Monitoring system of wind generating set

Abstract

The utility model discloses a monitoring system of a wind generating set. The monitoring system comprises a plurality of inverse synthetic aperture radars, a data center and a potential fault diagnosis server. Any inverse synthetic aperture radar is configured to monitor one or more wind driven generators to obtain radar monitoring data; the data center is configured to be in communication connection with the inverse synthetic aperture radar and receive and store radar monitoring data; and the potential fault diagnosis server is configured to be in communication connection with the data center, perform moving target imaging on fan blades of the wind generating set based on the radar monitoring data accumulated by the data center so as to obtain fan blade information, and perform intelligent fault diagnosis based on the fan blade information. According to the invention, the operation state of the wind driven generator blades can be monitored in a non-contact all-weather manner, the dynamic information of the wind driven generator is stably obtained, and intelligent fault diagnosis of the wind driven generator set is realized.

Description

Technical Field

[0001] This application relates to the field of wind power technology, and in particular, to a monitoring system for a wind turbine generator set. Background Art

[0002] The existing overall monitoring solution for wind turbine generator sets consists of a large number of sensors distributed on various components of each unit, a connection network, and an information processing center. The large number of sensors constitute a complex monitoring system, with a large number of devices, difficult maintenance, and a strong dependence on the reliability of the transmission network, resulting in many potential failure points in the monitoring system. Moreover, in a harsh environment, once the transmission network fails, the monitoring system will be unable to obtain the unit status and cannot ensure continuous and stable detection of the wind turbine generator set. Utility Model Content

[0003] In order to solve the deficiencies of the prior art, the following technical solutions are adopted in this application:

[0004] A monitoring system for a wind turbine generator set provided in this application includes a plurality of inverse synthetic aperture radars, a data center, and a potential fault diagnosis server. Any one of the inverse synthetic aperture radars is configured to monitor one or more wind turbines to obtain radar monitoring data; the data center is configured to be communicatively connected to the inverse synthetic aperture radar, receive and store the radar monitoring data; the potential fault diagnosis server is configured to be communicatively connected to the data center, perform moving target imaging on the blades of the wind turbine generator set based on the radar monitoring data accumulated by the data center to obtain blade information, and perform intelligent fault diagnosis based on the blade information.

[0005] In summary, a monitoring system for a wind turbine generator set provided in this application deploys a plurality of inverse synthetic aperture radars to monitor the wind turbine generator set, obtains the blade information of the wind turbine generator set, and realizes intelligent fault diagnosis of the wind turbine generator set. By using inverse synthetic aperture radars, non-contact all-weather monitoring of the operating status of wind turbine blades is realized. Even in harsh weather or at night, dynamic information of the wind turbine can still be stably obtained, providing reliable data support for the intelligent fault diagnosis of the monitoring system for the wind turbine generator set; and the redundancy of the monitoring system is enhanced through the design of multiple radar bases, which not only ensures multi-angle monitoring of blades at different angles, but also ensures that even if a single radar fails, other radars can still continue to monitor, guaranteeing the overall reliability of the monitoring system.

[0006] Further, the potential fault diagnosis server performs moving target imaging on the blades based on the inverse synthetic aperture radar imaging principle to obtain the blade information, and the blade information includes: blade rotation speed, blade deformation, and blade attitude.

[0007] Further, the monitoring system further includes an external data interface module, which is configured to be connected to the traditional monitoring network of the wind turbine generator through a gateway.

[0008] Further, the data center receives the monitoring data sent by the traditional monitoring network through the external data interface module.

[0009] Further, the potential fault diagnosis server is further configured to: combine the radar monitoring data with the monitoring data of the traditional monitoring network for intelligent fault diagnosis.

[0010] Further, the external data interface module is further configured to receive the historical reference data of the wind turbine generator from an external data source.

[0011] Further, the potential fault diagnosis server is further configured to: combine the radar monitoring data with the historical reference data for intelligent fault diagnosis.

[0012] Further, the monitoring system further includes a video monitoring module, which is configured to visually monitor the wind turbine generator within the visible range.

[0013] Further, the video monitoring module is communicatively connected to the data center through a network, and the potential fault diagnosis server is further configured to: combine the radar monitoring data with the video monitoring data for intelligent fault diagnosis.

[0014] Further, the monitoring system further includes a display module, which is used to display the imaging results of the moving targets in the video monitoring and / or the radar monitoring data. Description of the Drawings

[0015] Figure 1 Schematic diagram of the composition of the monitoring system of the wind turbine generator provided by an embodiment of the present application;

[0016] Figure 2 Schematic diagram of the connection of the monitoring system of the wind turbine generator provided by an embodiment of the present application;

[0017] Figure 3 Schematic diagram of the connection of the monitoring system of the wind turbine generator including an external data interface module provided by an embodiment of the present application;

[0018] Figure 4 Application schematic diagram of the monitoring system of the wind turbine generator including a video detection module provided by an embodiment of the present application. Detailed Embodiments

[0019] The present application will be described in detail below in conjunction with the specific implementation modes shown in the accompanying drawings, but these implementation modes do not limit the present application. Structural, methodological, or functional changes made by ordinary technicians in the field based on these implementation modes are included in the protection scope of the present application.

[0020] In order to solve the deficiencies of the prior art, the present application provides a monitoring system for a wind turbine generator set, such as Figure 1 As shown, the monitoring system includes multiple inverse synthetic aperture radars, a data center, and a potential fault diagnosis server. Any inverse synthetic aperture radar is configured to monitor one or more wind turbines to obtain radar monitoring data; the data center is configured to communicate with the inverse synthetic aperture radar to receive and store radar monitoring data; the potential fault diagnosis server is configured to communicate with the data center, and based on the radar monitoring data accumulated by the data center, the blades of the wind turbine are imaged for moving targets to obtain blade information, and intelligent fault diagnosis is performed based on the blade information.

[0021] like Figure 2 As shown in the figure, ISAR can be deployed in a wind farm in the form of multiple base stations. Each ISAR covers one or more wind turbines in a specific area and monitors the wind turbines by transmitting and receiving high-frequency electromagnetic wave signals. ISAR (Inverse Synthetic Aperture Radar) has the ability to observe beyond the horizon. The principle of ISAR is to replace the movement of the radar with the movement of the target itself, synthesize the virtual aperture through the movement of the target itself (such as the wind blade) (such as rotation or translation), and generate a high-resolution two-dimensional image of the target through signal processing technology, thus breaking through the resolution limit of traditional radar. At the same time, the "shooting" of multiple radars can obtain more abundant information about the target.

[0022] The use of inverse synthetic aperture radar can realize non-contact all-weather monitoring of the operating status of the wind turbine blades, and can stably obtain the dynamic information of the wind turbine even in bad weather or at night. The inverse synthetic aperture radar obtains radar monitoring data by capturing the reflected signal of the wind turbine. The deployment of the inverse synthetic aperture radar does not require physical modification of the existing wind turbine, avoiding the complexity and maintenance cost of the installation of traditional contact sensors. In addition, the multi-base station design enhances the redundancy and coverage of the system, ensuring that even if a single radar fails, other radars can still continue to monitor and ensure the overall reliability of the system. In addition, multiple inverse synthetic aperture radars can be used to monitor the blades of the same wind turbine, and the data of multiple inverse synthetic aperture radars are used to perform motion imaging on the blades of the same wind turbine, thereby improving the accuracy of blade monitoring. For ease of explanation, the radars referred to in the embodiments of the present application all refer to inverse synthetic aperture radars.

[0023] The data center is configured to be communicatively connected to the inverse synthetic aperture radar, and is responsible for receiving, storing, and managing the real-time monitoring data from the multi-base-station inverse synthetic aperture radar. Optionally, the data center has a disaster recovery and backup mechanism to ensure data security of the system in extreme environments or network interruptions, and to ensure the continuity of the monitoring service. The data center adopts a distributed storage architecture, supporting long-term preservation and efficient retrieval of massive data to ensure data integrity and traceability.

[0024] The potential fault diagnosis server is configured to be communicatively connected to the data center. Based on the radar monitoring data provided by the data center, it realizes early warning and diagnosis of wind blade faults through data-driven methods. The potential fault diagnosis server can adopt machine learning algorithms and signal processing technologies to extract features and perform pattern recognition on the wind blade motion data of the wind turbine generator, complete the motion imaging of the wind blade to obtain wind blade information, and realize intelligent fault diagnosis of the wind blade state. For example, by analyzing the periodic changes in the wind blade attitude, it can identify abnormal vibrations of the wind blade caused by structural fatigue or bolt loosening; or by predicting the trend of deformation data, it can evaluate the wear degree of the blade material. Optionally, the potential fault diagnosis server can have a built-in fault model library, integrating multiple typical fault cases, and combining the comparative analysis of real-time data and historical data to perform intelligent fault diagnosis on the wind blade.

[0025] According to the above description, a monitoring system for a wind turbine generator provided by this application deploys multiple inverse synthetic aperture radars to monitor the wind turbine generator, obtains the wind blade information of the wind turbine generator, and realizes intelligent fault diagnosis of the wind turbine generator. By using the inverse synthetic aperture radar, it realizes non-contact all-weather monitoring of the operating state of the wind turbine blades. Even in bad weather (such as low visibility weather like sandstorms, heavy rain, and fog) or at night, it can still stably obtain the dynamic information of the wind turbine, providing reliable data support for the intelligent fault diagnosis of the monitoring system for the wind turbine generator. Moreover, through the design of multiple radar bases, the redundancy of the monitoring system is enhanced, ensuring that even if a single radar fails, other radars can still continuously monitor, guaranteeing the overall reliability of the monitoring system; in addition, multiple inverse synthetic aperture radars can be used to monitor the wind blades of the same wind turbine generator, and the data of multiple inverse synthetic aperture radars are jointly used to perform motion imaging on the wind blades of the same wind turbine generator, thereby improving the accuracy of wind blade monitoring.

[0026] As an optional implementation, the potential fault diagnosis server performs motion target imaging on the wind blade based on the inverse synthetic aperture radar imaging principle to obtain wind blade information, where the wind blade information includes: wind blade rotation speed, wind blade deformation, and wind blade attitude.

[0027] Specifically, the potential fault diagnosis server first preprocesses the original radar monitoring data, including noise suppression, data alignment, and feature extraction of the monitoring data, to generate a standardized dynamic dataset of the wind turbine blades. Subsequently, based on the inverse synthetic aperture radar imaging principle, the potential fault diagnosis server real-time analyzes key parameters such as the rotation speed, attitude change, and minute deformation of the wind turbine blades, obtains wind turbine blade information such as the wind turbine blade rotation speed, wind turbine blade deformation, and wind turbine blade attitude, providing a data basis for the potential fault diagnosis server to perform intelligent fault diagnosis on the wind turbine blades.

[0028] As an alternative implementation, as Figure 3 shown, the monitoring system further includes an external data interface module, which is configured to be connected to the traditional monitoring network of the wind power generation unit through a gateway.

[0029] Specifically, the external data interface module realizes interconnection and interoperability with the traditional monitoring network of the wind power generation unit through the gateway. The traditional monitoring network usually consists of various sensors and data acquisition devices, which are scattered and installed in parts such as the drive chain, blades, and tower barrel of the wind turbine unit to collect the operation state parameters of the wind power generation unit. Further, the external data hardware interface supports multiple physical connection methods such as Ethernet and optical fiber to meet the long-distance and high-reliability communication requirements in complex environments such as offshore wind farms and high-altitude mountainous areas. Through the data access of the external data interface module, the monitoring system breaks through the limitations of the traditional single-sensor network, realizes the deep integration of non-contact radar monitoring data and traditional monitoring network monitoring data, and improves the comprehensive diagnosis ability of the monitoring system.

[0030] As an alternative implementation, the data center receives the monitoring data sent by the traditional monitoring network through the external data interface module. Specifically, the communication interface of the data center can be configured to support multiple data communication protocols. The data center realizes data docking with the monitoring data of the traditional sensor network through the external data interface module, and classifies and stores the monitoring data through a distributed storage architecture to form a multi-dimensional dataset, providing more comprehensive data support for the fault diagnosis of the monitoring system.

[0031] As an alternative implementation, the potential fault diagnosis server is further configured to: combine the radar monitoring data with the monitoring data of the traditional monitoring network to perform intelligent fault diagnosis.

[0032] Specifically, the potential fault diagnosis server standardizes the two types of data through data cleaning, time alignment, format conversion, etc., eliminating the analysis obstacles brought by data heterogeneity. Based on the radar monitoring data and the monitoring data collected by the traditional monitoring network, the status of the wind turbine unit is judged from the non-contact and contact sensing levels respectively. The inverse synthetic aperture radar monitoring data and the monitoring data of the traditional monitoring network are used to jointly conduct high-precision fault diagnosis on the wind turbine unit. The multi-source data collaborative diagnosis mechanism significantly improves the reliability and adaptability of the monitoring system, effectively reduces the false alarm rate of a single data source, and provides a solid technical support for the predictive maintenance of the wind turbine unit.

[0033] As an alternative implementation, the external data interface module is also configured to receive the historical reference data of the wind turbine monitoring from an external data source.

[0034] Specifically, the external data hardware interface is configured to dock with the historical database of the external data source to obtain the historical reference data of the long-term operation of the wind turbine, providing multi-dimensional data support for the intelligent fault diagnosis of the monitoring system. The historical reference data usually includes the operation records accumulated by the wind farm for a long time (such as fault event logs, maintenance work orders, performance degradation curves), the typical working condition data of the wind turbine unit (such as normal speed range, deformation threshold), and the environmental parameter data (such as historical wind speed, temperature fluctuation), etc. Through the historical reference data of the wind turbine monitoring from the external data source, the intelligent diagnosis ability of the monitoring system for the wind turbine unit is further improved.

[0035] As an alternative implementation, the potential fault diagnosis server is also configured to: combine the radar monitoring data with the historical reference data for intelligent fault diagnosis.

[0036] Specifically, the potential fault diagnosis server deeply integrates the radar monitoring data with the historical reference data to construct an intelligent fault diagnosis framework based on data-driven, and compares the blade information obtained from the radar monitoring data with the blade information of the historical reference data. For example, the potential fault diagnosis server can judge whether the current state deviates from the normal degradation path by comparing the current blade deformation rate with the deformation trend of the historical same-type wind turbines under similar working conditions; or, the potential fault diagnosis server can compare and analyze the real-time blade deformation data obtained by the inverse synthetic aperture radar with the historical blade stress data to identify abnormal trends. Through the deep collaboration of the radar data and the historical reference data, multi-dimensional comparison data is provided for fault diagnosis, improving the accuracy and predictability of the monitoring system for the status assessment of the wind turbine unit.

[0037] As an alternative implementation, such as Figure 4As shown, the monitoring system further includes a video monitoring module, which is configured to visually monitor the wind turbine within the visible range. Specifically, the video monitoring module generally includes a high-definition camera image acquisition unit. The video monitoring module is deployed on the top of the tower barrel around the wind turbine or near the monitoring base station. The monitoring area of the video monitoring module covers key parts such as the wind blades and nacelle of the wind turbine. By means of visualization, the wind turbine is visually monitored in real time to supplement the monitoring data of non-contact monitoring and enhance the multi-modal perception ability of the monitoring system for the operating state of the unit.

[0038] As an alternative implementation, the video monitoring module is communicatively connected to the data center through a network. The potential fault diagnosis server is further configured to: combine the radar monitoring data with the video monitoring data for intelligent fault diagnosis.

[0039] Specifically, the video monitoring module is communicatively connected to the data center through a network. The monitoring data of the video monitoring module is transmitted to the data center in real time for the monitoring system to perform intelligent fault diagnosis on the wind turbine. The potential fault diagnosis server calls the video monitoring data and radar monitoring data in the data center to perform intelligent diagnosis on the wind turbine. For example, when it is determined that the attitude of the wind blade is abnormal according to the radar monitoring data, the monitoring system can retrieve the video monitoring data at the corresponding time point to observe whether there is an external object collision or blade surface damage; conversely, if a suspicious shadow is found on the blade surface according to the video monitoring data, the monitoring system can combine the deformation data of the radar monitoring data to determine whether it is a visual abnormality caused by the structural deformation of the wind blade.

[0040] By combining the radar monitoring data with the video monitoring data, the monitoring system is based on multi-source data cross-validation, significantly reducing the false alarm rate of diagnosis through a single data and improving the accuracy of fault location.

[0041] As an alternative implementation, the monitoring system further includes a display module, which is used to display the moving target imaging results of video monitoring and / or radar monitoring data.

[0042] Specifically, the display module is connected to the data center, receives the high-definition video stream from the video monitoring module and the dynamic imaging results generated by the radar, and integrally displays the video monitoring data and the moving target imaging results of the inverse synthetic aperture radar through a graphical interface, providing an intuitive and multi-dimensional state monitoring view for the operation and maintenance personnel, and also providing an intuitive means of remote inspection for the operation and maintenance personnel.

[0043] According to the above description, a monitoring system for a wind turbine provided by the present application deploys multiple inverse synthetic aperture radars to monitor the wind turbine, obtains the blade information of the wind turbine, and realizes intelligent fault diagnosis of the wind turbine. By using inverse synthetic aperture radars, non-contact all-weather monitoring of the operating state of the wind turbine blades is achieved. Even under adverse weather or night conditions, dynamic information of the wind turbine can still be stably obtained, providing reliable data support for the intelligent fault diagnosis of the monitoring system for the wind turbine; and the redundancy of the monitoring system is enhanced through the design of multiple radar bases, ensuring that even if a single radar fails, other radars can still continuously monitor, guaranteeing the overall reliability of the monitoring system.

[0044] Furthermore, a monitoring system for a wind turbine provided by the present application also performs intelligent fault diagnosis on the wind turbine through multi-source data fusion and intelligent analysis, improving the intelligent diagnosis ability of the monitoring system for the wind turbine, significantly reducing the false alarm rate, effectively extending the service life of the fan and reducing the operation and maintenance cost.

[0045] It can be understood that the term "exemplary" used herein means "as an example, illustration, or description". Any embodiment described as "exemplary" is not necessarily preferred over or superior to other embodiments and / or does not exclude combining the features of other embodiments. It should be understood that certain features of the present application described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, the various features of the present application described in the context of a single embodiment may also be provided separately or in any suitable combination or as any other described embodiment of the present application.

[0046] The above-disclosed are only the preferred embodiments of the present application, but they are not intended to limit the scope of the rights of the present application. Those of ordinary skill in the art can understand that within the spirit and scope of the present application and the appended claims, changes, modifications, substitutions, combinations, and simplifications should all be equivalent replacement methods and still fall within the scope covered by the invention.

Claims

1. A monitoring system for a wind turbine generator set, characterized in that: The monitoring system comprises: A plurality of inverse synthetic aperture radars, any of which is configured to monitor one or more wind turbines to obtain radar monitoring data; A data center is configured to be in communication connection with the inverse synthetic aperture radar, receive and store the radar monitoring data; The potential fault diagnosis server is configured to be connected to the data center for communication, and based on the radar monitoring data accumulated by the data center, performs moving target imaging on the blades of the wind turbine generator set to obtain blade information, and performs intelligent fault diagnosis based on the blade information.

2. The monitoring system for a wind turbine generator set according to claim 1, characterized in that: The potential fault diagnosis server performs moving target imaging on the wind blade based on the inverse synthetic aperture radar imaging principle to obtain the wind blade information, and the wind blade information includes: wind blade rotation speed, wind blade deformation, and wind blade posture.

3. The monitoring system for a wind turbine generator set according to claim 1, characterized in that: The monitoring system further comprises an external data interface module, and the external data interface module is configured to be connected to a conventional monitoring network of the wind turbine generator set through a gateway.

4. The monitoring system for a wind turbine generator set according to claim 3, characterized in that: The data center receives the monitoring data sent by the traditional monitoring network through the external data interface module.

5. The monitoring system for a wind turbine generator set according to claim 4, characterized in that: The potential fault diagnosis server is further configured to combine the radar monitoring data with the monitoring data of the traditional monitoring network to perform intelligent fault diagnosis.

6. The monitoring system for a wind turbine generator set according to claim 3, characterized in that: The external data interface module is further configured to receive wind turbine monitoring historical reference data from an external data source.

7. The monitoring system for a wind turbine generator set according to claim 6, characterized in that: The potential fault diagnosis server is further configured to combine the radar monitoring data with the historical reference data to perform intelligent fault diagnosis.

8. The monitoring system for a wind turbine generator set according to claim 1, characterized in that: The monitoring system further comprises a video monitoring module, which is configured to perform visual monitoring on the wind turbine generator sets within a visible range.

9. The monitoring system for a wind turbine generator set according to claim 8, characterized in that: The video monitoring module is connected to the data center through a network, and the potential fault diagnosis server is further configured to combine the radar monitoring data with the video monitoring data to perform intelligent fault diagnosis.

10. The monitoring system for a wind turbine generator set according to claim 8, characterized in that: The monitoring system further comprises a display module, and the display module is used to display the moving target imaging results of the video monitoring and / or the radar monitoring data.

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