Real-time load assessment method, processing terminal and wind turbine generator system

Abstract

The application relates to the technical field of wind power generation and provides a real-time load evaluation method, a processing terminal and a wind turbine generator, which comprises the following steps: obtaining operation data of power generation equipment on site; correcting an output characteristic curve of the power generation equipment based on environmental parameters in the operation data to obtain a corrected output characteristic curve; and performing cumulative calculation based on the corrected output characteristic curve and a wind speed probability distribution to determine the evaluation load of the power generation equipment. Through on-site data acquisition and source-end calculation, network delay or interruption risk caused by data transmission is eliminated, real-time online load evaluation with low delay is realized, the output characteristic curve is corrected based on environmental parameters, the influence of complex environmental factors on the load is effectively compensated, cumulative calculation is performed in combination with the wind speed probability distribution, the substantial influence of wind speed variability on the load is fully considered, and the evaluation accuracy and reliability are significantly improved.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and more specifically, to a real-time load assessment method, a processing terminal, and a wind turbine generator set. Background Technology

[0002] Currently, in the wind power industry, load assessment of wind turbines is crucial for improving operational efficiency, optimizing maintenance plans, and supporting grid stability. Existing technologies typically involve periodically collecting turbine operating data and transmitting it remotely to a central server for load assessment. However, this traditional method has significant drawbacks: firstly, real-time data needs to be transmitted via communication networks, and network instability or interruptions can lead to data loss or delays, affecting the real-time nature and accuracy of load assessments and causing assessment lags; secondly, the variability of wind speed and direction, as well as the complexity of environmental factors such as temperature, humidity, and air pressure, significantly impact the efficiency and load of wind turbines. Traditional remote assessment models struggle to compensate for these variable factors in real time, leading to inaccurate assessments. Therefore, there is an urgent need for a real-time load assessment scheme that can eliminate transmission delays and compensate for the impact of environmental and wind speed variability on assessment accuracy. Summary of the Invention

[0003] The purpose of this application is to provide a real-time load assessment method, a processing terminal, and a wind turbine generator set, which acquires data locally at the source end and performs joint cumulative calculation based on environmental correction and probability distribution to achieve high-precision, low-latency real-time online load assessment.

[0004] Firstly, a real-time load assessment method is provided, including: Acquire operational data of power generation equipment on-site; Based on the environmental parameters in the operating data, the output characteristic curve of the power generation equipment is corrected to obtain the corrected output characteristic curve. Based on the corrected output characteristic curve and wind speed probability distribution, cumulative calculations are performed to determine the evaluation load of the power generation equipment.

[0005] In an optional implementation, acquiring the operating data of the power generation equipment locally includes: A processing terminal is deployed on the power generation equipment side, which directly collects the operating data from the power generation equipment and performs subsequent calculation steps on-site on the power generation equipment side.

[0006] In an optional implementation, the step of correcting the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data to obtain a corrected output characteristic curve includes: The output characteristic curve is corrected based on the temperature and air density in the operating data; The step of performing cumulative calculations based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment includes: The evaluation load is calculated by summing the corrected output characteristic curve with the wind speed probability distribution.

[0007] In an optional implementation, the wind speed probability distribution is calculated using the Weibull distribution model; Abnormal data in the operational data are removed using a statistical bias removal criterion based on normal distribution.

[0008] In an optional implementation, the step of correcting the output characteristic curve of the power generation equipment based on environmental parameters in the operating data further includes: The output characteristic curve is corrected based on altitude.

[0009] In an optional implementation, after acquiring the operating data of the power generation equipment locally, the method further includes: Based on the standard output characteristic curve, power limit data points that deviate from the rated output are eliminated.

[0010] In an optional implementation, it further includes: Based on the assessed load, the annual load change rate before and after optimization is compared to calculate the load increase.

[0011] Secondly, a processing terminal is provided, comprising: The acquisition module is configured to acquire the operating data of the power generation equipment locally. The environmental correction module is configured to correct the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data, so as to obtain the corrected output characteristic curve. The cumulative calculation evaluation module is configured to perform cumulative calculations based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment.

[0012] In an optional implementation, the processing terminal is deployed on the side of the power generation equipment and has a power interface for drawing power locally from the power generation equipment.

[0013] Thirdly, a wind turbine generator set is provided, including a power generation equipment body and a processing terminal according to the foregoing embodiments.

[0014] Beneficial effects: By acquiring the operating data of the power generation equipment locally and performing calculations at the source, the risk of network delay or interruption caused by data transmission is eliminated, and low-latency real-time online load assessment is achieved, providing a real-time basis for data-driven strategic planning and operational adjustments. By correcting the output characteristic curve based on environmental parameters, the impact of complex environmental factors such as temperature, air density, and altitude on the efficiency and load of power generation equipment is effectively compensated, significantly improving the evaluation accuracy. By summing the corrected output characteristic curve with the wind speed probability distribution, the substantial impact of wind speed variability and frequency distribution on the load is fully considered, so that different frequency distributions under the same average wind speed can be accurately quantified, further ensuring the accuracy of load assessment. By employing a statistical bias elimination criterion based on normal distribution to remove abnormal data and power-limited data points, the interference of abnormal power generation states on the evaluation model is eliminated, ensuring the validity of the input data and the reliability of the evaluation results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the real-time load evaluation method in an embodiment; Figure 2 This is a schematic diagram of the automated elimination process in the real-time load evaluation method of the embodiment; Figure 3 This is a schematic diagram of the load increase process in the real-time load evaluation method of the embodiment; Figure 4 This embodiment provides a schematic diagram of a processing terminal structure. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0018] like Figure 1 As shown, this embodiment provides a real-time load assessment method. The core logic loop of this method consists of three steps: local data acquisition, environmental correction, and probability distribution accumulation calculation. It aims to eliminate transmission delay from the data source and jointly compensate for the interference of wind speed variability and environmental complexity on assessment accuracy at the calculation level.

[0019] Step S110: Acquire the operating data of the power generation equipment locally.

[0020] Local acquisition refers to collecting data directly from the data source that is physically close to the power generation equipment, rather than transmitting the data back to a remote server via a remote transmission network for processing.

[0021] Traditional long-distance, periodic data acquisition methods heavily rely on the stability of the communication network. Network instability or interruption can easily lead to data loss or delay, causing significant lag in load assessment and failing to meet the requirements of real-time online evaluation. By acquiring operational data locally, the data is captured at the generation end and enters the subsequent calculation process, completely eliminating the time loss and uncertainty caused by network transmission. This lays the foundation for low-latency, real-time data efficiency for evaluation.

[0022] It should be understood that operational data is not limited to common operating parameters such as wind speed, wind direction, temperature, air density, and power. It can also include various sensor data that can reflect the real-time operating status of the power generation equipment, such as vibration frequency, rotational speed, and pitch angle, as long as they can provide an effective input source for subsequent load assessment.

[0023] Step S120: Based on the environmental parameters in the operating data, the output characteristic curve of the power generation equipment is corrected to obtain the corrected output characteristic curve.

[0024] Specifically, the actual output characteristics of power generation equipment are not fixed, but are significantly affected by surrounding environmental factors.

[0025] For example, changes in air density directly alter the base amount of kinetic energy captured by wind, and temperature fluctuations affect aerodynamic characteristics and the response of mechanical components. If these environmental complexities are not compensated for, the evaluation results based on standard curves will be seriously inaccurate.

[0026] This step extracts environmental parameters from locally acquired operational data and dynamically corrects the output characteristic curve, ensuring that the curve accurately reflects the device's output capability under the current environment.

[0027] Output characteristic curves are a higher-level summary of the operating characteristics of equipment. Their most common specific form is a power curve, which represents the mapping relationship between wind speed and output power. In other embodiments, it can be equivalently replaced by thrust curves, torque curves, or fatigue load spectra, as long as it characterizes the mapping relationship between the equipment's input and output responses. Similarly, the specific forms of the environmental parameters include not only temperature and air density, but also single or combined parameters such as humidity, air pressure, and altitude. The embodiments are merely illustrative and not restrictive.

[0028] Step S130: Based on the corrected output characteristic curve and wind speed probability distribution, perform cumulative calculations to determine the evaluation load of the power generation equipment.

[0029] Specifically, wind speed itself is highly variable. If the frequency distribution is different under the same average wind speed, the cumulative load effect on power generation equipment will be completely different.

[0030] Static table lookup assessments based solely on instantaneous or average wind speeds cannot accurately reflect the substantial impact of long-term wind speed fluctuations on equipment fatigue and load.

[0031] The output characteristic curve, which is more closely related to the real physical state after environmental correction, is jointly accumulated and calculated with the probability distribution that reflects the long-term statistical law of wind speed. In essence, the correction output at the micro moment is probabilistically weighted and integrated on the macro time scale, thereby extending the transient environmental compensation effect to the load assessment of the entire life cycle.

[0032] Environmental correction ensures the accuracy of the output response within each wind speed range, while probability distribution accumulation ensures the rationality of the weight allocation for each wind speed range. The combination of the two ensures that the final determined evaluation load is neither disturbed by environmental fluctuations nor misled by transient wind speed changes, achieving a high-precision quantitative approximation of complex real-world working conditions.

[0033] Through the coordinated operation of steps S110 to S130, this embodiment eliminates transmission delay at the data source, compensates for environmental complexity at the model level, and quantifies wind speed variability at the calculation level. The three form a closed loop linkage, jointly achieving high-precision, low-latency real-time online load assessment.

[0034] In some embodiments, acquiring the operating data of the power generation equipment locally includes: deploying a processing terminal on the side of the power generation equipment, having the processing terminal directly collect the operating data from the power generation equipment, and performing subsequent calculation steps locally on the side of the power generation equipment.

[0035] Specifically, the traditional solution transmits data back to a remote central server, which is not only limited by network bandwidth and stability, but also introduces uncontrollable delays in the process of data packaging, transmission, unpacking and queuing for processing. This delay is fatal when facing assessment scenarios that require millisecond-level response, such as sudden changes in wind speed.

[0036] This embodiment, by physically deploying a processing terminal on the power generation equipment side, enables data acquisition and consumption to be completed in a closed loop within the same physical domain, completely eliminating the time uncertainty introduced by network transmission. It should be understood that the processing terminal is not limited to a specific hardware box; it can be an edge computing gateway, a PLC controller, or even a dedicated data processing chip integrated into the main control board of the power generation equipment, as long as it satisfies the function of on-site data acquisition and on-site computation at the data source end. This embodiment is merely illustrative and not restrictive.

[0037] In some embodiments, the output characteristic curve of the power generation equipment is corrected based on environmental parameters in the operating data to obtain a corrected output characteristic curve, including: correcting the output characteristic curve based on temperature and air density in the operating data; and performing cumulative calculation based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment, including: summing the corrected output characteristic curve and wind speed probability distribution to calculate the evaluation load.

[0038] Specifically, the decrease in air density directly weakens the kinetic energy base captured by wind energy, while temperature fluctuations not only affect air density but also change the aerodynamic viscosity characteristics of the blade surface and the resistance loss of the generator windings. If the static output characteristic curve under standard operating conditions is used directly for evaluation, it will lead to a systematic deviation between the evaluation results and the actual physical state.

[0039] This embodiment extracts real-time temperature and air density data obtained locally to dynamically correct the output characteristic curve, ensuring that the output response at each instant matches the current real environment. Subsequently, the corrected output characteristic curve is cumulatively summed with the wind speed probability distribution. The physical essence of this is that, across the entire wind speed range, the corrected output power corresponding to each wind speed segment is multiplied by its probability weight and then integrally summed. This extends the environmental compensation effect at the microscopic moment to the load assessment at the macroscopic time scale, achieving a joint closed-loop calculation of environmental compensation and wind speed statistical laws.

[0040] In some embodiments, the Weibull distribution model is used to calculate the wind speed probability distribution; and a statistical bias elimination criterion based on normal distribution is used to eliminate abnormal data in the operational data.

[0041] Specifically, the calculation of wind speed probability distribution must rely on a mathematical model that conforms to natural laws. The Weibull distribution model is one of the optimal probability functions for describing wind speed distribution. Its true cumulative distribution function formula is reconstructed as follows: In the formula, This indicates that the wind speed is less than or equal to the given wind speed value. The cumulative probability; This refers to the specific wind speed value; The scale parameter of the Weibull distribution reflects the order of magnitude of the average wind speed. The shape parameter reflects the kurtosis and skewness of the wind speed distribution.

[0042] The Weibull distribution has strong inclusiveness and equivalent substitutability, when the shape parameter When the shape parameter is..., it degenerates into an exponential distribution; when the shape parameter... When, it is equivalent to the Rayleigh distribution, when When the value is approximately 3.5, its shape approximates a normal distribution. Those skilled in the art can flexibly choose the above equivalent distribution model for calculation based on the fitting of historical wind speed data from actual wind farms.

[0043] For outlier removal, the statistical bias removal criterion based on the normal distribution is specifically the 3σ criterion, which can be obtained by comparing historical data with the standard output characteristic curve to obtain the mean of the normal data distribution. with standard deviation If the actual collected operational data points deviate from the mean by more than 3 times the standard deviation, that is, if they fall within... to If a data point is outside the specified range, it is considered abnormal and removed.

[0044] In some embodiments, the correction of the output characteristic curve of the power generation equipment based on environmental parameters in the operating data further includes: correcting the output characteristic curve based on altitude.

[0045] As is well known, significant changes in altitude directly lead to changes in atmospheric pressure, which is one of the core factors determining air density. At high altitudes, atmospheric pressure decreases, and air density decreases accordingly. This means that the mass of air flowing through the blades of a wind turbine per unit volume is reduced, significantly decreasing the kinetic energy captured by the wind. Consequently, the actual output power of the turbine is far lower than the design value at the same wind speed and altitude. Although the aforementioned embodiments have incorporated correction logic for temperature and air density, in practical engineering applications, real-time accurate measurement of air density often faces challenges such as high sensor costs and susceptibility to humidity interference. Altitude, however, is a fixed and easily obtainable geographical constant for power generation equipment at a specific installation location, and it exerts a deterministic influence on air density through physical laws. Therefore, introducing altitude correction parameters, either alone or in combination, in addition to temperature and density correction has irreplaceable engineering value: on the one hand, altitude correction can serve as an independent supplementary defense for air density correction, providing a safety net of environmental compensation through the altitude-density mapping relationship when air density sensors fail or data is missing; on the other hand, in the joint correction mode, altitude parameters can provide a priori constraint benchmarks for air density calculation, effectively narrowing the error range of density correction and preventing overcompensation caused by sensor drift.

[0046] This embodiment dynamically adjusts the output characteristic curve by introducing an altitude correction coefficient. Specifically, the correction process can be illustrated by the following mapping relationship: The standard altitude is set to sea level (0 meters), corresponding to a standard air density of approximately 1.225 kg / m³; when the power generation equipment is installed at an altitude of H meters, an altitude correction coefficient is introduced based on the exponential decay of atmospheric pressure with altitude. Its calculation formula can be approximated as: Then, the output power corresponding to each wind speed point in the standard output characteristic curve is multiplied by the altitude correction factor. This will give you the corrected output characteristic curve after altitude compensation.

[0047] It should be understood that the above exponential decay formula is only a preferred physical approximation model. In practical applications, piecewise linear interpolation mapping tables or polynomial fitting curves can also be used to characterize the relationship between altitude and correction coefficient based on the micro-meteorological characteristics of different wind farms, as long as they can reflect the physical mechanism of the decrease in output capacity caused by the increase in altitude. The embodiments are only illustrative and not restrictive.

[0048] Through the aforementioned altitude correction mechanism, this embodiment enables the output characteristic curve to accurately match the real physical response under high altitude or complex terrain, avoiding the underestimation of the system due to neglecting geographical altitude differences, and further consolidating the comprehensiveness of environmental compensation and the accuracy of the assessment results.

[0049] In some embodiments, after acquiring the operating data of the power generation equipment locally, the method further includes: based on the standard output characteristic curve, eliminating power-limited data points that deviate from the rated output.

[0050] In actual operation, wind turbine generators frequently experience power-limited operation due to grid dispatch instructions or their own protection strategies. This means that even with sufficient wind speed, the generator is forced to reduce its output power below its rated power. This power-limited state does not accurately reflect the generator's actual power generation capacity but is an abnormal power generation state caused by external intervention. Directly inputting operating data under power-limited conditions into subsequent load assessment models will destructively interfere with the model. This is because the core logic of the assessment model is based on the joint cumulative calculation of the output characteristic curve and the wind speed probability distribution. Power-limited data points artificially lower the average output power within a specific wind speed range, causing severe distortion of the corrected output characteristic curve. Consequently, the final calculated assessment load is far lower than the actual physical load borne by the equipment. This is the fundamental reason why power-limited data points must be specifically removed, rather than being treated as ordinary low-power anomalies and retained or smoothed out.

[0051] In some embodiments, the automated rejection process includes the following sub-steps: Step S210: Obtain the standard output characteristic curve provided by the power generation equipment manufacturer. This curve defines the reference value of the rated output power that the unit should achieve in each wind speed range.

[0052] Step S220: For each set of operational data points acquired locally, extract its current wind speed value and actual output power value.

[0053] Step S230: Based on the current wind speed value, interpolate and look up the table on the standard output characteristic curve to obtain the reference value of the rated output power at the corresponding wind speed.

[0054] Step S240: Calculate the deviation of the actual output power from the rated output power reference value. For example, the deviation can be calculated using the relative deviation formula: Deviation = (Rated output power reference value - Actual output power) / Rated output power reference value.

[0055] Step S250: Compare the calculated deviation with the preset power limit identification threshold.

[0056] Since power limiting typically manifests as a significant reduction in output power, the deviation is often high. The preset threshold can be set as an empirical value between 30% and 50% or derived from statistical fitting of historical power limiting data. If the deviation exceeds the preset threshold and the actual output power is significantly lower than the rated output power benchmark, the data point is determined to be a power limiting data point and is discarded. If the deviation is within the normal fluctuation range, the data point is retained and included in subsequent calculations.

[0057] It should be understood that the above-described automated identification logic based on deviation threshold is only a preferred exemplary implementation and not a limitation thereof. In other implementations, the unit's control command status word can also be used to assist in identification. For example, when the locally acquired operating data contains a control flag indicating that the unit is in a power-limited operating mode, data points for the corresponding time period can be directly eliminated based on this flag without relying on deviation calculation. Furthermore, the calculation of deviation is not limited to relative deviation; it can also use mathematical mapping relationships such as absolute power difference and power ratio, as long as it can quantify the degree of abnormal attenuation between the actual output and the rated output. This embodiment effectively eliminates the interference of power-limited status on the evaluation model through the above-described automated elimination mechanism, providing a clean and reliable data source for subsequent environmental correction and cumulative calculation.

[0058] In some embodiments, the method further includes: calculating the load increase by comparing the annual load change rate before and after optimization based on the evaluated load.

[0059] After technical upgrades to wind turbine generators (such as blade lengthening and airfoil optimization), the core objective is to quantitatively assess the actual benefits of these upgrades. However, wind resources in wind farms naturally fluctuate from year to year. Simply comparing the absolute load values ​​of the upgraded units before and after the upgrade can easily lead to misjudging the gains from improved wind resources as the effectiveness of the upgrade, resulting in a distorted assessment. To eliminate external interference from wind resource fluctuations and purely extract the incremental value generated by the upgrade itself, this embodiment introduces calculation logic for the annual load change rate and load increase.

[0060] Specifically, the steps for calculating the load increase include: Step S310: Obtain the assessed load of the upgraded unit after the technical upgrade and compare the assessed load of the unit after the technical upgrade, and calculate the annual load change rate of the upgraded unit after the technical upgrade.

[0061] Among them, the upgraded turbine unit refers to the power generation equipment that has actually undergone technical upgrades; the reference turbine unit refers to the reference power generation equipment located in the same wind farm as the upgraded turbine unit, with similar terrain and wind resource conditions, but which has not undergone technical upgrades. The physical significance of introducing the reference turbine unit is to serve as an environmental reference baseline for natural fluctuations in wind resources. The formula for calculating the annual load change rate of the upgraded turbine unit is: In the formula, This indicates the annual load change rate of the upgraded unit. This represents the assessed load calculated cumulatively based on the corrected output characteristic curve and the annual wind speed probability distribution after the technical upgrade of the unit. This represents the assessed load calculated by cumulatively combining the modified output characteristic curve of the turbine unit after technical upgrades (i.e., in its normal operating state without upgrades) with the annual wind speed probability distribution. Through the above subtraction and ratio calculations, the linear influence of overall favorable or unfavorable wind resources on the absolute load of the upgraded turbine unit is eliminated.

[0062] Step S320: Obtain the assessment load of the unit before the technical upgrade and compare it with the assessment load of the unit before the technical upgrade, and calculate the annual load change rate of the unit before the technical upgrade.

[0063] Similarly, in order to establish a historical benchmark before the technological upgrade, data from the same historical period or the year before the technological upgrade need to be subjected to the same relativization process.

[0064] The formula for calculating the annual load change rate of the upgraded unit before the upgrade is: In the formula, This indicates the annual load change rate of the unit before the technical upgrade; This represents the assessed load calculated cumulatively from the corrected output characteristic curve and historical wind speed probability distribution before the technical upgrade of the unit. This represents the assessed load calculated cumulatively from the corrected output characteristic curve and historical wind speed probability distribution of the comparison unit during the same historical period.

[0065] Step S330: Calculate the load increase based on the annual load change rate before and after the technical upgrade.

[0066] The formula for calculating the load lifting amount is: In the formula, This represents the load increase that ultimately reflects the true effect of the technological upgrade. By subtracting the relative differences twice, this formula completely eliminates the systematic biases caused by interannual fluctuations in wind resources and the natural decline in the unit's own performance, ensuring that the final result only includes the pure increment contributed by the technological upgrade.

[0067] It should be understood that the above-mentioned logic of eliminating interference through dual-difference comparison of the comparison units is one of the most rigorous quantitative evaluation methods in the engineering field. However, in application scenarios where suitable comparison units are lacking, a single-difference method that standardizes the wind frequency distribution of the current year to the historical average wind frequency distribution can also be used as an alternative, as long as it can achieve the core purpose of separating external environmental fluctuations. The embodiments are only illustrative and not restrictive.

[0068] This embodiment, through the above post-processing logic, establishes a closed-loop connection to the practical application value of the evaluation load, transforming the high-precision evaluation load calculated in the aforementioned embodiment into a quantitative indicator of the technical improvement effect that can directly guide operation and maintenance decisions and commercial settlement. This achieves full-chain value integration from data collection, cleaning, correction, cumulative calculation to effect evaluation.

[0069] Figure 4 This embodiment provides a processing terminal, which includes: an acquisition module 401, an environment correction module 402, and a cumulative calculation and evaluation module 403.

[0070] The acquisition module 401 is configured to acquire the operating data of the power generation equipment locally.

[0071] Specifically, the acquisition module 401 is a set of front-end interfaces for interaction between the terminal and the physical sensing layer of the power generation equipment. It directly reads raw operating parameters such as wind speed, wind direction, temperature, air density, and power from the sensor array on the power generation equipment side through hard-wired connections, serial communication buses, or industrial Ethernet protocols. After acquiring the data, this module not only performs basic physical quantity conversions and unit normalization, but also temporarily stores the data in the terminal's local high-speed cache for downstream modules to access without delay.

[0072] It should be understood that the acquisition module 401 is not limited to a specific communication protocol board. It can also be a multi-channel ADC sampling circuit integrated on the main control chip, or an independent gateway module with protocol conversion function, as long as it meets the function of directly acquiring data close to the data source in physical location. The embodiments are for illustrative purposes only and not restrictive.

[0073] The environmental correction module 402 is configured to correct the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data, so as to obtain the corrected output characteristic curve.

[0074] Specifically, the environment correction module 402 receives a subset of environmental parameters output by the acquisition module, retrieves a preset standard output characteristic curve template from the terminal's non-volatile memory, and then substitutes the real-time environmental parameters into the correction algorithm logic. For example, when the environmental parameters include temperature and air density, this module dynamically adjusts the output power values ​​corresponding to each wind speed node of the curve through table lookup interpolation or polynomial fitting operations, generates a corrected output characteristic curve that fits the current micro-meteorological conditions, and pushes it to the next module. The hardware implementation of this module can be a dedicated DSP floating-point arithmetic unit or a software algorithm thread of the terminal's main processor.

[0075] The cumulative calculation evaluation module 403 is configured to perform cumulative calculations based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment.

[0076] Specifically, the cumulative calculation and evaluation module 403 is the core computing engine of the terminal. It receives the corrected curve output by the environmental correction module and, in conjunction with the locally stored historical wind speed statistical model or the real-time updated wind speed probability distribution parameters, performs a cumulative summation operation of probability-weighted integrals over the entire wind speed range. This operation involves a large number of matrix multiplications and additions and exponential operations. Therefore, this module is usually carried by a high-performance microprocessor or ARM core. The final calculated evaluation load result can be directly output to the local display interface of the terminal or synchronized to the remote monitoring center via a lightweight communication link for operation and maintenance decision-making reference. However, the core calculation process is always completed locally on the terminal side in a closed loop.

[0077] Furthermore, the processing terminal is deployed on the side of the power generation equipment and has a power interface for drawing power locally from the power generation equipment. Specifically, the processing terminal is physically installed directly inside the nacelle, at the bottom of the tower, or next to the control cabinet of the wind turbine generator, forming a close physical coupling with the power generation equipment itself. The terminal's power supply does not rely on external independent power supply network cables, but rather draws power directly from the auxiliary power bus or converter side of the power generation equipment through the power interface. This local power supply physical structure design has irreplaceable engineering protection value: wind farms are often located in remote areas, and external power supply networks are easily disrupted by extreme weather. If the terminal relies on external power supply, a power outage will directly cause blind spots and data gaps in load assessment; while local power supply allows the terminal and the power generation equipment to coexist. As long as the power generation equipment itself is in standby or generating state, the terminal can continuously obtain a stable power supply, thereby being unaffected by fluctuations or interruptions in the external power supply network, ensuring the absolute continuity and reliability of real-time online assessment. It should be understood that the specific form of the power interface can be a wide-voltage DC input module or an AC-to-DC adapter unit, as long as it can draw power locally from the power generation equipment.

[0078] Through the modular data flow and processing logic described above, as well as the physical structure features of local deployment and local power supply, the processing terminal of this embodiment completely solidifies the aforementioned method steps into the source hardware entity. This not only realizes a local low-latency closed loop for data acquisition, environmental correction, and cumulative calculation, but also ensures the robustness of terminal operation through the local power supply mechanism, providing a solid hardware support defense for high-precision, low-latency real-time online load assessment.

[0079] This embodiment provides a wind turbine generator set, which includes a power generation equipment body and the processing terminal described in the above embodiment.

[0080] Specifically, the power generation equipment body refers to the core physical entity in a wind turbine generator set that realizes wind energy capture and power conversion. It typically includes components such as impeller, transmission chain, generator and main control system. However, this embodiment does not impose restrictions on its conventional structure such as blade airfoil and tower height. The focus is on explaining the deep integration and connection relationship between the processing terminal and the power generation equipment body.

[0081] Regarding data acquisition connections, the processing terminal's acquisition module directly connects to the sensor array and main control data interface of the power generation equipment via physical communication links such as hardwired direct connection, CAN bus, or industrial Ethernet, achieving local direct acquisition of operational data and completely eliminating reliance on remote transmission networks. It should be understood that the hardwired direct connection, CAN bus, or industrial Ethernet listed above are only a few preferred examples of data acquisition connections. In other implementations, any industrial-grade communication medium capable of meeting the low-latency requirements of local direct acquisition, such as RS485 serial communication, fiber optic links, or wireless LAN modules, can also be used. These embodiments are illustrative only and not restrictive.

[0082] Regarding power supply connections, the processing terminal draws power locally from the auxiliary power bus or converter of the generator set via its own power interface. This shared power source design creates a mutually dependent power supply coupling between the terminal and the generator set: as long as the generator set is in standby or operating mode, its internal power supply system can continuously provide stable power to the processing terminal, unaffected by power outages caused by extreme weather or remote locations in the external independent power supply network of the wind farm, thus ensuring the absolute continuity and reliability of real-time online load assessment.

[0083] By embedding the processing terminal as a core evaluation component into the main body of the power generation equipment, the wind turbine generator set in this embodiment not only forms a local low-latency closed loop from data source acquisition, environmental correction to cumulative calculation and evaluation in terms of hardware form, but also establishes dual hard guarantees of local power supply and local direct acquisition in terms of physical connection. This enables the whole product to independently, in real time and with high precision complete its own load evaluation, and can output reliable evaluation results without relying on any remote server, thus realizing a protective closed loop for the complete product form of the industrial chain.

[0084] The computer program product of the readable storage medium provided in the embodiments of this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A real-time load assessment method, characterized in that, include: Acquire operational data of power generation equipment on-site; Based on the environmental parameters in the operating data, the output characteristic curve of the power generation equipment is corrected to obtain the corrected output characteristic curve. Based on the corrected output characteristic curve and wind speed probability distribution, cumulative calculations are performed to determine the evaluation load of the power generation equipment.

2. The real-time load assessment method according to claim 1, characterized in that, The on-site acquisition of operating data from the power generation equipment includes: A processing terminal is deployed on the power generation equipment side, which directly collects the operating data from the power generation equipment and performs subsequent calculation steps on-site on the power generation equipment side.

3. The real-time load evaluation method according to claim 2, characterized in that, The step of correcting the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data to obtain the corrected output characteristic curve includes: The output characteristic curve is corrected based on the temperature and air density in the operating data; The step of performing cumulative calculations based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment includes: The evaluation load is calculated by summing the corrected output characteristic curve with the wind speed probability distribution.

4. The real-time load evaluation method according to claim 3, characterized in that, The wind speed probability distribution is calculated using the Weibull distribution model; Abnormal data in the operational data are removed using a statistical bias removal criterion based on normal distribution.

5. The real-time load evaluation method according to claim 3, characterized in that, The correction of the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data further includes: The output characteristic curve is corrected based on altitude.

6. The real-time load assessment method according to claim 1, characterized in that, After acquiring the operating data of the power generation equipment on-site, the method further includes: Based on the standard output characteristic curve, power limit data points that deviate from the rated output are eliminated.

7. The real-time load assessment method according to claim 1, characterized in that, Also includes: Based on the assessed load, the annual load change rate before and after optimization is compared to calculate the load increase.

8. A processing terminal, characterized in that, include: The acquisition module is configured to acquire the operating data of the power generation equipment locally. The environmental correction module is configured to correct the output characteristic curve of the power generation equipment based on the environmental parameters in the operating data, so as to obtain the corrected output characteristic curve. The cumulative calculation evaluation module is configured to perform cumulative calculations based on the corrected output characteristic curve and wind speed probability distribution to determine the evaluation load of the power generation equipment.

9. The processing terminal according to claim 8, characterized in that, The processing terminal is deployed on the side of the power generation equipment and has a power interface for drawing power locally from the power generation equipment.

10. A wind turbine generator set, comprising a power generation equipment body, characterized in that, It also includes the processing terminal according to claim 8 or 9.

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