Fan running state wireless monitoring system and method based on Internet of Things

Abstract

The invention relates to the technical field of wireless monitoring, and particularly discloses a fan running state wireless monitoring system and method based on the Internet of Things, and the system comprises a fan state parameter collection module, a fan equipment health analysis module, a fan running performance analysis module, a fan running safety evaluation module and a fan running state control module. The method comprises the following steps: acquiring equipment state parameters and performance parameters; health state analysis and performance evaluation are carried out on the collected data, and safety comprehensive evaluation of the operation state of the draught fan is achieved; according to an evaluation result, a normal operation, load reduction operation or emergency shutdown control strategy is automatically adopted, and closed-loop intelligent control over the state of the draught fan is achieved; the problems that a traditional fan monitoring method is incomplete in parameter collection, insufficient in analysis capability, lack of linkage control and the like are solved, real-time monitoring, intelligent analysis and linkage control of the fan operation state are achieved, the safe operation level of a wind power plant is improved, and the service life of equipment is prolonged.

Description

Technical Field

[0001] This invention relates to the field of wireless monitoring technology, and in particular to a wireless monitoring system and method for wind turbine operating status based on the Internet of Things. Background Technology

[0002] As an important component of clean energy, wind power generation continues to expand in terms of installed capacity. Wind turbines, as core equipment, directly affect power generation efficiency and safety; therefore, real-time monitoring of turbine operating status is crucial for ensuring the safe and efficient operation of wind farms.

[0003] Traditional wind turbine monitoring mainly relies on regular manual inspections and wired sensor systems, which collect certain parameters through scheduled checks or fixed sensors. However, with the expansion of wind farms and the increasing demands for intelligent systems, these methods are no longer sufficient to meet the management needs of modern wind farms.

[0004] First, traditional wind turbine monitoring methods suffer from incomplete monitoring parameters, low acquisition frequency, and poor data continuity. They cannot perform real-time, continuous acquisition and monitoring of key equipment parameters such as bearing vibration frequency and temperature, as well as performance parameters such as operating load rate and speed deviation. This makes it difficult to detect early abnormal conditions in a timely manner, increasing potential safety hazards in wind turbine operation. Second, traditional methods generally lack intelligent analysis capabilities for the acquired data, remaining at the level of simple data recording and threshold alarms. They cannot conduct in-depth analysis of the health status of wind turbine equipment, nor can they quantitatively assess wind turbine operating performance, making it difficult to form a comprehensive safety evaluation of the overall wind turbine operating status. Furthermore, traditional monitoring systems lack an effective linkage mechanism with the wind turbine control system. They cannot intelligently adjust wind turbine operating strategies based on monitoring results, and lack the ability to automatically switch between multi-level control strategies such as normal operation, load reduction operation, and emergency shutdown. This prevents closed-loop intelligent control of wind turbine status, reducing the operating efficiency of wind farms and the service life of equipment. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a wireless monitoring system and method for wind turbine operation status based on the Internet of Things, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a wireless monitoring system for wind turbine operating status based on the Internet of Things, comprising: The wind turbine status parameter acquisition module includes a wind turbine equipment parameter acquisition unit and a wind turbine performance parameter acquisition unit. The wind turbine equipment parameter acquisition unit is used to acquire the status parameters of the wind turbine equipment, and the wind turbine performance parameter acquisition unit is used to acquire the performance parameters of the wind turbine during operation. Wind turbine equipment health analysis module: Based on the status parameters collected by the wind turbine equipment parameter acquisition unit, the module performs health status analysis on the wind turbine equipment, outputs the health status analysis results, and transmits the analysis results to the wind turbine operation performance analysis module and the wind turbine operation status control module; Wind turbine operation performance analysis module: Based on the performance parameters collected by the wind turbine performance parameter acquisition unit, the module analyzes the operation performance of the wind turbine during operation, obtains the wind turbine operation performance evaluation coefficient, and transmits it to the wind turbine operation status safety assessment module. Wind turbine operation safety assessment module: Based on the health status analysis results of wind turbine equipment and the wind turbine operation performance evaluation coefficient, it conducts a comprehensive safety assessment of the wind turbine operation status and outputs the wind turbine operation status safety assessment results; Wind turbine operation status control module: Based on the health status analysis results of wind turbine equipment and the safety assessment results of wind turbine operation status, the module adopts corresponding control strategies to control the wind turbine equipment and wind turbine operation process in real time.

[0007] Preferably, the execution method of the wind turbine status parameter acquisition module is as follows: The status parameters of the wind turbine equipment are collected in real time by the wind turbine equipment parameter acquisition unit and transmitted to the wind turbine equipment health analysis module. The status parameters include the vibration frequency and temperature value of the bearing. The wind turbine performance parameter acquisition unit collects the wind turbine's performance parameters in real time during operation and transmits the collected performance parameters to the wind turbine operation performance analysis module. The performance parameters include the wind turbine's operating load rate and speed deviation.

[0008] Preferably, the vibration frequency and temperature value of the bearing are obtained in the following way: Vibration sensors and temperature sensors are installed at the bearing position of the wind turbine equipment to form an Internet of Things (IoT) sensing layer. Status parameters are obtained through the IoT sensing layer and transmitted to the wind turbine equipment health analysis module via wireless transmission. The vibration sensor acquires the original bearing vibration signal at a preset first sampling frequency. By preprocessing the original bearing vibration signal, the characteristic frequency is extracted to obtain the bearing vibration frequency. The temperature sensor acquires the actual bearing temperature at a preset second sampling frequency. The wind turbine operating load rate and speed deviation are obtained in the following ways: A data acquisition device is installed at the interface of the wind turbine control system. It establishes a connection with the wind turbine control system through an industrial communication protocol to form an Internet of Things network layer, realizes the real-time acquisition of wind turbine performance parameters, and transmits the acquired performance parameters to the wind turbine operation performance analysis module through wireless transmission. The system collects the current output power of the wind turbine in real time, plots the turbine power curve within a preset period, obtains the theoretical maximum power at the current wind speed based on the power curve, and also obtains the actual output power of the wind turbine. The ratio of the actual output power to the theoretical maximum power at the current wind speed is calculated to determine the wind turbine operating load rate. Olr ; The actual fan speed is collected in real time, the difference between the actual fan speed and the preset ideal speed is calculated, and the ratio of the difference to the preset ideal speed is calculated to obtain the fan speed deviation. Fsd .

[0009] Preferably, the execution method of the wind turbine equipment health analysis module is as follows: The system receives status parameters collected by the wind turbine equipment parameter acquisition unit in the wind turbine status parameter acquisition module, extracts the bearing vibration frequency from the status parameters, analyzes the bearing vibration state, calculates the difference between the bearing vibration frequency and the preset bearing vibration frequency reference value, and calculates the ratio of the difference to the preset bearing vibration frequency reference value to obtain the bearing vibration frequency deviation rate. Fdr ; The actual bearing temperature is extracted from the condition parameters and combined with the preset bearing temperature reference value to analyze and obtain the bearing temperature deviation rate. Tdr ; Based on the bearing vibration frequency deviation rate and bearing temperature deviation rate, a health status analysis of the fan equipment is performed, and the results of the fan equipment health status analysis are output.

[0010] Preferably, the specific content of the health status analysis of the fan equipment based on the bearing vibration frequency deviation rate and the bearing temperature deviation rate is as follows: When the bearing vibration frequency deviation rate is less than the preset vibration frequency deviation threshold and the bearing temperature deviation rate is less than the preset temperature deviation threshold, the fan equipment is judged to be in normal health status, and the result of the normal health status judgment of the fan equipment is transmitted to the fan operation performance analysis module. When the bearing vibration frequency deviation rate is greater than the preset vibration frequency deviation threshold, it indicates that the bearing vibration of the fan equipment is abnormal, and the health status of the fan equipment is judged to be abnormal. The abnormal bearing vibration result of the fan equipment is transmitted to the fan operation status control module. When the bearing temperature deviation rate is greater than the preset temperature deviation threshold, it indicates that the bearing temperature of the fan equipment is abnormal, and the fan equipment is judged to be in an abnormal health state. The abnormal bearing temperature result of the fan equipment is then transmitted to the fan operation status control module.

[0011] Preferably, the execution method of the wind turbine operation performance analysis module is as follows: Based on the wind turbine equipment health analysis module, which determines that the wind turbine equipment is in normal health status, further analysis of the wind turbine operating performance is conducted. Receive performance parameters collected by the wind turbine performance parameter acquisition unit in the wind turbine status parameter acquisition module, and extract the wind turbine operating load rate. Olr and fan speed deviation Fsd ; This paper analyzes the operating load and speed status of wind turbines based on their operating load rate and speed deviation. Simultaneously, it extracts preset maximum allowable wind turbine operating load rates and maximum allowable speed deviations from the database to establish a wind turbine operating performance evaluation model and calculate the wind turbine operating performance evaluation coefficients. PEC The wind turbine's operating performance is evaluated based on the wind turbine operating performance evaluation coefficient, and the wind turbine performance evaluation results are output.

[0012] Preferably, the specific content of analyzing the wind turbine operating load status and wind turbine speed status based on the wind turbine operating load rate and wind turbine speed deviation is as follows: The fan operating load rate is compared with the preset standard range of operating load rate. When the fan operating load rate is within the preset standard range of operating load rate, the fan load rate is determined to be normal. When the fan operating load rate is greater than the preset upper limit of operating load rate, the fan load rate is determined to be too high. When the fan operating load rate is less than the preset lower limit of operating load rate, the fan load rate is determined to be too low. The fan speed deviation is compared with a preset speed deviation threshold. When the fan speed deviation is less than the preset speed deviation threshold, the fan speed is determined to be normal; when the fan speed deviation is greater than the preset speed deviation threshold, the fan speed is determined to be abnormal.

[0013] Preferably, the wind turbine operation safety assessment module is executed in the following manner: Based on the health status analysis results of the wind turbine equipment, the bearing vibration frequency deviation rate was extracted. Fdr and bearing temperature deviation rate Tdr In conjunction with the wind turbine operating performance evaluation coefficient PEC The system calculates the wind turbine operating status safety assessment index and conducts a comprehensive safety assessment of the wind turbine operating status based on the index. Specifically, it compares the wind turbine operating status safety assessment index with a preset threshold. If the index is greater than the threshold, the wind turbine is considered to be operating safely and without abnormalities; otherwise, it is considered to be operating abnormally. An early warning is issued for the abnormal result, the wind turbine operating status safety assessment result is output, and the result is transmitted to the wind turbine operating status control module.

[0014] Preferably, the wind turbine operation status control module is executed as follows: based on the wind turbine equipment health status analysis results and wind turbine operation status safety assessment results, a control strategy library including normal operation strategy, load reduction operation strategy and emergency shutdown strategy is adopted to generate corresponding control commands according to different status conditions, and the commands are sent to the wind turbine control system through the Internet of Things control layer to execute the corresponding control operations.

[0015] To achieve the above objectives, the present invention provides the following technical solution: a wireless monitoring method for wind turbine operating status based on the Internet of Things (IoT), comprising the following steps: S1: Fan status parameter acquisition: The status parameters of the fan equipment are acquired through the fan equipment parameter acquisition unit, and the performance parameters of the fan during operation are acquired through the fan performance parameter acquisition unit. S2: Wind Turbine Equipment Health Analysis: Based on the status parameters collected by the wind turbine equipment parameter acquisition unit, the health status analysis of the wind turbine equipment is performed, and the results of the wind turbine equipment health status analysis are output. S3: Wind Turbine Operation Performance Analysis: Based on the performance parameters collected by the wind turbine performance parameter acquisition unit, the operation performance of the wind turbine during operation is analyzed to obtain the wind turbine operation performance evaluation coefficient; S4: Wind Turbine Operation Safety Assessment: Based on the health status analysis results of wind turbine equipment and the wind turbine operation performance evaluation coefficient, a comprehensive safety assessment of the wind turbine operation status is conducted, and the wind turbine operation status safety assessment results are output. S5: Fan Operation Status Control: Based on the health status analysis results of the fan equipment and the safety assessment results of the fan operation status, corresponding control strategies are adopted to control the fan equipment and the fan operation process in real time.

[0016] As described above, the IoT-based wireless monitoring system and method for wind turbine operation status provided by the present invention has at least the following beneficial effects: This invention provides an IoT-based wireless monitoring system and method for wind turbine operation status. It collects bearing vibration frequency and temperature parameters in real time using sensors installed in the IoT sensing layer, and obtains wind turbine operating load rate and speed deviation performance parameters through industrial communication protocols. The system performs health status analysis and performance evaluation on the collected data to achieve a comprehensive safety assessment of the wind turbine's operating status. Based on the evaluation results, it automatically adopts normal operation, reduced load operation, or emergency shutdown control strategies, achieving closed-loop intelligent control of the wind turbine's status. This invention solves the problems of incomplete parameter collection, insufficient analysis capabilities, and lack of linkage control in traditional wind turbine monitoring methods. It achieves real-time monitoring, intelligent analysis, and linkage control of wind turbine operation status, improving the safety level of wind farm operation and extending equipment lifespan. This invention has the advantages of comprehensive monitoring parameters, real-time data acquisition, intelligent analysis and evaluation, and multi-level control strategies. It can effectively prevent equipment failures, reduce maintenance costs, extend wind turbine lifespan, simplify system wiring requirements, improve system reliability and scalability, and provide effective technical support for intelligent operation and maintenance of wind farms. Attached Figure Description

[0017] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of the wireless monitoring system for wind turbine operation status based on the Internet of Things of the present invention.

[0019] Figure 2 This is a flowchart illustrating the IoT-based wireless monitoring method for wind turbine operation status according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please see Figure 1 As shown, the present invention provides a wireless monitoring system for wind turbine operation status based on the Internet of Things, including a wind turbine status parameter acquisition module, a wind turbine equipment health analysis module, a wind turbine operation performance analysis module, a wind turbine operation safety assessment module, and a wind turbine operation status control module.

[0022] The wind turbine status parameter acquisition module includes a wind turbine equipment parameter acquisition unit and a wind turbine performance parameter acquisition unit. The wind turbine equipment parameter acquisition unit is used to acquire the status parameters of the wind turbine equipment, and the wind turbine performance parameter acquisition unit is used to acquire the performance parameters of the wind turbine during operation. In this embodiment, it should be specifically explained that the execution method of the wind turbine status parameter acquisition module is as follows: The status parameters of the wind turbine equipment are collected in real time by the wind turbine equipment parameter acquisition unit and transmitted to the wind turbine equipment health analysis module. The status parameters include the vibration frequency and temperature value of the bearing. The wind turbine performance parameters are collected in real time by the wind turbine performance parameter acquisition unit and transmitted to the wind turbine operation performance analysis module. The performance parameters include the wind turbine operating load rate and speed deviation. It should be noted that the vibration frequency and temperature values ​​of the bearing are obtained in the following ways: Vibration sensors and temperature sensors are installed at the bearing position of the wind turbine equipment to form an Internet of Things (IoT) sensing layer. Status parameters are obtained through the IoT sensing layer and transmitted to the wind turbine equipment health analysis module via wireless transmission. The vibration sensor acquires the original bearing vibration signal at a preset first sampling frequency. By preprocessing the original bearing vibration signal, the characteristic frequency is extracted to obtain the bearing vibration frequency. The temperature sensor acquires the actual bearing temperature at a preset second sampling frequency. It should be noted that, in a specific embodiment, the preset first sampling frequency can be set to 2kHz (2000 times per second), and the preset second sampling frequency can be set to collect abnormal bearing temperature every minute. Those skilled in the art can set these freely.

[0023] The wind turbine operating load rate and speed deviation are obtained in the following ways: A data acquisition device is installed at the interface of the wind turbine control system. It establishes a connection with the wind turbine control system through an industrial communication protocol to form an Internet of Things network layer, realizes the real-time acquisition of wind turbine performance parameters, and transmits the acquired performance parameters to the wind turbine operation performance analysis module through wireless transmission. The system collects the current output power of the wind turbine in real time, plots the turbine power curve within a preset period, obtains the theoretical maximum power at the current wind speed based on the power curve, and also obtains the actual output power of the wind turbine. The ratio of the actual output power to the theoretical maximum power at the current wind speed is calculated to determine the wind turbine operating load rate. Olr ; The actual fan speed is collected in real time, the difference between the actual fan speed and the preset ideal speed is calculated, and the ratio of the difference to the preset ideal speed is calculated to obtain the fan speed deviation.Fsd ; It should be noted that, in a specific embodiment, the sampling frequency of the wind turbine operating load rate and speed deviation can be set to be collected once every 5 minutes, and the data is pushed to the Internet of Things platform in real time using the MQTT Internet of Things protocol. Those skilled in the art can freely set this according to actual monitoring needs.

[0024] Wind turbine equipment health analysis module: Based on the status parameters collected by the wind turbine equipment parameter acquisition unit, the module performs health status analysis on the wind turbine equipment, outputs the health status analysis results, and transmits the analysis results to the wind turbine operation performance analysis module and the wind turbine operation status control module; In this embodiment, it should be specifically explained that the execution method of the wind turbine equipment health analysis module is as follows: The system receives status parameters collected by the wind turbine equipment parameter acquisition unit in the wind turbine status parameter acquisition module, extracts the bearing vibration frequency from the status parameters, analyzes the bearing vibration state, calculates the difference between the bearing vibration frequency and the preset bearing vibration frequency reference value, and calculates the ratio of the difference to the preset bearing vibration frequency reference value to obtain the bearing vibration frequency deviation rate. Fdr ; The actual bearing temperature is extracted from the condition parameters and combined with the preset bearing temperature reference value to analyze and obtain the bearing temperature deviation rate. Tdr ; Based on the bearing vibration frequency deviation rate and bearing temperature deviation rate, a health status analysis of the fan equipment is performed, and the results of the fan equipment health status analysis are output.

[0025] The bearing temperature deviation rate Tdr The calculation formula is: ,in, T Indicates the actual temperature of the bearing. T 0 This indicates the preset bearing temperature reference value; In this embodiment, it should be specifically explained that the specific content of the health status analysis of the fan equipment based on the bearing vibration frequency deviation rate and the bearing temperature deviation rate is as follows: When the bearing vibration frequency deviation rate is less than the preset vibration frequency deviation threshold and the bearing temperature deviation rate is less than the preset temperature deviation threshold, the fan equipment is judged to be in normal health status, and the result of the normal health status judgment of the fan equipment is transmitted to the fan operation performance analysis module. When the bearing vibration frequency deviation rate is greater than the preset vibration frequency deviation threshold, it indicates that the bearing vibration of the fan equipment is abnormal, and the health status of the fan equipment is judged to be abnormal. The abnormal bearing vibration result of the fan equipment is transmitted to the fan operation status control module. When the bearing temperature deviation rate is greater than the preset temperature deviation threshold, it indicates that the bearing temperature of the fan equipment is abnormal, and the fan equipment is judged to be in an abnormal health state. The abnormal bearing temperature result of the fan equipment is then transmitted to the fan operation status control module.

[0026] Wind turbine operation performance analysis module: Based on the performance parameters collected by the wind turbine performance parameter acquisition unit, the module analyzes the operation performance of the wind turbine during operation, obtains the wind turbine operation performance evaluation coefficient, and transmits it to the wind turbine operation status safety assessment module. In this embodiment, it should be specifically explained that the execution method of the wind turbine operation performance analysis module is as follows: Based on the wind turbine equipment health analysis module, which determines that the wind turbine equipment is in normal health status, further analysis of the wind turbine operating performance is conducted. Receive performance parameters collected by the wind turbine performance parameter acquisition unit in the wind turbine status parameter acquisition module, and extract the wind turbine operating load rate. Olr and fan speed deviation Fsd ; This paper analyzes the operating load and speed status of wind turbines based on their operating load rate and speed deviation. Simultaneously, it extracts preset maximum allowable wind turbine operating load rates and maximum allowable speed deviations from the database to establish a wind turbine operating performance evaluation model and calculate the wind turbine operating performance evaluation coefficients. PEC The wind turbine's operating performance is evaluated based on the wind turbine operating performance evaluation coefficient, and the wind turbine performance evaluation results are output.

[0027] The wind turbine operating performance evaluation coefficient PEC The calculation formula is: ,in, These represent the preset maximum allowable fan operating load rate and the preset maximum allowable fan speed deviation, respectively. These represent the weighting coefficients for the wind turbine operating load rate and the wind turbine speed deviation, respectively. .

[0028] In the formula, the wind turbine operating load rate Olr The smaller the deviation, the lower the fan speed. Fsd The smaller the value, the lower the wind turbine operating performance evaluation coefficient. PEC The larger.

[0029] In this embodiment, it should be specifically explained that the specific content of evaluating the wind turbine operating performance based on the wind turbine operating performance evaluation coefficient is as follows: Read the wind turbine operating performance evaluation coefficient, compare the wind turbine operating performance evaluation coefficient with the preset operating performance evaluation coefficient threshold. If the wind turbine operating performance evaluation coefficient is greater than the preset operating performance evaluation coefficient threshold, the wind turbine operating performance status is judged to be normal. Otherwise, the wind turbine operating performance status is judged to be abnormal. Issue a warning message for the abnormal result and output the wind turbine performance evaluation result.

[0030] It should be specifically noted that the analysis of the wind turbine operating load status and wind turbine speed status based on the wind turbine operating load rate and wind turbine speed deviation is as follows: The fan operating load rate is compared with the preset standard range of operating load rate. When the fan operating load rate is within the preset standard range of operating load rate, the fan load rate is determined to be normal. When the fan operating load rate is greater than the preset upper limit of operating load rate, the fan load rate is determined to be too high. When the fan operating load rate is less than the preset lower limit of operating load rate, the fan load rate is determined to be too low. The fan speed deviation is compared with a preset speed deviation threshold. When the fan speed deviation is less than the preset speed deviation threshold, the fan speed is determined to be normal; when the fan speed deviation is greater than the preset speed deviation threshold, the fan speed is determined to be abnormal.

[0031] Wind turbine operation safety assessment module: Based on the health status analysis results of wind turbine equipment and the wind turbine operation performance evaluation coefficient, it conducts a comprehensive safety assessment of the wind turbine operation status and outputs the wind turbine operation status safety assessment results; In this embodiment, it should be specifically explained that the execution method of the wind turbine operation safety assessment module is as follows: Based on the health status analysis results of the wind turbine equipment, the bearing vibration frequency deviation rate was extracted. Fdr and bearing temperature deviation rate Tdr In conjunction with the wind turbine operating performance evaluation coefficient PEC The system calculates the wind turbine operating status safety assessment index and conducts a comprehensive safety assessment of the wind turbine operating status based on the index. Specifically, it compares the wind turbine operating status safety assessment index with a preset threshold. If the index is greater than the threshold, the wind turbine is considered to be operating safely and without abnormalities; otherwise, it is considered to be operating abnormally. An early warning is issued for the abnormal result, the wind turbine operating status safety assessment result is output, and the result is transmitted to the wind turbine operating status control module.

[0032] The wind turbine operating status safety assessment index SI The calculation formula is as follows: ,in, These represent the preset maximum allowable bearing vibration frequency deviation rate and the maximum allowable bearing temperature deviation rate, respectively. This represents an exponential function.

[0033] In the formula, the bearing vibration frequency deviation rate Fdr The smaller the value, the lower the bearing temperature deviation rate. Tdr The smaller the value, the better the performance evaluation coefficient of the wind turbine. PEC The larger the value, the higher the safety assessment index for wind turbine operation. SI The larger the value, the safer and more stable the operation of the wind turbine.

[0034] Wind turbine operation status control module: Based on the health status analysis results of wind turbine equipment and the safety assessment results of wind turbine operation status, the module adopts corresponding control strategies to control the wind turbine equipment and wind turbine operation process in real time.

[0035] In this embodiment, it should be specifically explained that the wind turbine operation status control module is executed as follows: based on the wind turbine equipment health status analysis results and wind turbine operation status safety assessment results, a control strategy library including normal operation strategy, load reduction operation strategy and emergency shutdown strategy is adopted to generate corresponding control commands according to different status conditions, and the commands are sent to the wind turbine control system through the Internet of Things control layer to execute the corresponding control operations.

[0036] When the health status analysis results of the wind turbine equipment show that the health status of the wind turbine equipment is normal, and the safety assessment results of the wind turbine operation status show that the safety assessment index of the wind turbine operation status is greater than the preset safety assessment index threshold, the normal operation strategy is adopted to maintain the current operating parameters of the wind turbine unchanged.

[0037] When the wind turbine equipment health analysis module determines that the bearing vibration frequency deviation rate exceeds the preset threshold but is lower than the emergency shutdown threshold, or the bearing temperature deviation rate exceeds the preset threshold but is lower than the emergency shutdown threshold, or the wind turbine operating status safety assessment index is lower than the preset threshold but higher than the danger value, a load reduction operation strategy is adopted to generate control commands to reduce the wind turbine load to the preset safe load range.

[0038] When the wind turbine equipment health analysis module determines that the bearing vibration frequency deviation rate or bearing temperature deviation rate of the wind turbine equipment exceeds the emergency shutdown threshold, or the wind turbine operating status safety assessment index is lower than the dangerous value, an emergency shutdown strategy is adopted to generate an emergency shutdown control command for the wind turbine. The generated control commands are sent to the wind turbine control system in real time through the IoT control layer to execute the corresponding control operations; the execution process and results of the control commands are recorded to form a control record, which is then transmitted to the data center for storage through the IoT platform.

[0039] In this embodiment, it should be specifically noted that the above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0040] Example 2 Please see Figure 2 As shown, this invention provides a wireless monitoring method for wind turbine operating status based on the Internet of Things, including the following steps: S1: Fan status parameter acquisition: The status parameters of the fan equipment are acquired through the fan equipment parameter acquisition unit, and the performance parameters of the fan during operation are acquired through the fan performance parameter acquisition unit. S2: Wind Turbine Equipment Health Analysis: Based on the status parameters collected by the wind turbine equipment parameter acquisition unit, the health status analysis of the wind turbine equipment is performed, and the results of the wind turbine equipment health status analysis are output. S3: Wind Turbine Operation Performance Analysis: Based on the performance parameters collected by the wind turbine performance parameter acquisition unit, the operation performance of the wind turbine during operation is analyzed to obtain the wind turbine operation performance evaluation coefficient; S4: Wind Turbine Operation Safety Assessment: Based on the health status analysis results of wind turbine equipment and the wind turbine operation performance evaluation coefficient, a comprehensive safety assessment of the wind turbine operation status is conducted, and the wind turbine operation status safety assessment results are output. S5: Fan Operation Status Control: Based on the health status analysis results of the fan equipment and the safety assessment results of the fan operation status, corresponding control strategies are adopted to control the fan equipment and the fan operation process in real time.

[0041] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0042] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A wind turbine operating state wireless monitoring system based on the Internet of Things, characterized in that, The application relates to a wind turbine state parameter acquisition module, a wind turbine equipment health analysis module, a wind turbine operation performance analysis module, a wind turbine operation safety evaluation module and a wind turbine operation state control module. The wind turbine state parameter acquisition module comprises a wind turbine equipment parameter acquisition unit and a wind turbine performance parameter acquisition unit, the wind turbine equipment parameter acquisition unit is used for acquiring state parameters of wind turbine equipment, and the wind turbine performance parameter acquisition unit is used for acquiring performance parameters of a wind turbine operation process. The wind turbine equipment health analysis module is used for performing health state analysis on the wind turbine equipment based on the state parameters acquired by the wind turbine equipment parameter acquisition unit, outputting wind turbine equipment health state analysis results, and transmitting the analysis results to the wind turbine operation performance analysis module and the wind turbine operation state control module. The wind turbine operation performance analysis module is used for performing analysis on the operation performance in the wind turbine operation process based on the performance parameters acquired by the wind turbine performance parameter acquisition unit, obtaining a wind turbine operation performance evaluation coefficient, and transmitting the wind turbine operation performance evaluation coefficient to the wind turbine operation safety evaluation module. The wind turbine operation safety evaluation module is used for comprehensively evaluating the safety of the wind turbine operation state based on the wind turbine equipment health state analysis results and the wind turbine operation performance evaluation coefficient, and outputting wind turbine operation state safety evaluation results. The wind turbine operation state control module is used for performing real-time control on the wind turbine equipment and the wind turbine operation process by adopting corresponding control strategies based on the wind turbine equipment health state analysis results and the wind turbine operation state safety evaluation results. 2.The Internet of Things based wireless monitoring system for operating status of a fan as claimed in claim 1 wherein: The execution mode of the wind turbine state parameter acquisition module is as follows: The state parameters of the wind turbine equipment are acquired in real time by the wind turbine equipment parameter acquisition unit, and the acquired state parameters are transmitted to the wind turbine equipment health analysis module; the state parameters include the vibration frequency and temperature value of the bearing; The performance parameters of the wind turbine operation process are acquired in real time by the wind turbine performance parameter acquisition unit, and the acquired performance parameters are transmitted to the wind turbine operation performance analysis module; the performance parameters include the wind turbine operation load rate and the speed deviation. 3.The system according to claim 2, wherein: The vibration frequency and temperature value of the bearing are acquired in the following mode: Vibration sensors and temperature sensors are installed at the bearing position of the wind turbine equipment to form an Internet of Things sensing layer, the state parameters are acquired through the Internet of Things sensing layer, and the acquired state parameters are transmitted to the wind turbine equipment health analysis module through wireless transmission; The vibration sensors acquire the bearing vibration original signal at a preset first sampling frequency, the characteristic frequency is extracted by preprocessing the bearing vibration original signal, and the bearing vibration frequency is acquired; the temperature sensors acquire the actual temperature of the bearing according to a preset second sampling frequency; The wind turbine operation load rate and the speed deviation are acquired in the following mode: A data acquisition device is installed at the interface of the wind turbine control system, the wind turbine control system is connected through an industrial communication protocol to form an Internet of Things network layer, the performance parameters of the wind turbine are acquired in real time, and the acquired performance parameters are transmitted to the wind turbine operation performance analysis module through wireless transmission. Real-time acquisition of the current output power of the fan, drawing the fan power curve in the preset period, obtaining the theoretical maximum power under the current wind speed based on the fan power curve, and obtaining the actual output power of the fan, calculating the ratio of the actual output power of the fan to the theoretical maximum power under the current wind speed, and calculating the fan operation load rate Olr ; The actual rotating speed value of the fan is collected in real time, a difference between the actual rotating speed value and a preset ideal rotating speed value is calculated, a ratio between the difference and the preset ideal rotating speed value is calculated, and a rotating speed deviation of the fan is obtained The execution mode of the wind turbine equipment health analysis module is as follows: . 4.The system according to claim 1, wherein: The execution mode of the wind turbine operation performance analysis module is as follows: Receive the state parameter collected by the fan equipment parameter collection unit in the fan state parameter collection module, extract the bearing vibration frequency in the state parameter, analyze the bearing vibration state, calculate the difference between the bearing vibration frequency and the preset bearing vibration frequency reference value, and calculate the ratio of the difference to the preset bearing vibration frequency reference value, and analyze the bearing vibration frequency deviation rate The execution mode of the wind turbine operation safety evaluation module is as follows: ; The bearing actual temperature in the extracted state parameter is combined with a preset bearing temperature reference value to analyze a bearing temperature deviation rate The health state of the wind turbine equipment is analyzed based on the bearing vibration frequency deviation rate and the bearing temperature deviation rate, and the wind turbine equipment health state analysis results are output. ; The specific content of the health state analysis on the wind turbine equipment based on the bearing vibration frequency deviation rate and the bearing temperature deviation rate is as follows: 5.The system according to claim 4, wherein: ​ When the bearing vibration frequency deviation rate is less than the preset vibration frequency deviation threshold value, and the bearing temperature deviation rate is less than the preset temperature deviation threshold value, it is judged that the fan equipment health state is normal, and the fan equipment health state normal judgment result is transmitted to the fan operation performance analysis module; When the bearing vibration frequency deviation rate is greater than the preset vibration frequency deviation threshold value, it indicates that the fan equipment bearing vibration is abnormal, and it is judged that the fan equipment health state is abnormal, and the fan equipment bearing vibration abnormal result is transmitted to the fan operation state control module; When the bearing temperature deviation rate is greater than the preset temperature deviation threshold value, it indicates that the fan equipment bearing temperature is abnormal, and it is judged that the fan equipment health state is abnormal, and the fan equipment bearing temperature abnormal result is transmitted to the fan operation state control module. 6.The system according to claim 1, wherein: The execution mode of the fan operation performance analysis module is specifically as follows: Based on the fan equipment health analysis module, the fan equipment health state is normal, and the fan operation performance is further analyzed; Receive the performance parameters collected by the fan performance parameter collection unit in the fan state parameter collection module, extract the fan operation load rate Olr and the fan speed deviation Fsd ; Based on the fan operation load rate and the fan speed deviation, the fan operation load state and the fan speed state are analyzed, and the preset maximum allowed fan operation load rate and the preset maximum allowed fan speed deviation are extracted from the database to establish a fan operation performance evaluation model, and a fan operation performance evaluation coefficient is calculated PEC Based on the fan operation performance evaluation coefficient, the fan operation performance is evaluated, and a fan performance evaluation result is output. 7.The system according to claim 6, wherein: The specific content of analyzing the fan operation load state and the fan speed state based on the fan operation load rate and the fan speed deviation is as follows: The fan operation load rate is compared with the preset operation load rate standard interval, when the fan operation load rate is within the preset operation load rate standard interval, it is determined that the fan load rate state is normal; When the fan operation load rate is greater than the preset upper limit value of the operation load rate, it is determined that the fan load rate is too high, and when the fan operation load rate is less than the preset lower limit value of the operation load rate, it is determined that the fan load rate is too low; The fan speed deviation is compared with the preset speed deviation threshold value, when the fan speed deviation is less than the preset speed deviation threshold value, it is determined that the fan speed state is normal; when the fan speed deviation is greater than the preset speed deviation threshold value, it is determined that the fan speed is abnormal. 8.The system according to claim 1, wherein: The execution mode of the fan operation safety evaluation module is specifically as follows: Based on the fan equipment health state analysis result, the bearing vibration frequency deviation rate is extracted Fdr and bearing temperature deviation rate Tdr , combined with the fan operation performance evaluation coefficient PEC , the fan operation state safety evaluation index is calculated, and the safety of the fan operation state is comprehensively evaluated based on the fan operation state safety evaluation index. Specifically, the fan operation state safety evaluation index is compared with the preset operation state safety evaluation index threshold value. If the fan operation state safety evaluation index is greater than the preset operation state safety evaluation index threshold value, it is judged that the fan operation state safety is normal, otherwise, it is judged that the fan operation state is abnormal, an early warning information is sent for the abnormal result, the fan operation state safety evaluation result is output, and the fan operation state safety evaluation result is transmitted to the fan operation state control module. 9.The system according to claim 1, wherein: The execution mode of the fan operation state control module is specifically: based on the fan equipment health state analysis result and the fan operation state safety evaluation result, a control strategy library including normal operation strategy, load reduction operation strategy and emergency shutdown strategy is adopted, corresponding control instructions are generated according to different state conditions, and the instructions are sent to the fan control system through the Internet of Things control layer to execute corresponding control operation.

10. The method for wireless monitoring of the running state of a fan based on the Internet of Things, used in the system for wireless monitoring of the running state of a fan based on the Internet of Things according to any one of claims 1-9, characterized in that, The steps include: S1: fan state parameter acquisition: the state parameters of the fan equipment are acquired through the fan equipment parameter acquisition unit, and the performance parameters of the fan operation process are acquired through the fan performance parameter acquisition unit; S2: fan equipment health analysis: based on the state parameters acquired by the fan equipment parameter acquisition unit, the health state of the fan equipment is analyzed, and the fan equipment health state analysis result is output; S3: fan operation performance analysis: based on the performance parameters acquired by the fan performance parameter acquisition unit, the operation performance in the fan operation process is analyzed, and the fan operation performance evaluation coefficient is obtained; S4: fan operation safety evaluation: based on the fan equipment health state analysis result and the fan operation performance evaluation coefficient, the safety of the fan operation state is comprehensively evaluated, and the fan operation state safety evaluation result is output; S5: Fan operation state control: based on the fan equipment health state analysis result and the fan operation state safety evaluation result, the corresponding control strategy is adopted to control the fan equipment and the fan operation process in real time.

Images