Fan power generation operation and maintenance system based on intelligent construction of station

Abstract

The invention relates to the technical field of wind generating sets, and particularly discloses a fan power generation operation and maintenance system based on intelligent construction of a station, which is characterized in that an audio-visual monitoring end is arranged for acquiring operation data of a wind generating set monitoring project of the station; the data acquisition module is used for acquiring operation data acquired by the audio-visual monitoring end through a plurality of sensors and preprocessing the operation data; a data analysis module is arranged and used for analyzing the preprocessed operation data to obtain abnormal type information data; the state evaluation module is used for inputting abnormal type information data to construct a state evaluation model, outputting an evaluation coefficient, outputting an operation and maintenance strategy according to the evaluation coefficient and sending out an early warning signal; an intelligent wind field display end is arranged for displaying an operation and maintenance strategy and an early warning signal; the operation and maintenance degree and blade loss analysis are judged through monitoring operation analysis, the accuracy of the operation and maintenance system is improved, and potential safety hazards of the wind generating set are avoided in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine generator sets, and in particular to a wind turbine power generation operation and maintenance system based on intelligent station construction. Background Art

[0002] With the rapid development of the wind power industry, the number of various large-scale wind turbines is large, the distribution range is wide and the location is scattered. Due to the influence of the long-term operating environment, wind turbines are very prone to safety accidents. In order to reduce and avoid the occurrence of safety accidents, it is necessary to carry out life cycle maintenance and patrol management of wind turbines. The method of regular manual on-site operation and maintenance and inspection has problems such as low efficiency, poor real-time performance and greater safety hazards.

[0003] Introduce intelligent site management technology combined with the Internet of Things, big data and artificial intelligence equipment to carry out operation and maintenance of wind turbines, realize the intelligent management of wind farms, and obtain the operating status data of wind turbines through intelligent systems. Analyze the operating status of wind turbines according to the model built by machine algorithms to judge the health status of wind turbines, thereby realizing the intelligent judgment process of wind turbine power generation operation and maintenance system.

[0004] The existing intelligent operation and maintenance management system mainly performs centralized intelligent maintenance on the operation of wind turbines, and the staff completes the real-time monitoring and centralized recording of operation and maintenance data. However, in the operation and maintenance analysis process, only the power and related equipment operating status of the wind turbine are analyzed to determine the equipment operation loss, and the environmental loss impact of the wind turbine is lacking. In addition, when judging the equipment loss of wind turbines, most of them rely on the naked eye observation and analysis of the operation and maintenance personnel, which mostly depends on the experience and technical level of the operation and maintenance personnel, resulting in low efficiency in judging the equipment loss, especially the blade loss and operation and maintenance status, which account for an important part of the wind turbine. Through real-time blade loss analysis and unit operation and maintenance analysis, blade failure problems can be eliminated in advance. Summary of the invention

[0005] The purpose of the present invention is to provide a wind turbine power generation operation and maintenance system based on intelligent station construction to solve the following technical problems: How to judge the degree of operation and maintenance and blade loss analysis through monitoring operation analysis to improve the accuracy of the operation and maintenance system and avoid the occurrence of safety hazards of wind turbines in advance.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A wind turbine power generation operation and maintenance system based on intelligent construction of a station, the system comprising: Audio-visual monitoring terminal, used to obtain the operation data of the wind turbine monitoring project at the site; A data acquisition module is used to collect the operation data obtained by the audio-visual monitoring terminal through multiple sensors and pre-process the operation data; A data analysis module is used to analyze the pre-processed operation data to obtain abnormal type information data; The status assessment module is used to input abnormal type information data to build a status assessment model, output the assessment coefficient, output the operation and maintenance strategy according to the assessment coefficient, and issue an early warning signal; Smart wind farm display terminal, used to display operation and maintenance strategies and early warning signals.

[0007] Preferably, the wind turbine generator monitoring items of the station include: patrol route optimization monitoring, listening and playback monitoring and remote diagnosis monitoring; The patrol route optimization monitoring is used to obtain a patrol route image and obtain the operation and maintenance patrol workload of the wind turbine generator set of all monitoring nodes corresponding to the current patrol route image; The monitoring playback monitoring is used to obtain the blade vibration and sound signals of the wind turbine generator set at the patrol route image monitoring node through a microphone device; The remote diagnostic monitoring is used to perform a comprehensive diagnosis on the health status of the monitored wind turbine generator set in combination with the interactive messages between the on-site operation and maintenance patrol personnel and the control terminal to obtain health score data.

[0008] Preferably, since the types of operation data collected by the data collection module include structured data and unstructured data, it is necessary to select different databases for different heterogeneous homogeneous data and perform preprocessing of data normalization; the preprocessing includes data cleaning, data conversion and data arrangement; The pre-processed operation data includes: operation and maintenance inspection workload parameters , Blade vibration and sound signal parameters and health score .

[0009] Preferably, the data analysis module obtains the abnormal type information in the following manner: By formula Calculate and obtain the operation and maintenance coefficient of wind turbines at all monitoring nodes within the preset maintenance period under the current maintenance cycle ;in, is the total number of all monitoring nodes, and ∈ ; For the The operation and maintenance inspection workload parameters of wind turbine generator sets at each monitoring node; For the Group monitoring node wind turbine blade vibration and sound signal parameters; For the The health score of the wind turbine generator set at the group monitoring node; is the standard operation and maintenance inspection workload parameter, is the standard blade vibration and sound signal parameter, is the standard health score value; , , is the preset weight coefficient; For the The location coefficient of each monitoring node.

[0010] Preferably, the operation and maintenance coefficient The preset operation and maintenance coefficient threshold range To compare: like < , then the operation and maintenance level is judged to be low, and blade fault detection is performed; like > , then the operation and maintenance level is judged to be high, and the operation and maintenance inspection workload is optimized; like ≤ ≤ , then the operation and maintenance level is judged to be normal.

[0011] Preferably, the blade fault detection process is: Get the current The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Curves changing over time ; Get history The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Standard curve over time ; The curve Derivative calculation to obtain , the standard curve Derivative calculation to obtain ; By formula Calculate the failure factor ;in, is the preset fault correlation coefficient; The failure factor With the preset fault coefficient threshold To compare: like , it is judged that the blade is faulty and a fault alarm signal is issued; Furthermore, if < , then it is judged that the blade power is insufficient; like > , it is judged that the blade power is too large.

[0012] Preferably, the operation and maintenance inspection workload optimization analysis process is: By formula Calculate the first Optimization parameters of wind turbine generator sets with monitoring nodes ;in, For preset The weight coefficient of the wind turbine generator set at each monitoring node; is the leaf degradation value under the influence of the current environment; For the Maintenance cycle of wind turbine generator sets with monitoring nodes; The optimization parameters With preset optimization parameter threshold To compare: like > , then reduce the operation and maintenance inspection time or improve the operation and maintenance inspection efficiency; like ≤ , increase the maintenance cycle and perform blade degradation detection.

[0013] Preferably, the abnormal type information data of the state assessment module includes blade fault detection result data and operation and maintenance inspection workload optimization result data; The state assessment model is constructed by inputting the SCADA data of wind turbine operation for machine training, and initially constructing a state assessment index system; The abnormal type information data is input into the status assessment index system to realize the update of the status assessment model; the evaluation coefficient is output by obtaining the comprehensive operation and maintenance strategy results through fuzzy calculation and an early warning signal is issued according to the degree of blade degradation.

[0014] The beneficial effects of the present invention are as follows: the present invention sets up a data analysis module to ensure that the staff can timely monitor and obtain the working status that meets the current operation and maintenance control through the analysis of the operation data, and analyze the abnormal situation according to the operation and maintenance situation, obtain the operation and maintenance abnormal type information and obtain abnormal data in combination with the abnormal type, provide conditions for the next step of evaluation and analysis, and improve the process of evaluation and analysis of wind turbine monitoring projects; by setting up a state evaluation module, a state evaluation model is constructed according to the input abnormal type information data, an evaluation coefficient is output, and an operation and maintenance strategy is output according to the evaluation coefficient and an early warning signal is issued, thereby improving the accuracy of the operation and maintenance system and avoiding the occurrence of safety hazards of wind turbines in advance.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a module diagram of a wind turbine power generation operation and maintenance system based on intelligent station construction according to the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The existing intelligent operation and maintenance management system mainly performs centralized intelligent maintenance on the operation of wind turbines, and the staff completes the real-time monitoring and centralized recording of operation and maintenance data. However, in the operation and maintenance analysis process, only the power and related equipment operating status of the wind turbine are analyzed to determine the equipment operation loss, and the environmental loss impact of the wind turbine is lacking. In addition, when judging the equipment loss of wind turbines, most of them rely on the naked eye observation and analysis of the operation and maintenance personnel, which mostly depends on the experience and technical level of the operation and maintenance personnel, resulting in low efficiency in judging the equipment loss, especially the blade loss and operation and maintenance status, which account for an important part of the wind turbine. Through real-time blade loss analysis and unit operation and maintenance analysis, blade failure problems can be eliminated in advance.

[0020] To solve the above technical problems, please refer to Figure 1 As shown, the present invention is a wind turbine power generation operation and maintenance system based on intelligent construction of a station, and the system includes: Audio-visual monitoring terminal, used to obtain the operation data of the wind turbine monitoring project at the site; A data acquisition module is used to collect the operation data obtained by the audio-visual monitoring terminal through multiple sensors and pre-process the operation data; A data analysis module is used to analyze the pre-processed operation data to obtain abnormal type information data; The status assessment module is used to input abnormal type information data to build a status assessment model, output the assessment coefficient, output the operation and maintenance strategy according to the assessment coefficient, and issue an early warning signal; The smart wind farm display terminal shows operation and maintenance strategies and early warning signals.

[0021] Through the above technical scheme, in this embodiment, the wind turbine power generation operation and maintenance system is designed to specifically include: an audio-visual monitoring terminal, a data acquisition module, a data analysis module, a status assessment module and a smart wind farm display terminal to complete the operation and maintenance process of the power operation status of the wind turbine generator set, thereby realizing the operation and maintenance control process of the wind turbine generator set equipment.

[0022] An audio-visual monitoring terminal is set up to obtain the operating data of the wind turbine monitoring project at the site. By obtaining the operating data of the wind turbine monitoring project, the operation and maintenance status can be grasped in real time to improve the accuracy of operation and maintenance. The audio-visual monitoring terminal is usually controlled and information extracted by arranging staff or intelligent artificial systems, and the control project operation data obtained by the audio-visual monitoring terminal include image information and sound signals, etc. Therefore, these acquisition machines have a large amount of heterogeneous homogeneous data, which are divided into structured and unstructured data, and these data need to be comprehensively analyzed.

[0023] The data acquisition module is set to collect the operation data obtained by the audio-visual monitoring terminal through multiple sensors, and pre-process the operation data, such as filtering, denoising, etc.; the operation data is obtained through the data acquisition module and the data is pre-processed to ensure the unified analysis and standardized adjustment of the data; in order to ensure the reliability of the data and reduce the risk of data loss, this embodiment also includes the setting of redundant storage of the data; A data analysis module is set up to ensure the analysis of the pre-processed operation data and obtain the abnormal type information data. Through the analysis of the operation data, it is ensured that the staff can timely monitor and obtain the working status that meets the current operation and maintenance control, and analyze the abnormal situation according to the operation and maintenance situation, obtain the operation and maintenance abnormal type information and obtain abnormal data in combination with the abnormal type, so as to provide conditions for the next step of evaluation and analysis and improve the evaluation and analysis process of the wind turbine monitoring project.

[0024] The status assessment module is set to construct a status assessment model for the input abnormal type information data, output the assessment coefficient, and output the operation and maintenance strategy and issue a warning signal based on the assessment coefficient, thereby improving the accuracy of the operation and maintenance system and avoiding the occurrence of safety hazards of wind turbines in advance; this embodiment also provides a smart wind farm display terminal for displaying operation and maintenance strategies and warning signals, and uses only the wind farm display segment for visual expression of charts to facilitate operation and maintenance personnel to intuitively obtain operation and maintenance results.

[0025] As an implementation mode of the present invention, the monitoring items of the wind turbine generator set at the station include: patrol route optimization monitoring, listening playback monitoring and remote diagnosis monitoring; The patrol route optimization monitoring is used to obtain the patrol route image and obtain the operation and maintenance patrol workload of the wind turbine generator set of all monitoring nodes corresponding to the current patrol route image; The monitoring playback monitoring is used to obtain the blade vibration and sound signals of the wind turbine generator set at the patrol route image monitoring node through the microphone device; Remote diagnostic monitoring is used to conduct a comprehensive diagnosis of the health status of the monitored wind turbines by combining the interactive messages between on-site operation and maintenance patrol personnel and the control terminal to obtain health score data.

[0026] Through the above technical solution, since it is necessary to obtain the operation and maintenance status of the wind turbine generator sets at each monitoring node of the station, obtaining the operation and maintenance images and sound signals during the operation and maintenance process can more completely obtain the on-site operation and maintenance efficiency. Specifically, the wind turbine generator set monitoring items set at the station in this embodiment include: patrol route optimization monitoring, monitoring and playback monitoring and remote diagnosis monitoring; wherein, the patrol route optimization monitoring is set to obtain the patrol route image and obtain the operation and maintenance patrol workload of the wind turbine generator sets at all monitoring nodes corresponding to the current patrol route image; the monitoring and playback monitoring is set to obtain the blade vibration and sound signals of the wind turbine generator sets at the patrol route image monitoring nodes through a microphone device; the remote diagnosis monitoring is set to combine the on-site operation and maintenance patrol personnel with the interactive messages of the control terminal to conduct a comprehensive diagnosis of the health status of the monitored wind turbine generator sets, and obtain health score data.

[0027] As an implementation mode of the present invention, since the types of operation data collected by the data collection module include structured data and unstructured data, it is necessary to select different databases for different heterogeneous homogeneous data and perform data normalization preprocessing, and the preprocessing includes data cleaning, data conversion and data arrangement; The pre-processed operation data includes: operation and maintenance inspection workload parameters , Blade vibration and sound signal parameters and health score .

[0028] Through the above technical solution, the operation data obtained in this embodiment includes structured data and unstructured data. It is necessary to select different databases for different heterogeneous homogeneous data and perform data normalization preprocessing. The preprocessing in this design specifically includes database matching for heterogeneous homogeneous data, selecting data cleaning, conversion and sorting modes that meet the database, and then processing the preprocessed operation data to extract and obtain the operation and maintenance inspection workload parameters. , Blade vibration and sound signal parameters and health score ; Through the operation and maintenance inspection workload parameters It can reflect the efficiency of manual management and artificial intelligence inspection of wind turbine equipment, as well as the number and distribution of equipment corresponding to the operation and maintenance work.

[0029] The operation and maintenance inspection workload refers to the workload required for routine maintenance and upkeep of equipment, systems or facilities. Correctly calculating the operation and maintenance inspection workload is crucial to ensuring the normal operation of equipment and extending the service life of equipment. The calculation of the operation and maintenance inspection workload needs to consider multiple factors, including equipment type, frequency of use, maintenance cycle, maintenance content, etc. It can fully reflect the operation status of the unit blades, and the blade vibration and sound signal data is collected and calculated at the acoustic monitoring node; health score It can reflect the overall health of the wind turbine equipment.

[0030] As an implementation mode of the present invention, the data analysis module obtains the abnormal type information in the following manner: By formula Calculate and obtain the operation and maintenance coefficient of wind turbines at all monitoring nodes within the preset maintenance period under the current maintenance cycle ;in, is the total number of all monitoring nodes, and ∈ ; For the The operation and maintenance inspection workload parameters of wind turbine generator sets at each monitoring node; For the Group monitoring node wind turbine blade vibration and sound signal parameters; For the The health score of the wind turbine generator set at the group monitoring node; is the standard operation and maintenance inspection workload parameter, is the standard blade vibration and sound signal parameter, is the standard health score value; , , is the preset weight coefficient; For the The location coefficient of each monitoring node.

[0031] Through the above technical solution, the data analysis module in this embodiment obtains the abnormal type information through the formula Calculate and obtain the operation and maintenance coefficient of wind turbines at all monitoring nodes within the preset maintenance period under the current maintenance cycle ; According to the operation and maintenance coefficient of wind turbines It can reflect the overall operation and maintenance situation, thereby ensuring further optimization based on abnormal operation and maintenance situations.

[0032] Among them, the preset weight coefficient , , They are all obtained in advance based on historical experience data; , and All are standard data obtained based on historical experience; location coefficient In order to select settings according to the status of different monitoring points, they will not be described in detail here.

[0033] As an implementation mode of the present invention, the operation and maintenance coefficient The preset operation and maintenance coefficient threshold range To compare: like < , then the operation and maintenance level is judged to be low, and blade fault detection is performed; like > , then the operation and maintenance level is judged to be high, and the operation and maintenance inspection workload is optimized; like ≤ ≤ , then the operation and maintenance level is judged to be normal.

[0034] Through the above technical solution, since the blade is one of the key components of the wind turbine generator set, once a fault occurs, the normal troubleshooting time is the most important. Therefore, it is necessary to combine the existing technical means to focus on monitoring the blade fault conditions of the wind turbine generator set.

[0035] In this embodiment, the operation and maintenance coefficient The preset operation and maintenance coefficient threshold range Comparison of size guarantees further analysis of the degree of operation and maintenance. Specifically, determine if If it falls within the preset operation coefficient threshold range, the current operation degree is judged to be normal; otherwise, the operation degree is judged to be abnormal. Less than If the operation and maintenance level is low, the abnormal information can be extracted through blade fault detection; Greater than , then the operation and maintenance level is judged to be high, and the operation and maintenance inspection workload is optimized to obtain abnormal information during the optimization process.

[0036] As an implementation mode of the present invention, the blade fault detection process is as follows: Get the current The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Curves changing over time ; Get history The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Standard curve over time ; The curve Derivative calculation to obtain , the standard curve Derivative calculation to obtain ; By formula Calculate the failure factor ;in, is the preset fault correlation coefficient; The failure factor With the preset fault coefficient threshold To compare: like , it is judged that the blade is faulty and a fault alarm signal is issued; Furthermore, if < , then it is judged that the blade power is insufficient; like > , it is judged that the blade power is too large.

[0037] Through the above technical solution, the blade fault detection process in this embodiment is as follows: first, obtain the current The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Curves changing over time ; Then, get the history The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Standard curve over time ; Finally, the curve Derivative calculation to obtain , the standard curve Derivative calculation to obtain ; and through the formula Calculate the failure factor , through the failure coefficient The size of the blade is used to determine whether there is a problem with the blade, and then determine the cause of the blade failure, and output the corresponding failure coefficient as abnormal information data; the failure coefficient With the preset fault coefficient threshold Compare the sizes, if , then the blade is judged to be faulty and a fault alarm signal is issued; further, if < , then it is judged that the blade power is insufficient; if > , it is judged that the blade power is too large.

[0038] It should be noted that the preset fault correlation coefficient In order to simulate the calculation based on the historical blade vibration and sound signals and the occurrence of blade failures and pre-select the results based on historical experience data, no further explanation will be given here.

[0039] As an implementation method of the present invention, the operation and maintenance inspection workload optimization analysis process is: By formula Calculate the first Optimization parameters of wind turbine generator sets with monitoring nodes ;in, For preset The weight coefficient of the wind turbine generator set at each monitoring node; is the leaf degradation value under the influence of the current environment; For the Maintenance cycle of wind turbine generator sets with monitoring nodes; The optimization parameters With preset optimization parameter threshold To compare: like > , then reduce the operation and maintenance inspection time or improve the operation and maintenance inspection efficiency; like ≤ , increase the maintenance cycle and perform blade degradation detection.

[0040] Through the above technical solution, the specific process of optimizing the operation and maintenance inspection workload in this embodiment is to obtain the first Optimization parameters of wind turbine generator sets with monitoring nodes , according to the optimization parameters The size determines the optimization type and obtains the current optimization exception data; among them, the weight coefficient Since it is obtained in advance based on historical experience data, it will not be elaborated here.

[0041] In this embodiment, it is also necessary to explain that: the blade degradation value It is a selected indicator to describe the health of the blades of the current wind turbine generator set, with a certain range of values. This design combines the influence of the current environment to ensure The result of comprehensive consideration of the wear and consumption caused by environmental factors ensures more accurate detection of blade degradation.

[0042] Among them, the degradation value The calculation formula is: In the formula, is the measured value of blade parameters affected by the environment, is the normal working range of the blade parameters; among them, Indicates the parameter value of the current running status. The parameter value that indicates the current operating status is poor, and the intermediate value is between good and poor.

[0043] As an embodiment of the present invention, the state assessment module includes: Abnormal type information data includes blade fault detection result data and operation and maintenance inspection workload optimization result data; The construction of the condition assessment model is to input the wind turbine operation SCADA data for machine training and initially build a condition assessment indicator system; The abnormal type information data is input into the status assessment index system to realize the update of the status assessment model; the evaluation coefficient is output by obtaining the comprehensive operation and maintenance strategy results through fuzzy calculation and an early warning signal is issued according to the degree of blade degradation.

[0044] Through the above technical scheme, the abnormal type information data in the state assessment module in this embodiment includes blade fault detection result data and operation and maintenance inspection workload optimization result data; and the state assessment model is constructed by inputting the wind turbine operation SCADA data for machine training to preliminarily construct a state assessment index system; then the abnormal type information data is input into the state assessment index system to realize the state assessment model update; finally, the comprehensive operation and maintenance strategy result output evaluation coefficient is obtained through fuzzy calculation and an early warning signal is issued according to the degree of blade degradation.

[0045] Among them, the SCADA data in this embodiment is based on the SCADA system, which is a computer-based DCS and power automation monitoring system with a wide range of applications. It can be applied to data acquisition and monitoring control in the fields of electricity, metallurgy, petroleum, chemical industry, gas, railways, and process control. In addition, the SCADA data does not include blade vibration and sound signals. Therefore, when constructing the status assessment index system in this embodiment, the abnormal type information data is matched with the wind turbine operation SCADA data and input into the status assessment model for construction, so as to ensure timely warning of the degree of blade degradation and improve the warning analysis capability of blade environmental impact.

[0046] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A wind turbine power generation operation and maintenance system based on intelligent station construction, characterized in that: The system comprises: Audio-visual monitoring terminal, used to obtain the operation data of the wind turbine monitoring project at the site; A data acquisition module is used to collect the operation data obtained by the audio-visual monitoring terminal through multiple sensors and pre-process the operation data; A data analysis module is used to analyze the pre-processed operation data to obtain abnormal type information data; The status assessment module is used to input abnormal type information data to build a status assessment model, output the assessment coefficient, output the operation and maintenance strategy according to the assessment coefficient, and issue an early warning signal; Smart wind farm display terminal, used to display operation and maintenance strategies and early warning signals.

2. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 1, characterized in that: The wind turbine monitoring projects of the station include: patrol route optimization monitoring, monitoring and playback monitoring and remote diagnosis monitoring; The patrol route optimization monitoring is used to obtain a patrol route image and obtain the operation and maintenance patrol workload of the wind turbine generator set of all monitoring nodes corresponding to the current patrol route image; The monitoring playback monitoring is used to obtain the blade vibration and sound signals of the wind turbine generator set at the patrol route image monitoring node through a microphone device; The remote diagnosis monitoring is used to perform a comprehensive diagnosis on the health status of the monitored wind turbine generator set in combination with the interactive messages between the on-site operation and maintenance patrol control personnel and the control terminal to obtain health score data.

3. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 1, characterized in that: Since the types of operation data collected by the data collection module include structured data and unstructured data, it is necessary to select different databases for different heterogeneous homogeneous data and perform preprocessing of data normalization; The preprocessing includes data cleaning, data conversion and data arrangement; The pre-processed operation data includes: operation and maintenance inspection workload parameters , Blade vibration and sound signal parameters and health score .

4. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 3, characterized in that: The data analysis module obtains the abnormal type information in the following way: By formula Calculate and obtain the operation and maintenance coefficient of wind turbines at all monitoring nodes within the preset maintenance period under the current maintenance cycle ;in, is the total number of all monitoring nodes, and ∈ ; For the The operation and maintenance inspection workload parameters of wind turbine generator sets at each monitoring node; For the Group monitoring node wind turbine blade vibration and sound signal parameters; For the The health score of the wind turbine generator set at the group monitoring node; is the standard operation and maintenance inspection workload parameter, is the standard blade vibration and sound signal parameter, is the standard health score value; , , is the preset weight coefficient; For the The location coefficient of each monitoring node.

5. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 4, characterized in that: Operation and maintenance factor The preset operation and maintenance coefficient threshold range To compare: like < , then the operation and maintenance level is judged to be low, and blade fault detection is performed; like > , then the operation and maintenance level is judged to be high, and the operation and maintenance inspection workload is optimized; like ≤ ≤ , then the operation and maintenance level is judged to be normal.

6. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 5, characterized in that: The blade fault detection process is as follows: Get the current The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Curves changing over time ; Get history The mean value of all blade vibration and sound signal parameters of the wind turbine generator set at the group monitoring node Standard curve over time ; The curve Derivative calculation to obtain , the standard curve Derivative calculation to obtain ; By formula Calculate the failure factor ;in, is the preset fault correlation coefficient; The failure factor With the preset fault coefficient threshold To compare: like , it is judged that the blade is faulty and a fault alarm signal is issued; Furthermore, if < , then it is judged that the blade power is insufficient; like > , it is judged that the blade power is too large.

7. A wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 5, characterized in that: The operation and maintenance inspection workload optimization analysis process is as follows: By formula Calculate the first Optimization parameters of wind turbine generator sets with monitoring nodes ;in, For preset The weight coefficient of the wind turbine generator set at each monitoring node; is the leaf degradation value under the influence of the current environment; For the Maintenance cycle of wind turbine generator sets with monitoring nodes; The optimization parameters With preset optimization parameter threshold To compare: like > , then reduce the operation and maintenance inspection time or improve the operation and maintenance inspection efficiency; like ≤ , increase the maintenance cycle and perform blade degradation detection.

8. The wind turbine power generation operation and maintenance system based on intelligent station construction according to claim 1 is characterized in that: The abnormal type information data of the state assessment module includes blade fault detection result data and operation and maintenance inspection workload optimization result data; The state assessment model is constructed by inputting the SCADA data of wind turbine operation for machine training, and initially constructing a state assessment index system; The abnormal type information data is input into the status assessment index system to realize the update of the status assessment model; the evaluation coefficient is output by obtaining the comprehensive operation and maintenance strategy results through fuzzy calculation and an early warning signal is issued according to the degree of blade degradation.

Images