote redundant data storage systems, forming a formal and traceable green electricity consumption master table.

[0158] Step S46: Construct a green electricity traceability link diagram based on green electricity consumption data and upload it to the green electricity management platform to execute the green electricity consumption traceability task.

[0159] In this embodiment, the green electricity traceability link graph is constructed using the graph database Neo4j. Node types include "generation unit," "user node," "transmission and distribution node," "storage unit," and "time node," while edge types include "generation-transmission and distribution," "transmission and distribution-user," and "generation-storage." Each edge records the green electricity transfer path and its corresponding quantified value (in kWh). First, the green electricity consumption data from step S45 is structurally transformed to construct the node and edge information of the graph data. Node attributes include ID, type, number, and time, while edge attributes include energy value, timestamp, and voucher number. Subsequently, a full-path structure chain is constructed using the Cypher statement in the graph database, tracing the entire process of transmission, distribution, storage, and consumption from each green electricity generation, generating a complete traceability path graph. Graph data rendering uses D3.js for visualization, with the image generation resolution set to 1920×1080 and output in SVG format. Finally, the generated traceability link diagram and its data structure are uploaded to the green electricity management platform deployed in the government cloud or energy regulatory center via HTTPS encryption protocol. The platform records the data upload time, node signature information, path hash fingerprint and other information, and forms a green electricity consumption traceability task log archive.

[0160] Preferably, this specification also provides a green energy consumption traceability system for executing the green energy consumption traceability method described above, the green energy consumption traceability system comprising:

[0161] The wind power generation simulation module acquires wind turbine design drawings and constructs a wind power generation model; it then performs wind power generation simulation based on the model to obtain wind power generation data.

[0162] The bearing crack identification module performs gear meshing anomaly detection on the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identifies bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; and locates the crack defect location based on the bearing crack data.

[0163] The green electricity digital certificate generation module designs fatigue-resistant wind power structures based on the location of crack defects; integrates the fatigue-resistant wind power structures into the wind power generation model and predicts wind power output; and generates green electricity digital certificates based on the wind power output.

[0164] The green electricity traceability module acquires user grid load data; maps green electricity digital certificates to user grid load data to obtain user green electricity load data; performs green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; constructs a green electricity traceability link diagram based on green electricity consumption data and uploads it to the green electricity management platform to execute green electricity consumption traceability tasks.

[0165] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application be incorporated into the invention.

[0166] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for tracing the source of green energy consumption, characterized in that, Includes the following steps: Step S1: Obtain the design drawings of the wind turbine and build a wind power generation model; perform wind power generation simulation based on the wind power generation model to obtain wind power generation data; Step S2: Detect gear meshing anomalies in the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identify bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; Based on bearing crack data, the location of crack defects is determined. Step S2, identifying bearing cracks, includes: Gear rotation data is obtained by simulating gear rotation based on gear meshing anomaly data; The gear load is statistically analyzed based on gear rotation data; gear shaft data is obtained; load transmission simulation is performed on the gear shaft data based on the gear load to obtain the gear shaft load; Based on gear shaft load, gear load types are classified to obtain helical gear load data and spiral gear load data; Extract axial load data of helical gears from helical gear load data; perform stress detection on the raceway of thrust bearings based on the axial load data of helical gears to obtain the stress on the raceway of thrust bearings; identify the concentrated area of ​​stress on the raceway of thrust bearings. Extract helical gear eccentric load data from helical gear load data; construct a rolling bearing load transfer model based on the helical gear eccentric load data; simulate bearing load transfer based on the rolling bearing load transfer model to obtain bearing load transfer data; Calculate the load ellipse offset based on bearing load transfer; identify high-offset bearing raceway regions based on load ellipse offset; The bearing fatigue zone is determined based on the concentrated area of ​​the bearing raceway and the high offset bearing raceway area. Based on the bearing fatigue region, the probability of bearing cracks is predicted, and bearing crack data is obtained. Step S3: Design a fatigue-resistant wind power structure based on the location of crack defects; integrate the fatigue-resistant wind power structure into the wind power generation model and predict the wind power output; generate green electricity digital vouchers based on the wind power output. Step S4: Obtain user grid load data; map green electricity digital vouchers to user grid load data to obtain user green electricity load data; perform green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; construct a green electricity traceability link diagram based on green electricity consumption data and upload it to the green electricity management platform to execute the green electricity consumption traceability task.

2. The method for tracing the source of green energy consumption according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the design drawings of the wind turbine and extract the blade structure data and gearbox structure data; Step S12: Acquire wind data and perform blade aerodynamic response detection on the blade structure data to obtain blade aerodynamic response data; Step S13: Perform power transmission simulation on gearbox structure data based on blade aerodynamic response data, where the input torque is set to 40000-70000Nm and the input speed is set to 10-20rpm to obtain power transmission data; Step S14: Determine the power generation energy based on the power transmission data; Step S15: Construct a wind power generation model based on power transmission data and generated energy; Step S16: Perform wind power generation simulation based on the wind power generation model to obtain wind power generation data.

3. The method for tracing the source of green energy consumption according to claim 2, characterized in that, Step S16 is as follows: Step S161: Import the wind power generation model into the simulation software; Step S162: In the simulation software, set the wind speed range to 3-25m / s, the air density to 1.225kg / m³, and the wind speed angle to 0–360°; Step S163: In the simulation software, set the blade length to 40-60m and the blade angle to 0-45°; Step S164: In the simulation software, set the gearbox transmission ratio to 1:10-1:20 and the generator efficiency to 90%-98%; Step S165: Run the wind power generation simulation program in the simulation software and output the wind power generation data.

4. The method for tracing the source of green energy consumption according to claim 1, characterized in that, The gear meshing abnormality detection in step S2 includes: Extract gear vibration signals from wind power generation data and convert the gear vibration signals into gear vibration spectra; The meshing harmonic frequencies are statistically analyzed based on the gear vibration spectrum, and the third meshing harmonic frequency is calculated based on the meshing harmonic frequencies. Based on the third meshing harmonic frequency, the three meshing gear segments were screened, and the gear thermal load of the three meshing gear segments was analyzed to obtain gear thermal load data; The high heat load period of the gear is statistically analyzed based on gear heat load data; the gear lubricating oil film thickness is measured based on the high heat load period of the gear; abnormal thickness of the gear lubricating oil film is identified according to the preset gear lubricating oil film thickness threshold, and abnormal thickness data of gear lubricating oil film is obtained. Abnormal lubricating oil film thickness of gear segments in wind power generation model based on abnormal gear lubricating oil film thickness data; Based on the abnormal lubricating oil film thickness of the gear segment and the three meshing gear segments, the gear meshing intersection calculation is performed to obtain the abnormal gear meshing data.

5. The method for tracing the source of green energy consumption according to claim 1, characterized in that, The location of the crack defect mentioned in step S2 includes: Based on bearing crack data acquisition, bearing crack signals are obtained. Extracting the high-frequency response crack band of bearing crack signal; Identifying abnormal areas in bearing raceways based on high-frequency response crack bands; The crack coordinates are obtained by inverting the crack coordinates in the abnormal region of the bearing raceway based on the high-frequency response crack band. The crack coordinates are mapped to the abnormal area of ​​the bearing raceway to obtain the location of the crack defect.

6. The method for tracing the source of green energy consumption according to claim 1, characterized in that, Step S3 is as follows: Step S31: Set the axial flange thickness based on the location of the crack defect; evaluate the spindle rigidity based on the axial flange thickness; Step S32: Adjust the hub connection angle based on the location of the crack defect; detect the off-center load stress dispersion force based on the hub connection angle; Step S33: Design the diameter of the connecting flange based on the spindle rigidity and the stress dispersion force of the off-center load; Step S34: Construct a fatigue-resistant wind power structure based on the connecting flange diameter and axial flange thickness; Step S35: Integrate the fatigue-resistant wind power structure into the wind power generation model and predict the wind power output; Step S36: Generate green electricity digital vouchers based on wind power output.

7. The method for tracing the source of green energy consumption according to claim 6, characterized in that, Step S36 is as follows: Step S361: Perform wind power output credibility processing based on wind power output to obtain credible wind power output data; Step S362: Obtain the geographical location identifier of the wind farm and bind the spatiotemporal tags to the trusted wind power output data to obtain the unique spatiotemporal identifier of wind power generation; Step S363: Generate the original structure data of green electricity certificates based on the unique spatiotemporal identifier of wind power generation; Step S364: Perform digital signature authentication processing based on the original structure data of the green electricity certificate to obtain the original structure data of the authenticated green electricity certificate; Step S365: Write the original structure data of the certified green electricity certificate into the blockchain node to generate a green electricity digital certificate.

8. The method for tracing the source of green energy consumption according to claim 1, characterized in that, Step S4 is as follows: Step S41: Obtain user power grid load data; Step S42: Map the green electricity digital certificate to the user's grid load data to obtain the user's green electricity load data; Step S43: Determine the amount of green electricity to be transmitted and distributed based on user green electricity load data; Step S44: Determine the amount of green electricity stored based on the amount of green electricity consumed by transmission and distribution; Step S45: Integrate the green electricity consumption and storage capacity to obtain green electricity consumption data; Step S46: Construct a green electricity traceability link diagram based on green electricity consumption data and upload it to the green electricity management platform to execute the green electricity consumption traceability task.

9. A traceability system for green energy consumption, characterized in that, For performing the green energy consumption traceability method as described in claim 1, the green energy consumption traceability system comprises: The wind power generation simulation module acquires wind turbine design drawings and constructs a wind power generation model; it then performs wind power generation simulation based on the model to obtain wind power generation data. The bearing crack identification module performs gear meshing anomaly detection on the wind power generation model based on wind power generation data to obtain gear meshing anomaly data; identifies bearing cracks based on the gear meshing anomaly data to obtain bearing crack data; and locates the crack defect location based on the bearing crack data. The green electricity digital certificate generation module designs fatigue-resistant wind power structures based on the location of crack defects; integrates the fatigue-resistant wind power structures into the wind power generation model and predicts wind power output; and generates green electricity digital certificates based on the wind power output. The green electricity traceability module acquires user grid load data; maps green electricity digital certificates to user grid load data to obtain user green electricity load data; performs green electricity consumption analysis based on user green electricity load data to obtain green electricity consumption data; constructs a green electricity traceability link diagram based on green electricity consumption data and uploads it to the green electricity management platform to execute green electricity consumption traceability tasks.

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