Risk assessment method and device for tower clearance monitoring system

Abstract

The invention provides a risk assessment method and device for a tower clearance monitoring system. The risk assessment method comprises the steps of obtaining real-time monitoring data of a target tower clearance monitoring system; environment data of the position where the target tower clearance monitoring system is located are obtained; determining a first risk prediction model of the target tower clearance monitoring system based on the position of the target tower clearance monitoring system; according to the real-time monitoring data, the environment data and the first risk prediction model, the failure mode of the target tower clearance monitoring system is identified, and the failure mode is used for reflecting the fault type of the target tower clearance monitoring system.

Description

Technical Field

[0001] The present disclosure generally relates to the field of wind power, and more specifically, to a risk assessment method and device for a tower clearance monitoring system. Background Art

[0002] With the development of wind power generation technology and the improvement of energy efficiency requirements, the blades of wind turbines (which can be simply referred to as wind turbines, units or wind turbine units) are getting longer and more flexible. At the same time, due to the complex geographical environment and wind conditions of the wind turbine positions, as well as the influence of complex meteorological conditions such as cold snaps and typhoons, there is a risk of blade tower sweeping (contact between the blade and the tower). In the event of blade tower sweeping, at least the blade needs to be replaced, and in the worst case, the entire unit will be scrapped, which will cause huge losses.

[0003] As a system that can monitor the tip clearance distance in real time, the tower clearance monitoring system plays a crucial role in the safety of wind turbines and has received extensive attention in the relevant designs of wind turbine safety monitoring and early warning. However, in the actual operation process of the tower clearance monitoring system, there are problems such as failure, critical failure, and performance degradation, which pose great potential hazards to the safe and reliable operation of the tower clearance monitoring system and even wind turbines.

[0004] At present, there is no systematic and theoretical support for the fault identification of the tower clearance monitoring system itself, and it is difficult to ensure the effectiveness and accuracy of its own fault identification. At present, most analyses and treatments are carried out only after failure or fault occurs, and the occurrence of failure or fault is not analyzed and warned in advance, resulting in greater risks and uncertainties during use. Summary of the Invention

[0005] One of the objectives of the exemplary embodiments of the present disclosure is to overcome at least one of the above technical problems.

[0006] One of the objectives of the exemplary embodiments of the present disclosure is to provide a wind power assessment method and device capable of identifying faults in a tower clearance monitoring system.

[0007] According to a first aspect of the present disclosure, there is provided a risk assessment method for a tower clearance monitoring system, the risk assessment method comprising: obtaining real-time monitoring data of a target tower clearance monitoring system; obtaining environmental data of a location where the target tower clearance monitoring system is located; determining a first risk prediction model of the target tower clearance monitoring system based on the location where the target tower clearance monitoring system is located; and identifying a failure mode of the target tower clearance monitoring system according to the real-time monitoring data, the environmental data, and the first risk prediction model, wherein the failure mode is used to reflect the type of fault of the target tower clearance monitoring system.

[0008] According to an embodiment of the present disclosure, the steps of determining a first risk prediction model of the target tower clearance monitoring system based on the location of the target tower clearance monitoring system may include: classifying the target tower clearance monitoring system based on the geographical location of the wind farm where the target tower clearance monitoring system is located and the environmental type of the location of the target tower clearance monitoring system; selecting at least one risk prediction model from the failure mode information database according to the type of the target tower clearance monitoring system; and determining one of the at least one risk prediction models as the first risk prediction model.

[0009] According to an embodiment of the present disclosure, the geographical location may include multiple regions divided according to the location of the wind farm where the tower clearance monitoring system is located, and the environmental type may include at least one of desert, gobi and wasteland, plateau, intertidal zone and deep sea.

[0010] According to an embodiment of the present disclosure, the failure mode information database may be established through the following steps: obtaining a plurality of historical monitoring data of a plurality of tower clearance monitoring systems of wind farms located in different geographical locations and in different environmental types; respectively performing design failure mode and effect analysis on the monitoring data of the tower clearance monitoring systems of the wind farms belonging to the same geographical location and in the same type of environment among the plurality of historical monitoring data to obtain corresponding failure modes; respectively training the monitoring data and the corresponding failure modes by using a neural network algorithm to obtain corresponding risk prediction models; and establishing a risk prediction model library based on the corresponding risk prediction models, wherein the failure modes may include historical failure modes associated with the monitoring data and future potential failure modes.

[0011] According to an embodiment of the present disclosure, the steps of determining one of the at least one risk prediction models as the first risk prediction model may include: counting the accuracy rates of the at least one risk prediction model; and determining the risk prediction model with the highest accuracy rate among the at least one risk prediction model as the first risk prediction model.

[0012] According to an embodiment of the present disclosure, the steps of establishing the failure mode information database may further include: performing SOD scoring on the historical failure modes and future potential failure modes in the failure modes; determining the coping methods for the historical failure modes and / or potential failure modes according to the results of the SOD scoring; and recording the coping methods in the failure mode information database.

[0013] According to an embodiment of the present disclosure, the risk assessment method may further include: determining risk points according to the identified failure modes of the target tower clearance monitoring system; and determining the coping methods for the risk points of the target tower clearance monitoring system based on the coping methods corresponding to the identified failure modes of the target tower clearance monitoring system recorded in the failure mode information database.

[0014] According to an embodiment of the present disclosure, the steps of establishing a failure mode information library may further include: obtaining real-time monitoring data of tower clearance monitoring systems of wind farms located at different geographical locations and in different types of environments; and updating the failure mode information library by using the real-time monitoring data.

[0015] According to an embodiment of the present disclosure, the steps of obtaining real-time monitoring data of a target tower clearance monitoring system may include: obtaining at least one of real-time heartbeat data, real-time status data, real-time laser beam distance value, real-time laser beam intensity value, and real-time laser beam data valid flag bit of each device in the target tower clearance monitoring system.

[0016] According to an embodiment of the present disclosure, the steps of obtaining environmental data of the location where the target tower clearance monitoring system is located may include: obtaining at least one of temperature data, humidity data, and particulate matter concentration data of the location where the target tower clearance monitoring system is located.

[0017] According to an embodiment of the present disclosure, the target tower clearance monitoring system may include: a lidar for tower clearance monitoring, a camera device, and a digital storage unit for storing monitoring data.

[0018] According to an embodiment of the present disclosure, the failure modes may include at least one of: abnormal output value of the tower clearance monitoring system output to the master control system, abnormal lidar ranging value of the tower clearance monitoring system, communication abnormality of the tower clearance monitoring system, laser camera failure of the tower clearance monitoring system, damage to the network cable interface of the tower clearance monitoring system, abnormal power-on indicator light of the tower clearance monitoring system, and no light spot of the laser light source of the tower clearance monitoring system.

[0019] According to a second aspect of the present disclosure, there is provided a computer-readable storage medium storing instructions or programs, which, when executed by a processor, cause the processor to execute the above-mentioned risk assessment method.

[0020] According to a third aspect of the present disclosure, there is provided a risk assessment device for a tower clearance monitoring system, the risk assessment device including a processor and a memory, the memory storing instructions or programs, which, when executed by the processor, cause the processor to execute the above-mentioned risk assessment method.

[0021] The risk assessment method and device for a tower clearance monitoring system according to an embodiment of the present disclosure can perform modeling and identification in combination with monitoring data, and can accurately locate risk points.

[0022] The risk assessment method and device for a tower clearance monitoring system according to an embodiment of the present disclosure can improve the accuracy, real-time performance, and effectiveness of fault analysis. Description of the Drawings

[0023] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent from the following description with reference to the accompanying drawings that exemplarily illustrate the embodiments, wherein:

[0024] Figure 1 is an interactive topology diagram of a tower laser clearance monitoring system according to an embodiment of the present disclosure;

[0025] Figure 2 is a fault and risk location data access topology structure of a tower clearance monitoring system according to an embodiment of the present disclosure;

[0026] Figure 3 is a flowchart of a risk assessment method according to an embodiment of the present disclosure;

[0027] Figure 4 is a flowchart of determining a first risk prediction model according to an embodiment of the present disclosure;

[0028] Figure 5 is a flowchart of establishing a failure mode information library according to an embodiment of the present disclosure;

[0029] Figure 6 is a block diagram showing a risk assessment device of a tower clearance monitoring system according to an embodiment of the present disclosure. Detailed Embodiments

[0030] The following detailed embodiments are used to help obtain a comprehensive understanding of the methods, devices, and / or systems described herein. However, the order of operations described herein is merely exemplary and is not limited to those set forth herein. That is, equivalent substitutions or changes can be made, except for operations that must occur or be performed in a specific order. In addition, for greater clarity and conciseness, descriptions of well-known content in the art will be omitted or simplified.

[0031] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present disclosure pertains after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their context in the relevant art and the present disclosure, and should not be idealized or interpreted too formally.

[0032] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals that appear in a previous embodiment and then again in a subsequent embodiment may be omitted. Additionally, the technical features described in different or the same embodiments can be combined in any manner, as long as the combined embodiment or technical solution is complete and can solve the technical problems of this application or achieve the technical effects described in this application or that can be determined based on the above complete technical solution even if not described.

[0033] First, a brief description of the terms used in this disclosure will be given.

[0034] LiDAR: A radar system that detects the position, speed, and other characteristic quantities of a target by emitting laser beams. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal (target echo) reflected from the target with the emitted signal. After appropriate processing, parameters such as the position, speed, azimuth, altitude, speed, attitude, and even shape of the target can be obtained.

[0035] Tower clearance: The vertical distance between the plane where the tip of the wind turbine blade is located and the tower barrel.

[0036] Backscatter intensity: When a laser beam hits an object to be measured, backscatter is generated, and the magnitude of the backscatter energy is the backscatter intensity. The following will describe this in detail in conjunction with the embodiments of this disclosure.

[0037] Figure 1 is an interaction topology diagram of a tower laser clearance monitoring system according to an embodiment of this disclosure, Figure 2 is a fault and risk location data access topology structure of a tower clearance monitoring system according to an embodiment of this disclosure.

[0038] The tower laser clearance monitoring system according to the embodiments of this disclosure may include a LiDAR for tower clearance monitoring, a camera device, and a digital storage unit for storing monitoring data, etc.

[0039] Refer to Figure 1 , the tower clearance monitoring system can emit light to the blades of the wind turbine through the LiDAR and receive the backscatter. The tower clearance monitoring system can send the monitoring data to the wind turbine (e.g., the main controller of the wind turbine, hereinafter referred to as the "main control") through a DP communication cable and can be connected to a debugging computer through network communication. The camera device (e.g., a laser camera) of the tower clearance monitoring system can be connected to the debugging computer through a USB data cable. The tower clearance monitoring system can obtain 24V DC power from the wind turbine and can indicate the power-on state through an indicator light. Although not shown, the tower clearance monitoring system may also include a lightning protection system, etc. The lightning protection system, LiDAR, camera device, digital storage unit, etc. can all be regarded as subsystems, sub-devices, or sub-modules of the tower clearance monitoring system.

[0040] Reference Figure 2 Figure 2 , various data of the tower clearance monitoring system (e.g., self-parameters of sub-devices or subsystems, monitoring data, etc.) can be provided to the main control system of the wind turbine generator set, and can be stored in the cloud server through the gateway. Mobile phones and PC terminals can access the cloud server, and the central control room and PC terminals of the wind turbine generator set can communicate with the main control system remotely.

[0041] The failure situations of the tower clearance monitoring system may include: the output value output by the tower clearance monitoring system to the main control system is abnormal, the lidar ranging value of the tower clearance monitoring system is abnormal, the communication of the tower clearance monitoring system is abnormal (e.g., DP communication is abnormal), the laser camera of the tower clearance monitoring system fails, the network cable interface of the tower clearance monitoring system is damaged, the power-on indicator of the tower clearance monitoring system is abnormal, there is no light spot in the laser light source of the tower clearance monitoring system, at least one of the power protection diode and the fuse is burned out. The failure mode of the present disclosure is used to reflect the fault type of the tower clearance monitoring system. That is to say, there may be faults such as the output value output by the tower clearance monitoring system to the main control system being abnormal, the lidar ranging value being abnormal, the communication being abnormal, the laser camera failing, the network cable interface being damaged, the power-on indicator being abnormal, and there being no light spot in the laser light source in the tower laser clearance monitoring system. With the differences in functions, etc. of the tower clearance monitoring system, the faults or failure modes of the tower clearance monitoring system may change. The process of identifying the failure mode of the tower clearance monitoring system will be described in detail below.

[0042] Figure 3 is a flowchart of the risk assessment method according to an embodiment of the present disclosure.

[0043] The risk assessment method according to an embodiment of the present disclosure may include step S310, step S320, step S330, and step S340.

[0044] In step S310, the real-time monitoring data of the target tower clearance monitoring system is obtained. The target tower clearance monitoring system here may be Figure 1 the tower clearance monitoring system shown (i.e., the tower laser clearance monitoring system).

[0045] The real-time monitoring data of the target tower clearance monitoring system may include the heartbeat and status data of sub-devices or subsystems, the monitoring data of sub-devices and subsystems (e.g., laser beam distance value, laser beam intensity value, laser beam data valid flag bit), etc. As an example, the real-time monitoring data can be directly obtained from the corresponding sub-devices or subsystems, or the monitoring data of sub-devices or subsystems can be collected through the corresponding sensors.

[0046] The steps of obtaining the real-time monitoring data of the target tower clearance monitoring system may include: obtaining at least one of the real-time heartbeat data, real-time status data, real-time laser beam distance value, real-time laser beam intensity value, and real-time laser beam data valid flag bit of each device in the target tower clearance monitoring system. The real-time monitoring data of the target tower clearance monitoring system is not limited to this. According to needs, the real-time monitoring data can be screened to select at least one of the real-time heartbeat data, real-time status data, real-time laser beam distance value, real-time laser beam intensity value, and real-time laser beam data valid flag bit.

[0047] In step S320, obtain the environmental data of the location where the target tower clearance monitoring system is located.

[0048] As the location where the target tower clearance monitoring system is located is different, the environmental data may be different. The environmental data here may include common data such as corrosion parameters, particulate matter concentration, temperature, humidity, altitude, etc. The corrosion parameters, temperature, particulate matter concentration, humidity, altitude and other data can be obtained through corresponding sensors.

[0049] The steps of obtaining the environmental data of the location where the target tower clearance monitoring system is located may include: obtaining at least one of the temperature data, humidity data, and particulate matter concentration data of the location where the target tower clearance monitoring system is located. In addition, the altitude data of the location where the target tower clearance monitoring system is located can also be obtained.

[0050] The location where the target tower clearance monitoring system is located may refer to the geographical location or regional location of the wind farm where the target tower clearance monitoring system is located. Different geographical locations or regional locations are divided based on significant differences in common data such as corrosion parameters, temperature, humidity, altitude, etc. Taking China as an example, different geographical locations may include the northwest, northeast, southwest, southeast, and central regions. The faults or failures in each region can be classified, and the historical fault or failure information database can be continuously updated and iterated to be improved.

[0051] In step S330, based on the location where the target tower clearance monitoring system is located, determine the first risk prediction model of the target tower clearance monitoring system. The first risk prediction model may be a prediction model of the relationship between the characteristic data in the monitoring data and the failure mode, and the first risk prediction model can be established in advance according to the historical monitoring data and historical failure modes.

[0052] Figure 4 It is a flowchart for determining the first risk prediction model according to an embodiment of the present disclosure.

[0053] Refer to Figure 4 , based on the location where the target tower clearance monitoring system is located, the steps of determining the first risk prediction model of the target tower clearance monitoring system may include step S331, step S332, and step S333.

[0054] In step S331, the target tower clearance monitoring system is classified based on the geographical location of the wind farm where the target tower clearance monitoring system is located and the environmental type of the location where the target tower clearance monitoring system is located.

[0055] The geographical location here may include multiple regions divided according to the location of the wind farm where the tower clearance monitoring system is located, and the environmental type may include at least one of desert, gobi, plateau, intertidal zone, and deep sea. The environmental type can be determined based on environmental data, that is, the specific environmental types of desert, gobi, plateau, intertidal zone, and deep sea can be determined by altitude, temperature, humidity, particulate matter concentration, etc. as described above.

[0056] As an example, the tower clearance monitoring systems of wind farms located in the northwest region and with the altitude, temperature, humidity, particulate matter concentration, temperature, etc. of the environment in the corresponding first preset range (for example, the corresponding environmental type is plateau) can be classified into the first category, and the tower clearance monitoring systems in wind farms in the northwest region and with the altitude, temperature, humidity, particulate matter concentration, temperature, etc. of the environment in the corresponding second preset range (for example, the corresponding environmental type is desert, gobi) can be classified into the second category. As an example, the tower clearance monitoring systems of wind farms located in the northwest region and with the humidity in the environment within the preset humidity range can be classified into the same category. When classifying, different environmental factors such as season, hail weather, rain and snow weather, sand and dust weather, foggy weather, etc. can also be considered. For example, the tower clearance monitoring systems with rain and snow weather exceeding the preset number of days in the same region and the same time period and the average temperature in the same time period being higher than the preset value can be classified into the same category. The temperature, humidity, particulate matter concentration, etc. of the environment can all be the average values within a certain time period. As an example, the tower clearance monitoring systems with similar environmental types or similar environmental data in different regions can also be classified into one category.

[0057] The above classification methods are only examples. When classifying, it can be divided in the order of region, temperature, humidity, and particulate matter concentration. The tower clearance monitoring systems in the same region (or location), the same temperature range (the average temperature within a certain time period is in the same temperature range), the same humidity range (the average humidity within a certain time period is in the same humidity range), and the particulate matter concentration (the average particulate matter concentration within a certain time period is in the same particulate matter concentration range) can be classified into the same category. In addition, when classifying, it can be classified by region or location first, and then at least one of temperature, humidity, and particulate matter concentration can be selected as the classification basis, and the tower clearance monitoring systems with the at least one in the same range can be classified into the same category.

[0058] Thus, the target tower clearance monitoring system can be classified according to the geographical location of the wind farm where the target tower clearance monitoring system is located and the environmental type of the location where the target tower clearance monitoring system is located (i.e., determine the type of the target tower clearance monitoring system).

[0059] In step S332, at least one risk prediction model is selected from the failure mode information database according to the type of the target tower clearance monitoring system.

[0060] For example, if the target tower clearance monitoring system belongs to the first category as described above, at least one risk prediction model corresponding to the first category (e.g., five risk prediction models) can be selected from the failure mode information database.

[0061] In step S333, one of the at least one risk prediction models is determined as the first risk prediction model.

[0062] The risk prediction model with the highest accuracy rate among the at least one risk prediction models can be determined as the first risk prediction model. As an example, the step of determining one of the at least one risk prediction models as the first risk prediction model may include: counting the accuracy rates of the at least one risk prediction models; determining the risk prediction model with the highest accuracy rate among the at least one risk prediction models as the first risk prediction model. As an example, the monitoring data not used for modeling, environmental data, etc. in the historical monitoring data can be used to count the accuracy rate of the risk prediction model.

[0063] As an example, the accuracy rates of the at least one risk prediction models can also be counted, and any one of the risk prediction models with the top-ranked (e.g., top five) accuracy rates can be selected as the first risk prediction model.

[0064] The failure mode information database stores historical failure modes associated with the monitoring data, future potential failure modes, characteristic data (monitoring data within a preset range characterizing the failure mode) corresponding to the historical failure modes and future potential failure modes, and corresponding countermeasures (i.e., fault handling methods). As an example, the characteristic data may include: the laser beam distance values within a preset range corresponding to the fault of abnormal laser beam distance value in the historical monitoring data, the laser beam intensity values within a preset range corresponding to the fault of abnormal laser beam intensity value in the historical monitoring data, and the laser beam data valid flag bits corresponding to the fault of the laser beam data valid flag bit set in the historical monitoring data.

[0065] The failure mode information database here is pre-established. During the pre-establishment process, the risk prediction models corresponding to each type of tower clearance monitoring system can be statistically analyzed, as well as the accuracy rates of the failure modes predicted by the corresponding risk prediction models. The top five risk prediction models can be selected for selection, so as to provide multiple countermeasures when avoiding risks (such as redundant design, program optimization, and hardware improvement). That is to say, the failure mode information database stores characteristic data, failure modes, risk prediction models, etc.

[0066] Figure 5 It is a flowchart for determining the first risk prediction model according to an embodiment of the present disclosure.

[0067] Refer to Figure 5 , according to the embodiment of the present disclosure, the steps for determining the first risk prediction model may include step S510, step S520, step S530, and step S540.

[0068] In step S510, a plurality of historical monitoring data of a plurality of tower clearance monitoring systems of wind farms located in different geographical locations and in different environmental types are obtained.

[0069] The plurality of historical monitoring data can be pre-stored monitoring data, and the plurality of historical monitoring data can be obtained from a memory storing the historical monitoring data. The plurality of historical monitoring data here can be classified data. As an example, it can be classified according to geographical location and / or environmental type. The specific monitoring data may include heartbeat and status data of sub-devices or subsystems, detection data of sub-devices and subsystems (such as laser beam distance values, laser beam intensity values, laser beam data valid flag bits), and so on.

[0070] In step S520, the monitoring data of the tower clearance monitoring systems of wind farms belonging to the same geographical location and in the same type of environment among the plurality of historical monitoring data are respectively subjected to Design Failure Mode and Effects Analysis (DFMEA, a reliability tool similar to brainstorming) to obtain the corresponding failure modes. As shown above, the failure modes include historical failure modes and future potential failure modes. The future potential failure modes can be exhaustively listed through DFMEA. The future potential failure modes refer to the failure modes that may occur but have not occurred.

[0071] In step S530, the neural network algorithm is respectively used to train the monitoring data and the corresponding failure modes to obtain the corresponding risk prediction models.

[0072] For example, design failure mode and effects analysis (DFMEA) can be performed on the laser beam distance values of the tower clearance monitoring systems of wind farms that belong to the first geographical location and are in the first environmental type among multiple historical monitoring data to obtain the corresponding failure modes (i.e., abnormal laser beam distance values). Then, the monitored laser beam distance values and the abnormal laser beam distance values are used for BP neural network training using the neural network algorithm, thereby obtaining the corresponding risk prediction model. That is to say, tower clearance monitoring systems in the same geographical location but with different environmental types may have different risk prediction models.

[0073] In step S540, a risk prediction model library is established based on the corresponding risk prediction models, and the risk prediction model library contains multiple risk prediction models.

[0074] As an example, the steps of establishing the failure mode information library may further include: performing SOD scoring on historical failure modes and future potential failure modes in the failure mode; determining the coping methods for historical failure modes and / or potential failure modes according to the results of the SOD scoring; and recording the coping methods in the failure mode information library.

[0075] In other words, the severity S, occurrence frequency O, and detectability D of the failure mode can be scored according to the actual design situation. The higher the product score of the three, the greater the risk. For failure modes with SOD≥100, corresponding countermeasures (such as maintenance, replacement) are required. For key failure modes with SOD<100, corresponding countermeasures (such as inspection or troubleshooting) are also required. Here, the detectability D refers to the degree to which a failure mode can be detected through experiments, simulations, or calculations. The higher the detectability D, the easier it is to detect the corresponding failure mode.

[0076] As an example, the steps of establishing the failure mode information library may further include obtaining the real-time monitoring data of the tower clearance monitoring systems of wind farms located in different geographical locations and in different types of environments; and updating the failure mode information library using the real-time monitoring data. The failure mode information library can be updated according to the real-time monitored data, thereby improving the historical failure mode information library.

[0077] Return reference Figure 3 , in step S340, according to the real-time monitoring data, environmental data, and the first risk prediction model, the failure modes of the target tower clearance monitoring system are identified. The failure modes here are used to reflect the fault types of the target tower clearance monitoring system.

[0078] The real-time monitoring data and environmental data can be used as the input of the first risk prediction model, and the output of the first risk prediction model may include the specific failure modes of the target tower clearance monitoring system.

[0079] The risk assessment method according to an embodiment of the present disclosure may further include risk point identification and risk response.

[0080] As an example, the risk assessment method according to an embodiment of the present disclosure may further include determining risk points according to the identified failure modes of the target tower clearance monitoring system; determining the response methods for the risk points of the target tower clearance monitoring system based on the response methods recorded in the failure mode information database corresponding to the identified failure modes of the target tower clearance monitoring system.

[0081] As an example, when the failure mode is that the output value output from the tower clearance monitoring system to the main control system is abnormal, the risk point can be determined as the communication module of the tower clearance monitoring system, and the response method recorded in the failure mode information database corresponding to the identified failure mode is further inspection or troubleshooting; when the failure mode is that the lidar ranging value of the tower clearance monitoring system is abnormal, the risk point can be determined as the lidar, and the response method recorded in the failure mode information database corresponding to the identified failure mode is further inspection or troubleshooting; when the failure mode is a laser camera failure, the risk point can be determined as the laser camera, and the response method recorded in the failure mode information database corresponding to the identified failure mode is replacement; when the failure mode is that there is no light spot in the laser light source, the risk point can be determined as the laser light source, and the response method recorded in the failure mode information database corresponding to the identified failure mode is repair. After troubleshooting or inspection, the specific fault type can be determined (for example, the output data is out of sync in the abnormal output value). As an example, according to the degree of subdivision of the failure mode, more specific risk points can be directly determined according to the failure mode.

[0082] For a new tower clearance monitoring system without any usage data, such as a tower clearance monitoring system with a brand-new design in the industry and no relevant usage information, it is possible to reverse-infer whether the subsystem or sub-device meets the requirements based on the device reliability test. As an example, historical failure information, failure causes, and / or failure mechanisms can be obtained through disassembly, and continuous iterative updates and improvements can be made in combination with usage data after subsequent use.

[0083] Figure 6 It is a block diagram showing a risk assessment device for a tower clearance monitoring system according to an embodiment of the present disclosure.

[0084] The risk assessment device 600 for a tower clearance monitoring system according to an embodiment of the present disclosure may include a monitoring data acquisition module 610, an environmental data acquisition module 620, a risk model determination module 630, and a failure mode identification module 640.

[0085] The monitoring data acquisition module 610 may obtain real-time monitoring data of the target tower clearance monitoring system.

[0086] As an example, the monitoring data acquisition module 610 may acquire at least one of the real-time heartbeat data, real-time status data, real-time laser beam distance value, real-time laser beam intensity value, and real-time laser beam data valid flag of each device in the target tower clearance monitoring system. The monitoring data acquisition module 610 may execute any one of the above monitoring data acquisition steps.

[0087] The environmental data acquisition module 620 may acquire the environmental data of the location where the target tower clearance monitoring system is located. For example, the environmental data acquisition module 620 may acquire the temperature data, humidity data, altitude data, and particulate matter concentration data of the location where the target tower clearance monitoring system is located.

[0088] The risk model determination module 630 may determine the first risk prediction model of the target tower clearance monitoring system based on the location where the target tower clearance monitoring system is located. The risk model determination module 630 may execute steps S331 to S333 and the like described above.

[0089] The failure mode identification module 640 may identify the failure mode of the target tower clearance monitoring system according to the real-time monitoring data, environmental data, and the first risk prediction model. The failure mode identification module 640 may execute the above failure mode identification steps.

[0090] The risk assessment method of the tower clearance monitoring system according to the embodiments of the present disclosure may be executed by a processor and may be written as a corresponding computer program or code. The risk assessment method, device, etc. according to the embodiments of the present disclosure have been described above with reference to the accompanying drawings. However, it should be understood that the units, devices, and modules shown in the drawings may be respectively configured as software, hardware, firmware, or any combination of the above for performing specific functions. For example, these systems and modules may correspond to application-specific integrated circuits, may also correspond to pure software code, or may also correspond to modules combining software and hardware. In addition, one or more functions implemented by these devices and modules may also be uniformly executed by components in a physical entity device (such as a processor, a client, or a server, etc.).

[0091] The instructions stored in the above computer-readable storage medium may run in an environment deployed in computer devices such as clients, hosts, proxy devices, servers, etc. It should be noted that the instructions may also be used to execute additional steps other than the above steps or perform more specific processing when executing the above steps. The content of these additional steps and further processing has been mentioned in the description of related modules, methods, and devices with reference to the accompanying drawings, so it will not be repeated here to avoid redundancy.

[0092] It should be noted that the risk assessment method and device according to the embodiments of the present disclosure can fully rely on the running of computer programs or instructions to implement corresponding functions, that is, each device corresponds to each step in the functional architecture of the computer program, so that the entire system is called through a dedicated software package (for example, a lib library) to implement the corresponding functions.

[0093] On the other hand, when the device or system is implemented in software, firmware, middleware or microcode, the program code or code segment for performing corresponding operations can be stored in a computer-readable medium such as a storage medium, so that at least one processor or at least one computing device can perform corresponding operations by reading and running the corresponding program code or code segment. Additionally, when the computer-readable medium or storage medium is executed by the processor, the processor is prompted to execute the above-mentioned risk assessment method.

[0094] For example, according to an exemplary embodiment of the present disclosure, a computer device including a readable medium storing computer program instructions may be provided, wherein when the instructions are run by at least one computing device, the at least one computing device is prompted to execute at least one of the above steps.

[0095] According to an embodiment of the present disclosure, a computer-readable storage medium is provided, and the computer-readable storage medium stores instructions or programs, which when executed by a processor, prompt the processor to execute the above-mentioned risk assessment method.

[0096] The computer-readable storage medium includes a non-transitory computer-readable storage medium. For example, it may include magnetic media such as floppy disks and magnetic tapes, optical media (including CD-ROMs and DVD-ROMs), magneto-optical media such as floppy optical disks, hardware devices such as ROM and RAM designed to store and execute program commands, and flash memories. The program commands include language codes executable by a computer using an interpreter and machine language codes generated by a compiler. The above-mentioned hardware devices can be implemented by one or more software modules for performing the operations of the various embodiments of the present disclosure.

[0097] The risk assessment device of the tower clearance monitoring system according to the embodiment of the present disclosure may include a processor and a memory, and the memory stores instructions or programs, which when executed by the processor, prompt the processor to execute the above-mentioned risk assessment method.

[0098] The risk assessment method and device of the tower clearance monitoring system according to the embodiment of the present disclosure can identify faults in the tower clearance monitoring system.

[0099] The risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure perform modeling in combination with historical monitoring data, and can accurately locate risk points. The failure or fault point location of the risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure is more accurate, facilitating timely replacement or repair of corresponding subsystems, sub-devices, etc.

[0100] The risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure can implement failure and fault analysis under various extreme environmental conditions, making the risk prediction model more comprehensive and diversified.

[0101] The risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure can make the failure mode clearer and more specific.

[0102] The failure and fault model established by the risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure is more targeted and diverse.

[0103] The risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure can identify in advance the critical failure and performance degradation of the tower clearance monitoring system and its sub-devices and subsystems, so as to replace or repair in advance and avoid safety problems.

[0104] The risk assessment method and device of the tower clearance monitoring system according to an embodiment of the present disclosure guide the preparation of spare parts in advance by grasping the failure mode of relevant sub-devices, subsystems, etc. in real time.

[0105] Although some exemplary embodiments of the present disclosure have been shown and described, those skilled in the art should understand that these embodiments can be modified without departing from the principle and spirit of the present disclosure defined by the claims and their equivalents. For example, the technical features of different embodiments can be combined.

Claims

1. A risk assessment method for a tower clearance monitoring system, characterized in that, the risk assessment method includes: obtaining real-time monitoring data of the target tower clearance monitoring system; obtaining environmental data of the location where the target tower clearance monitoring system is located; determining a first risk prediction model of the target tower clearance monitoring system based on the location where the target tower clearance monitoring system is located; identifying a failure mode of the target tower clearance monitoring system according to the real-time monitoring data, the environmental data and the first risk prediction model, wherein the failure mode is used to reflect the fault type of the target tower clearance monitoring system.

2. The risk assessment method for a tower clearance monitoring system according to claim 1, characterized in that, the step of determining a first risk prediction model of the target tower clearance monitoring system based on the location where the target tower clearance monitoring system is located includes: classifying the target tower clearance monitoring system based on the geographical location of the wind farm where the target tower clearance monitoring system is located and the environmental type of the location where the target tower clearance monitoring system is located; selecting at least one risk prediction model from a failure mode information database according to the type of the target tower clearance monitoring system; determining one of the at least one risk prediction model as the first risk prediction model.

3. The risk assessment method for a tower clearance monitoring system according to claim 2, characterized in that, the geographical location includes multiple regions divided according to the location of the wind farm where the tower clearance monitoring system is located, and the environmental type includes at least one of desert, gobi, plateau, intertidal zone and deep sea.

4. The risk assessment method for a tower clearance monitoring system according to claim 3, characterized in that, the failure mode information database is established through the following steps: obtaining multiple historical monitoring data of multiple tower clearance monitoring systems of wind farms located in different geographical locations and in different environmental types; performing design failure mode and effects analysis on the monitoring data of the tower clearance monitoring systems of the wind farms belonging to the same geographical location and in the same type of environment among the multiple historical monitoring data respectively to obtain corresponding failure modes; training the monitoring data and the corresponding failure modes respectively by using a neural network algorithm to obtain corresponding risk prediction models; establishing the risk prediction model database based on the corresponding risk prediction models, wherein the failure mode includes historical failure modes associated with the monitoring data and future potential failure modes.

5. The risk assessment method for a tower clearance monitoring system according to claim 4, characterized in that, the step of determining one of the at least one risk prediction model as the first risk prediction model includes: statistically analyzing the accuracy rates of the at least one risk prediction model; determining the risk prediction model with the highest accuracy rate among the at least one risk prediction model as the first risk prediction model.

6. The risk assessment method for a tower clearance monitoring system according to claim 4, characterized in that, the step of establishing the failure mode information database further includes: Perform SOD scoring on the historical failure modes and future potential failure modes in the failure modes; Determine the response methods for the historical failure modes and / or potential failure modes according to the results of the SOD scoring; Record the response methods in the failure mode information database.

7. The risk assessment method for a tower clearance monitoring system according to claim 6, wherein, the risk assessment method further includes: Determine risk points according to the identified failure modes of the target tower clearance monitoring system; Based on the response methods corresponding to the identified failure modes of the target tower clearance monitoring system recorded in the failure mode information database, determine the response methods for the risk points of the target tower clearance monitoring system.

8. The risk assessment method for a tower clearance monitoring system according to claim 6, wherein, The step of establishing the failure mode information database further includes: Obtain the real-time monitoring data of the tower clearance monitoring systems of wind farms located in different geographical locations and different types of environments; Update the failure mode information database using the real-time monitoring data.

9. The risk assessment method for a tower clearance monitoring system according to claim 1, wherein, The step of obtaining the real-time monitoring data of the target tower clearance monitoring system includes: Obtain at least one of the real-time heartbeat data, real-time status data, real-time laser beam distance value, real-time laser beam intensity value, and real-time laser beam data valid flag bit of each device in the target tower clearance monitoring system.

10. The risk assessment method for a tower clearance monitoring system according to claim 1, wherein, The step of obtaining the environmental data of the location where the target tower clearance monitoring system is located includes: Obtain at least one of the temperature data, humidity data, and particulate matter concentration data of the location where the target tower clearance monitoring system is located.

11. The risk assessment method for a tower clearance monitoring system according to any one of claims 1 to 10, wherein, The target tower clearance monitoring system includes: a lidar for tower clearance monitoring, a camera device, and a digital storage unit for storing monitoring data.

12. The risk assessment method for a tower clearance monitoring system according to any one of claims 1 to 10, wherein, The failure modes include: at least one of abnormal output values output by the tower clearance monitoring system to the main control system, abnormal lidar ranging values of the tower clearance monitoring system, communication anomalies of the tower clearance monitoring system, laser camera failures of the tower clearance monitoring system, damage to the network cable interface of the tower clearance monitoring system, abnormal power-on indicator lights of the tower clearance monitoring system, and no light spot of the laser light source of the tower clearance monitoring system.

13. A computer-readable storage medium, wherein, The computer-readable storage medium stores instructions or programs, and when the instructions or programs are executed by a processor, the processor is prompted to execute the risk assessment method according to any one of claims 1 to 12.

14. A risk assessment device for a tower clearance monitoring system, wherein, The risk assessment device includes a processor and a memory, and the memory stores instructions or programs. When the instructions or programs are executed by the processor, the processor is caused to execute the risk assessment method according to any one of claims 1 to 12.

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