Wind power plant station end data processing method, device, equipment and medium

Abstract

The embodiment of the invention relates to a wind power plant station end data processing method and device, equipment and a medium. The method comprises the steps of obtaining multi-source data; wherein the multi-source data comprises but is not limited to wind power equipment data, ground station meteorological data and meteorological reanalysis and numerical mode data; performing comprehensive inspection on the multi-source data, and processing the multi-source data based on an inspection result to obtain to-be-identified data; wherein the comprehensive inspection comprises but not limited limit value inspection, internal consistency inspection, time consistency inspection, space consistency inspection and missing measurement value inspection; performing interpolation processing on the to-be-identified data to obtain target data; wherein the interpolation processing comprises but not limited inverse distance weight interpolation, spline interpolation and space-time bilinear interpolation; and performing exception identification on the target data to obtain an exception identification result. By adopting the technical scheme, the data processing efficiency and the wind power prediction precision can be improved, and technical support is provided for safe and efficient operation of the wind power plant.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, device, equipment and medium for processing data at a wind farm station. Background Art

[0002] At present, as the world attaches great importance to energy conservation and emission reduction, green energy, low-carbon economy and sustainable development, the proportion of clean energy and renewable energy in the energy structure continues to rise. Among them, wind energy has become the fastest growing type of energy with its rapid development momentum. Wind power generation relies on natural wind energy. As a clean and renewable energy form, it is increasingly gaining widespread attention and favor.

[0003] Generally speaking, for wind power systems, efficient and accurate data processing is of great value in ensuring the safe and stable operation of the system and optimizing the dispatch of the power market. The data sources at the wind farm station are rich and complex, including but not limited to wind power equipment data, ground site meteorological data, meteorological reanalysis and numerical model data. These multi-source data vary greatly in format, spatiotemporal resolution and data quality, which brings great challenges to data integration and processing. In particular, the complexity and real-time requirements of wind power data have prompted wind farms to put forward higher requirements for data preprocessing efficiency in order to improve the accuracy of wind power forecasting. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a wind farm station-side data processing method, device, equipment and medium.

[0005] The present disclosure provides a method for processing wind farm station-side data, the method comprising:

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

[0007] Performing a comprehensive check on the multi-source data, and processing the multi-source data based on the check result to obtain the data to be identified; wherein the comprehensive check includes but is not limited to a limit value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check;

[0008] Performing 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;

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

[0010] Optionally, the performing a comprehensive inspection on the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified includes:

[0011] Acquire the limit value of the equipment operation parameter corresponding to the wind power equipment data, perform a limit value check on the wind power equipment data based on the limit value of the equipment operation parameter, and delete the wind power equipment data that is greater than the limit value of the equipment operation parameter;

[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, and the boundary value checks are performed on the ground station meteorological data and the meteorological reanalysis and numerical model data, and the ground station meteorological data and the meteorological reanalysis and numerical model data that are greater than the boundary value are deleted to obtain the data to be identified.

[0013] Optionally, the comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified includes:

[0014] Determining the internal consistency of the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data based on the logical relationship between each data within the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data;

[0015] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the internal consistency are deleted to obtain the data to be identified.

[0016] Optionally, the performing a comprehensive inspection on the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified includes:

[0017] Determining the time consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the time series information of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0018] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the time consistency are deleted to obtain the data to be identified.

[0019] Optionally, the comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified includes:

[0020] Determining the spatial consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the spatial distribution characteristics of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0021] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

[0022] Optionally, the comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified includes:

[0023] Check the missing values ​​in the multi-source data, identify and record the data points corresponding to the missing values, and obtain the data to be identified.

[0024] Optionally, performing interpolation processing on the data to be identified to obtain target data includes:

[0025] For the uniformly distributed data in the data to be identified, interpolation processing is performed based on the weight determined according to the distance of the uniformly distributed data based on the inverse distance weighted interpolation algorithm; for the changed data in the data to be identified, interpolation processing is performed on the changed data based on the spline interpolation algorithm; and interpolation processing is performed on the missing data in the data to be identified based on the spatiotemporal bilinear interpolation algorithm to obtain the target data.

[0026] Optionally, the method further includes:

[0027] Generate an exception log based on the exception identification result, wherein the exception log includes an exception type, exception data, a timestamp, and a device identifier;

[0028] The abnormal log is stored in a designated log storage system so that a user can retrieve the abnormal log based on the log storage system.

[0029] Optionally, the method further includes:

[0030] When it is determined that the target type is abnormal based on the abnormal identification result, an alarm prompt message is generated and the user is prompted.

[0031] The present disclosure also provides a wind farm station-side data processing device, the device comprising:

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

[0033] A check module, used to perform a comprehensive check on the multi-source data, and process the multi-source data based on the check result to obtain the data to be identified; wherein the comprehensive check includes but is not limited to a limit value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check;

[0034] An interpolation module, 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 recognition module is used to perform anomaly recognition on the target data to obtain anomaly recognition results.

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

[0037] Acquire the limit value of the equipment operation parameter corresponding to the wind power equipment data, perform a limit value check on the wind power equipment data based on the limit value of the equipment operation parameter, and delete the wind power equipment data that is greater than the limit value of the equipment operation parameter;

[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, and the boundary value checks are performed on the ground station meteorological data and the meteorological reanalysis and numerical model data, and the ground station meteorological data and the meteorological reanalysis and numerical model data that are greater than the boundary value are deleted to obtain the data to be identified.

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

[0040] Determining the internal consistency of the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data based on the logical relationship between each data within the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data;

[0041] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the internal consistency are deleted to obtain the data to be identified.

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

[0043] Determining the time consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the time series information of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0044] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the time consistency are deleted to obtain the data to be identified.

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

[0046] Determining the spatial consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the spatial distribution characteristics of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0047] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

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

[0049] Check the missing values ​​in the multi-source data, identify and record the data points corresponding to the missing values, and obtain the data to be identified.

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

[0051] For the uniformly distributed data in the data to be identified, interpolation processing is performed based on the weight determined according to the distance of the uniformly distributed data based on the inverse distance weighted interpolation algorithm; for the changed data in the data to be identified, interpolation processing is performed on the changed data based on the spline interpolation algorithm; and interpolation processing is performed on the missing data in the data to be identified based on the spatiotemporal bilinear interpolation algorithm to obtain the target data.

[0052] Optionally, the device further comprises:

[0053] A generating module, configured to generate an exception log based on the exception identification result, wherein the exception log includes an exception type, exception data, a timestamp, and a device identifier;

[0054] The storage module is used to store the abnormal log in a designated log storage system so that a user can retrieve and obtain the abnormal log based on the log storage system.

[0055] Optionally, the device further comprises:

[0056] The prompt module is used to generate an alarm prompt message and prompt a user when it is determined that the target type is abnormal based on the abnormal identification result.

[0057] An embodiment of the present disclosure also provides an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the wind farm station-side data processing method provided in the embodiment of the present disclosure.

[0058] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the wind farm station-side data processing method provided by the embodiment of the present disclosure.

[0059] Compared with the prior art, the technical solution provided by the embodiment of the present disclosure has the following advantages: the wind farm station-side data processing solution provided by the embodiment of the present disclosure obtains 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; a comprehensive check is performed on the multi-source data, and the multi-source data is processed based on the check result to obtain the data to be identified; wherein, the comprehensive check includes but is not limited to the limit value check, the internal consistency check, the time consistency check and the spatial consistency check and the missing value check; the data to be identified is interpolated to obtain the target data; wherein, the interpolation processing includes but is not limited to the inverse distance weighted interpolation, the spline interpolation and the space-time bilinear interpolation; the target data is anomaly identified to obtain the anomaly identification result. The above technical solution can not only effectively cope with the increasingly complex needs in the field of wind power generation, but also significantly improve the data processing efficiency and prediction accuracy; especially in dealing with the complexity and real-time requirements of wind power data, the embodiment of the present disclosure optimizes the preprocessing process 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent 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 schematic diagram of a flow chart of a wind farm station-side data processing method provided in an embodiment of the present disclosure;

[0062] Figure 2 A schematic diagram of a scenario of a wind farm station-side data processing method provided by an embodiment of the present disclosure;

[0063] Figure 3 This is a structural example diagram of a wind farm station-side data processing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

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

[0066] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part 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". The relevant definitions of other terms will be given in the following description.

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

[0068] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

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

[0070] The present application proposes a method for processing data at the wind farm station end, which can improve the real-time and accuracy of data processing and meet the needs of wind power systems for efficient data preprocessing. Specifically, by integrating a multifunctional data processing unit, comprehensive inspection, optimization processing, anomaly identification and marking of multi-source data are realized; first, quality control is performed on wind power equipment data, ground meteorological data, meteorological reanalysis and numerical model data, covering boundary value checks, internal consistency checks, time consistency checks, spatial consistency checks and missing value checks; secondly, methods such as inverse distance weighted interpolation and spline interpolation are used to improve data consistency and refined analysis, and missing data is repaired through spatiotemporal bilinear interpolation, and repair logs are recorded to ensure the traceability of the data processing process; finally, wind power equipment and meteorological data anomalies are classified and identified, logs are generated and alarms are triggered, and data monitoring and emergency management capabilities are enhanced. Compared with the prior art, the disclosed embodiment significantly improves data processing efficiency and wind power prediction accuracy, providing technical support for the safe and efficient operation of wind farms. The following is combined with Figure 1 Describe in detail.

[0071] Specifically, Figure 1 The present invention provides a flow chart of a method for processing wind farm station-side data, which can be executed by a wind farm station-side data processing device, wherein the device can be implemented by software and / or hardware, and can generally be integrated into an electronic device. Figure 1 As shown, the method includes:

[0072] Step 101, obtaining multi-source data; wherein the multi-source data includes but is not limited to wind power equipment data, ground site meteorological data, and meteorological reanalysis and numerical model data.

[0073] In the disclosed embodiment, multi-source data includes but is not limited to wind power equipment data, ground site 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 active power output of the actual generator and the reactive power used to maintain the grid voltage; equipment status monitoring data involves wind turbine operating status parameters, such as wind wheel and generator speed, main bearing and generator temperature, current and voltage, etc.; ground site meteorological data includes wind speed, wind direction, temperature, humidity and air pressure, etc.; these environmental data provide necessary support for evaluating the wind energy conversion efficiency and operating conditions of wind farms; meteorological reanalysis and numerical model data include reanalysis data containing potential height field, high-altitude wind speed and wind direction and other elements, which can be used to predict future meteorological conditions and assist in the operation scheduling and power forecasting of wind farms; numerical model forecast data covers important meteorological elements, which are used for comparison and verification with the results of wind farm forecast models.

[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; wherein the comprehensive check includes but is not limited to a boundary value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check.

[0075] In some embodiments, the limit values ​​of equipment operating parameters corresponding to wind power equipment data are obtained, and a limit value check is performed on the wind power equipment data based on the limit values ​​of the equipment operating parameters, and the wind power equipment data that is greater than the limit values ​​of the equipment operating parameters is deleted; the limit values ​​of ground site meteorological data and meteorological reanalysis and numerical model data determined based on historical observations and regional extreme values ​​are obtained, and a limit value check is performed on the ground site meteorological data and meteorological reanalysis and numerical model data, and the ground site meteorological data and meteorological reanalysis and numerical model data that are greater than the limit values ​​are deleted to obtain data to be identified.

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

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

[0078] Specifically, the internal consistency check is based on the logical relationship within the multi-source data; for example, the operating status of wind power equipment and the output power should have a reasonable correspondence, and the monitoring data between different components of the equipment should remain consistent; for ground stations and reanalysis data, check the relationship between meteorological elements to ensure that the data conforms to the expected change pattern, and identify potential anomalies caused by data processing software.

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

[0080] Specifically, the time consistency check is used to detect the continuity and rationality of the data in the time dimension; the wind power equipment parameters should show stable or gradual changes during operation, and sudden changes will be marked as abnormalities; meteorological data should be consistent with the time series, and the data before and after 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 site meteorological data, meteorological reanalysis and numerical model data is determined based on the spatial distribution characteristics of wind power equipment data, ground site meteorological data, meteorological reanalysis and numerical model data; the data in the wind power equipment data, ground site meteorological data, meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

[0082] Specifically, the spatial consistency check is based on the spatial distribution characteristics of multi-source data. For reanalysis data and ground site meteorological data, the data between adjacent sites or regions should show spatial correlation. Interpolation methods are used to estimate the tested data through neighboring data points to determine whether the deviation is reasonable to ensure the spatial consistency of the data. The distribution characteristics of wind power equipment can also be similarly analyzed to detect and correct local anomalies.

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

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

[0085] Step 103: interpolate the data to be identified to obtain target data; wherein the interpolation process 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: interpolation processing is performed on uniformly distributed data in the data to be identified based on an inverse distance weighted interpolation algorithm to determine weights according to the distance of the uniformly distributed data, interpolation processing is performed on the changing data in the data to be identified based on a spline interpolation algorithm, and interpolation processing is performed on missing data in the data to be identified based on a spatiotemporal bilinear interpolation algorithm to obtain target data.

[0087] Specifically, for uniformly distributed meteorological data (such as pressure and temperature potential height), when using Inverse Distance Weighting (IDW) for spatial data interpolation, IDW interpolates according to the distance weight, and the closer the known data point is, the greater the weight is. The interpolation formula (1) of IDW is:

[0088]

[0089] Among them, 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 weight index, which usually determines the influence of the distance.

[0090] Specifically, for wind speed and direction data that are significantly affected by terrain complexity, the spline interpolation method 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 the spline function in the interval, a i , b i , c i , d i are the coefficients of the spline function, which are determined by satisfying the requirements of continuity, smoothness and boundary conditions.

[0093] Specifically, for missing data, the spatiotemporal bilinear interpolation method is used to fill in the missing data to ensure the continuity and integrity of the data. This method combines the data change trends in the 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), (x2, y2), and the corresponding data values ​​are f(x1, y1), f(x1, y2), f(x2, y1), f(x2, y2); the goal is to interpolate 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 at different time points t1 and t2 are f(x, y, t1) and f(x, y, t2) respectively, which is the interpolation value f(x, y, t) at the calculation time t, as shown in formula (4):

[0097]

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

[0099] Step 104: perform anomaly identification on the target data to obtain anomaly identification results.

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

[0101] In some embodiments, when it is determined that the target type is abnormal based on the abnormality identification result, an alarm prompt message is generated and the user is prompted.

[0102] Specifically, the anomaly identification results include wind power equipment data anomalies, ground site meteorological data anomalies, and extreme weather event anomalies; wind power equipment data anomalies detect anomalies in wind turbine operation data, such as main bearing temperature anomalies (such as setting temperature thresholds for identification), current and voltage anomalies, etc.; generate equipment anomaly logs to support equipment fault diagnosis and maintenance decisions; ground site meteorological data anomalies identify anomalies or missing values ​​in meteorological data, and automatically generate meteorological data anomaly logs; log content includes the time, spatial location and anomaly type of the abnormal data point, which is convenient for the operation and maintenance team to repair and analyze; extreme weather event anomalies identify and mark extreme weather events (such as strong winds, drastic temperature changes, etc.), and generate risk warning reports; warning information helps wind farms take protective measures in advance to reduce the negative impact of extreme weather on equipment operation.

[0103] Specifically, formulate a standardized log template, clarify the basic format of the log and the information fields it contains, such as exception type, exception data, timestamp, device identification, etc., to ensure the uniformity and standardization of log records; fill in the specified fields of the log template with relevant information based on the classified and marked exception data, generate a complete exception log, and ensure that all key information is accurately recorded; save the generated exception log in the specified log storage system to facilitate subsequent data retrieval and analysis operations. Provide a convenient log query function, users can perform accurate retrieval based on time range, exception type and other conditions to quickly locate and analyze abnormal events; regularly analyze the stored exception logs to identify the laws and trends of data anomalies, and provide a reliable basis for improving data quality and formulating risk management strategies; when serious data anomalies are detected, the system will automatically trigger the alarm mechanism to promptly notify relevant personnel to take necessary measures to ensure that the problem can be quickly handled. This processing step ensures efficient management and real-time response of abnormal data, which helps to improve overall data quality and system reliability.

[0104] Based on the above-mentioned embodiment description, the wind farm station-side data processing method of the embodiment of the present disclosure can realize efficient preprocessing of multi-source data, ensure the real-time and accuracy of wind farm data processing, and meet the high requirements of wind power generation field for data. As an example, 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 site meteorological data, involving wind direction and speed and other basic meteorological data; meteorological reanalysis and numerical model data, involving potential height field, high-altitude wind speed and wind direction and other elements; comprehensive quality control of multi-source data at the wind farm station, including basic parameter check, missing data check, variation range check, internal consistency check, time consistency check, spatial consistency check, and climatological limit value check of meteorological data, as well as annotation of detected abnormal data.

[0105] Among them, the limit value check is used to detect whether the multi-source data exceeds the set safety or operating limit range. The check is based on the design limit values ​​of the wind power equipment operating parameters and the historical extreme value range of the ground station meteorological data. The detected out-of-limit data is marked as abnormal; the internal consistency check analyzes the logical relationship between multi-source data and the consistency of monitoring data to identify unreasonable changes between equipment operating status and power parameters and abnormal relationships between meteorological elements.

[0106] Furthermore, the multi-source data format is optimized and outliers are effectively processed to improve data consistency and analysis accuracy. The coarse-resolution reanalysis and numerical model data are interpolated to a spatial resolution consistent with the wind farm site data to match the refined requirements of wind power equipment and ground meteorological site data. For uniformly distributed meteorological data (such as pressure and temperature potential height), inverse distance weighted interpolation is used to ensure interpolation accuracy. That is, when calculating the value of the interpolation point, the interpolation value is assigned based on the distance weight. The closer the known data point is, the greater the weight is, so as to improve the interpolation accuracy. For wind speed and direction data that are significantly affected by terrain complexity, the spline interpolation method is applied to ensure the accuracy of interpolation. Better capture local feature changes, that is, for wind speed and wind direction data affected by terrain complexity, the spline interpolation method is used for interpolation to better capture local feature changes. The spline interpolation method meets the continuity and smoothness of the data; the missing data is completed by spatiotemporal bilinear interpolation to avoid the overall analysis accuracy being affected by data breakpoints. In other words, the missing data is completed by spatiotemporal bilinear interpolation, combining the data change trends in time and space dimensions to ensure the accuracy of the interpolation results and the continuity of the data; for the outliers to be repaired, a processing log is generated in real time to record the interpolation process and parameter settings to ensure the traceability and transparency of the data repair process.

[0107] Finally, it automatically identifies, classifies and marks various data anomalies to enhance data monitoring and emergency management capabilities, including identifying and classifying anomalies in wind power equipment data, ground meteorological data and extreme weather events, generating detailed logs according to the anomaly type, and storing them in categories to support different user needs. As a result, it can generate standardized anomaly logs, including anomaly type, anomaly data, timestamp and device identification, and store the logs in a designated log storage system to support subsequent analysis and query; at the same time, it provides a log query function, allowing users to accurately retrieve logs based on time range, anomaly type and other conditions, quickly locate and analyze abnormal data, and also has a log alarm function. When serious data anomalies are detected, the alarm mechanism is automatically triggered to notify relevant personnel to take timely processing measures to reduce the impact of abnormal data on system operation.

[0108] As a result, it can not only effectively respond to the complex demands in the field of wind power generation, but also significantly improve data processing efficiency and prediction accuracy. This is a technical problem that urgently needs to be solved in the current wind power industry.

[0109] The wind farm station-side data processing scheme provided by the embodiment of the present disclosure obtains 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; a comprehensive check is performed on the multi-source data, 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 the limit value check, the internal consistency check, the time consistency check, the space consistency check and the missing value check; the data to be identified is interpolated to obtain the target data; wherein, the interpolation processing includes but is not limited to the inverse distance weighted interpolation, the spline interpolation and the space-time bilinear interpolation; the target data is anomaly identified to obtain the anomaly identification result. The adoption of the above technical scheme can not only effectively cope with the increasingly complex needs in the field of wind power generation, but also significantly improve the data processing efficiency and prediction accuracy; especially in dealing with the complexity and real-time requirements of wind power data, the embodiment of the present disclosure optimizes the preprocessing process 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 the structure of a wind farm station-side data processing device provided in an embodiment of the present disclosure. The device can be implemented by software and / or hardware and can generally be integrated into an electronic device.

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

[0112] An 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 site 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 a limit value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check;

[0114] An 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 perform abnormality identification on the target data to obtain an abnormality identification result.

[0116] Optionally, the checking module 302 is specifically configured to:

[0117] Acquire the limit value of the equipment operation parameter corresponding to the wind power equipment data, perform a limit value check on the wind power equipment data based on the limit value of the equipment operation parameter, and delete the wind power equipment data that is greater than the limit value of the equipment operation parameter;

[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, and the boundary value checks are performed on the ground station meteorological data and the meteorological reanalysis and numerical model data, and the ground station meteorological data and the meteorological reanalysis and numerical model data that are greater than the boundary value are deleted to obtain the data to be identified.

[0119] Optionally, the checking module 302 is specifically configured to:

[0120] Determining the internal consistency of the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data based on the logical relationship between each data within the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data;

[0121] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the internal consistency are deleted to obtain the data to be identified.

[0122] Optionally, the checking module 302 is specifically configured to:

[0123] Determining the time consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the time series information of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0124] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the time consistency are deleted to obtain the data to be identified.

[0125] Optionally, the checking module 302 is specifically configured to:

[0126] Determining the spatial consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the spatial distribution characteristics of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data;

[0127] The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

[0128] Optionally, the checking module 302 is specifically configured to:

[0129] Check the missing values ​​in the multi-source data, identify and record the data points corresponding to the missing values, and obtain the data to be identified.

[0130] Optionally, the interpolation module 303 is specifically used for:

[0131] For the uniformly distributed data in the data to be identified, interpolation processing is performed based on the weight determined according to the distance of the uniformly distributed data based on the inverse distance weighted interpolation algorithm; for the changed data in the data to be identified, interpolation processing is performed on the changed data based on the spline interpolation algorithm; and interpolation processing is performed on the missing data in the data to be identified based on the spatiotemporal bilinear interpolation algorithm to obtain the target data.

[0132] Optionally, the device further includes:

[0133] A generating module, configured to generate an exception log based on the exception identification result, wherein the exception log includes an exception type, exception data, a timestamp, and a device identifier;

[0134] The storage module is used to store the abnormal log in a designated log storage system so that a user can retrieve and obtain the abnormal log based on the log storage system.

[0135] Optionally, the device further includes:

[0136] The prompt module is used to generate an alarm prompt message and prompt a user when it is determined that the target type is abnormal based on the abnormal identification result.

[0137] The wind farm station-side data processing system provided in the embodiments of the present disclosure can execute the wind farm station-side data processing method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0138] The embodiments of the present disclosure further provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the wind farm station-side data processing method provided by any embodiment of the present disclosure.

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

[0140] processor;

[0141] a memory for storing instructions executable by the processor;

[0142] The processor is used to read the executable instructions from the memory and execute the instructions to implement any wind farm station-side data processing method provided in the present disclosure.

[0143] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute any of the wind farm station-side data processing methods provided by the present disclosure.

[0144] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0145] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0146] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, 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. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A wind farm station-side data processing method, characterized in that: include: Acquire multi-source data; wherein the multi-source data includes but is not limited to wind power equipment data, ground site meteorological data, and meteorological reanalysis and numerical model data; Performing a comprehensive check on the multi-source data, and processing the multi-source data based on the check result to obtain the data to be identified; wherein the comprehensive check includes but is not limited to a limit value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check; Performing 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; Anomaly identification is performed on the target data to obtain anomaly identification results.

2. The method according to claim 1, characterized in that The comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified include: Acquire the limit value of the equipment operation parameter corresponding to the wind power equipment data, perform a limit value check on the wind power equipment data based on the limit value of the equipment operation parameter, and delete the wind power equipment data that is greater than the limit value of the equipment operation parameter; 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, and the boundary value checks are performed on the ground station meteorological data and the meteorological reanalysis and numerical model data, and the ground station meteorological data and the meteorological reanalysis and numerical model data that are greater than the boundary value are deleted to obtain the data to be identified.

3. The method according to claim 1, characterized in that The comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified include: Determining the internal consistency of the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data based on the logical relationship between each data within the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the internal consistency are deleted to obtain the data to be identified.

4. The method according to claim 1, characterized in that: The comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified include: Determining the time consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the time series information of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the time consistency are deleted to obtain the data to be identified.

5. The method according to claim 1, characterized in that The comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified include: Determining the spatial consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the spatial distribution characteristics of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

6. The method according to claim 1, characterized in that The comprehensive inspection of the multi-source data and processing the multi-source data based on the inspection result to obtain the data to be identified include: Check the missing values ​​in the multi-source data, identify and record the data points corresponding to the missing values, and obtain the data to be identified.

7. The method according to claim 1, characterized in that The interpolation processing is performed on the data to be identified to obtain target data, including: For the uniformly distributed data in the data to be identified, interpolation processing is performed based on the weight determined according to the distance of the uniformly distributed data based on the inverse distance weighted interpolation algorithm; for the changed data in the data to be identified, interpolation processing is performed on the changed data based on the spline interpolation algorithm; and interpolation processing is performed on the missing data in the data to be identified based on the spatiotemporal bilinear interpolation algorithm to obtain the target data.

8. The method according to claim 1, characterized in that: The method further comprises: Generate an exception log based on the exception identification result, wherein the exception log includes an exception type, exception data, a timestamp, and a device identifier; The abnormal log is stored in a designated log storage system so that a user can retrieve the abnormal log based on the log storage system.

9. The method according to claim 1, characterized in that: The method further comprises: When it is determined that the target type is abnormal based on the abnormal identification result, an alarm prompt message is generated and the user is prompted.

10. A wind farm station-side data processing device, characterized in that: include: An acquisition module, used to acquire multi-source data; wherein the multi-source data includes but is not limited to wind power equipment data, ground site meteorological data, and meteorological reanalysis and numerical model data; A check module, used to perform a comprehensive check on the multi-source data, and process the multi-source data based on the check result to obtain the data to be identified; wherein the comprehensive check includes but is not limited to a limit value check, an internal consistency check, a time consistency check, a spatial consistency check, and a missing value check; An interpolation module, 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; The recognition module is used to perform anomaly recognition on the target data to obtain anomaly recognition results.

11. The device according to claim 10, characterized in that The inspection module is specifically used for: Acquire the limit value of the equipment operation parameter corresponding to the wind power equipment data, perform a limit value check on the wind power equipment data based on the limit value of the equipment operation parameter, and delete the wind power equipment data that is greater than the limit value of the equipment operation parameter; 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, and the boundary value checks are performed on the ground station meteorological data and the meteorological reanalysis and numerical model data, and the ground station meteorological data and the meteorological reanalysis and numerical model data that are greater than the boundary value are deleted to obtain the data to be identified.

12. The device according to claim 10, characterized in that The inspection module is specifically used for: Determining the internal consistency of the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data based on the logical relationship between each data within the wind power equipment data, the ground site meteorological data and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the internal consistency are deleted to obtain the data to be identified.

13. The device according to claim 10, characterized in that The inspection module is specifically used for: Determining the time consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the time series information of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the time consistency are deleted to obtain the data to be identified.

14. The device according to claim 10, characterized in that The inspection module is specifically used for: Determining the spatial consistency of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data based on the spatial distribution characteristics of the wind power equipment data, the ground site meteorological data, and the meteorological reanalysis and numerical model data; The wind power equipment data, the ground site meteorological data, and the data in the meteorological reanalysis and numerical model data that do not meet the spatial consistency are deleted to obtain the data to be identified.

15. The device according to claim 10, characterized in that The inspection module is specifically used for: Check the missing values ​​in the multi-source data, identify and record the data points corresponding to the missing values, and obtain the data to be identified.

16. The device according to claim 10, characterized in that The interpolation module is specifically used for: For the uniformly distributed data in the data to be identified, interpolation processing is performed based on the weight determined according to the distance of the uniformly distributed data based on the inverse distance weighted interpolation algorithm; for the changed data in the data to be identified, interpolation processing is performed on the changed data based on the spline interpolation algorithm; and interpolation processing is performed on the missing data in the data to be identified based on the spatiotemporal bilinear interpolation algorithm to obtain the target data.

17. The device according to claim 10, characterized in that The device also includes: A generating module, configured to generate an exception log based on the exception identification result, wherein the exception log includes an exception type, exception data, a timestamp, and a device identifier; The storage module is used to store the abnormal log in a designated log storage system so that a user can retrieve and obtain the abnormal log based on the log storage system.

18. The device according to claim 10, characterized in that The device also includes: The prompt module is used to generate an alarm prompt message and prompt a user when it is determined that the target type is abnormal based on the abnormal identification result.

19. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the wind farm station-side data processing method described in any one of claims 1 to 9.

20. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the wind farm station-side data processing method according to any one of claims 1 to 9.

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