A wind power plant terminal data processing method, device, equipment and medium

Abstract

This disclosure relates to a method, apparatus, equipment, and medium for processing data at wind farm sites. The method includes: acquiring multi-source data, including but not limited to wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; performing comprehensive checks on the multi-source data and processing the data based on the check results to obtain data to be identified; wherein the comprehensive checks include but not limited to boundary value checks, internal consistency checks, temporal consistency checks, spatial consistency checks, and missing value checks; performing interpolation processing on the data to be identified to obtain target data; wherein the interpolation processing includes but not limited to inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation; and performing anomaly identification on the target data to obtain anomaly identification results. Using the above technical solution can improve data processing efficiency and wind power prediction accuracy, providing technical support for the safe and efficient operation of wind farms.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a data processing method, apparatus, equipment and medium for wind farms. Background Technology

[0002] Currently, with the world placing great emphasis on energy conservation and emission reduction, green energy, low-carbon economy, and sustainable development, the proportion of clean and renewable energy in the energy structure continues to rise. Among them, wind energy, with its rapid development momentum, has become the fastest-growing energy type. Wind power generation relies on natural wind energy and, as a clean and renewable energy form, is receiving increasing attention and favor.

[0003] For wind power systems, efficient and accurate data processing is crucial for ensuring safe and stable operation and optimizing power market dispatch. Wind farm data sources are diverse and complex, including but not limited to wind turbine data, ground-based meteorological data, meteorological reanalysis, and numerical model data. These multi-source data vary significantly in format, spatiotemporal resolution, and data quality, posing considerable challenges to data integration and processing. In particular, the complexity and real-time requirements of wind power data have driven wind farms to demand higher efficiency in data preprocessing to improve the accuracy of wind power forecasting. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method, apparatus, equipment, and medium for processing data at wind farm sites.

[0005] This disclosure provides a method for processing data at a wind farm site, the method comprising:

[0006] Acquire multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data;

[0007] The multi-source data is comprehensively checked, and the data is processed based on the check results to obtain the data to be identified; wherein, the comprehensive check includes, but is not limited to, boundary value check, internal consistency check, temporal consistency check, spatial consistency check, and missing value check;

[0008] The data to be identified is interpolated to obtain the target data; wherein, the interpolation includes, but is not limited to, inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation;

[0009] Anomaly identification is performed on the target data to obtain anomaly identification results.

[0010] Optionally, the step of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes:

[0011] Obtain the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data, perform boundary value checks on the wind power equipment data based on the limit values ​​of the equipment operating parameters, and delete the wind power equipment data that exceeds the limit values ​​of the equipment operating parameters;

[0012] The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data, determined based on historical observations and regional extreme values, are obtained. The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data are checked, and the ground station meteorological data and the meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

[0013] Optionally, the step of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes:

[0014] The internal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0015] Data that does not meet the internal consistency requirements of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data are deleted to obtain the data to be identified.

[0016] Optionally, the step of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes:

[0017] The temporal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the time series information of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0018] Data that does not meet the time consistency requirement, such as wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0019] Optionally, the step of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes:

[0020] The spatial consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0021] Data that does not meet spatial consistency requirements, including wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0022] Optionally, the step of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes:

[0023] The missing values ​​in the multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

[0024] Optionally, the step of interpolating the data to be identified to obtain the target data includes:

[0025] For the uniformly distributed data in the data to be identified, the weights are determined based on the distance of the uniformly distributed data using the inverse distance weighted interpolation algorithm for interpolation processing. For the variable data in the data to be identified, the variable data is interpolated using the spline interpolation algorithm, and the missing data in the data to be identified is interpolated using the spatiotemporal bilinear interpolation algorithm, to obtain the target data.

[0026] Optionally, the method further includes:

[0027] An anomaly log is generated based on the anomaly identification results, wherein the anomaly log includes anomaly type, anomaly data, timestamp, and device identifier;

[0028] The exception logs are stored in a designated log storage system so that users can retrieve the exception logs based on the log storage system.

[0029] Optionally, the method further includes:

[0030] When the anomaly identification result determines that the target type is an anomaly, an alarm message is generated and the user is notified.

[0031] This disclosure also provides a wind farm terminal data processing device, the device comprising:

[0032] The acquisition module is used to acquire multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data;

[0033] The inspection module is used to perform a comprehensive inspection on the multi-source data and process the multi-source data based on the inspection results to obtain the data to be identified; wherein, the comprehensive inspection includes, but is not limited to, boundary value inspection, internal consistency inspection, temporal consistency inspection, spatial consistency inspection, and missing value inspection.

[0034] An interpolation module is used to perform interpolation processing on the data to be identified to obtain target data; wherein, the interpolation processing includes, but is not limited to, inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation;

[0035] The identification module is used to identify anomalies in the target data and obtain anomaly identification results.

[0036] Optionally, the inspection module is specifically used for:

[0037] Obtain the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data, perform boundary value checks on the wind power equipment data based on the limit values ​​of the equipment operating parameters, and delete the wind power equipment data that exceeds the limit values ​​of the equipment operating parameters;

[0038] The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data, determined based on historical observations and regional extreme values, are obtained. The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data are checked, and the ground station meteorological data and the meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

[0039] Optionally, the inspection module is specifically used for:

[0040] The internal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0041] Data that does not meet the internal consistency requirements of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data are deleted to obtain the data to be identified.

[0042] Optionally, the inspection module is specifically used for:

[0043] The temporal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the time series information of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0044] Data that does not meet the time consistency requirement, such as wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0045] Optionally, the inspection module is specifically used for:

[0046] The spatial consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0047] Data that does not meet spatial consistency requirements, including wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0048] Optionally, the inspection module is specifically used for:

[0049] The missing values ​​in the multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

[0050] Optionally, the interpolation module is specifically used for:

[0051] For the uniformly distributed data in the data to be identified, the weights are determined based on the distance of the uniformly distributed data using the inverse distance weighted interpolation algorithm for interpolation processing. For the variable data in the data to be identified, the variable data is interpolated using the spline interpolation algorithm, and the missing data in the data to be identified is interpolated using the spatiotemporal bilinear interpolation algorithm, to obtain the target data.

[0052] Optionally, the device further includes:

[0053] The generation module is used to generate an anomaly log based on the anomaly identification result, wherein the anomaly log includes an anomaly type, anomaly data, a timestamp, and a device identifier;

[0054] The storage module is used to store the exception logs to a designated log storage system so that users can retrieve the exception logs based on the log storage system.

[0055] Optionally, the device further includes:

[0056] The prompting module is used to generate alarm prompts and prompt the user when the anomaly identification result determines that the target type is an anomaly.

[0057] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the wind farm terminal data processing method provided in this disclosure.

[0058] This disclosure also provides a computer-readable storage medium storing a computer program for executing the wind farm terminal data processing method provided in this disclosure.

[0059] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: The wind farm terminal data processing scheme provided in this disclosure acquires multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; the multi-source data is comprehensively checked, and the multi-source data is processed based on the check results to obtain the data to be identified; wherein, the comprehensive check includes, but is not limited to, boundary value check, internal consistency check, temporal consistency check, spatial consistency check, and missing value check; the data to be identified is interpolated to obtain the target data; wherein, the interpolation process includes, but is not limited to, inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation; the target data is anomaly identified to obtain the anomaly identification result. Adopting the above technical solution can not only effectively address the increasingly complex needs in the wind power generation field, but also significantly improve data processing efficiency and prediction accuracy; especially in addressing the complexity and real-time requirements of wind power data, this disclosure optimizes the preprocessing flow of multi-source data to meet the higher standards of wind farms for data processing speed and quality, thereby providing strong support for improving the accuracy of wind power prediction. Attached Figure Description

[0060] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0061] Figure 1 A flowchart illustrating a wind farm terminal data processing method provided in this embodiment of the disclosure;

[0062] Figure 2 A schematic diagram illustrating a scenario for a wind farm terminal data processing method provided in an embodiment of this disclosure;

[0063] Figure 3 This is a structural example diagram of a wind farm terminal data processing device provided in an embodiment of the present disclosure. Detailed Implementation

[0064] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0065] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0066] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0067] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0068] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0069] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0070] This application proposes a data processing method for wind farms, which improves the real-time performance and accuracy of data processing, meeting the needs of wind power systems for efficient data preprocessing. Specifically, by integrating a multi-functional data processing unit, it achieves comprehensive inspection, optimization processing, and anomaly identification and marking of multi-source data. First, quality control is performed on wind turbine equipment data, surface meteorological data, meteorological reanalysis, and numerical model data, covering boundary value checks, internal consistency checks, temporal consistency checks, spatial consistency checks, and missing value checks. Second, methods such as inverse distance weighted interpolation and spline interpolation are used to improve data consistency and refine analysis, and missing data is repaired through spatiotemporal bilinear interpolation, with repair logs recorded to ensure the traceability of the data processing flow. Finally, anomalies in wind turbine equipment and meteorological data are classified and identified, logs are generated, and alarms are triggered, enhancing data monitoring and emergency management capabilities. Compared with existing technologies, the embodiments of this disclosure significantly improve data processing efficiency and wind power prediction accuracy, providing technical support for the safe and efficient operation of wind farms. The following is a detailed explanation... Figure 1 Provide a detailed description.

[0071] Specifically, Figure 1 This is a flowchart illustrating a wind farm terminal data processing method provided in an embodiment of this disclosure. The method can be executed by a wind farm terminal data processing device, which can be implemented using software and / or hardware, and is generally integrated into an electronic device. Figure 1 As shown, the method includes:

[0072] Step 101: Obtain multi-source data; where multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data.

[0073] In this embodiment, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data. Wind power equipment data includes power parameters and equipment status monitoring data; wherein, power parameters include the actual active power output by the generator and the reactive power used to maintain grid voltage; equipment status monitoring data involves wind turbine operating status parameters, such as rotor and generator speeds, main bearing and generator temperatures, current, and voltage; ground station meteorological data includes wind speed, wind direction, air temperature, humidity, and air pressure; these environmental data provide necessary support for assessing the wind energy conversion efficiency and operating conditions of wind farms; meteorological reanalysis and numerical model data include reanalysis data containing geopotential height field, upper-level wind speed, and wind direction, which can be used to predict future meteorological conditions and assist in the operation scheduling and power prediction of wind farms; numerical model forecast data covers important meteorological elements and is used for comparison and verification with wind farm forecast model results.

[0074] Step 102: Perform a comprehensive check on the multi-source data, and process the multi-source data based on the check results to obtain the data to be identified; the comprehensive check includes, but is not limited to, boundary value check, internal consistency check, temporal consistency check, spatial consistency check, and missing value check.

[0075] In some embodiments, the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data are obtained, and the wind power equipment data is checked for boundary values ​​based on the limit values ​​of the equipment operating parameters. The wind power equipment data that exceeds the limit values ​​of the equipment operating parameters is deleted. The boundary values ​​of the ground station meteorological data and meteorological reanalysis and numerical model data determined based on historical observations and regional extreme values ​​are obtained, and the ground station meteorological data and meteorological reanalysis and numerical model data are checked for boundary values. The ground station meteorological data and meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

[0076] Specifically, the limit value check is used to determine whether multi-source data exceeds the set safety or operating limits. For wind power equipment data, such as main bearing temperature, current and voltage, the check will be based on the design limits of the equipment operating parameters. For ground station meteorological data and reanalysis data, the limit values ​​are determined based on historical observations and regional extreme values. Data that exceeds the limit is marked as abnormal and cannot pass the check.

[0077] In some embodiments, the internal consistency of wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; data in wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data that do not meet the internal consistency requirements are deleted to obtain the data to be identified.

[0078] Specifically, internal consistency checks are based on the logical relationships within multi-source data; for example, the operating status and output power of wind power equipment should have a reasonable correspondence, and the monitoring data between different components of the equipment should be consistent; for ground stations and reanalysis data, the interrelationships between meteorological elements are checked to ensure that the data conforms to the expected change patterns, while identifying potential anomalies caused by data processing software.

[0079] In some embodiments, the temporal consistency of wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data is determined based on the time series information of wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; data in wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data that do not meet the time consistency requirement are deleted to obtain the data to be identified.

[0080] Specifically, time consistency checks are used to detect the continuity and rationality of data over time; wind power equipment parameters should exhibit stable or gradually changing characteristics during operation, and sudden changes will be marked as abnormal; meteorological data should be consistent with the time series, and data from different time points should be compared to verify the rationality of the data and detect anomalies.

[0081] In some embodiments, the spatial consistency of wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; data in wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data that do not meet the spatial consistency requirements are deleted to obtain the data to be identified.

[0082] Specifically, spatial consistency checks are based on the spatial distribution characteristics of multi-source data; for reanalysis data and meteorological data from ground stations, data from adjacent stations or regions should show spatial correlation; interpolation methods are used to estimate the data under test through neighboring data points to determine whether the deviation is reasonable, so as to ensure the spatial consistency of the data; similar analysis can also be performed on the distribution characteristics of wind power equipment to detect and correct local anomalies.

[0083] In some embodiments, missing values ​​in multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

[0084] Specifically, check for missing values ​​in all types of data, identify and record incomplete data points.

[0085] Step 103: Perform interpolation processing on the data to be identified to obtain the target data; wherein, the interpolation processing includes, but is not limited to, inverse distance weighted interpolation, spline interpolation and spatiotemporal bilinear interpolation.

[0086] In some embodiments, interpolation processing is performed on the data to be identified to obtain target data, including: interpolating uniformly distributed data in the data to be identified by determining weights based on the distance of the uniformly distributed data using an inverse distance weighted interpolation algorithm; interpolating variable data in the data to be identified by a spline interpolation algorithm; and interpolating missing data in the data to be identified by a spatiotemporal bilinear interpolation algorithm to obtain target data.

[0087] Specifically, for uniformly distributed meteorological data (such as air pressure, temperature, geopotential height), when using Inverse Distance Weighting (IDW) for spatial data interpolation, IDW performs interpolation based on distance weights, with closer known data points receiving greater weights. The IDW interpolation formula (1) is:

[0088]

[0089] Where Z(x) is the estimated value of the interpolation point x, Zi is the value of the known data point i, di is the distance between the interpolation point and the known data point i, and p is the weighting index, which usually determines the degree of influence of the distance.

[0090] Specifically, for wind speed and direction data that are significantly affected by terrain complexity, spline interpolation is used to better capture local feature changes; taking cubic spline interpolation as an example, as shown in formula (2):

[0091] S i (x)=a i +b i (xx i )+c i (xx i ) 2 +d i (xx i ) 3 (2)

[0092] Among them, S i (x) is a spline function in the interval, a i b i c i d i The coefficients of the spline function are determined by satisfying the requirements of continuity, smoothness, and boundary conditions.

[0093] Specifically, for missing data, spatiotemporal bilinear interpolation is used to complete the data to ensure the continuity and integrity of the data. This method combines the data change trends in time and space dimensions to ensure the accuracy and consistency of the interpolation results, as shown in formula (3):

[0094]

[0095] Specifically, spatial bilinear interpolation can be understood as assuming that the missing data point is located on a two-dimensional plane, surrounded by four known data points (x1, y1), (x1, y2), (x2, y1), and (x2, y2), with corresponding data values ​​f(x1, y1), f(x1, y2), f(x2, y1), and f(x2, y2); the goal is to interpolate and calculate the data value f(x, y) of the point (x, y).

[0096] The specific time linear interpolation can be understood as the spatial interpolation results f(x, y, t1) and f(x, y, t2) at different time points t1 and t2, respectively, where f(x, y, t) is the interpolation value at time t, as shown in formula (4):

[0097]

[0098] Using the above formula, the spatiotemporal bilinear interpolation method combines spatial and temporal dimensions to achieve smooth completion of missing data.

[0099] Step 104: Perform anomaly identification on the target data and obtain the anomaly identification results.

[0100] In some embodiments, an anomaly log is generated based on the anomaly identification result, wherein the anomaly log includes an anomaly type, anomaly data, a timestamp, and a device identifier; the anomaly log is stored in a designated log storage system so that users can retrieve the anomaly log based on the log storage system.

[0101] In some embodiments, when an anomaly is determined to be a target type anomaly based on the anomaly identification result, an alarm message is generated and the user is notified.

[0102] Specifically, the anomaly identification results include wind turbine data anomalies, ground station meteorological data anomalies, and extreme weather event anomalies. Wind turbine data anomaly detection identifies abnormalities in wind turbine operating data, such as main bearing temperature anomalies (e.g., by setting a temperature threshold for identification), current and voltage anomalies, etc.; it generates equipment anomaly logs to support equipment fault diagnosis and maintenance decisions. Ground station meteorological data anomaly identification identifies abnormal or missing values ​​in meteorological data and automatically generates meteorological data anomaly logs; the log content includes the time, spatial location, and anomaly type of the abnormal data point, facilitating repair and analysis by the operation and maintenance team. Extreme weather event anomaly identification identifies and marks extreme weather events (such as strong winds, drastic temperature changes, etc.) and generates risk warning reports; the warning information helps wind farms take protective measures in advance, reducing the negative impact of extreme weather on equipment operation.

[0103] Specifically, standardized log templates are developed, clearly defining the basic format and included information fields, such as anomaly type, anomaly data, timestamp, and device identifier, to ensure the uniformity and standardization of log recording. Based on the categorized and tagged anomaly data, relevant information is filled into the designated fields of the log template to generate a complete anomaly log, ensuring that all key information is accurately recorded. The generated anomaly logs are stored in a designated log storage system for convenient subsequent data retrieval and analysis. A convenient log query function is provided, allowing users to perform precise searches based on time range, anomaly type, and other conditions to quickly locate and analyze anomaly events. Stored anomaly logs are analyzed regularly to identify patterns and trends in data anomalies, providing a reliable basis for improving data quality and developing risk management strategies. When serious data anomalies are detected, the system automatically triggers an alarm mechanism to promptly notify relevant personnel to take necessary measures, ensuring that the problem is handled quickly. This process ensures efficient management and real-time response to anomaly data, contributing to improved overall data quality and system reliability.

[0104] Based on the above embodiments, the wind farm terminal data processing method of this disclosure can achieve efficient preprocessing of multi-source data, ensuring the real-time performance and accuracy of wind farm data processing, thereby meeting the high data requirements of the wind power generation field. As an example, such as... Figure 2 As shown, the acquisition includes, but is not limited to, wind power equipment data, involving power parameters and equipment status monitoring data; ground station meteorological data, involving wind direction, wind speed and other basic meteorological data; meteorological reanalysis and numerical model data, involving geopotential height field, upper-level wind speed and wind direction and other elements; comprehensive quality control is carried out on the multi-source data at the wind farm site, including basic parameter checks, missing data checks, variation range checks, internal consistency checks, temporal consistency checks, spatial consistency checks, and climatological boundary value checks of meteorological data, as well as annotation of detected abnormal data.

[0105] Among them, the limit value check is used to detect whether multi-source data exceeds the set safety or operating limit range. The check is set based on the design limit value of the wind power equipment operating parameters and the historical extreme value range of meteorological data from ground stations. The detected data exceeding the limit is marked as abnormal. The internal consistency check identifies unreasonable changes between equipment operating status and power parameters, as well as abnormal relationships between meteorological elements, by analyzing the logical relationship between multi-source data and the consistency of monitoring data.

[0106] Furthermore, the multi-source data format is optimized and outliers are effectively handled to improve data consistency and analytical accuracy. Coarse-resolution reanalysis and numerical model data are interpolated to a spatial resolution consistent with wind farm site data to match the refined requirements of wind power equipment and ground meteorological station data. For uniformly distributed meteorological data (such as air pressure, temperature, geopotential height), inverse distance-weighted interpolation is used to ensure interpolation accuracy. That is, when calculating interpolation points, interpolation values ​​are assigned based on distance weights, with closer known data points receiving greater weights to improve interpolation accuracy. For wind speed and direction data significantly affected by terrain complexity, spline interpolation is applied to... To better capture local feature changes, spline interpolation is used for wind speed and direction data affected by terrain complexity, ensuring data continuity and smoothness. For missing data, spatiotemporal bilinear interpolation is employed to complete the data, avoiding the impact of data breakpoints on overall analysis accuracy. This involves combining time and spatial data trends to guarantee the accuracy of the interpolation results and data continuity. For outliers that are repaired, a processing log is generated in real-time, recording the interpolation process and parameter settings to ensure the traceability and transparency of the data repair process.

[0107] Finally, the system automatically identifies, classifies, and tags various data anomalies, enhancing data monitoring and emergency management capabilities. This includes identifying and classifying anomalies in wind power equipment data, surface meteorological data, and extreme weather events. Detailed logs are generated based on anomaly type and stored in categories to support diverse user needs. This enables the generation of standardized anomaly logs, including anomaly type, anomaly data, timestamps, and equipment identifiers, which are then stored in a designated log storage system for subsequent analysis and querying. The system also provides log query functionality, allowing users to accurately retrieve logs based on time range, anomaly type, and other criteria, quickly locating and analyzing anomaly data. Furthermore, it includes a log alarm function; when a serious data anomaly is detected, an alarm mechanism is automatically triggered to notify relevant personnel to take timely action and mitigate the impact of the anomaly on system operation.

[0108] Therefore, it can not only effectively address the complex needs of the wind power generation field, but also significantly improve the efficiency of data processing and the accuracy of prediction, which is a technical problem that the wind power industry urgently needs to solve.

[0109] The wind farm data processing scheme provided in this disclosure acquires multi-source data, including but not limited to wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data. The multi-source data undergoes comprehensive checks, and based on the check results, it is processed to obtain data to be identified. These comprehensive checks include, but are not limited to, boundary value checks, internal consistency checks, temporal consistency checks, spatial consistency checks, and missing value checks. The data to be identified is then interpolated to obtain target data, including but not limited to inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation. Anomaly identification is performed on the target data to obtain anomaly identification results. This technical solution effectively addresses the increasingly complex needs in the wind power generation field and significantly improves data processing efficiency and prediction accuracy. Particularly in addressing the complexity and real-time requirements of wind power data, this disclosure optimizes the preprocessing flow of multi-source data to meet the higher standards of wind farms for data processing speed and quality, thereby providing strong support for improving the accuracy of wind power prediction.

[0110] Figure 3 This is a schematic diagram of a data processing device for a wind farm station provided in an embodiment of the present disclosure. The device can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0111] like Figure 3 As shown, the device includes:

[0112] The acquisition module 301 is used to acquire multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data;

[0113] The inspection module 302 is used to perform a comprehensive inspection on the multi-source data and process the multi-source data based on the inspection results to obtain the data to be identified; wherein, the comprehensive inspection includes, but is not limited to, limit value inspection, internal consistency inspection, temporal consistency inspection, spatial consistency inspection, and missing value inspection.

[0114] Interpolation module 303 is used to perform interpolation processing on the data to be identified to obtain target data; wherein, the interpolation processing includes, but is not limited to, inverse distance weighted interpolation, spline interpolation and spatiotemporal bilinear interpolation;

[0115] The identification module 304 is used to identify anomalies in the target data and obtain anomaly identification results.

[0116] Optionally, the inspection module 302 is specifically used for:

[0117] Obtain the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data, perform boundary value checks on the wind power equipment data based on the limit values ​​of the equipment operating parameters, and delete the wind power equipment data that exceeds the limit values ​​of the equipment operating parameters;

[0118] The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data, determined based on historical observations and regional extreme values, are obtained. The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data are checked, and the ground station meteorological data and the meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

[0119] Optionally, the inspection module 302 is specifically used for:

[0120] The internal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0121] Data that does not meet the internal consistency requirements of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data are deleted to obtain the data to be identified.

[0122] Optionally, the inspection module 302 is specifically used for:

[0123] The temporal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the time series information of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0124] Data that does not meet the time consistency requirement, such as wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0125] Optionally, the inspection module 302 is specifically used for:

[0126] The spatial consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data.

[0127] Data that does not meet spatial consistency requirements, including wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

[0128] Optionally, the inspection module 302 is specifically used for:

[0129] The missing values ​​in the multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

[0130] Optional, interpolation module 303, specifically used for:

[0131] For the uniformly distributed data in the data to be identified, the weights are determined based on the distance of the uniformly distributed data using the inverse distance weighted interpolation algorithm for interpolation processing. For the variable data in the data to be identified, the variable data is interpolated using the spline interpolation algorithm, and the missing data in the data to be identified is interpolated using the spatiotemporal bilinear interpolation algorithm, to obtain the target data.

[0132] Optionally, the device further includes:

[0133] The generation module is used to generate an anomaly log based on the anomaly identification result, wherein the anomaly log includes an anomaly type, anomaly data, a timestamp, and a device identifier;

[0134] The storage module is used to store the exception logs to a designated log storage system so that users can retrieve the exception logs based on the log storage system.

[0135] Optionally, the device further includes:

[0136] The prompting module is used to generate alarm prompts and prompt the user when the anomaly identification result determines that the target type is an anomaly.

[0137] The wind farm terminal data processing system provided in this disclosure can execute the wind farm terminal data processing method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of executing the method.

[0138] This disclosure also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the wind farm terminal data processing method provided in any embodiment of this disclosure.

[0139] According to one or more embodiments of this disclosure, this disclosure provides an electronic device, including:

[0140] processor;

[0141] Memory used to store the processor's executable instructions;

[0142] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the wind farm terminal data processing method as provided in any of the present disclosure.

[0143] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium storing a computer program for performing a wind farm terminal data processing method as described in any of the present disclosure.

[0144] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0145] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0146] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for processing data at a wind farm station, characterized in that, include: Acquire multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; The multi-source data is comprehensively checked, and the data is processed based on the check results to obtain the data to be identified; wherein, the comprehensive check includes, but is not limited to, boundary value check, internal consistency check, temporal consistency check, spatial consistency check, and missing value check; Specifically, the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data are obtained, and the wind power equipment data is checked for boundary values ​​based on the limit values ​​of the equipment operating parameters. The wind power equipment data that exceeds the limit values ​​of the equipment operating parameters is deleted. The internal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. Specifically, the temporal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the time series information of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. Specifically, the spatial consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. The data to be identified is interpolated to obtain target data; wherein, the interpolation includes, but is not limited to, inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation; wherein, for uniformly distributed data in the data to be identified, interpolation is performed based on the inverse distance weighted interpolation algorithm to determine the weights according to the distance of the uniformly distributed data; for variable data in the data to be identified, interpolation is performed based on the spline interpolation algorithm; and for missing data in the data to be identified, interpolation is performed based on the spatiotemporal bilinear interpolation algorithm to obtain the target data; Anomaly identification is performed on the target data to obtain anomaly identification results.

2. The method according to claim 1, characterized in that, The method further includes: The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data, determined based on historical observations and regional extreme values, are obtained. The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data are checked, and the ground station meteorological data and the meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

3. The method according to claim 1, characterized in that, The method further includes: Data that does not meet the internal consistency requirements of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data are deleted to obtain the data to be identified.

4. The method according to claim 1, characterized in that, The method further includes: Data that does not meet the time consistency requirement, such as wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

5. The method according to claim 1, characterized in that, The method further includes: Data that does not meet spatial consistency requirements, including wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

6. The method according to claim 1, characterized in that, The process of comprehensively checking the multi-source data and processing the multi-source data based on the check results to obtain the data to be identified includes: The missing values ​​in the multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

7. The method according to claim 1, characterized in that, The method further includes: An anomaly log is generated based on the anomaly identification results, wherein the anomaly log includes anomaly type, anomaly data, timestamp, and device identifier; The exception logs are stored in a designated log storage system so that users can retrieve the exception logs based on the log storage system.

8. The method according to claim 1, characterized in that, The method further includes: When the anomaly identification result determines that the target type is an anomaly, an alarm message is generated and the user is notified.

9. A data processing device for a wind farm station, characterized in that, include: The acquisition module is used to acquire multi-source data; wherein, the multi-source data includes, but is not limited to, wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data; The inspection module is used to perform a comprehensive inspection on the multi-source data and process the multi-source data based on the inspection results to obtain the data to be identified; wherein, the comprehensive inspection includes, but is not limited to, boundary value inspection, internal consistency inspection, temporal consistency inspection, spatial consistency inspection, and missing value inspection. Specifically, the limit values ​​of the equipment operating parameters corresponding to the wind power equipment data are obtained, and the wind power equipment data is checked for boundary values ​​based on the limit values ​​of the equipment operating parameters. The wind power equipment data that exceeds the limit values ​​of the equipment operating parameters is deleted. The internal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the logical relationships between the various data within the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. Specifically, the temporal consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the time series information of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. Specifically, the spatial consistency of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data. An interpolation module is used to perform interpolation processing on the data to be identified to obtain target data; wherein the interpolation processing includes, but is not limited to, inverse distance weighted interpolation, spline interpolation, and spatiotemporal bilinear interpolation; wherein, for uniformly distributed data in the data to be identified, interpolation processing is performed based on the inverse distance weighted interpolation algorithm to determine the weights according to the distance of the uniformly distributed data; for variable data in the data to be identified, interpolation processing is performed based on the spline interpolation algorithm; and interpolation processing is performed based on the spatiotemporal bilinear interpolation algorithm for missing data in the data to be identified to obtain the target data; The identification module is used to identify anomalies in the target data and obtain anomaly identification results.

10. The apparatus according to claim 9, characterized in that, The inspection module is further used for: The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data, determined based on historical observations and regional extreme values, are obtained. The boundary values ​​of the ground station meteorological data and the meteorological reanalysis and numerical model data are checked, and the ground station meteorological data and the meteorological reanalysis and numerical model data that exceed the boundary values ​​are deleted to obtain the data to be identified.

11. The apparatus according to claim 9, characterized in that, The inspection module is further used for: Data that does not meet the internal consistency requirements of the wind power equipment data, the ground station meteorological data, and the meteorological reanalysis and numerical model data are deleted to obtain the data to be identified.

12. The apparatus according to claim 9, characterized in that, The inspection module is further used for: Data that does not meet the time consistency requirement, such as wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

13. The apparatus according to claim 9, characterized in that, The inspection module is further used for: Data that does not meet spatial consistency requirements, including wind power equipment data, ground station meteorological data, and meteorological reanalysis and numerical model data, are deleted to obtain the data to be identified.

14. The apparatus according to claim 9, characterized in that, The inspection module is specifically used for: The missing values ​​in the multi-source data are checked, and the data points corresponding to the missing values ​​are identified and recorded to obtain the data to be identified.

15. The apparatus according to claim 9, characterized in that, The device further includes: The generation module is used to generate an anomaly log based on the anomaly identification result, wherein the anomaly log includes an anomaly type, anomaly data, a timestamp, and a device identifier; The storage module is used to store the exception logs to a designated log storage system so that users can retrieve the exception logs based on the log storage system.

16. The apparatus according to claim 9, characterized in that, The device further includes: The prompting module is used to generate alarm prompts and prompt the user when the anomaly identification result determines that the target type is an anomaly.

17. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the wind farm terminal data processing method according to any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which is used to execute the wind farm terminal data processing method according to any one of claims 1-8.

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